From 2b26ea22c4e632c7c966d54d366ca98ebb069579 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 3 Jul 2025 15:20:47 +0200 Subject: [PATCH 01/52] setting up the branch --- DEV_WORKFLOW_GUIDE.md | 253 ++++++++++++++++++ {docker => docker_fs}/.env.dev | 0 {docker => docker_fs}/.env.example | 0 {docker => docker_fs}/.env.test | 0 {docker => docker_fs}/docker-compose.dev.yml | 0 {docker => docker_fs}/docker-compose.prod.yml | 0 scripts/init-docker-dev.sh | 8 +- 7 files changed, 257 insertions(+), 4 deletions(-) create mode 100644 DEV_WORKFLOW_GUIDE.md rename {docker => docker_fs}/.env.dev (100%) rename {docker => docker_fs}/.env.example (100%) rename {docker => docker_fs}/.env.test (100%) rename {docker => docker_fs}/docker-compose.dev.yml (100%) rename {docker => docker_fs}/docker-compose.prod.yml (100%) diff --git a/DEV_WORKFLOW_GUIDE.md b/DEV_WORKFLOW_GUIDE.md new file mode 100644 index 0000000..3ae74de --- /dev/null +++ b/DEV_WORKFLOW_GUIDE.md @@ -0,0 +1,253 @@ +# **Development Workflow & Architecture Guide** + +This document outlines the standardized development workflow, repository architecture, and branching strategy for this project. Adhering to these guidelines ensures consistency, maintainability, and a scalable development process. + +## 1. Technology Stack + +The project is built upon the following core technologies: + +- **Backend**: FastAPI +- **Backend Package Manager**: Poetry +- **Frontend**: React + JavaScript +- **Database**: PostgreSQL +- **Caching**: Redis +- **Containerization**: Docker + +## 2. Architectural Overview: A Multi-Repo Strategy + +To promote scalability, team autonomy, and clear separation of concerns, this project adopts a **multi-repo architecture**. Each core component of the system resides in its own dedicated repository. This approach allows for independent development cycles, testing, and deployment. + +Our architecture is composed of three main repositories: + +### 2.1. Backend Service (`project-backend`) + +This repository contains all code related to the FastAPI backend service. Its primary responsibility is to handle business logic, interact with the database, and expose a RESTful API. + +**Folder Structure:** +``` +project-backend/ +├── .github/ # CI workflows: tests, builds, and publishes the Docker image +│ └── workflows/ +│ └── main.yml +├── src/ +│ └── app/ # Main Python package +│ ├── api/ # API routers & endpoints +│ ├── db/ # Database session management & base models +│ ├── models/ # SQLAlchemy ORM models (database table definitions) +│ ├── schemas/ # Pydantic schemas for validation/serialization +│ ├── core/ # Business logic (services, utilities, etc.) +│ ├── config/ # settings.py & constants.py for configuration +│ └── main.py # FastAPI application entrypoint +├── Alembic/ # Database migrations managed with Alembic +│ ├── versions/ # Generated migration files +│ ├── env.py # Alembic environment setup +│ └── script.py.mako # Template for new migrations +├── tests/ # Unit and integration tests +├── alembic.ini # Alembic configuration file +├── py.ini # Python tool configurations (flake8, mypy, etc.) +├── .env.example # Template for environment variables +├── .gitignore # Files and paths ignored by Git +├── docker-compose.override.yml # Local overrides (e.g., hot-reload) +├── docker-compose.test.yml # Docker Compose setup for isolated testing +├── docker-compose.yml # Base Docker Compose configuration for development +├── Dockerfile # Instructions to build the production Docker image +├── poetry.lock # Locked dependency versions for Poetry +├── pyproject.toml # Poetry project configuration (including src layout) +└── README.md # Setup instructions and project overview + +``` + +**Key Responsibilities:** +* To be testable in isolation. +* To produce a versioned Docker image (`backend:`) as its main artifact. + +### 2.2. Frontend Service (`project-frontend`) + +This repository contains all code for the React web application. Its responsibility is to provide the user interface and interact with the backend via its API. + +**Folder Structure:** +``` +project-frontend/ +├── .github/ +│ └── workflows/ +│ └── main.yml # CI: Tests and publishes the frontend Docker image +├── public/ # Static assets (index.html, favicon, etc.) +├── src/ # Application source code +│ ├── api/ # Functions for backend API calls +│ ├── components/ # Reusable UI components +│ ├── hooks/ # Custom React hooks +│ ├── mocks/ # Service worker mocks for API (for isolated testing) +│ ├── pages/ # Page components +│ └── index.js # React application entrypoint +├── .env.example +├── .gitignore +├── docker-compose.yml # Base local development definition +├── Dockerfile # Multi-stage build for a lean production image +├── package.json +├── package-lock.json +└── README.md # Setup instructions for the frontend service +``` + +**Key Responsibilities:** +* To be testable in isolation (using a mocked API). +* To produce a versioned, production-ready Docker image (`frontend:`), typically served by Nginx. + +### 2.3. Infrastructure & E2E Tests (`project-master`) + +This repository is the "glue" that holds the system together. It does not contain application code but rather the configuration to orchestrate, test, and deploy the entire system. + +**Folder Structure:** +``` +project-master/ +├── .github/ +│ └── workflows/ +│ ├── e2e-tests.yml # CI: Runs End-to-End tests on a complete stack +│ └── deploy.yml # CD: Handles deployment to environments +├── e2e-tests/ # End-to-End test suite (e.g., Cypress, Playwright) +│ ├── cypress/ # Test code +│ └── cypress.config.js +├── environments/ # Environment-specific configurations +│ ├── staging/ +│ │ ├── docker-compose.yml # Docker Compose file for the Staging environment +│ │ └── .env.example +│ └── production/ +│ ├── docker-compose.yml # Docker Compose file for the Production environment +│ └── .env.example +├── scripts/ # Deployment and utility scripts +│ ├── deploy-staging.sh +│ └── deploy-prod.sh +└── README.md # Main project README: explains the overall architecture +``` + +**Key Responsibilities:** +* To define the composition of services for each environment (Staging, Production). +* To run End-to-End tests that validate the integration between services. +* To manage the Continuous Deployment (CD) process. + +## 3. Branching Strategy: Git Flow + +To manage code development and releases in a structured manner, we use the **Git Flow** branching model. + +### Git Flow Workflow Diagram + +```mermaid +--- +title: Git Flow +--- + +gitGraph + commit id: "Initial commit" + branch develop + checkout develop + commit id: "Setup Project" + + branch feature/user-authentication + checkout feature/user-authentication + commit id: "feat: Add login form" + commit id: "feat: Add form validation" + checkout develop + merge feature/user-authentication + + branch release/v1.0.0 + checkout release/v1.0.0 + commit id: "fix: Pre-release bug fixes" tag: "v1.0.0" + checkout main + merge release/v1.0.0 + checkout develop + merge release/v1.0.0 + + checkout main + branch hotfix/critical-login-bug + checkout hotfix/critical-login-bug + commit id: "fix: Critical production bug" tag: "v1.0.1" + checkout main + merge hotfix/critical-login-bug + checkout develop + merge hotfix/critical-login-bug +``` + +--- + +### Git Flow Explained + +This workflow is built upon two long-lived branches and several temporary, supporting branches. + +#### Main Branches + +1. **`main`** + * **Purpose**: This branch contains **production-ready, stable code**. Every commit on `main` represents a new, official release. + * **Rules**: You should **never** commit directly to `main`. It only receives merges from `release/*` and `hotfix/*` branches. Each merge should be tagged with a version number (e.g., `v1.0.0`). + +2. **`develop`** + * **Purpose**: This is the main **integration branch** for ongoing development. It contains all completed and tested features that are planned for the next release. + * **Rules**: It's the base for creating new `feature/*` branches. It reflects the most up-to-date state of development. + +#### Supporting Branches + +3. **`feature/*`** (e.g., `feature/user-authentication`) + * **Purpose**: To develop a new, specific feature in isolation. + * **Lifecycle**: + 1. Branched off of **`develop`**. + 2. Once development is complete, a **Pull Request (PR)** is opened to merge the changes back into **`develop`**. + 3. After the merge, the `feature/*` branch can be deleted. + +4. **`release/*`** (e.g., `release/v1.2.0`) + * **Purpose**: To prepare for a new production release. This branch is used for final bug fixes, documentation updates, and last-minute testing. It freezes the feature set for the release. + * **Lifecycle**: + 1. Branched off of **`develop`** when it's decided that the next version is feature-complete. + 2. When ready, it is merged into **`main`** (and tagged) and also back into **`develop`** to ensure any fixes made during the release phase are not lost. + 3. After merging, the `release/*` branch can be deleted. + +5. **`hotfix/*`** (e.g., `hotfix/critical-login-bug`) + * **Purpose**: To quickly patch a critical bug discovered in the production version (`main`). + * **Lifecycle**: + 1. Branched off of **`main`** (from the specific version tag). + 2. Once the fix is ready, it is merged into **`main`** (and a new patch version tag is created, e.g., `v1.0.1`). + 3. It is also merged into **`develop`** to ensure the fix is included in future releases. + 4. After merging, the `hotfix/*` branch can be deleted. + +## 4. Continuous Integration / Continuous Delivery (CI/CD) Pipeline + +A robust CI/CD pipeline guarantees that every change pushed to the repository is automatically validated, packaged, and—when appropriate—promoted to the next environment. +Our pipeline is built with **GitHub Actions** and follows a layered approach that mirrors the Git Flow branching model. + +We will start to describe the CI part related to push and PR in the develop branch of the backend service. + +### 4.1 CI for project-backend on `develop` + +#### 4.1.1 Goals + +* **Fast feedback** – linting, type-checking, and unit tests finish in under a minute for every Pull Request. +* **Confidence in integration** – migrations, integration tests, and Docker smoke-tests run on every push to `develop`. +* **Deployment safety** – only artifacts produced from a green pipeline can be released or deployed. +* **Parity with production** – the same multi-stage Dockerfile is built and probed in CI, preventing “works-on-my-machine” surprises. + +### 4.1.2 Pipeline Layers + +* **Quick Suite (PR to `develop`)** + *Runs in seconds; no external services or containers.* + + * Black, isort, Flake8 + * mypy static type-checking + * Unit tests only (`pytest -m "not integration"`) + +* **Full Suite (push to `develop`)** + *Runs in a few minutes; includes real services and Docker.* + + * All steps from the Quick Suite + * PostgreSQL service container started via `services:` + * Alembic migrations applied to the test database + * Full test suite, including `@pytest.mark.integration` tests + * Multi-stage Docker build of the backend image + * Smoke test: container started with Uvicorn → `curl /health` + + +### 4.1.3 Key Implementation Details + +* **Service containers** – PostgreSQL 17 is spun up in CI with a health-check to ensure migrations run against a live instance. +* **Test markers** – integration tests are isolated with `@pytest.mark.integration`, enabling selective execution. +* **Caching** – Poetry’s download cache is restored to cut installation time; Docker layer cache is reused between builds. +* **Smoke test logic** – after the image is built, CI launches it in detached mode, polls the `/health` endpoint, prints logs, and stops the container. The job fails if the endpoint is unreachable. +* **Secrets management** – database credentials and registry tokens are stored in GitHub Secrets and injected as environment variables only at runtime. + + diff --git a/docker/.env.dev b/docker_fs/.env.dev similarity index 100% rename from docker/.env.dev rename to docker_fs/.env.dev diff --git a/docker/.env.example b/docker_fs/.env.example similarity index 100% rename from docker/.env.example rename to docker_fs/.env.example diff --git a/docker/.env.test b/docker_fs/.env.test similarity index 100% rename from docker/.env.test rename to docker_fs/.env.test diff --git a/docker/docker-compose.dev.yml b/docker_fs/docker-compose.dev.yml similarity index 100% rename from docker/docker-compose.dev.yml rename to docker_fs/docker-compose.dev.yml diff --git a/docker/docker-compose.prod.yml b/docker_fs/docker-compose.prod.yml similarity index 100% rename from docker/docker-compose.prod.yml rename to docker_fs/docker-compose.prod.yml diff --git a/scripts/init-docker-dev.sh b/scripts/init-docker-dev.sh index 6e1e270..80f06d8 100644 --- a/scripts/init-docker-dev.sh +++ b/scripts/init-docker-dev.sh @@ -11,9 +11,9 @@ set -euo pipefail SCRIPT_PATH="$(realpath "${BASH_SOURCE[0]}")" SCRIPT_DIR="$(cd "$(dirname "$SCRIPT_PATH")" && pwd)" PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" -COMPOSE_FILE="$PROJECT_ROOT/docker/docker-compose.dev.yml" -ENV_DEV="$PROJECT_ROOT/docker/.env.dev" -ENV_DOT="$PROJECT_ROOT/docker/.env" +COMPOSE_FILE="$PROJECT_ROOT/docker_fs/docker-compose.dev.yml" +ENV_DEV="$PROJECT_ROOT/docker_fs/.env.dev" +ENV_DOT="$PROJECT_ROOT/docker_fs/.env" # ────────────────────────────────────────────────────────────────────────────── # 0.1 Make script executable @@ -23,7 +23,7 @@ if [[ ! -x "$SCRIPT_PATH" ]]; then fi # ────────────────────────────────────────────────────────────────────────────── -# 0.2 Ensure docker/.env exists for Compose interpolation +# 0.2 Ensure docker_fs/.env exists for Compose interpolation # ────────────────────────────────────────────────────────────────────────────── if [[ -f "$ENV_DEV" && ! -f "$ENV_DOT" ]]; then echo ">>> Copying .env.dev → .env for Compose interpolation" From 9e48446d9bc693e55c743e062027d507f34eb3e2 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 3 Jul 2025 15:23:05 +0200 Subject: [PATCH 02/52] minor correction --- alembic/env.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/alembic/env.py b/alembic/env.py index 9f0ce10..2e671fd 100644 --- a/alembic/env.py +++ b/alembic/env.py @@ -5,7 +5,7 @@ from app.config.settings import settings from app.db.base import Base -import app.models.table_users + # this is the Alembic Config object, which provides # access to the values within the .ini file in use. From 7a0f86079ff3a3df36cdcf825184ba32f07e9639 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 3 Jul 2025 15:26:04 +0200 Subject: [PATCH 03/52] minor changes --- pyproject.toml | 2 -- src/app/main.py | 4 ++-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0fb29d2..2d76c41 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,8 +19,6 @@ gunicorn = "^23.0.0" pydantic-settings = "^2.10.1" pydantic = {extras = ["email"], version = "^2.11.7"} asyncpg = "^0.30.0" -passlib = {extras = ["argon2"], version = "^1.7.4"} -argon2-cffi = "^25.1.0" sqlalchemy-utils = "^0.41.2" [tool.poetry.group.dev.dependencies] diff --git a/src/app/main.py b/src/app/main.py index 4e85b19..4a78144 100644 --- a/src/app/main.py +++ b/src/app/main.py @@ -5,7 +5,7 @@ from fastapi import FastAPI -from app.api.auth import router as auth_router + from app.api.health_check import router as health_router from app.config.settings import settings from app.db.init_db import close_engine, init_models @@ -31,4 +31,4 @@ async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]: ) app.include_router(health_router) -app.include_router(auth_router) + From 1ab21d52f236508b4016a809f64e7f76692fb642 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 3 Jul 2025 15:55:40 +0200 Subject: [PATCH 04/52] improved script for linux --- poetry.lock | 1592 +++++++++++++++++ scripts/init-docker-dev.sh | 17 +- src/app/config/constants.py | 19 - src/app/config/settings.py | 4 +- .../db_initialization/test_init_models.py | 17 +- 5 files changed, 1617 insertions(+), 32 deletions(-) create mode 100644 poetry.lock diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..de1465a --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1592 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "alembic" +version = "1.16.2" +description = "A database migration tool for SQLAlchemy." +optional = false 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b/scripts/init-docker-dev.sh @@ -59,8 +59,23 @@ docker compose \ -f "$COMPOSE_FILE" up -d db pgadmin # ────────────────────────────────────────────────────────────────────────────── -# 4. Run Alembic migrations +# 4. Wait for Postgres to be ready, then run Alembic migrations # ────────────────────────────────────────────────────────────────────────────── +PGUSER="${DB_USER:-${POSTGRES_USER:-postgres}}" + +echo ">>> Waiting for Postgres to be ready..." +MAX_RETRIES=30 +RETRIES=0 +until docker exec docker_fs-db-1 pg_isready -U "$PGUSER" > /dev/null 2>&1; do + sleep 1 + RETRIES=$((RETRIES+1)) + if [ "$RETRIES" -ge "$MAX_RETRIES" ]; then + echo "ERROR: Postgres non si è avviato in tempo." >&2 + exit 1 + fi +done +echo ">>> Postgres is ready!" + echo ">>> Applying database migrations (Alembic)…" cd "$PROJECT_ROOT" poetry run alembic upgrade head diff --git a/src/app/config/constants.py b/src/app/config/constants.py index e5e9d0b..14ed238 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -1,23 +1,4 @@ """Application constants and configuration values.""" -from enum import Enum - - -class UserLevel(str, Enum): - """constants to define the different levels the user can have in the application""" - - USER = "user" - ADMIN = "admin" - -class SubscriptionLevel(str, Enum): - """ - constants to define the different subscription levels the user can have - in the application - """ - - FREE = "free" - EXPERT = "expert" - PRO = "pro" - ENTERPRISE = "enterprise" diff --git a/src/app/config/settings.py b/src/app/config/settings.py index 69ee4ad..7fc3d9d 100644 --- a/src/app/config/settings.py +++ b/src/app/config/settings.py @@ -7,7 +7,7 @@ from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict -env_path = Path(__file__).resolve().parents[3] / "docker" / ".env.dev" +env_path = Path(__file__).resolve().parents[3] / "docker_fs" / ".env.dev" if env_path.exists(): load_dotenv(dotenv_path=env_path) @@ -17,7 +17,7 @@ class Settings(BaseSettings): model_config = SettingsConfigDict(env_file=None, extra="ignore") - app_name: str = "Project Backend" + app_name: str = "FastSim" environment: Literal["development", "staging", "production", "test"] = Field( default="development", diff --git a/tests/integration/db_initialization/test_init_models.py b/tests/integration/db_initialization/test_init_models.py index 7783bdc..05721eb 100644 --- a/tests/integration/db_initialization/test_init_models.py +++ b/tests/integration/db_initialization/test_init_models.py @@ -1,6 +1,7 @@ import pytest from sqlalchemy import text from sqlalchemy.ext.asyncio import create_async_engine +from sqlalchemy.exc import SQLAlchemyError from app.config.settings import settings @@ -8,13 +9,9 @@ async def test_users_table_exists_after_migrations() -> None: engine = create_async_engine(settings.db_url, echo=False) - async with engine.connect() as conn: - result = await conn.execute( - text( - "SELECT COUNT(*) " - "FROM information_schema.tables " - "WHERE table_schema = 'public' " - " AND table_name = 'users';", - ), - ) - assert result.scalar_one() == 1 + try: + async with engine.connect() as conn: + await conn.execute(text("SELECT 1")) + except SQLAlchemyError: + pytest.fail("Database connection or Alembic setup failed.") + From 2a4675fb8693df1957501ea3bf3ad963d71b953c Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 3 Jul 2025 15:58:40 +0200 Subject: [PATCH 05/52] minor change --- tests/conftest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index 842f5ae..5fcd4bc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -23,7 +23,7 @@ from app.main import app # Load test environment variables from .env.test -ENV_PATH = Path(__file__).resolve().parents[1] / "docker" / ".env.test" +ENV_PATH = Path(__file__).resolve().parents[1] / "docker_fs" / ".env.test" load_dotenv(dotenv_path=ENV_PATH, override=True) From 91c0a7eb17a89f182ff928edebe2f23027d1cd40 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 4 Jul 2025 18:20:06 +0200 Subject: [PATCH 06/52] Delete docker_fs/.env.dev --- docker_fs/.env.dev | 11 ----------- 1 file changed, 11 deletions(-) delete mode 100644 docker_fs/.env.dev diff --git a/docker_fs/.env.dev b/docker_fs/.env.dev deleted file mode 100644 index 911bc85..0000000 --- a/docker_fs/.env.dev +++ /dev/null @@ -1,11 +0,0 @@ -DB_HOST=db -DB_PORT=5432 -DB_NAME=project_backend_dev -DB_USER=dev_user -DB_PASSWORD=dev_pass -DB_URL=postgresql+asyncpg://dev_user:dev_pass@db:5432/project_backend_dev - -PGADMIN_DEFAULT_EMAIL=admin@example.com -PGADMIN_DEFAULT_PASSWORD=secret - -ENVIRONMENT=development \ No newline at end of file From ab6c48ae1513ed502971b2ab8c5fab9710c4068c Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 4 Jul 2025 18:20:17 +0200 Subject: [PATCH 07/52] Delete docker_fs/.env.test --- docker_fs/.env.test | 12 ------------ 1 file changed, 12 deletions(-) delete mode 100644 docker_fs/.env.test diff --git a/docker_fs/.env.test b/docker_fs/.env.test deleted file mode 100644 index 2ae2f2e..0000000 --- a/docker_fs/.env.test +++ /dev/null @@ -1,12 +0,0 @@ - -ENVIRONMENT=test - - -DB_HOST=db -DB_PORT=5432 -DB_USER=postgres -DB_PASSWORD=testpassword -DB_NAME=test_db - - -DB_URL=postgresql+asyncpg://postgres:testpassword@db:5432/test_db From c06166b7eff7a02406376ed63efd43ee10854d35 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Sun, 6 Jul 2025 15:44:16 +0200 Subject: [PATCH 08/52] Features/event generator (#1) * setting up the generator * poisson-poisson requests sampling * Delete docker_fs/.env.dev * Delete docker_fs/.env.test * changes * refactor for event generator * added gaussian truncated generator and unit tests for helpers * imprved documentantion and simulation time handling * simulation start with the vent generator * small refactor + tests for the generation of events * minor changes * Update src/app/schemas/simulation_input.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update tests/unit/sampler/test_sampler_helper.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update scripts/quality-check.sh Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update tests/integration/db_initialization/test_init_models.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * minor changes after PR review --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .github/workflows/ci-develop.yml | 2 + .gitignore | 2 + .../DEV_WORKFLOW_GUIDE.md | 73 +------ .../integration_tests/test_sampler_helper.md | 50 +++++ .../test_simulation_input.md | 93 ++++++++ poetry.lock | 73 ++++++- pyproject.toml | 6 +- scripts/quality-check.sh | 10 + src/app/api/simulation.py | 18 ++ src/app/config/constants.py | 7 + src/app/core/auth_helpers.py | 62 ------ src/app/core/event_samplers/common_helpers.py | 33 +++ .../core/event_samplers/gaussian_poisson.py | 98 +++++++++ .../core/event_samplers/poisson_poisson.py | 86 ++++++++ src/app/core/simulation/requests_generator.py | 51 +++++ src/app/core/simulation/simulation_run.py | 56 +++++ src/app/main.py | 1 - src/app/schemas/simulation_input.py | 58 +++++ src/app/schemas/simulation_output.py | 12 ++ .../db_initialization/test_init_models.py | 2 +- tests/unit/sampler/test_sampler_helper.py | 130 ++++++++++++ .../simulation/test_requests_generator.py | 198 ++++++++++++++++++ .../unit/simulation/test_simulation_input.py | 93 ++++++++ 23 files changed, 1076 insertions(+), 138 deletions(-) rename DEV_WORKFLOW_GUIDE.md => documentation/DEV_WORKFLOW_GUIDE.md (70%) create mode 100644 documentation/tests_documentation/integration_tests/test_sampler_helper.md create mode 100644 documentation/tests_documentation/integration_tests/test_simulation_input.md create mode 100644 scripts/quality-check.sh create mode 100644 src/app/api/simulation.py delete mode 100644 src/app/core/auth_helpers.py create mode 100644 src/app/core/event_samplers/common_helpers.py create mode 100644 src/app/core/event_samplers/gaussian_poisson.py create mode 100644 src/app/core/event_samplers/poisson_poisson.py create mode 100644 src/app/core/simulation/requests_generator.py create mode 100644 src/app/core/simulation/simulation_run.py create mode 100644 src/app/schemas/simulation_input.py create mode 100644 src/app/schemas/simulation_output.py create mode 100644 tests/unit/sampler/test_sampler_helper.py create mode 100644 tests/unit/simulation/test_requests_generator.py create mode 100644 tests/unit/simulation/test_simulation_input.py diff --git a/.github/workflows/ci-develop.yml b/.github/workflows/ci-develop.yml index edcd143..480f075 100644 --- a/.github/workflows/ci-develop.yml +++ b/.github/workflows/ci-develop.yml @@ -55,6 +55,8 @@ jobs: # Unit-tests only (exclude integration markers) - name: Run unit tests + env: + ENVIRONMENT: test run: poetry run pytest -m "not integration" --disable-warnings diff --git a/.gitignore b/.gitignore index 56d248a..58026c9 100644 --- a/.gitignore +++ b/.gitignore @@ -30,6 +30,8 @@ pip-wheel-metadata/ venv/ ENV/ env/ +docker_fs/.env +docker_fs/.env.* # Poetry-specific .cache/pypoetry/ diff --git a/DEV_WORKFLOW_GUIDE.md b/documentation/DEV_WORKFLOW_GUIDE.md similarity index 70% rename from DEV_WORKFLOW_GUIDE.md rename to documentation/DEV_WORKFLOW_GUIDE.md index 3ae74de..c08cea4 100644 --- a/DEV_WORKFLOW_GUIDE.md +++ b/documentation/DEV_WORKFLOW_GUIDE.md @@ -1,6 +1,6 @@ # **Development Workflow & Architecture Guide** -This document outlines the standardized development workflow, repository architecture, and branching strategy for this project. Adhering to these guidelines ensures consistency, maintainability, and a scalable development process. +This document outlines the standardized development workflow, repository architecture, and branching strategy for the backend of the FastSim project. Adhering to these guidelines ensures consistency, maintainability, and a scalable development process. ## 1. Technology Stack @@ -13,13 +13,7 @@ The project is built upon the following core technologies: - **Caching**: Redis - **Containerization**: Docker -## 2. Architectural Overview: A Multi-Repo Strategy - -To promote scalability, team autonomy, and clear separation of concerns, this project adopts a **multi-repo architecture**. Each core component of the system resides in its own dedicated repository. This approach allows for independent development cycles, testing, and deployment. - -Our architecture is composed of three main repositories: - -### 2.1. Backend Service (`project-backend`) +### 2.1. Backend Service (`FastSim-backend`) This repository contains all code related to the FastAPI backend service. Its primary responsibility is to handle business logic, interact with the database, and expose a RESTful API. @@ -61,69 +55,6 @@ project-backend/ * To be testable in isolation. * To produce a versioned Docker image (`backend:`) as its main artifact. -### 2.2. Frontend Service (`project-frontend`) - -This repository contains all code for the React web application. Its responsibility is to provide the user interface and interact with the backend via its API. - -**Folder Structure:** -``` -project-frontend/ -├── .github/ -│ └── workflows/ -│ └── main.yml # CI: Tests and publishes the frontend Docker image -├── public/ # Static assets (index.html, favicon, etc.) -├── src/ # Application source code -│ ├── api/ # Functions for backend API calls -│ ├── components/ # Reusable UI components -│ ├── hooks/ # Custom React hooks -│ ├── mocks/ # Service worker mocks for API (for isolated testing) -│ ├── pages/ # Page components -│ └── index.js # React application entrypoint -├── .env.example -├── .gitignore -├── docker-compose.yml # Base local development definition -├── Dockerfile # Multi-stage build for a lean production image -├── package.json -├── package-lock.json -└── README.md # Setup instructions for the frontend service -``` - -**Key Responsibilities:** -* To be testable in isolation (using a mocked API). -* To produce a versioned, production-ready Docker image (`frontend:`), typically served by Nginx. - -### 2.3. Infrastructure & E2E Tests (`project-master`) - -This repository is the "glue" that holds the system together. It does not contain application code but rather the configuration to orchestrate, test, and deploy the entire system. - -**Folder Structure:** -``` -project-master/ -├── .github/ -│ └── workflows/ -│ ├── e2e-tests.yml # CI: Runs End-to-End tests on a complete stack -│ └── deploy.yml # CD: Handles deployment to environments -├── e2e-tests/ # End-to-End test suite (e.g., Cypress, Playwright) -│ ├── cypress/ # Test code -│ └── cypress.config.js -├── environments/ # Environment-specific configurations -│ ├── staging/ -│ │ ├── docker-compose.yml # Docker Compose file for the Staging environment -│ │ └── .env.example -│ └── production/ -│ ├── docker-compose.yml # Docker Compose file for the Production environment -│ └── .env.example -├── scripts/ # Deployment and utility scripts -│ ├── deploy-staging.sh -│ └── deploy-prod.sh -└── README.md # Main project README: explains the overall architecture -``` - -**Key Responsibilities:** -* To define the composition of services for each environment (Staging, Production). -* To run End-to-End tests that validate the integration between services. -* To manage the Continuous Deployment (CD) process. - ## 3. Branching Strategy: Git Flow To manage code development and releases in a structured manner, we use the **Git Flow** branching model. diff --git a/documentation/tests_documentation/integration_tests/test_sampler_helper.md b/documentation/tests_documentation/integration_tests/test_sampler_helper.md new file mode 100644 index 0000000..9084ed8 --- /dev/null +++ b/documentation/tests_documentation/integration_tests/test_sampler_helper.md @@ -0,0 +1,50 @@ +Below is a guided walkthrough of **`tests/unit/simulation/test_sampler_helper.py`**, explaining core ideas and each test’s intent. + +--- + +## File purpose + +This file verifies that your three helper functions— + +* `uniform_variable_generator` +* `poisson_variable_generator` +* `truncated_gaussian_generator` + +—correctly delegate to whatever RNG you pass in, and fall back to NumPy’s default RNG when you don’t provide one. + +--- + +## Key testing patterns + +1. **Dependency injection via `rng`** + Each helper takes an `rng` parameter. In production you’ll pass a `np.random.Generator`; in tests we inject a **`DummyRNG`** with predictable outputs to make our tests **deterministic**. + +2. **Duck typing** + Python doesn’t require `rng` to be a specific class—only that it implements the required methods (`random()`, `poisson(mean)`, `normal(mean, sigma)`). Our `DummyRNG` simply implements those three methods. + +3. **`typing.cast` for static typing** + We wrap `DummyRNG` instances in `cast("np.random.Generator", DummyRNG(...))` so mypy sees them as satisfying the generator type, but at runtime they remain our dummy. + +--- + +## Test-by-test breakdown + +| Test name | What it checks | +| -------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | +| **`test_uniform_variable_generator_with_dummy_rng`** | Passing a `DummyRNG(uniform_value=0.75)`, `rng.random()` returns 0.75 → helper must return exactly 0.75. | +| **`test_uniform_variable_generator_default_rng_range`** | Without supplying `rng`, the helper uses `default_rng()`. We call it 100× to ensure it always returns a `float` in \[0.0, 1.0). | +| **`test_poisson_variable_generator_with_dummy_rng`** | With `DummyRNG(poisson_value=3)`, `rng.poisson(mean)` yields 3 → helper returns 3. | +| **`test_poisson_variable_generator_reproducible`** | Two NumPy generators created with the same seed (`12345`) must produce the same Poisson sample for `mean=10.0`. | +| **`test_truncated_gaussian_generator_truncates_negative`** | `DummyRNG(normal_value=-2.7)` forces a negative draw: helper must clamp it to **0**. | +| **`test_truncated_gaussian_generator_truncates_toward_zero`** | `DummyRNG(normal_value=3.9)` forces a positive draw: helper must cast/round toward zero (int(3.9) → **3**). | +| **`test_truncated_gaussian_generator_default_rng_non_negative_int`** | With a real seeded RNG, helper must produce **some** non-negative `int` (verifies default fallback path is valid). | + +--- + +## Why this matters + +* **Deterministic behavior**: by forcing the RNG’s output via `DummyRNG`, we can assert exactly how our helpers transform that value (clamping, rounding, type conversion). +* **Fallbacks work**: tests with **no** `rng` verify that calling `default_rng()` still gives valid outputs of the correct type and range. +* **Type safety**: using `cast(...)` silences mypy errors while still executing our dummy logic at runtime—ensuring we meet both static‐typing and functional correctness goals. + +With this suite, you have **full confidence** that your sampling helpers behave correctly under both controlled (dummy) and uncontrolled (default) RNG conditions. diff --git a/documentation/tests_documentation/integration_tests/test_simulation_input.md b/documentation/tests_documentation/integration_tests/test_simulation_input.md new file mode 100644 index 0000000..f555571 --- /dev/null +++ b/documentation/tests_documentation/integration_tests/test_simulation_input.md @@ -0,0 +1,93 @@ +Below an explanation for the unit tests in the file `tests/unit/simulation/test_simulation_input.py` + +### 1. `test_normal_sets_variance_to_mean` + +**Purpose:** +Checks that when you create an `RVConfig` with `distribution="normal"` and omit the `variance` field, the model automatically sets `variance = mean`. + +* Verifies the “default variance” logic in the post‐init validator. + +--- + +### 2. `test_poisson_keeps_variance_none` + +**Purpose:** +Ensures that if you choose the Poisson distribution (`distribution="poisson"`) and do **not** supply a variance, the model **does not** fill in any default variance (keeps it `None`). + +* Confirms that defaulting only applies to “normal”/“gaussian,” not to Poisson. + +--- + +### 3. `test_explicit_variance_is_preserved` + +**Purpose:** +Validates that if you explicitly pass a `variance` value—even for a distribution that would normally default—it remains exactly what you provided, and is coerced to float. + +* Guards against accidental overwriting of user‐supplied variance. + +--- + +### 4. `test_mean_must_be_numeric` + +**Purpose:** +Verifies that giving a non‐numeric `mean` (e.g. a string) raises a `ValidationError` with our custom message `"mean must be a number"`. + +* Tests the “before” validator on the `mean` field for type checking and coercion. + +--- + +### 5. `test_missing_mean_field` + +**Purpose:** +Ensures that completely omitting the `mean` key triggers a standard “field required” error. + +* Confirms that `mean` is mandatory in the schema. + +--- + +### 6. `test_gaussian_sets_variance_to_mean` + +**Purpose:** +Exactly like the “normal” test above, but for `distribution="gaussian"`. + +* Demonstrates that “gaussian” is treated as an alias for “normal” in the default‐variance logic. + +--- + +### 7. `test_default_distribution_is_poisson` + +**Purpose:** +Checks two things simultaneously: + +1. When you omit `distribution`, it defaults to `"poisson"`. +2. In that default‐poisson case, `variance` remains `None`. + +* Validates both the default distribution and its variance behavior in one test. + +--- + +### 8. `test_explicit_variance_kept_for_poisson` + +**Purpose:** +Confirms that even if you supply a `variance` when `distribution="poisson"`, the model preserves it rather than discarding it or forcing it back to `None`. + +* Provides symmetry to the “explicit variance” test for non‐Poisson cases. + +--- + +### 9. `test_invalid_distribution_raises` + +**Purpose:** +Ensures that passing a value for `distribution` outside of the allowed literals (`"poisson"`, `"normal"`, `"gaussian"`) results in a `ValidationError`. + +* Confirms that the `Literal[...]` constraint on `distribution` is enforced. + +--- + +With these nine tests you fully cover: + +1. **Defaulting behavior** for both “normal” and “gaussian.” +2. **No‐op behavior** for Poisson defaults. +3. **Preservation** of explicit user input. +4. **Type‐checking** on required fields. +5. 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"sha256:806bbc17f1104fd57451a98a58df35388ee3ab422e029e8f5cf30aa4af2c138c"}, ] +[[package]] +name = "simpy" +version = "4.1.1" +description = "Event discrete, process based simulation for Python." +optional = false +python-versions = ">=3.8" +files = [ + {file = "simpy-4.1.1-py3-none-any.whl", hash = "sha256:7c5ae380240fd2238671160e4830956f8055830a8317edf5c05e495b3823cd88"}, + {file = "simpy-4.1.1.tar.gz", hash = "sha256:06d0750a7884b11e0e8e20ce0bc7c6d4ed5f1743d456695340d13fdff95001a6"}, +] + [[package]] name = "sniffio" version = "1.3.1" @@ -1589,4 +1660,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.12" -content-hash = "387c837941fad4e69a6a11d22f94c0b0ac3f5d2688fe196b31a9beb3a00b3dbe" +content-hash = "71f5d3bb002c33ab3bad9d5e9efadc144b033c272b4319c2686728026dd5d849" diff --git a/pyproject.toml b/pyproject.toml index 2d76c41..f5fc34a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] -name = "project-backend" +name = "FastSim-backend" version = "0.1.0" -description = "solo professional project" +description = "Simulate fastapi event loop to manage resources" authors = ["Gioele Botta"] readme = "README.md" @@ -20,6 +20,8 @@ pydantic-settings = "^2.10.1" pydantic = {extras = ["email"], version = "^2.11.7"} asyncpg = "^0.30.0" sqlalchemy-utils = "^0.41.2" +numpy = "^2.3.1" +simpy = "^4.1.1" [tool.poetry.group.dev.dependencies] pytest = "^8.4.1" diff --git a/scripts/quality-check.sh b/scripts/quality-check.sh new file mode 100644 index 0000000..eb2a66f --- /dev/null +++ b/scripts/quality-check.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Lint & format with ruff, automatic corrections applied (--fix) +poetry run ruff check src tests --fix + +# Type‐check with mypy +poetry run mypy src tests + +echo "✅ Linting and type‐checking completed SUCCESSFULLY" \ No newline at end of file diff --git a/src/app/api/simulation.py b/src/app/api/simulation.py new file mode 100644 index 0000000..55081c6 --- /dev/null +++ b/src/app/api/simulation.py @@ -0,0 +1,18 @@ +""""Api to simulate the process""" + +import numpy as np +from fastapi import APIRouter + +from app.core.simulation.simulation_run import run_simulation +from app.schemas.simulation_input import SimulationInput +from app.schemas.simulation_output import SimulationOutput + +router = APIRouter() + +@router.post("/simulation") +async def event_loop_simulation(input_data: SimulationInput) -> SimulationOutput: + """Run the simulation and return aggregate KPIs.""" + rng = np.random.default_rng() + return run_simulation(input_data, rng=rng) + + diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 14ed238..fb0c231 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -1,4 +1,11 @@ """Application constants and configuration values.""" +from enum import IntEnum +class TimeDefaults(IntEnum): + """Default time-related constants (all in seconds).""" + + MIN_TO_SEC = 60 # 1 minute → 60 s + SAMPLING_WINDOW = 60 # keep U(t) constant for 60 s + SIMULATION_HORIZON = 3_600 # run 1 h if user gives no other value diff --git a/src/app/core/auth_helpers.py b/src/app/core/auth_helpers.py deleted file mode 100644 index 4a3437a..0000000 --- a/src/app/core/auth_helpers.py +++ /dev/null @@ -1,62 +0,0 @@ -"""helper functions for the auth process""" - -import string -from typing import cast - -from passlib.context import CryptContext - -MAX_PASSWORD_LENGTH = 128 # prevent DoS -MIN_PASSWORD_LENGTH = 8 -SPECIAL_CHARS = set(string.punctuation) - -pwd_context = CryptContext( - schemes=["argon2"], - deprecated="auto", - -) - -def is_password_safe(password: str) -> bool: - """Function to verify if a password is safe""" - has_upper = False - has_lower = False - has_digit = False - has_special = False - - if len(password) < MIN_PASSWORD_LENGTH or len(password) > MAX_PASSWORD_LENGTH: - return False - - for char in password: - if char.isupper(): - has_upper = True - elif char.islower(): - has_lower = True - elif char.isdigit(): - has_digit = True - elif char in SPECIAL_CHARS: - has_special = True - - if has_upper and has_lower and has_digit and has_special: - return True - - return False - - -def verify_passwords_equality(password: str, confirmed_password: str) -> bool: - """Verify if the passwords given in the registration form are equivalent.""" - return password == confirmed_password - - -def hash_password(password: str) -> str: - """ - Receive the password - give back the hashed password - """ - return cast("str", pwd_context.hash(password)) - - -def verify_hashed_pwd_plain_pwd_equality(plain_pwd: str, hashed_pwd: str) -> bool: - """ - Verify during the login if the plain password - correspond to the hashed password - """ - return cast("bool", pwd_context.verify(plain_pwd, hashed_pwd)) diff --git a/src/app/core/event_samplers/common_helpers.py b/src/app/core/event_samplers/common_helpers.py new file mode 100644 index 0000000..0f80870 --- /dev/null +++ b/src/app/core/event_samplers/common_helpers.py @@ -0,0 +1,33 @@ +"""Helpers function for the request generator""" + + +import numpy as np + + +def uniform_variable_generator(rng: np.random.Generator | None = None) -> float: + """Return U~Uniform(0, 1).""" + rng = rng or np.random.default_rng() + return float(rng.random()) + + +def poisson_variable_generator( + mean: float, + rng: np.random.Generator | None = None, +) -> int: + """Return a Poisson-distributed integer with expectation *mean*.""" + rng = rng or np.random.default_rng() + return int(rng.poisson(mean)) + + +def truncated_gaussian_generator( + mean: float, + variance: float, + rng: np.random.Generator, +) -> int: + """ + Generate a Normal-distributed variable + with mean and variance + """ + rng = rng or np.random.default_rng() + value = rng.normal(mean, variance) + return max(0, int(value)) diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/core/event_samplers/gaussian_poisson.py new file mode 100644 index 0000000..2239626 --- /dev/null +++ b/src/app/core/event_samplers/gaussian_poisson.py @@ -0,0 +1,98 @@ +""" +event sampler in the case of gaussian distribution +for concurrent user and poisson distribution for rqs per minute per user. +The rationale behind this choice is about considering scenario +with variance bigger or smaller w.r.t the one inherited from +the Poisson distribution +""" + +import math +from collections.abc import Generator + +import numpy as np + +from app.config.constants import TimeDefaults +from app.core.event_samplers.common_helpers import ( + truncated_gaussian_generator, + uniform_variable_generator, +) +from app.schemas.simulation_input import SimulationInput + + +def gaussian_poisson_sampling( + input_data: SimulationInput, + *, + sampling_window_s: int = TimeDefaults.SAMPLING_WINDOW.value, + rng: np.random.Generator | None = None, +) -> Generator[float, None, None]: + """ + Yield inter-arrival gaps (seconds) for the compound Gaussian-Poisson process. + + Algorithm + --------- + 1. Every *sampling_window_s* seconds, draw + U ~ Gaussian(mean_concurrent_user, variance). + 2. Compute the aggregate rate + Λ = U * (mean_req_per_minute_per_user / 60) [req/s]. + 3. While inside the current window, draw gaps + Δt ~ Exponential(Λ) using inverse-CDF. + 4. Stop once the virtual clock exceeds *simulation_time*. + """ + rng = rng or np.random.default_rng() + + simulation_time = input_data.total_simulation_time + # pydantic in the validation assign a value and mypy is not + # complaining because a None cannot be compared in the loop + # to a float + assert simulation_time is not None + + # λ_u : mean concurrent users per window + mean_concurrent_user = float(input_data.avg_active_users.mean) + + # Let's be sure that the variance is not None (guaranteed from pydantic) + variance_concurrent_user = input_data.avg_active_users.variance + assert variance_concurrent_user is not None + variance_concurrent_user = float(variance_concurrent_user) + + # λ_r / 60 : mean req/s per user + mean_req_per_sec_per_user = ( + float( + input_data.avg_request_per_minute_per_user.mean) + / TimeDefaults.MIN_TO_SEC.value + ) + + now = 0.0 # virtual clock (s) + window_end = 0.0 # end of the current user window + lam = 0.0 # aggregate rate Λ (req/s) + + while now < simulation_time: + # (Re)sample U at the start of each window + if now >= window_end: + window_end = now + float(sampling_window_s) + users = truncated_gaussian_generator( + mean_concurrent_user, + variance_concurrent_user, + rng, + ) + lam = users * mean_req_per_sec_per_user + + # No users → fast-forward to next window + if lam <= 0.0: + now = window_end + continue + + # Exponential gap from a protected uniform value + u_raw = max(uniform_variable_generator(rng), 1e-15) + delta_t = -math.log(1.0 - u_raw) / lam + + # End simulation if the next event exceeds the horizon + if now + delta_t > simulation_time: + break + + # If the gap crosses the window boundary, jump to it + if now + delta_t >= window_end: + now = window_end + continue + + now += delta_t + yield delta_t diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/core/event_samplers/poisson_poisson.py new file mode 100644 index 0000000..37c2da5 --- /dev/null +++ b/src/app/core/event_samplers/poisson_poisson.py @@ -0,0 +1,86 @@ +""" +event sampler in the case of poisson distribution +both for concurrent user and rqs per minute per user +""" + +import math +from collections.abc import Generator + +import numpy as np + +from app.config.constants import TimeDefaults +from app.core.event_samplers.common_helpers import ( + poisson_variable_generator, + uniform_variable_generator, +) +from app.schemas.simulation_input import SimulationInput + + +def poisson_poisson_sampling( + input_data: SimulationInput, + *, + sampling_window_s: int = TimeDefaults.SAMPLING_WINDOW.value, + rng: np.random.Generator | None = None, +) -> Generator[float, None, None]: + """ + Yield inter-arrival gaps (seconds) for the compound Poisson-Poisson process. + + Algorithm + --------- + 1. Every *sampling_window_s* seconds, draw + U ~ Poisson(mean_concurrent_user). + 2. Compute the aggregate rate + Λ = U * (mean_req_per_minute_per_user / 60) [req/s]. + 3. While inside the current window, draw gaps + Δt ~ Exponential(Λ) using inverse-CDF. + 4. Stop once the virtual clock exceeds *simulation_time*. + """ + rng = rng or np.random.default_rng() + + simulation_time = input_data.total_simulation_time + # pydantic in the validation assign a value and mypy is not + # complaining because a None cannot be compared in the loop + # to a float + assert simulation_time is not None + + # λ_u : mean concurrent users per window + mean_concurrent_user = float(input_data.avg_active_users.mean) + + # λ_r / 60 : mean req/s per user + mean_req_per_sec_per_user = ( + float( + input_data.avg_request_per_minute_per_user.mean) + / TimeDefaults.MIN_TO_SEC.value + ) + + now = 0.0 # virtual clock (s) + window_end = 0.0 # end of the current user window + lam = 0.0 # aggregate rate Λ (req/s) + + while now < simulation_time: + # (Re)sample U at the start of each window + if now >= window_end: + window_end = now + float(sampling_window_s) + users = poisson_variable_generator(mean_concurrent_user, rng) + lam = users * mean_req_per_sec_per_user + + # No users → fast-forward to next window + if lam <= 0.0: + now = window_end + continue + + # Exponential gap from a protected uniform value + u_raw = max(uniform_variable_generator(rng), 1e-15) + delta_t = -math.log(1.0 - u_raw) / lam + + # End simulation if the next event exceeds the horizon + if now + delta_t > simulation_time: + break + + # If the gap crosses the window boundary, jump to it + if now + delta_t >= window_end: + now = window_end + continue + + now += delta_t + yield delta_t diff --git a/src/app/core/simulation/requests_generator.py b/src/app/core/simulation/requests_generator.py new file mode 100644 index 0000000..0da1c1c --- /dev/null +++ b/src/app/core/simulation/requests_generator.py @@ -0,0 +1,51 @@ +""" +Continuous-time event sampling for the Poisson-Poisson +and Gaussian-Poisson workload model. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling +from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling + +if TYPE_CHECKING: + from collections.abc import Generator + + import numpy as np + + from app.schemas.simulation_input import SimulationInput + + +def requests_generator( + input_data: SimulationInput, + *, + rng: np.random.Generator | None = None, +) -> Generator[float, None, None]: + """ + Return an iterator of inter-arrival gaps (seconds) according to the model + chosen in *input_data*. + + Notes + ----- + * If ``avg_active_users.distribution`` is ``"gaussian"`` or ``"normal"``, + the Gaussian-Poisson sampler is used. + * Otherwise the default Poisson-Poisson sampler is returned. + + """ + dist = input_data.avg_active_users.distribution.lower() + + if dist in {"gaussian", "normal"}: + #Gaussian-Poisson model + return gaussian_poisson_sampling( + input_data=input_data, + rng=rng, + + ) + + # Poisson + Poisson + return poisson_poisson_sampling( + input_data=input_data, + rng=rng, + ) diff --git a/src/app/core/simulation/simulation_run.py b/src/app/core/simulation/simulation_run.py new file mode 100644 index 0000000..b2f72f3 --- /dev/null +++ b/src/app/core/simulation/simulation_run.py @@ -0,0 +1,56 @@ +"""simulation of the server""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import simpy + +from app.core.simulation.requests_generator import requests_generator +from app.schemas.simulation_output import SimulationOutput + +if TYPE_CHECKING: + from collections.abc import Generator + + import numpy as np + + from app.schemas.simulation_input import SimulationInput + + + + +def run_simulation( + data: SimulationInput, + *, + rng: np.random.Generator, +) -> SimulationOutput: + """Simulation executor in Simpy""" + gaps: Generator[float, None, None] = requests_generator(data, rng=rng) + env = simpy.Environment() + + simulation_time = data.total_simulation_time + # pydantic in the validation assign a value and mypy is not + # complaining because a None cannot be compared in the loop + # to a float + assert simulation_time is not None + + total_request_per_time_period = { + "simulation_time": simulation_time, + "total_requests": 0, + } + + def arrival_process( + env: simpy.Environment, + ) -> Generator[simpy.events.Event, None, None]: + for gap in gaps: + yield env.timeout(gap) + total_request_per_time_period["total_requests"] += 1 + + env.process(arrival_process(env)) + env.run(until=simulation_time) + + return SimulationOutput( + total_requests=total_request_per_time_period, + metric_2=str(data.avg_request_per_minute_per_user.mean), + metric_n=str(data.avg_active_users.mean), + ) diff --git a/src/app/main.py b/src/app/main.py index 4a78144..b5ef876 100644 --- a/src/app/main.py +++ b/src/app/main.py @@ -5,7 +5,6 @@ from fastapi import FastAPI - from app.api.health_check import router as health_router from app.config.settings import settings from app.db.init_db import close_engine, init_models diff --git a/src/app/schemas/simulation_input.py b/src/app/schemas/simulation_input.py new file mode 100644 index 0000000..2197be1 --- /dev/null +++ b/src/app/schemas/simulation_input.py @@ -0,0 +1,58 @@ +"""Define the schemas for the simulator""" + +from typing import Literal + +from pydantic import BaseModel, field_validator, model_validator + +from app.config.constants import TimeDefaults + + +class RVConfig(BaseModel): + """class to configure random variables""" + + mean: float + distribution: Literal["poisson", "normal", "gaussian"] = "poisson" + variance: float | None = None + + @field_validator("mean", mode="before") + def check_mean_is_number( + cls, # noqa: N805 + v: object, + ) -> float: + """Ensure `mean` is numeric, then coerce to float.""" + err_msg = "mean must be a number (int or float)" + if not isinstance(v, (float, int)): + raise ValueError(err_msg) # noqa: TRY004 + return float(v) + + @model_validator(mode="after") # type: ignore[arg-type] + def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 + """Set variance = mean when distribution == 'normal' and variance is missing.""" + if model.variance is None and model.distribution in {"normal", "gaussian"}: + model.variance = model.mean + return model + +class SimulationInput(BaseModel): + """Define the expected variables for the simulation""" + + avg_active_users: RVConfig + avg_request_per_minute_per_user: RVConfig + total_simulation_time: int | None = None + + @field_validator("total_simulation_time", mode="before") + def check_simulation_time(cls, v: object) -> int: # noqa: N805 + """ + Assign constant value to total sim time if is None + check if it is of the right type + impose a lower boundary for the simulation + """ + if v is None: + v = TimeDefaults.SIMULATION_HORIZON.value + if not isinstance(v, int): + err_msg_type = "the simulation time must be an integer" + raise ValueError(err_msg_type) # noqa: TRY004 + if v <= 60: + err_msg_val = "the simulation must be at least 60 seconds" + raise ValueError(err_msg_val) + return v + diff --git a/src/app/schemas/simulation_output.py b/src/app/schemas/simulation_output.py new file mode 100644 index 0000000..b02c172 --- /dev/null +++ b/src/app/schemas/simulation_output.py @@ -0,0 +1,12 @@ +"""Define the output of the simulation""" + +from pydantic import BaseModel + + +class SimulationOutput(BaseModel): + """Define the output of the simulation""" + + total_requests: dict[str, int | float] + metric_2: str + #...... + metric_n: str diff --git a/tests/integration/db_initialization/test_init_models.py b/tests/integration/db_initialization/test_init_models.py index 05721eb..204f478 100644 --- a/tests/integration/db_initialization/test_init_models.py +++ b/tests/integration/db_initialization/test_init_models.py @@ -14,4 +14,4 @@ async def test_users_table_exists_after_migrations() -> None: await conn.execute(text("SELECT 1")) except SQLAlchemyError: pytest.fail("Database connection or Alembic setup failed.") - + diff --git a/tests/unit/sampler/test_sampler_helper.py b/tests/unit/sampler/test_sampler_helper.py new file mode 100644 index 0000000..0222196 --- /dev/null +++ b/tests/unit/sampler/test_sampler_helper.py @@ -0,0 +1,130 @@ +from typing import cast + +import numpy as np + +from app.core.event_samplers.common_helpers import ( + poisson_variable_generator, + truncated_gaussian_generator, + uniform_variable_generator, +) + + +class DummyRNG: + """Dummy RNG for testing: returns fixed values for random(), poisson(), normal().""" + + def __init__( + self, + uniform_value: float | None = None, + poisson_value: int | None = None, + normal_value: float | None = None, + ) -> None: + """ + Initialize the dummy RNG with optional preset outputs. + + Args: + uniform_value: value to return from random(), if not None. + poisson_value: value to return from poisson(), if not None. + normal_value: value to return from normal(), if not None. + + """ + self.uniform_value = uniform_value + self.poisson_value = poisson_value + self.normal_value = normal_value + + def random(self) -> float: + """ + Return the preset uniform_value or fall back to a real RNG. + + Returns: + A float in [0.0, 1.0). + + """ + if self.uniform_value is not None: + return self.uniform_value + return np.random.default_rng().random() + + def poisson(self, mean: float) -> int: + """ + Return the preset poisson_value or fall back to a real RNG. + + Args: + mean: the λ parameter for a Poisson draw (ignored if poisson_value is set). + + Returns: + An integer sample from a Poisson distribution. + + """ + if self.poisson_value is not None: + return self.poisson_value + return int(np.random.default_rng().poisson(mean)) + + def normal(self, mean: float, sigma: float) -> float: + """ + Return the preset normal_value or fall back to a real RNG. + + Args: + mean: the mean of the Normal distribution. + sigma: the standard deviation of the Normal distribution. + + Returns: + A float sample from a Normal distribution. + + """ + if self.normal_value is not None: + return self.normal_value + return float(np.random.default_rng().normal(mean, sigma)) + + +def test_uniform_variable_generator_with_dummy_rng() -> None: + """Ensure uniform_variable_generator returns the dummy RNGs uniform_value.""" + dummy = cast("np.random.Generator", DummyRNG(uniform_value=0.75)) + assert uniform_variable_generator(dummy) == 0.75 + + +def test_uniform_variable_generator_default_rng_range() -> None: + """Ensure the default RNG produces a float in [0.0, 1.0).""" + for _ in range(100): + val = uniform_variable_generator() + assert isinstance(val, float) + assert 0.0 <= val < 1.0 + + +def test_poisson_variable_generator_with_dummy_rng() -> None: + """Ensure poisson_variable_generator returns the dummy RNGs poisson_value.""" + dummy = cast("np.random.Generator", DummyRNG(poisson_value=3)) + assert poisson_variable_generator(mean=5.0, rng=dummy) == 3 + + +def test_poisson_variable_generator_reproducible() -> None: + """Ensure two generators with the same seed produce the same Poisson sample.""" + rng1 = np.random.default_rng(12345) + rng2 = np.random.default_rng(12345) + v1 = poisson_variable_generator(mean=10.0, rng=rng1) + v2 = poisson_variable_generator(mean=10.0, rng=rng2) + assert v1 == v2 + + +def test_truncated_gaussian_generator_truncates_negative() -> None: + """Ensure truncated_gaussian_generator clamps negative draws to zero.""" + dummy = cast("np.random.Generator", DummyRNG(normal_value=-2.7)) + result = truncated_gaussian_generator(mean=10.0, variance=5.0, rng=dummy) + assert result == 0 + + +def test_truncated_gaussian_generator_truncates_toward_zero() -> None: + """Ensure truncated_gaussian_generator rounds toward zero for positive draws.""" + dummy = cast("np.random.Generator", DummyRNG(normal_value=3.9)) + result = truncated_gaussian_generator(mean=10.0, variance=5.0, rng=dummy) + assert isinstance(result, int) + assert result == 3 + + +def test_truncated_gaussian_generator_default_rng_non_negative_int() -> None: + """ + Ensure the default RNG produces + a non-negative integer from the truncated Gaussian. + """ + rng = np.random.default_rng(321) + val = truncated_gaussian_generator(mean=10.0, variance=2.0, rng=rng) + assert isinstance(val, int) + assert val >= 0 diff --git a/tests/unit/simulation/test_requests_generator.py b/tests/unit/simulation/test_requests_generator.py new file mode 100644 index 0000000..e7fad85 --- /dev/null +++ b/tests/unit/simulation/test_requests_generator.py @@ -0,0 +1,198 @@ +"""Unit test to verify the behaviour of the rqs generator""" + +from __future__ import annotations + +from types import GeneratorType +from typing import TYPE_CHECKING + +import numpy as np +import pytest + +from app.core.simulation.requests_generator import requests_generator +from app.core.simulation.simulation_run import run_simulation +from app.schemas.simulation_input import SimulationInput + +if TYPE_CHECKING: + + from collections.abc import Iterator + + from app.schemas.simulation_output import SimulationOutput + +# -------------------------------------------------------------- +# TESTS INPUT +# -------------------------------------------------------------- + +@pytest.fixture +def base_input() -> SimulationInput: + """Return a SimulationInput with a 120-second simulation horizon.""" + return SimulationInput( + avg_active_users={"mean": 1.0}, + avg_request_per_minute_per_user={"mean": 2.0}, + total_simulation_time=120, + ) + +# -------------------------------------------------------------- +# REQUESTS GENERATOR FUNCTION TESTS +# -------------------------------------------------------------- + +def test_default_requests_generator_uses_poisson_poisson_sampling( + base_input: SimulationInput, +) -> None: + """ + Verify that when avg_active_users.distribution is the default 'poisson', + requests_generator returns an iterator whose code object is from + poisson_poisson_sampling. + """ + rng = np.random.default_rng(0) + gen = requests_generator(base_input, rng=rng) + # It must be a generator. + assert isinstance(gen, GeneratorType) + + # Internally, it should call poisson_poisson_sampling. + assert gen.gi_code.co_name == "poisson_poisson_sampling" + +@pytest.mark.parametrize( + ("dist", "expected_sampler"), + [ + ("poisson", "poisson_poisson_sampling"), + ("normal", "gaussian_poisson_sampling"), + ("gaussian", "gaussian_poisson_sampling"), + ], +) +def test_requests_generator_dispatches_to_correct_sampler( + dist: str, + expected_sampler: str, +) -> None: + """ + Verify that requests_generator returns a generator whose code object + comes from the appropriate sampler function based on distribution: + - 'poisson' → poisson_poisson_sampling + - 'normal' → gaussian_poisson_sampling + - 'gaussian'→ gaussian_poisson_sampling + """ + input_data = SimulationInput( + avg_active_users={"mean": 1.0, "distribution": dist}, + avg_request_per_minute_per_user={"mean": 1.0}, + total_simulation_time=120, # Must be > 60 to pass schema validation + ) + rng = np.random.default_rng(0) + gen = requests_generator(input_data, rng=rng) + + # It must be a generator object. + assert isinstance(gen, GeneratorType) + # Check which underlying sampler function produced it. + assert gen.gi_code.co_name == expected_sampler + +# -------------------------------------------------------------- +# REQUESTS GENERATOR INSIDE SIMULATION TESTS +# -------------------------------------------------------------- + +def test_run_simulation_counts_events_up_to_horizon( + monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, +) -> None: + """ + Verify that all events whose cumulative inter-arrival times + fall within the simulation horizon are counted. + For gaps [1, 2, 3, 4], cumulative times [1, 3, 6, 10] + yield 4 events by t=10. + """ + def fake_requests_generator_fixed( + data: SimulationInput, *, rng: np.random.Generator, + ) -> Iterator[float]: + # Replace the complex Poisson-Poisson sampler with a deterministic sequence. + yield from [1.0, 2.0, 3.0, 4.0] + + # Monkeypatch the internal requests_generator to use our simple generator. + monkeypatch.setattr( + "app.core.simulation.simulation_run.requests_generator", + fake_requests_generator_fixed, + ) + + # The rng argument is unused in this deterministic test. + rng = np.random.default_rng(42) + output: SimulationOutput = run_simulation(base_input, rng=rng) + + assert output.total_requests["total_requests"] == 4 + # The returned metrics should reflect the input means as strings. + assert output.metric_2 == str(base_input.avg_request_per_minute_per_user.mean) + assert output.metric_n == str(base_input.avg_active_users.mean) + + +def test_run_simulation_includes_event_at_exact_horizon( + monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, +) -> None: + """ + Confirm that an event scheduled exactly at the simulation horizon + is not processed, since SimPy stops at t == horizon. + """ + def fake_generator_at_horizon( + data: SimulationInput, *, rng: np.random.Generator, + ) -> Iterator[float]: + + # mypy assertion, pydantic guaranteed + assert base_input.total_simulation_time is not None + # Yield a single event at exactly t == simulation_time. + yield float(base_input.total_simulation_time) + + monkeypatch.setattr( + "app.core.simulation.simulation_run.requests_generator", + fake_generator_at_horizon, + ) + + rng = np.random.default_rng(123) + output: SimulationOutput = run_simulation(base_input, rng=rng) + + # SimPy does not execute events scheduled exactly at the stop time. + assert output.total_requests["total_requests"] == 0 + + +def test_run_simulation_excludes_event_beyond_horizon( + monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, +) -> None: + """ + Ensure that events scheduled after the simulation horizon + are not counted. + """ + def fake_generator_beyond_horizon( + data: SimulationInput, *, rng: np.random.Generator, + ) -> Iterator[float]: + + # mypy assertion, pydantic guaranteed + assert base_input.total_simulation_time is not None + # Yield a single event just beyond the horizon. + yield float(base_input.total_simulation_time) + 0.1 + + monkeypatch.setattr( + "app.core.simulation.simulation_run.requests_generator", + fake_generator_beyond_horizon, + ) + + rng = np.random.default_rng(999) + output: SimulationOutput = run_simulation(base_input, rng=rng) + + assert output.total_requests["total_requests"] == 0 + + +def test_run_simulation_zero_events_when_generator_empty( + monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, +) -> None: + """ + Check that run_simulation reports zero requests when no + inter-arrival times are yielded. + """ + def fake_generator_empty( + data: SimulationInput, *, rng: np.random.Generator, + ) -> Iterator[float]: + # Empty generator yields nothing. + if False: + yield # pragma: no cover + + monkeypatch.setattr( + "app.core.simulation.simulation_run.requests_generator", + fake_generator_empty, + ) + + rng = np.random.default_rng(2025) + output: SimulationOutput = run_simulation(base_input, rng=rng) + + assert output.total_requests["total_requests"] == 0 diff --git a/tests/unit/simulation/test_simulation_input.py b/tests/unit/simulation/test_simulation_input.py new file mode 100644 index 0000000..20a32d9 --- /dev/null +++ b/tests/unit/simulation/test_simulation_input.py @@ -0,0 +1,93 @@ +import pytest +from pydantic import ValidationError + +from app.schemas.simulation_input import RVConfig, SimulationInput + + +def test_normal_sets_variance_to_mean() -> None: + """When distribution='normal' and variance is omitted, variance == mean.""" + cfg = RVConfig(mean=10, distribution="normal") + assert cfg.variance == 10.0 + + +def test_poisson_keeps_variance_none() -> None: + """When distribution='poisson' and variance is omitted, variance stays None.""" + cfg = RVConfig(mean=5, distribution="poisson") + assert cfg.variance is None + + +def test_explicit_variance_is_preserved() -> None: + """If the user supplies variance explicitly, it is preserved unchanged.""" + cfg = RVConfig(mean=8, distribution="normal", variance=4) + assert cfg.variance == 4.0 + + +def test_mean_must_be_numeric() -> None: + """A non-numeric mean raises a ValidationError with our custom message.""" + with pytest.raises(ValidationError) as excinfo: + RVConfig(mean="not a number", distribution="poisson") + + # Check that at least one error refers to the 'mean' field + assert any(err["loc"] == ("mean",) for err in excinfo.value.errors()) + assert "mean must be a number" in excinfo.value.errors()[0]["msg"] + + +def test_missing_mean_field() -> None: + """Omitting the mean field raises a 'field required' ValidationError.""" + with pytest.raises(ValidationError) as excinfo: + # Using model_validate avoids the constructor signature check + RVConfig.model_validate({"distribution": "normal"}) + + assert any( + err["loc"] == ("mean",) and err["type"] == "missing" + for err in excinfo.value.errors() + ) + +def test_gaussian_sets_variance_to_mean() -> None: + """When distribution='gaussian' and variance is omitted, variance == mean.""" + cfg = RVConfig(mean=12.5, distribution="gaussian") + assert cfg.variance == pytest.approx(12.5) + + +def test_default_distribution_is_poisson() -> None: + """ + When distribution is omitted, it defaults to 'poisson' and + variance stays None. + """ + cfg = RVConfig(mean=3.3) + assert cfg.distribution == "poisson" + assert cfg.variance is None + + +def test_explicit_variance_kept_for_poisson() -> None: + """If the user supplies variance even for poisson, it is preserved.""" + cfg = RVConfig(mean=4.0, distribution="poisson", variance=2.2) + assert cfg.variance == pytest.approx(2.2) + + +def test_invalid_distribution_raises() -> None: + """Supplying a non-supported distribution literal raises ValidationError.""" + with pytest.raises(ValidationError) as excinfo: + RVConfig(mean=5.0, distribution="not_a_dist") + + errors = excinfo.value.errors() + # Only assert there is at least one error for the 'distribution' field: + assert any(e["loc"] == ("distribution",) for e in errors) + + +def test_simulation_time_below_minimum_raises() -> None: + """ + Passing total_simulation_time <= 60 must raise a ValidationError, + because the minimum allowed simulation time is 61 seconds. + """ + with pytest.raises(ValidationError) as excinfo: + SimulationInput( + avg_active_users={"mean": 1.0}, + avg_request_per_minute_per_user={"mean": 1.0}, + total_simulation_time=60, # exactly at the boundary + ) + errors = excinfo.value.errors() + assert any( + err["loc"] == ("total_simulation_time",) and "at least 60 seconds" in err["msg"] + for err in errors + ) From 86501f95a07d7f324f0c76fc51e95896cd9beb7c Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Sun, 6 Jul 2025 15:46:09 +0200 Subject: [PATCH 09/52] minor changes --- tests/integration/db_initialization/test_init_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/integration/db_initialization/test_init_models.py b/tests/integration/db_initialization/test_init_models.py index 05721eb..48389d0 100644 --- a/tests/integration/db_initialization/test_init_models.py +++ b/tests/integration/db_initialization/test_init_models.py @@ -1,7 +1,7 @@ import pytest from sqlalchemy import text -from sqlalchemy.ext.asyncio import create_async_engine from sqlalchemy.exc import SQLAlchemyError +from sqlalchemy.ext.asyncio import create_async_engine from app.config.settings import settings From 5c6f5db3a5967d58910c690e3e9acdd9c9a1ab02 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Thu, 10 Jul 2025 17:30:08 +0200 Subject: [PATCH 10/52] Features/event generator documentation test improvements (#2) * moving file to correct folder * Documentation added * Update requests_generator.md * test Added, introduced constants for the sampling window * Update tests/unit/sampler/test_poisson_posson.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- documentation/FASTSIM_VISION.md | 41 +++ .../requests_generator.md | 281 ++++++++++++++++++ .../test_sampler_helper.md | 0 .../test_simulation_input.md | 0 src/app/config/constants.py | 9 +- .../core/event_samplers/gaussian_poisson.py | 5 +- .../core/event_samplers/poisson_poisson.py | 5 +- src/app/core/simulation/simulation_run.py | 10 +- src/app/schemas/simulation_input.py | 39 +-- tests/unit/sampler/test_gaussian_poisson.py | 93 ++++++ tests/unit/sampler/test_poisson_posson.py | 119 ++++++++ .../simulation/test_events_generator_input.py | 210 +++++++++++++ .../simulation/test_requests_generator.py | 5 +- .../unit/simulation/test_simulation_input.py | 93 ------ 14 files changed, 784 insertions(+), 126 deletions(-) create mode 100644 documentation/FASTSIM_VISION.md create mode 100644 documentation/backend_documentation/requests_generator.md rename documentation/tests_documentation/{integration_tests => unit_tests}/test_sampler_helper.md (100%) rename documentation/tests_documentation/{integration_tests => unit_tests}/test_simulation_input.md (100%) create mode 100644 tests/unit/sampler/test_gaussian_poisson.py create mode 100644 tests/unit/sampler/test_poisson_posson.py create mode 100644 tests/unit/simulation/test_events_generator_input.py delete mode 100644 tests/unit/simulation/test_simulation_input.py diff --git a/documentation/FASTSIM_VISION.md b/documentation/FASTSIM_VISION.md new file mode 100644 index 0000000..7119807 --- /dev/null +++ b/documentation/FASTSIM_VISION.md @@ -0,0 +1,41 @@ +## 1 Why FastSim? + +FastAPI + Uvicorn gives Python teams a lightning-fast async stack, yet sizing it for production still means guess-work, costly cloud load-tests or late surprises. **FastSim** fills that gap by becoming a **digital twin** of your actual service: + +* It **replays** your FastAPI + Uvicorn event-loop behavior in SimPy, generating exactly the same kinds of asynchronous steps (parsing, CPU work, I/O, LLM calls) that happen in real code. +* It **models** your infrastructure primitives—CPU cores (via a SimPy `Resource`), database pools, rate-limiters, even GPU inference quotas—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency. +* It **outputs** the very metrics you’d scrape in production (p50/p95/p99 latency, ready-queue lag, current & max concurrency, throughput, cost per LLM call), but entirely offline, in seconds. + +With FastSim you can ask *“What happens if traffic doubles on Black Friday?”*, *“How many cores to keep p95 < 100 ms?”* or *“Is our LLM-driven endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. + +**Outcome:** data-driven capacity planning, early performance tuning, and far fewer “surprises” once you hit production. + +--- + +## 2 Project Goals + +| # | Goal | Practical Outcome | +| - | ------------------------- | ------------------------------------------------------------------------ | +| 1 | **Pre-production sizing** | Know core-count, pool-size, replica-count to hit SLA. | +| 2 | **Scenario lab** | Explore traffic models, endpoint mixes, latency distributions, RTT, etc. | +| 3 | **Twin metrics** | Produce the same metrics you’ll scrape in prod (latency, queue, CPU). | +| 4 | **Rapid iteration** | One YAML/JSON config or REST call → full report. | +| 5 | **Educational value** | Visualise how GIL lag, queue length, concurrency react to load. | + +--- + +## 3 Who benefits & why (detailed) + +| Audience | Pain-point solved | FastSim value | +| ------------------------------ | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| **Backend engineers** | Unsure if 4 vCPU container survives a marketing spike | Run *what-if* load, tweak CPU cores / pool size, get p95 & max-concurrency before merging. | +| **DevOps / SRE** | Guesswork in capacity planning; cost of over-provisioning | Simulate 1 → N replicas, autoscaler thresholds, DB-pool size; pick the cheapest config meeting SLA. | +| **ML / LLM product teams** | LLM inference cost & latency hard to predict | Model the LLM step with a price + latency distribution; estimate \$/req and GPU batch gains without real GPU. | +| **Educators / Trainers** | Students struggle to “see” event-loop internals | Visualise GIL ready-queue lag, CPU vs I/O steps, effect of blocking code—perfect for live demos and labs. | +| **Consultants / Architects** | Need a quick PoC of new designs for clients | Drop endpoint definitions in YAML and demo throughput / latency under projected load in minutes. | +| **Open-source community** | Lacks a lightweight Python simulator for ASGI workloads | Extensible codebase; easy to plug in new resources (rate-limit, cache) or traffic models (spike, uniform ramp). | +| **System-design interviewees** | Hard to quantify trade-offs in whiteboard interviews | Prototype real-time metrics—queue lengths, concurrency, latency distributions—to demonstrate in interviews how your design scales and where bottlenecks lie. | + +--- + +**Bottom-line:** FastSim turns abstract architecture diagrams into concrete numbers—*before* spinning up expensive cloud environments—so you can build, validate and discuss your designs with full confidence. diff --git a/documentation/backend_documentation/requests_generator.md b/documentation/backend_documentation/requests_generator.md new file mode 100644 index 0000000..556c4f4 --- /dev/null +++ b/documentation/backend_documentation/requests_generator.md @@ -0,0 +1,281 @@ +# Requests Generator + +This document describes the design of the **requests generator**, which models a stream of user requests to a given endpoint over time. + +--- + +## Model Inputs and Output + +Following the FastSim philosophy, we accept a small set of input parameters to drive a “what-if” analysis in a pre-production environment. These inputs let you explore reliability and cost implications under different traffic scenarios. + +**Inputs** + +1. **Average concurrent users** – expected number of users (or sessions) simultaneously hitting the endpoint. +2. **Average requests per minute per user** – average number of requests each user issues per minute. +3. **Simulation time** – total duration of the simulation, in seconds. + +**Output** +A continuous sequence of timestamps (seconds) marking individual request arrivals. + +--- + +## Model Assumptions + +* *Concurrent users* and *requests per minute per user* are **random variables**. +* *Simulation time* is **deterministic**. + +We model: + +* **Requests per minute per user** as Poisson($\lambda_r$). +* **Concurrent users** as either Poisson($\lambda_u$) or truncated Normal. +* **The variables are independent** + +```python +from pydantic import BaseModel +from typing import Literal + +class RVConfig(BaseModel): + """Configure a random-variable parameter.""" + mean: float + distribution: Literal["poisson", "normal", "gaussian"] = "poisson" + variance: float | None = None # required only for normal/gaussian + +class SimulationInput(BaseModel): + """Define simulation inputs.""" + avg_active_users: RVConfig + avg_request_per_minute_per_user: RVConfig + total_simulation_time: int | None = None +``` + +--- + +## Aggregate Request Rate + +From the two random inputs we define the **per-second aggregate rate** $\Lambda$: + +$$ +\Lambda + = \text{concurrent\_users} + \;\times\; + \frac{\text{requests\_per\_minute\_per\_user}}{60} + \quad[\text{requests/s}]. +$$ + +--- + +## 1. Poisson → Exponential Refresher + +### 1.1 Homogeneous Poisson process + +A Poisson process of rate $\lambda$ has + +$$ +\Pr\{N(t)=k\} + = e^{-\lambda t}\,\frac{(\lambda t)^{k}}{k!},\quad k=0,1,2,\dots +$$ + +### 1.2 Waiting time to first event + +Define $T_1=\inf\{t>0:N(t)=1\}$. +The survival function is + +$$ +\Pr\{T_1>t\} + = \Pr\{N(t)=0\} + = e^{-\lambda t}, +$$ + +so the CDF is + +$$ +F_{T_1}(t) = 1 - e^{-\lambda t},\quad t\ge0, +$$ + +and the density $f(t)=\lambda\,e^{-\lambda t}$. Thus + +$$ +T_1 \sim \mathrm{Exp}(\lambda), +$$ + +and by memorylessness every inter-arrival gap $\Delta t_i$ is i.i.d. Exp($\lambda$). + +### 1.3 Inverse-CDF sampling + +To draw $\Delta t\sim\mathrm{Exp}(\lambda)$: + +1. Sample $U\sim\mathcal U(0,1)$. +2. Solve $U=1-e^{-\lambda\,\Delta t}$;$\Rightarrow\;\Delta t=-\ln(1-U)/\lambda$. +3. Equivalent compact form: + $\displaystyle \Delta t = -\,\ln(U)/\lambda$. + +--- + +## 2. Poisson × Poisson Workload + +### 2.1 Notation + +| Symbol | Meaning | Law | +| --------------------------------- | --------------------------------------- | -------- | +| $U\sim\mathrm{Pois}(\lambda_u)$ | active users in current 1-minute window | Poisson | +| $R_i\sim\mathrm{Pois}(\lambda_r)$ | requests per minute by user *i* | Poisson | +| $N=\sum_{i=1}^U R_i$ | total requests in that minute | compound | +| $\Lambda=N/60$ | aggregate rate (requests / second) | compound | + +The procedure here rely heavily on the independence of our random variables. + +### 2.2 Conditional sum ⇒ Poisson + +Given $U=u$: + +$$ +N\mid U=u +=\sum_{i=1}^{u}R_i +\;\sim\;\mathrm{Pois}(u\,\lambda_r). +$$ + +### 2.3 Unconditional law of $N$ + +By the law of total probability: + +$$ +\Pr\{N=n\} +=\sum_{u=0}^{\infty} +\Pr\{U=u\}\; +\Pr\{N=n\mid U=u\} +\;=\; +e^{-\lambda_u}\,\frac1{n!} +\sum_{u=0}^{\infty} +\frac{\lambda_u^u}{u!}\, +e^{-u\lambda_r}\,(u\lambda_r)^n. +$$ + +This is the **Poisson–Poisson compound** (Borel–Tanner) distribution. + +--- + +## 3. Exact Hierarchical Sampler + +Rather than invert the discrete CDF above, we exploit the conditional structure: + +```python +# Hierarchical sampler code snippet +now = 0.0 # virtual clock (s) +window_end = 0.0 # end of the current user window +Lambda = 0.0 # aggregate rate Λ (req/s) + +while now < simulation_time: + # (Re)sample U at the start of each window + if now >= window_end: + window_end = now + float(sampling_window_s) + users = poisson_variable_generator(mean_concurrent_user, rng) + Lambda = users * mean_req_per_sec_per_user + + # No users → fast-forward to next window + if Lambda <= 0.0: + now = window_end + continue + + # Exponential gap from a protected uniform value + u_raw = max(uniform_variable_generator(rng), 1e-15) + delta_t = -math.log(1.0 - u_raw) / Lambda + + # End simulation if the next event exceeds the horizon + if now + delta_t > simulation_time: + break + + # If the gap crosses the window boundary, jump to it + if now + delta_t >= window_end: + now = window_end + continue + + now += delta_t + yield delta_t +``` + +Because each conditional step matches the exact Poisson→Exponential law, this two-stage algorithm reproduces the same joint distribution as analytically inverting the compound CDF, but with minimal computation. + +--- + +## 4. Validity of the hierarchical sampler + +The validity of the hierarchical sampler relies on a structural property of the model: + +$$ +N \;=\; \sum_{i=1}^{U} R_i, +$$ + +where each $R_i \sim \mathrm{Pois}(\lambda_r)$ is independent of the others and of $U$. Because the Poisson family is closed under convolution, + +$$ +N \,\big|\, U=u \;\sim\; \mathrm{Pois}\!\bigl(u\,\lambda_r\bigr). +$$ + +This result has two important consequences: + +1. **Deterministic conditional rate** – Given $U=u$, the aggregate request arrivals constitute a homogeneous Poisson process with the *deterministic* rate + + $$ + \Lambda = \frac{u\,\lambda_r}{60}. + $$ + + All inter-arrival gaps are therefore i.i.d. exponential with parameter $\Lambda$, allowing us to use the standard inverse–CDF formula for each gap. + +2. **Layered uncertainty handling** – The randomness associated with $U$ is handled in an outer step (sampling $U$ once per window), while the inner step leverages the well-known Poisson→Exponential correspondence. This two-level construction reproduces exactly the joint distribution obtained by first drawing $\Lambda = N/60$ from the compound Poisson law and then drawing gaps conditional on $\Lambda$. + +If the total count could **not** be written as a sum of independent Poisson variables, the conditional distribution of $N$ would no longer be Poisson and the exponential-gap shortcut would not apply. In that situation one would need to work directly with the (generally more complex) mixed distribution of $\Lambda$ or adopt another specialized sampling scheme. + + + +## 5. Equivalence to CDF Inversion + +By the law of total probability, for any event set $A$: + +$$ +\Pr\{(\Lambda,\Delta t_1,\dots)\in A\} +=\sum_{u=0}^\infty +\Pr\{U=u\}\; +\Pr\{(\Lambda,\Delta t_1,\dots)\in A\mid U=u\}. +$$ + +Step 1 samples $\Pr\{U=u\}$, step 2–3 sample the conditional exponential gaps. Because these two factors exactly match the mixture definition of the compound CDF, the hierarchical sampler **is** an exact implementation of two-stage CDF inversion, avoiding any explicit inversion of an infinite series. + +--- + +## 6. Gaussian × Poisson Variant + +If concurrent users follow a truncated Normal, + +$$ +U\sim \max\{0,\;\mathcal N(\mu_u,\sigma_u^2)\}, +$$ + +steps 2–3 remain unchanged; only step 1 draws $U$ from a continuous law. The resulting mixture is continuous, yet the hierarchical sampler remains exact. + +--- + +## 7. Time Window + +The sampling window length governs how often we re-sample $U$. It should reflect the timescale over which user count fluctuations become significant. Our default is **60 s**, but you can adjust this parameter in your configuration before each simulation. + +--- + +## Limitations of the Requests Model + +1. **Independence assumption** + Assumes per-user streams and $U$ are independent. Real traffic often exhibits user-behavior correlations (e.g., flash crowds). + +2. **Exponential inter-arrival times** + Implies memorylessness; cannot capture self-throttling or long-range dependence found in real workloads. + +3. **No diurnal/trend component** + User count $U$ is IID per window. To model seasonality or trends, you must vary $\lambda_u(t)$ externally. + +4. **No burst-control or rate-limiting** + Does not simulate client-side throttling or server back-pressure. Any rate-limit logic must be added externally. + +5. **Gaussian truncation artifacts** + In the Gaussian–Poisson variant, truncating negatives to zero and rounding can under-estimate extreme user counts. + + +**Key takeaway:** By structuring the generator as +$\Lambda = U\,\lambda_r/60$ with a two-stage Poisson→Exponential sampler, FastSim efficiently reproduces compound Poisson traffic dynamics without any complex CDF inversion. diff --git a/documentation/tests_documentation/integration_tests/test_sampler_helper.md b/documentation/tests_documentation/unit_tests/test_sampler_helper.md similarity index 100% rename from documentation/tests_documentation/integration_tests/test_sampler_helper.md rename to documentation/tests_documentation/unit_tests/test_sampler_helper.md diff --git a/documentation/tests_documentation/integration_tests/test_simulation_input.md b/documentation/tests_documentation/unit_tests/test_simulation_input.md similarity index 100% rename from documentation/tests_documentation/integration_tests/test_simulation_input.md rename to documentation/tests_documentation/unit_tests/test_simulation_input.md diff --git a/src/app/config/constants.py b/src/app/config/constants.py index fb0c231..5146c62 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -6,6 +6,9 @@ class TimeDefaults(IntEnum): """Default time-related constants (all in seconds).""" - MIN_TO_SEC = 60 # 1 minute → 60 s - SAMPLING_WINDOW = 60 # keep U(t) constant for 60 s - SIMULATION_HORIZON = 3_600 # run 1 h if user gives no other value + MIN_TO_SEC = 60 # 1 minute → 60 s + USER_SAMPLING_WINDOW = 60 # keep U(t) constant for 60 s, default + SIMULATION_TIME = 3_600 # run 1 h if user gives no other value + MIN_SIMULATION_TIME = 1800 # min simulation time + MIN_USER_SAMPLING_WINDOW = 1 # 1 second + MAX_USER_SAMPLING_WINDOW = 120 # 2 minutes diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/core/event_samplers/gaussian_poisson.py index 2239626..03434b5 100644 --- a/src/app/core/event_samplers/gaussian_poisson.py +++ b/src/app/core/event_samplers/gaussian_poisson.py @@ -22,7 +22,6 @@ def gaussian_poisson_sampling( input_data: SimulationInput, *, - sampling_window_s: int = TimeDefaults.SAMPLING_WINDOW.value, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: """ @@ -41,10 +40,12 @@ def gaussian_poisson_sampling( rng = rng or np.random.default_rng() simulation_time = input_data.total_simulation_time + user_sampling_window = input_data.user_sampling_window # pydantic in the validation assign a value and mypy is not # complaining because a None cannot be compared in the loop # to a float assert simulation_time is not None + assert user_sampling_window is not None # λ_u : mean concurrent users per window mean_concurrent_user = float(input_data.avg_active_users.mean) @@ -68,7 +69,7 @@ def gaussian_poisson_sampling( while now < simulation_time: # (Re)sample U at the start of each window if now >= window_end: - window_end = now + float(sampling_window_s) + window_end = now + float(user_sampling_window) users = truncated_gaussian_generator( mean_concurrent_user, variance_concurrent_user, diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/core/event_samplers/poisson_poisson.py index 37c2da5..5f0598c 100644 --- a/src/app/core/event_samplers/poisson_poisson.py +++ b/src/app/core/event_samplers/poisson_poisson.py @@ -19,7 +19,6 @@ def poisson_poisson_sampling( input_data: SimulationInput, *, - sampling_window_s: int = TimeDefaults.SAMPLING_WINDOW.value, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: """ @@ -38,10 +37,12 @@ def poisson_poisson_sampling( rng = rng or np.random.default_rng() simulation_time = input_data.total_simulation_time + user_sampling_window = input_data.user_sampling_window # pydantic in the validation assign a value and mypy is not # complaining because a None cannot be compared in the loop # to a float assert simulation_time is not None + assert user_sampling_window is not None # λ_u : mean concurrent users per window mean_concurrent_user = float(input_data.avg_active_users.mean) @@ -60,7 +61,7 @@ def poisson_poisson_sampling( while now < simulation_time: # (Re)sample U at the start of each window if now >= window_end: - window_end = now + float(sampling_window_s) + window_end = now + float(user_sampling_window) users = poisson_variable_generator(mean_concurrent_user, rng) lam = users * mean_req_per_sec_per_user diff --git a/src/app/core/simulation/simulation_run.py b/src/app/core/simulation/simulation_run.py index b2f72f3..d3f52f6 100644 --- a/src/app/core/simulation/simulation_run.py +++ b/src/app/core/simulation/simulation_run.py @@ -20,15 +20,15 @@ def run_simulation( - data: SimulationInput, + input_data: SimulationInput, *, rng: np.random.Generator, ) -> SimulationOutput: """Simulation executor in Simpy""" - gaps: Generator[float, None, None] = requests_generator(data, rng=rng) + gaps: Generator[float, None, None] = requests_generator(input_data, rng=rng) env = simpy.Environment() - simulation_time = data.total_simulation_time + simulation_time = input_data.total_simulation_time # pydantic in the validation assign a value and mypy is not # complaining because a None cannot be compared in the loop # to a float @@ -51,6 +51,6 @@ def arrival_process( return SimulationOutput( total_requests=total_request_per_time_period, - metric_2=str(data.avg_request_per_minute_per_user.mean), - metric_n=str(data.avg_active_users.mean), + metric_2=str(input_data.avg_request_per_minute_per_user.mean), + metric_n=str(input_data.avg_active_users.mean), ) diff --git a/src/app/schemas/simulation_input.py b/src/app/schemas/simulation_input.py index 2197be1..ed702bb 100644 --- a/src/app/schemas/simulation_input.py +++ b/src/app/schemas/simulation_input.py @@ -2,7 +2,7 @@ from typing import Literal -from pydantic import BaseModel, field_validator, model_validator +from pydantic import BaseModel, Field, field_validator, model_validator from app.config.constants import TimeDefaults @@ -37,22 +37,23 @@ class SimulationInput(BaseModel): avg_active_users: RVConfig avg_request_per_minute_per_user: RVConfig - total_simulation_time: int | None = None - - @field_validator("total_simulation_time", mode="before") - def check_simulation_time(cls, v: object) -> int: # noqa: N805 - """ - Assign constant value to total sim time if is None - check if it is of the right type - impose a lower boundary for the simulation - """ - if v is None: - v = TimeDefaults.SIMULATION_HORIZON.value - if not isinstance(v, int): - err_msg_type = "the simulation time must be an integer" - raise ValueError(err_msg_type) # noqa: TRY004 - if v <= 60: - err_msg_val = "the simulation must be at least 60 seconds" - raise ValueError(err_msg_val) - return v + total_simulation_time: int = Field( + default=TimeDefaults.SIMULATION_TIME.value, + ge=TimeDefaults.MIN_SIMULATION_TIME.value, # minimum simulation time in seconds + description=( + f"Simulation time in seconds (>= {TimeDefaults.MIN_SIMULATION_TIME.value})." + ), + ) + + user_sampling_window: int = Field( + default=TimeDefaults.USER_SAMPLING_WINDOW.value, + ge=TimeDefaults.MIN_USER_SAMPLING_WINDOW.value, + le=TimeDefaults.MAX_USER_SAMPLING_WINDOW.value, + description=( + "Sampling window in seconds " + f"({TimeDefaults.MIN_USER_SAMPLING_WINDOW.value}-" + f"{TimeDefaults.MAX_USER_SAMPLING_WINDOW.value})." + ), + ) + diff --git a/tests/unit/sampler/test_gaussian_poisson.py b/tests/unit/sampler/test_gaussian_poisson.py new file mode 100644 index 0000000..6d49902 --- /dev/null +++ b/tests/unit/sampler/test_gaussian_poisson.py @@ -0,0 +1,93 @@ +"""Unit tests for gaussian_poisson_sampling.""" + +from __future__ import annotations + +import itertools +from types import GeneratorType + +import numpy as np +import pytest + +from app.config.constants import TimeDefaults +from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling +from app.schemas.simulation_input import RVConfig, SimulationInput + +# --------------------------------------------------------------------------- +# Fixture +# --------------------------------------------------------------------------- + + +@pytest.fixture +def base_input() -> SimulationInput: + """Return a minimal, valid SimulationInput for the Gaussian-Poisson sampler.""" + return SimulationInput( + avg_active_users=RVConfig( + mean=10.0, variance=4.0, distribution="gaussian", + ), + avg_request_per_minute_per_user=RVConfig(mean=30.0), + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW.value, + ) + + +# --------------------------------------------------------------------------- +# Basic behaviour +# --------------------------------------------------------------------------- + + +def test_returns_generator_type(base_input: SimulationInput) -> None: + """The function must return a generator object.""" + rng = np.random.default_rng(0) + gen = gaussian_poisson_sampling(base_input, rng=rng) + assert isinstance(gen, GeneratorType) + + +def test_generates_positive_gaps(base_input: SimulationInput) -> None: + """ + With nominal parameters the sampler should emit at least a few positive + gaps and no gap must be non-positive. + """ + rng = np.random.default_rng(42) + gaps: list[float] = list( + itertools.islice(gaussian_poisson_sampling(base_input, rng=rng), 1000), + ) + + # At least one event is expected. + assert gaps + # No gap may be negative or zero. + assert all(gap > 0.0 for gap in gaps) + # The cumulative time of gaps must stay below the horizon. + assert sum(gaps) < base_input.total_simulation_time + + +# --------------------------------------------------------------------------- +# Edge-case: zero users ⇒ no events +# --------------------------------------------------------------------------- + + +def test_zero_users_produces_no_events( + monkeypatch: pytest.MonkeyPatch, + base_input: SimulationInput, +) -> None: + """ + If every Gaussian draw returns 0 users, Λ == 0, + hence the generator must yield no events at all. + """ + + def fake_truncated_gaussian( + mean: float, + var: float, + rng: np.random.Generator, + ) -> float: + return 0.0 # force U = 0 + + # Patch the helper so that it always returns 0 users. + monkeypatch.setattr( + "app.core.event_samplers.gaussian_poisson.truncated_gaussian_generator", + fake_truncated_gaussian, + ) + + rng = np.random.default_rng(123) + gaps = list(gaussian_poisson_sampling(base_input, rng=rng)) + + assert gaps == [] # no events should be generated diff --git a/tests/unit/sampler/test_poisson_posson.py b/tests/unit/sampler/test_poisson_posson.py new file mode 100644 index 0000000..c0a2f3e --- /dev/null +++ b/tests/unit/sampler/test_poisson_posson.py @@ -0,0 +1,119 @@ +"""Unit tests for the poisson_poisson_sampling generator.""" + +from __future__ import annotations + +import itertools +import math +from types import GeneratorType + +import numpy as np +import pytest + +from app.config.constants import TimeDefaults +from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling +from app.schemas.simulation_input import RVConfig, SimulationInput + + +@pytest.fixture +def base_input() -> SimulationInput: + """Return a minimal-valid SimulationInput for the sampler tests.""" + return SimulationInput( + # 1 average concurrent user … + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + # … sending on average 60 req/min → 1 req/s + avg_request_per_minute_per_user={"mean": 60.0, "distribution": "poisson"}, + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, # 30 min + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW.value, # 60 s + ) + + +# --------------------------------------------------------------------- +# BASIC SHAPE / TYPE TESTS +# --------------------------------------------------------------------- + + +def test_sampler_returns_generator(base_input: SimulationInput) -> None: + """The function must return a real generator object.""" + rng = np.random.default_rng(0) + gen = poisson_poisson_sampling(base_input, rng=rng) + + assert isinstance(gen, GeneratorType) + + +def test_all_gaps_are_positive(base_input: SimulationInput) -> None: + """Every yielded inter-arrival gap Δt must be > 0.""" + rng = np.random.default_rng(1) + gaps: list[float] = list( + itertools.islice(poisson_poisson_sampling(base_input, rng=rng), 1_000), + ) + + # None of the first 1 000 gaps (if any) can be negative or zero + assert all(gap > 0.0 for gap in gaps) + + +# --------------------------------------------------------------------- +# REPRODUCIBILITY WITH FIXED RNG SEED +# --------------------------------------------------------------------- + + +def test_sampler_is_reproducible_with_fixed_seed(base_input: SimulationInput) -> None: + """Same seed ⇒ identical first N gaps.""" + seed = 42 + n_samples = 15 + + gaps_1 = list( + itertools.islice( + poisson_poisson_sampling( + base_input, rng=np.random.default_rng(seed), + ), + n_samples, + ), + ) + gaps_2 = list( + itertools.islice( + poisson_poisson_sampling( + base_input, rng=np.random.default_rng(seed), + ), + n_samples, + ), + ) + + assert gaps_1 == gaps_2 + + +# --------------------------------------------------------------------- +# EDGE-CASE: ZERO USERS ⇒ NO EVENTS +# --------------------------------------------------------------------- + + +def test_zero_users_produces_no_events(base_input: SimulationInput) -> None: + """ + With mean concurrent users == 0 the Poisson draw is almost surely 0, + so Λ = 0 and the generator should yield no events. + """ + input_data = SimulationInput( + avg_active_users=RVConfig(mean=0.0, distribution="poisson"), + avg_request_per_minute_per_user=RVConfig(mean=60.0, distribution="poisson"), + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW.value, + ) + + rng = np.random.default_rng(123) + gaps = list(poisson_poisson_sampling(input_data, rng=rng)) + + assert gaps == [] # no events expected + +# --------------------------------------------------------------------- +# CUMULATIVE TIME ALWAYS < SIMULATION HORIZON +# --------------------------------------------------------------------- + + +def test_cumulative_time_never_exceeds_horizon(base_input: SimulationInput) -> None: + """ΣΔt (virtual clock) must stay strictly below total_simulation_time.""" + rng = np.random.default_rng(7) + gaps = list(poisson_poisson_sampling(base_input, rng=rng)) + + cum_time = math.fsum(gaps) + # Even if the virtual clock can jump when λ == 0, + # the summed gaps must never exceed the horizon. + assert cum_time < base_input.total_simulation_time diff --git a/tests/unit/simulation/test_events_generator_input.py b/tests/unit/simulation/test_events_generator_input.py new file mode 100644 index 0000000..39c5216 --- /dev/null +++ b/tests/unit/simulation/test_events_generator_input.py @@ -0,0 +1,210 @@ +import pytest +from pydantic import ValidationError + +from app.config.constants import TimeDefaults +from app.schemas.simulation_input import RVConfig, SimulationInput + +# -------------------------------------------------------------------------- +# TEST RANDOM VARIABLE CONFIGURATION +# -------------------------------------------------------------------------- + +def test_normal_sets_variance_to_mean() -> None: + """When distribution='normal' and variance is omitted, variance == mean.""" + cfg = RVConfig(mean=10, distribution="normal") + assert cfg.variance == 10.0 + + +def test_poisson_keeps_variance_none() -> None: + """When distribution='poisson' and variance is omitted, variance stays None.""" + cfg = RVConfig(mean=5, distribution="poisson") + assert cfg.variance is None + + +def test_explicit_variance_is_preserved() -> None: + """If the user supplies variance explicitly, it is preserved unchanged.""" + cfg = RVConfig(mean=8, distribution="normal", variance=4) + assert cfg.variance == 4.0 + + +def test_mean_must_be_numeric() -> None: + """A non-numeric mean raises a ValidationError with our custom message.""" + with pytest.raises(ValidationError) as excinfo: + RVConfig(mean="not a number", distribution="poisson") + + # Check that at least one error refers to the 'mean' field + assert any(err["loc"] == ("mean",) for err in excinfo.value.errors()) + assert "mean must be a number" in excinfo.value.errors()[0]["msg"] + + +def test_missing_mean_field() -> None: + """Omitting the mean field raises a 'field required' ValidationError.""" + with pytest.raises(ValidationError) as excinfo: + # Using model_validate avoids the constructor signature check + RVConfig.model_validate({"distribution": "normal"}) + + assert any( + err["loc"] == ("mean",) and err["type"] == "missing" + for err in excinfo.value.errors() + ) + +def test_gaussian_sets_variance_to_mean() -> None: + """When distribution='gaussian' and variance is omitted, variance == mean.""" + cfg = RVConfig(mean=12.5, distribution="gaussian") + assert cfg.variance == pytest.approx(12.5) + + +def test_default_distribution_is_poisson() -> None: + """ + When distribution is omitted, it defaults to 'poisson' and + variance stays None. + """ + cfg = RVConfig(mean=3.3) + assert cfg.distribution == "poisson" + assert cfg.variance is None + + +def test_explicit_variance_kept_for_poisson() -> None: + """If the user supplies variance even for poisson, it is preserved.""" + cfg = RVConfig(mean=4.0, distribution="poisson", variance=2.2) + assert cfg.variance == pytest.approx(2.2) + + +def test_invalid_distribution_raises() -> None: + """Supplying a non-supported distribution literal raises ValidationError.""" + with pytest.raises(ValidationError) as excinfo: + RVConfig(mean=5.0, distribution="not_a_dist") + + errors = excinfo.value.errors() + # Only assert there is at least one error for the 'distribution' field: + assert any(e["loc"] == ("distribution",) for e in errors) + +# -------------------------------------------------------------------------- +# TEST FIELD VALIDATOR USER SAMPLING WINDOW +# -------------------------------------------------------------------------- + +def test_default_user_sampling_window() -> None: + """When user_sampling_window is omitted, it defaults to USER_SAMPLING_WINDOW.""" + inp = SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + ) + assert inp.user_sampling_window == TimeDefaults.USER_SAMPLING_WINDOW.value + + +def test_explicit_user_sampling_window_kept() -> None: + """An explicit user_sampling_window value is preserved unchanged.""" + custom_window = 30 + inp = SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + user_sampling_window=custom_window, + ) + assert inp.user_sampling_window == custom_window + + +def test_user_sampling_window_not_int_raises() -> None: + """A non-integer user_sampling_window raises a ValidationError.""" + with pytest.raises(ValidationError) as excinfo: + + SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + user_sampling_window="not-an-int", + ) + + errors = excinfo.value.errors() + assert any(err["loc"] == ("user_sampling_window",) for err in errors) + + # Pydantic v2 wording + assert any("valid integer" in err["msg"] for err in errors) + + + +def test_user_sampling_window_above_max_raises() -> None: + """ + Passing user_sampling_window > MAX_USER_SAMPLING_WINDOW + must raise a ValidationError. + """ + too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW.value + 1 + with pytest.raises(ValidationError) as excinfo: + SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + user_sampling_window=too_large, + ) + + errors = excinfo.value.errors() + assert any(err["loc"] == ("user_sampling_window",) for err in errors) + + expected_snippet = ( + f"less than or equal to {TimeDefaults.MAX_USER_SAMPLING_WINDOW.value}" + ) + assert any(expected_snippet in err["msg"] for err in errors) + + + +# -------------------------------------------------------------------------- +# TEST FIELD VALIDATOR TOTAL SIMULATION TIME +# -------------------------------------------------------------------------- + +def test_default_total_simulation_time() -> None: + """When total_simulation_time is omitted, it defaults to SIMULATION_TIME.""" + inp = SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + ) + assert inp.total_simulation_time == TimeDefaults.SIMULATION_TIME.value + + +def test_explicit_total_simulation_time_kept() -> None: + """An explicit total_simulation_time value is preserved unchanged.""" + custom_time = 3_000 + inp = SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + total_simulation_time=custom_time, + ) + assert inp.total_simulation_time == custom_time + + +def test_total_simulation_time_not_int_raises() -> None: + """A non-integer total_simulation_time raises a ValidationError.""" + with pytest.raises(ValidationError) as excinfo: + + SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + total_simulation_time="three thousand", + ) + + errors = excinfo.value.errors() + assert any(err["loc"] == ("total_simulation_time",) for err in errors) + + # Pydantic v2 wording: “Input should be a valid integer” + assert any("valid integer" in err["msg"] for err in errors) + + + +def test_total_simulation_time_below_minimum_raises() -> None: + """ + Passing total_simulation_time < MIN_SIMULATION_TIME + must raise a ValidationError. + """ + too_small = TimeDefaults.MIN_SIMULATION_TIME.value - 1 + with pytest.raises(ValidationError) as excinfo: + SimulationInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + total_simulation_time=too_small, + ) + + errors = excinfo.value.errors() + # c'è almeno un errore sul campo giusto + assert any(err["loc"] == ("total_simulation_time",) for err in errors) + + expected_snippet = ( + f"greater than or equal to {TimeDefaults.MIN_SIMULATION_TIME.value}" + ) + assert any(expected_snippet in err["msg"] for err in errors) + + diff --git a/tests/unit/simulation/test_requests_generator.py b/tests/unit/simulation/test_requests_generator.py index e7fad85..0bf5872 100644 --- a/tests/unit/simulation/test_requests_generator.py +++ b/tests/unit/simulation/test_requests_generator.py @@ -8,6 +8,7 @@ import numpy as np import pytest +from app.config.constants import TimeDefaults from app.core.simulation.requests_generator import requests_generator from app.core.simulation.simulation_run import run_simulation from app.schemas.simulation_input import SimulationInput @@ -28,7 +29,7 @@ def base_input() -> SimulationInput: return SimulationInput( avg_active_users={"mean": 1.0}, avg_request_per_minute_per_user={"mean": 2.0}, - total_simulation_time=120, + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, ) # -------------------------------------------------------------- @@ -73,7 +74,7 @@ def test_requests_generator_dispatches_to_correct_sampler( input_data = SimulationInput( avg_active_users={"mean": 1.0, "distribution": dist}, avg_request_per_minute_per_user={"mean": 1.0}, - total_simulation_time=120, # Must be > 60 to pass schema validation + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, ) rng = np.random.default_rng(0) gen = requests_generator(input_data, rng=rng) diff --git a/tests/unit/simulation/test_simulation_input.py b/tests/unit/simulation/test_simulation_input.py deleted file mode 100644 index 20a32d9..0000000 --- a/tests/unit/simulation/test_simulation_input.py +++ /dev/null @@ -1,93 +0,0 @@ -import pytest -from pydantic import ValidationError - -from app.schemas.simulation_input import RVConfig, SimulationInput - - -def test_normal_sets_variance_to_mean() -> None: - """When distribution='normal' and variance is omitted, variance == mean.""" - cfg = RVConfig(mean=10, distribution="normal") - assert cfg.variance == 10.0 - - -def test_poisson_keeps_variance_none() -> None: - """When distribution='poisson' and variance is omitted, variance stays None.""" - cfg = RVConfig(mean=5, distribution="poisson") - assert cfg.variance is None - - -def test_explicit_variance_is_preserved() -> None: - """If the user supplies variance explicitly, it is preserved unchanged.""" - cfg = RVConfig(mean=8, distribution="normal", variance=4) - assert cfg.variance == 4.0 - - -def test_mean_must_be_numeric() -> None: - """A non-numeric mean raises a ValidationError with our custom message.""" - with pytest.raises(ValidationError) as excinfo: - RVConfig(mean="not a number", distribution="poisson") - - # Check that at least one error refers to the 'mean' field - assert any(err["loc"] == ("mean",) for err in excinfo.value.errors()) - assert "mean must be a number" in excinfo.value.errors()[0]["msg"] - - -def test_missing_mean_field() -> None: - """Omitting the mean field raises a 'field required' ValidationError.""" - with pytest.raises(ValidationError) as excinfo: - # Using model_validate avoids the constructor signature check - RVConfig.model_validate({"distribution": "normal"}) - - assert any( - err["loc"] == ("mean",) and err["type"] == "missing" - for err in excinfo.value.errors() - ) - -def test_gaussian_sets_variance_to_mean() -> None: - """When distribution='gaussian' and variance is omitted, variance == mean.""" - cfg = RVConfig(mean=12.5, distribution="gaussian") - assert cfg.variance == pytest.approx(12.5) - - -def test_default_distribution_is_poisson() -> None: - """ - When distribution is omitted, it defaults to 'poisson' and - variance stays None. - """ - cfg = RVConfig(mean=3.3) - assert cfg.distribution == "poisson" - assert cfg.variance is None - - -def test_explicit_variance_kept_for_poisson() -> None: - """If the user supplies variance even for poisson, it is preserved.""" - cfg = RVConfig(mean=4.0, distribution="poisson", variance=2.2) - assert cfg.variance == pytest.approx(2.2) - - -def test_invalid_distribution_raises() -> None: - """Supplying a non-supported distribution literal raises ValidationError.""" - with pytest.raises(ValidationError) as excinfo: - RVConfig(mean=5.0, distribution="not_a_dist") - - errors = excinfo.value.errors() - # Only assert there is at least one error for the 'distribution' field: - assert any(e["loc"] == ("distribution",) for e in errors) - - -def test_simulation_time_below_minimum_raises() -> None: - """ - Passing total_simulation_time <= 60 must raise a ValidationError, - because the minimum allowed simulation time is 61 seconds. - """ - with pytest.raises(ValidationError) as excinfo: - SimulationInput( - avg_active_users={"mean": 1.0}, - avg_request_per_minute_per_user={"mean": 1.0}, - total_simulation_time=60, # exactly at the boundary - ) - errors = excinfo.value.errors() - assert any( - err["loc"] == ("total_simulation_time",) and "at least 60 seconds" in err["msg"] - for err in errors - ) From d43c91585a3d92a0b27bf761e9f66168b864384d Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 10 Jul 2025 17:38:39 +0200 Subject: [PATCH 11/52] README update --- README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 7a5e6bf..8180e20 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -## 🚀 How to Start the Backend with Docker (Development) +## How to Start the Backend with Docker (Development) To spin up the backend and its supporting services in development mode: @@ -17,7 +17,7 @@ To spin up the backend and its supporting services in development mode: --- -## 🏗️ Development Architecture & Philosophy +## Development Architecture & Philosophy We split responsibilities between Docker-managed services and local workflows: @@ -29,13 +29,13 @@ We split responsibilities between Docker-managed services and local workflows: **Why?** -* ⚡ **Instant feedback** on code changes -* 🛠️ **Full IDE support** (debugging, autocomplete, refactoring) -* ⏱️ **Blistering speed**—no rebuilding images on every change +* **Fater feedback** on code changes +* **Full IDE support** (debugging, autocomplete, refactoring) +* **Speed**—no rebuilding images on every change --- -### 🧪 Local Quality & Testing Workflow +### Local Quality & Testing Workflow All code quality tools, migrations, and tests execute on your host machine: @@ -52,7 +52,7 @@ All code quality tools, migrations, and tests execute on your host machine: --- -## ⚙️ CI/CD with GitHub Actions +## CI/CD with GitHub Actions We maintain two jobs on the `develop` branch: @@ -60,7 +60,7 @@ We maintain two jobs on the `develop` branch: * Ruff & MyPy * Unit tests only -* **No database** → < 1-minute feedback +* **No database** ### 🛠️ Full (on pushes to `develop`) @@ -75,12 +75,12 @@ We maintain two jobs on the `develop` branch: --- -## 🧠 Summary +## Summary 1. **Docker-Compose** for services & hot-reload of the app code 2. **Local** execution of migrations, tests, and QA for speed and IDE integration 3. **CI pipeline** split into quick PR checks and full develop-branch validation -This hybrid setup delivers **fast development** without sacrificing **production-grade safety** in CI. + From 02b47569e26dd27e12ccc06cfa86411577c12b6f Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 10 Jul 2025 18:17:51 +0200 Subject: [PATCH 12/52] Improved constants management --- src/app/config/constants.py | 11 +++++++- .../core/event_samplers/gaussian_poisson.py | 2 +- .../core/event_samplers/poisson_poisson.py | 2 +- src/app/core/simulation/requests_generator.py | 3 +- src/app/schemas/simulation_input.py | 28 ++++++++++--------- tests/unit/sampler/test_gaussian_poisson.py | 4 +-- tests/unit/sampler/test_poisson_posson.py | 8 +++--- .../simulation/test_events_generator_input.py | 12 ++++---- .../simulation/test_requests_generator.py | 4 +-- 9 files changed, 43 insertions(+), 31 deletions(-) diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 5146c62..ac5ca2b 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -1,6 +1,6 @@ """Application constants and configuration values.""" -from enum import IntEnum +from enum import Enum, IntEnum class TimeDefaults(IntEnum): @@ -12,3 +12,12 @@ class TimeDefaults(IntEnum): MIN_SIMULATION_TIME = 1800 # min simulation time MIN_USER_SAMPLING_WINDOW = 1 # 1 second MAX_USER_SAMPLING_WINDOW = 120 # 2 minutes + + +class Distribution(str, Enum): + """Allowed probability distributions for an RVConfig.""" + + POISSON = "poisson" + NORMAL = "normal" + GAUSSIAN = "gaussian" + diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/core/event_samplers/gaussian_poisson.py index 03434b5..4cd0463 100644 --- a/src/app/core/event_samplers/gaussian_poisson.py +++ b/src/app/core/event_samplers/gaussian_poisson.py @@ -59,7 +59,7 @@ def gaussian_poisson_sampling( mean_req_per_sec_per_user = ( float( input_data.avg_request_per_minute_per_user.mean) - / TimeDefaults.MIN_TO_SEC.value + / TimeDefaults.MIN_TO_SEC ) now = 0.0 # virtual clock (s) diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/core/event_samplers/poisson_poisson.py index 5f0598c..8637798 100644 --- a/src/app/core/event_samplers/poisson_poisson.py +++ b/src/app/core/event_samplers/poisson_poisson.py @@ -51,7 +51,7 @@ def poisson_poisson_sampling( mean_req_per_sec_per_user = ( float( input_data.avg_request_per_minute_per_user.mean) - / TimeDefaults.MIN_TO_SEC.value + / TimeDefaults.MIN_TO_SEC ) now = 0.0 # virtual clock (s) diff --git a/src/app/core/simulation/requests_generator.py b/src/app/core/simulation/requests_generator.py index 0da1c1c..2551ed6 100644 --- a/src/app/core/simulation/requests_generator.py +++ b/src/app/core/simulation/requests_generator.py @@ -7,6 +7,7 @@ from typing import TYPE_CHECKING +from app.config.constants import Distribution from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling @@ -36,7 +37,7 @@ def requests_generator( """ dist = input_data.avg_active_users.distribution.lower() - if dist in {"gaussian", "normal"}: + if dist in {Distribution.GAUSSIAN, Distribution.NORMAL}: #Gaussian-Poisson model return gaussian_poisson_sampling( input_data=input_data, diff --git a/src/app/schemas/simulation_input.py b/src/app/schemas/simulation_input.py index ed702bb..fca5701 100644 --- a/src/app/schemas/simulation_input.py +++ b/src/app/schemas/simulation_input.py @@ -1,21 +1,20 @@ """Define the schemas for the simulator""" -from typing import Literal from pydantic import BaseModel, Field, field_validator, model_validator -from app.config.constants import TimeDefaults +from app.config.constants import Distribution, TimeDefaults class RVConfig(BaseModel): """class to configure random variables""" mean: float - distribution: Literal["poisson", "normal", "gaussian"] = "poisson" + distribution: Distribution = Distribution.POISSON variance: float | None = None @field_validator("mean", mode="before") - def check_mean_is_number( + def ensure_mean_is_numeric( cls, # noqa: N805 v: object, ) -> float: @@ -28,7 +27,10 @@ def check_mean_is_number( @model_validator(mode="after") # type: ignore[arg-type] def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 """Set variance = mean when distribution == 'normal' and variance is missing.""" - if model.variance is None and model.distribution in {"normal", "gaussian"}: + if model.variance is None and model.distribution in { + Distribution.NORMAL, + Distribution.GAUSSIAN, + }: model.variance = model.mean return model @@ -38,21 +40,21 @@ class SimulationInput(BaseModel): avg_active_users: RVConfig avg_request_per_minute_per_user: RVConfig total_simulation_time: int = Field( - default=TimeDefaults.SIMULATION_TIME.value, - ge=TimeDefaults.MIN_SIMULATION_TIME.value, # minimum simulation time in seconds + default=TimeDefaults.SIMULATION_TIME, + ge=TimeDefaults.MIN_SIMULATION_TIME, # minimum simulation time in seconds description=( - f"Simulation time in seconds (>= {TimeDefaults.MIN_SIMULATION_TIME.value})." + f"Simulation time in seconds (>= {TimeDefaults.MIN_SIMULATION_TIME})." ), ) user_sampling_window: int = Field( - default=TimeDefaults.USER_SAMPLING_WINDOW.value, - ge=TimeDefaults.MIN_USER_SAMPLING_WINDOW.value, - le=TimeDefaults.MAX_USER_SAMPLING_WINDOW.value, + default=TimeDefaults.USER_SAMPLING_WINDOW, + ge=TimeDefaults.MIN_USER_SAMPLING_WINDOW, + le=TimeDefaults.MAX_USER_SAMPLING_WINDOW, description=( "Sampling window in seconds " - f"({TimeDefaults.MIN_USER_SAMPLING_WINDOW.value}-" - f"{TimeDefaults.MAX_USER_SAMPLING_WINDOW.value})." + f"({TimeDefaults.MIN_USER_SAMPLING_WINDOW}-" + f"{TimeDefaults.MAX_USER_SAMPLING_WINDOW})." ), ) diff --git a/tests/unit/sampler/test_gaussian_poisson.py b/tests/unit/sampler/test_gaussian_poisson.py index 6d49902..419a68d 100644 --- a/tests/unit/sampler/test_gaussian_poisson.py +++ b/tests/unit/sampler/test_gaussian_poisson.py @@ -25,8 +25,8 @@ def base_input() -> SimulationInput: mean=10.0, variance=4.0, distribution="gaussian", ), avg_request_per_minute_per_user=RVConfig(mean=30.0), - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, - user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW.value, + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, ) diff --git a/tests/unit/sampler/test_poisson_posson.py b/tests/unit/sampler/test_poisson_posson.py index c0a2f3e..c11e187 100644 --- a/tests/unit/sampler/test_poisson_posson.py +++ b/tests/unit/sampler/test_poisson_posson.py @@ -22,8 +22,8 @@ def base_input() -> SimulationInput: avg_active_users={"mean": 1.0, "distribution": "poisson"}, # … sending on average 60 req/min → 1 req/s avg_request_per_minute_per_user={"mean": 60.0, "distribution": "poisson"}, - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, # 30 min - user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW.value, # 60 s + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, # 30 min + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, # 60 s ) @@ -94,8 +94,8 @@ def test_zero_users_produces_no_events(base_input: SimulationInput) -> None: input_data = SimulationInput( avg_active_users=RVConfig(mean=0.0, distribution="poisson"), avg_request_per_minute_per_user=RVConfig(mean=60.0, distribution="poisson"), - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, - user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW.value, + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, ) rng = np.random.default_rng(123) diff --git a/tests/unit/simulation/test_events_generator_input.py b/tests/unit/simulation/test_events_generator_input.py index 39c5216..e794c50 100644 --- a/tests/unit/simulation/test_events_generator_input.py +++ b/tests/unit/simulation/test_events_generator_input.py @@ -88,7 +88,7 @@ def test_default_user_sampling_window() -> None: avg_active_users={"mean": 1.0, "distribution": "poisson"}, avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, ) - assert inp.user_sampling_window == TimeDefaults.USER_SAMPLING_WINDOW.value + assert inp.user_sampling_window == TimeDefaults.USER_SAMPLING_WINDOW def test_explicit_user_sampling_window_kept() -> None: @@ -125,7 +125,7 @@ def test_user_sampling_window_above_max_raises() -> None: Passing user_sampling_window > MAX_USER_SAMPLING_WINDOW must raise a ValidationError. """ - too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW.value + 1 + too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW + 1 with pytest.raises(ValidationError) as excinfo: SimulationInput( avg_active_users={"mean": 1.0, "distribution": "poisson"}, @@ -137,7 +137,7 @@ def test_user_sampling_window_above_max_raises() -> None: assert any(err["loc"] == ("user_sampling_window",) for err in errors) expected_snippet = ( - f"less than or equal to {TimeDefaults.MAX_USER_SAMPLING_WINDOW.value}" + f"less than or equal to {TimeDefaults.MAX_USER_SAMPLING_WINDOW}" ) assert any(expected_snippet in err["msg"] for err in errors) @@ -153,7 +153,7 @@ def test_default_total_simulation_time() -> None: avg_active_users={"mean": 1.0, "distribution": "poisson"}, avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, ) - assert inp.total_simulation_time == TimeDefaults.SIMULATION_TIME.value + assert inp.total_simulation_time == TimeDefaults.SIMULATION_TIME def test_explicit_total_simulation_time_kept() -> None: @@ -190,7 +190,7 @@ def test_total_simulation_time_below_minimum_raises() -> None: Passing total_simulation_time < MIN_SIMULATION_TIME must raise a ValidationError. """ - too_small = TimeDefaults.MIN_SIMULATION_TIME.value - 1 + too_small = TimeDefaults.MIN_SIMULATION_TIME - 1 with pytest.raises(ValidationError) as excinfo: SimulationInput( avg_active_users={"mean": 1.0, "distribution": "poisson"}, @@ -203,7 +203,7 @@ def test_total_simulation_time_below_minimum_raises() -> None: assert any(err["loc"] == ("total_simulation_time",) for err in errors) expected_snippet = ( - f"greater than or equal to {TimeDefaults.MIN_SIMULATION_TIME.value}" + f"greater than or equal to {TimeDefaults.MIN_SIMULATION_TIME}" ) assert any(expected_snippet in err["msg"] for err in errors) diff --git a/tests/unit/simulation/test_requests_generator.py b/tests/unit/simulation/test_requests_generator.py index 0bf5872..fcd2a55 100644 --- a/tests/unit/simulation/test_requests_generator.py +++ b/tests/unit/simulation/test_requests_generator.py @@ -29,7 +29,7 @@ def base_input() -> SimulationInput: return SimulationInput( avg_active_users={"mean": 1.0}, avg_request_per_minute_per_user={"mean": 2.0}, - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, ) # -------------------------------------------------------------- @@ -74,7 +74,7 @@ def test_requests_generator_dispatches_to_correct_sampler( input_data = SimulationInput( avg_active_users={"mean": 1.0, "distribution": dist}, avg_request_per_minute_per_user={"mean": 1.0}, - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME.value, + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, ) rng = np.random.default_rng(0) gen = requests_generator(input_data, rng=rng) From 6f06bb74b28da76fda751756b420bfe79b43494e Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Fri, 11 Jul 2025 17:25:34 +0200 Subject: [PATCH 13/52] Clean and refactor --- .../unit_tests/test_sampler_helper.md | 50 ---------- .../unit_tests/test_simulation_input.md | 93 ------------------- src/app/config/constants.py | 6 +- .../core/event_samplers/gaussian_poisson.py | 7 +- .../core/event_samplers/poisson_poisson.py | 7 +- src/app/core/simulation/requests_generator.py | 4 +- ...n_input.py => requests_generator_input.py} | 7 +- tests/unit/sampler/test_gaussian_poisson.py | 4 +- tests/unit/sampler/test_poisson_posson.py | 2 +- .../simulation/test_requests_generator.py | 4 +- ...ut.py => test_requests_generator_input.py} | 76 +++++++++------ 11 files changed, 63 insertions(+), 197 deletions(-) delete mode 100644 documentation/tests_documentation/unit_tests/test_sampler_helper.md delete mode 100644 documentation/tests_documentation/unit_tests/test_simulation_input.md rename src/app/schemas/{simulation_input.py => requests_generator_input.py} (88%) rename tests/unit/simulation/{test_events_generator_input.py => test_requests_generator_input.py} (72%) diff --git a/documentation/tests_documentation/unit_tests/test_sampler_helper.md b/documentation/tests_documentation/unit_tests/test_sampler_helper.md deleted file mode 100644 index 9084ed8..0000000 --- a/documentation/tests_documentation/unit_tests/test_sampler_helper.md +++ /dev/null @@ -1,50 +0,0 @@ -Below is a guided walkthrough of **`tests/unit/simulation/test_sampler_helper.py`**, explaining core ideas and each test’s intent. - ---- - -## File purpose - -This file verifies that your three helper functions— - -* `uniform_variable_generator` -* `poisson_variable_generator` -* `truncated_gaussian_generator` - -—correctly delegate to whatever RNG you pass in, and fall back to NumPy’s default RNG when you don’t provide one. - ---- - -## Key testing patterns - -1. **Dependency injection via `rng`** - Each helper takes an `rng` parameter. In production you’ll pass a `np.random.Generator`; in tests we inject a **`DummyRNG`** with predictable outputs to make our tests **deterministic**. - -2. **Duck typing** - Python doesn’t require `rng` to be a specific class—only that it implements the required methods (`random()`, `poisson(mean)`, `normal(mean, sigma)`). Our `DummyRNG` simply implements those three methods. - -3. **`typing.cast` for static typing** - We wrap `DummyRNG` instances in `cast("np.random.Generator", DummyRNG(...))` so mypy sees them as satisfying the generator type, but at runtime they remain our dummy. - ---- - -## Test-by-test breakdown - -| Test name | What it checks | -| -------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | -| **`test_uniform_variable_generator_with_dummy_rng`** | Passing a `DummyRNG(uniform_value=0.75)`, `rng.random()` returns 0.75 → helper must return exactly 0.75. | -| **`test_uniform_variable_generator_default_rng_range`** | Without supplying `rng`, the helper uses `default_rng()`. We call it 100× to ensure it always returns a `float` in \[0.0, 1.0). | -| **`test_poisson_variable_generator_with_dummy_rng`** | With `DummyRNG(poisson_value=3)`, `rng.poisson(mean)` yields 3 → helper returns 3. | -| **`test_poisson_variable_generator_reproducible`** | Two NumPy generators created with the same seed (`12345`) must produce the same Poisson sample for `mean=10.0`. | -| **`test_truncated_gaussian_generator_truncates_negative`** | `DummyRNG(normal_value=-2.7)` forces a negative draw: helper must clamp it to **0**. | -| **`test_truncated_gaussian_generator_truncates_toward_zero`** | `DummyRNG(normal_value=3.9)` forces a positive draw: helper must cast/round toward zero (int(3.9) → **3**). | -| **`test_truncated_gaussian_generator_default_rng_non_negative_int`** | With a real seeded RNG, helper must produce **some** non-negative `int` (verifies default fallback path is valid). | - ---- - -## Why this matters - -* **Deterministic behavior**: by forcing the RNG’s output via `DummyRNG`, we can assert exactly how our helpers transform that value (clamping, rounding, type conversion). -* **Fallbacks work**: tests with **no** `rng` verify that calling `default_rng()` still gives valid outputs of the correct type and range. -* **Type safety**: using `cast(...)` silences mypy errors while still executing our dummy logic at runtime—ensuring we meet both static‐typing and functional correctness goals. - -With this suite, you have **full confidence** that your sampling helpers behave correctly under both controlled (dummy) and uncontrolled (default) RNG conditions. diff --git a/documentation/tests_documentation/unit_tests/test_simulation_input.md b/documentation/tests_documentation/unit_tests/test_simulation_input.md deleted file mode 100644 index f555571..0000000 --- a/documentation/tests_documentation/unit_tests/test_simulation_input.md +++ /dev/null @@ -1,93 +0,0 @@ -Below an explanation for the unit tests in the file `tests/unit/simulation/test_simulation_input.py` - -### 1. `test_normal_sets_variance_to_mean` - -**Purpose:** -Checks that when you create an `RVConfig` with `distribution="normal"` and omit the `variance` field, the model automatically sets `variance = mean`. - -* Verifies the “default variance” logic in the post‐init validator. - ---- - -### 2. `test_poisson_keeps_variance_none` - -**Purpose:** -Ensures that if you choose the Poisson distribution (`distribution="poisson"`) and do **not** supply a variance, the model **does not** fill in any default variance (keeps it `None`). - -* Confirms that defaulting only applies to “normal”/“gaussian,” not to Poisson. - ---- - -### 3. `test_explicit_variance_is_preserved` - -**Purpose:** -Validates that if you explicitly pass a `variance` value—even for a distribution that would normally default—it remains exactly what you provided, and is coerced to float. - -* Guards against accidental overwriting of user‐supplied variance. - ---- - -### 4. `test_mean_must_be_numeric` - -**Purpose:** -Verifies that giving a non‐numeric `mean` (e.g. a string) raises a `ValidationError` with our custom message `"mean must be a number"`. - -* Tests the “before” validator on the `mean` field for type checking and coercion. - ---- - -### 5. `test_missing_mean_field` - -**Purpose:** -Ensures that completely omitting the `mean` key triggers a standard “field required” error. - -* Confirms that `mean` is mandatory in the schema. - ---- - -### 6. `test_gaussian_sets_variance_to_mean` - -**Purpose:** -Exactly like the “normal” test above, but for `distribution="gaussian"`. - -* Demonstrates that “gaussian” is treated as an alias for “normal” in the default‐variance logic. - ---- - -### 7. `test_default_distribution_is_poisson` - -**Purpose:** -Checks two things simultaneously: - -1. When you omit `distribution`, it defaults to `"poisson"`. -2. In that default‐poisson case, `variance` remains `None`. - -* Validates both the default distribution and its variance behavior in one test. - ---- - -### 8. `test_explicit_variance_kept_for_poisson` - -**Purpose:** -Confirms that even if you supply a `variance` when `distribution="poisson"`, the model preserves it rather than discarding it or forcing it back to `None`. - -* Provides symmetry to the “explicit variance” test for non‐Poisson cases. - ---- - -### 9. `test_invalid_distribution_raises` - -**Purpose:** -Ensures that passing a value for `distribution` outside of the allowed literals (`"poisson"`, `"normal"`, `"gaussian"`) results in a `ValidationError`. - -* Confirms that the `Literal[...]` constraint on `distribution` is enforced. - ---- - -With these nine tests you fully cover: - -1. **Defaulting behavior** for both “normal” and “gaussian.” -2. **No‐op behavior** for Poisson defaults. -3. **Preservation** of explicit user input. -4. **Type‐checking** on required fields. -5. **Literal‐constraint** enforcement. diff --git a/src/app/config/constants.py b/src/app/config/constants.py index ac5ca2b..03448de 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -1,6 +1,6 @@ """Application constants and configuration values.""" -from enum import Enum, IntEnum +from enum import IntEnum, StrEnum class TimeDefaults(IntEnum): @@ -14,10 +14,10 @@ class TimeDefaults(IntEnum): MAX_USER_SAMPLING_WINDOW = 120 # 2 minutes -class Distribution(str, Enum): +class Distribution(StrEnum): """Allowed probability distributions for an RVConfig.""" POISSON = "poisson" NORMAL = "normal" - GAUSSIAN = "gaussian" + diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/core/event_samplers/gaussian_poisson.py index 4cd0463..98f9121 100644 --- a/src/app/core/event_samplers/gaussian_poisson.py +++ b/src/app/core/event_samplers/gaussian_poisson.py @@ -16,7 +16,7 @@ truncated_gaussian_generator, uniform_variable_generator, ) -from app.schemas.simulation_input import SimulationInput +from app.schemas.requests_generator_input import SimulationInput def gaussian_poisson_sampling( @@ -41,11 +41,6 @@ def gaussian_poisson_sampling( simulation_time = input_data.total_simulation_time user_sampling_window = input_data.user_sampling_window - # pydantic in the validation assign a value and mypy is not - # complaining because a None cannot be compared in the loop - # to a float - assert simulation_time is not None - assert user_sampling_window is not None # λ_u : mean concurrent users per window mean_concurrent_user = float(input_data.avg_active_users.mean) diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/core/event_samplers/poisson_poisson.py index 8637798..d25c4dd 100644 --- a/src/app/core/event_samplers/poisson_poisson.py +++ b/src/app/core/event_samplers/poisson_poisson.py @@ -13,7 +13,7 @@ poisson_variable_generator, uniform_variable_generator, ) -from app.schemas.simulation_input import SimulationInput +from app.schemas.requests_generator_input import SimulationInput def poisson_poisson_sampling( @@ -38,11 +38,6 @@ def poisson_poisson_sampling( simulation_time = input_data.total_simulation_time user_sampling_window = input_data.user_sampling_window - # pydantic in the validation assign a value and mypy is not - # complaining because a None cannot be compared in the loop - # to a float - assert simulation_time is not None - assert user_sampling_window is not None # λ_u : mean concurrent users per window mean_concurrent_user = float(input_data.avg_active_users.mean) diff --git a/src/app/core/simulation/requests_generator.py b/src/app/core/simulation/requests_generator.py index 2551ed6..e19d8f0 100644 --- a/src/app/core/simulation/requests_generator.py +++ b/src/app/core/simulation/requests_generator.py @@ -16,7 +16,7 @@ import numpy as np - from app.schemas.simulation_input import SimulationInput + from app.schemas.requests_generator_input import SimulationInput def requests_generator( @@ -37,7 +37,7 @@ def requests_generator( """ dist = input_data.avg_active_users.distribution.lower() - if dist in {Distribution.GAUSSIAN, Distribution.NORMAL}: + if dist == Distribution.NORMAL: #Gaussian-Poisson model return gaussian_poisson_sampling( input_data=input_data, diff --git a/src/app/schemas/simulation_input.py b/src/app/schemas/requests_generator_input.py similarity index 88% rename from src/app/schemas/simulation_input.py rename to src/app/schemas/requests_generator_input.py index fca5701..9090fed 100644 --- a/src/app/schemas/simulation_input.py +++ b/src/app/schemas/requests_generator_input.py @@ -27,10 +27,7 @@ def ensure_mean_is_numeric( @model_validator(mode="after") # type: ignore[arg-type] def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 """Set variance = mean when distribution == 'normal' and variance is missing.""" - if model.variance is None and model.distribution in { - Distribution.NORMAL, - Distribution.GAUSSIAN, - }: + if model.variance is None and model.distribution == Distribution.NORMAL: model.variance = model.mean return model @@ -41,7 +38,7 @@ class SimulationInput(BaseModel): avg_request_per_minute_per_user: RVConfig total_simulation_time: int = Field( default=TimeDefaults.SIMULATION_TIME, - ge=TimeDefaults.MIN_SIMULATION_TIME, # minimum simulation time in seconds + ge=TimeDefaults.MIN_SIMULATION_TIME, description=( f"Simulation time in seconds (>= {TimeDefaults.MIN_SIMULATION_TIME})." ), diff --git a/tests/unit/sampler/test_gaussian_poisson.py b/tests/unit/sampler/test_gaussian_poisson.py index 419a68d..f0abbf8 100644 --- a/tests/unit/sampler/test_gaussian_poisson.py +++ b/tests/unit/sampler/test_gaussian_poisson.py @@ -10,7 +10,7 @@ from app.config.constants import TimeDefaults from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling -from app.schemas.simulation_input import RVConfig, SimulationInput +from app.schemas.requests_generator_input import RVConfig, SimulationInput # --------------------------------------------------------------------------- # Fixture @@ -22,7 +22,7 @@ def base_input() -> SimulationInput: """Return a minimal, valid SimulationInput for the Gaussian-Poisson sampler.""" return SimulationInput( avg_active_users=RVConfig( - mean=10.0, variance=4.0, distribution="gaussian", + mean=10.0, variance=4.0, distribution="normal", ), avg_request_per_minute_per_user=RVConfig(mean=30.0), total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, diff --git a/tests/unit/sampler/test_poisson_posson.py b/tests/unit/sampler/test_poisson_posson.py index c11e187..5d89686 100644 --- a/tests/unit/sampler/test_poisson_posson.py +++ b/tests/unit/sampler/test_poisson_posson.py @@ -11,7 +11,7 @@ from app.config.constants import TimeDefaults from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling -from app.schemas.simulation_input import RVConfig, SimulationInput +from app.schemas.requests_generator_input import RVConfig, SimulationInput @pytest.fixture diff --git a/tests/unit/simulation/test_requests_generator.py b/tests/unit/simulation/test_requests_generator.py index fcd2a55..5164413 100644 --- a/tests/unit/simulation/test_requests_generator.py +++ b/tests/unit/simulation/test_requests_generator.py @@ -11,7 +11,7 @@ from app.config.constants import TimeDefaults from app.core.simulation.requests_generator import requests_generator from app.core.simulation.simulation_run import run_simulation -from app.schemas.simulation_input import SimulationInput +from app.schemas.requests_generator_input import SimulationInput if TYPE_CHECKING: @@ -57,7 +57,6 @@ def test_default_requests_generator_uses_poisson_poisson_sampling( [ ("poisson", "poisson_poisson_sampling"), ("normal", "gaussian_poisson_sampling"), - ("gaussian", "gaussian_poisson_sampling"), ], ) def test_requests_generator_dispatches_to_correct_sampler( @@ -69,7 +68,6 @@ def test_requests_generator_dispatches_to_correct_sampler( comes from the appropriate sampler function based on distribution: - 'poisson' → poisson_poisson_sampling - 'normal' → gaussian_poisson_sampling - - 'gaussian'→ gaussian_poisson_sampling """ input_data = SimulationInput( avg_active_users={"mean": 1.0, "distribution": dist}, diff --git a/tests/unit/simulation/test_events_generator_input.py b/tests/unit/simulation/test_requests_generator_input.py similarity index 72% rename from tests/unit/simulation/test_events_generator_input.py rename to tests/unit/simulation/test_requests_generator_input.py index e794c50..bbd84ae 100644 --- a/tests/unit/simulation/test_events_generator_input.py +++ b/tests/unit/simulation/test_requests_generator_input.py @@ -1,8 +1,8 @@ import pytest from pydantic import ValidationError -from app.config.constants import TimeDefaults -from app.schemas.simulation_input import RVConfig, SimulationInput +from app.config.constants import Distribution, TimeDefaults +from app.schemas.requests_generator_input import RVConfig, SimulationInput # -------------------------------------------------------------------------- # TEST RANDOM VARIABLE CONFIGURATION @@ -10,26 +10,26 @@ def test_normal_sets_variance_to_mean() -> None: """When distribution='normal' and variance is omitted, variance == mean.""" - cfg = RVConfig(mean=10, distribution="normal") + cfg = RVConfig(mean=10, distribution=Distribution.NORMAL) assert cfg.variance == 10.0 def test_poisson_keeps_variance_none() -> None: """When distribution='poisson' and variance is omitted, variance stays None.""" - cfg = RVConfig(mean=5, distribution="poisson") + cfg = RVConfig(mean=5, distribution=Distribution.POISSON) assert cfg.variance is None def test_explicit_variance_is_preserved() -> None: """If the user supplies variance explicitly, it is preserved unchanged.""" - cfg = RVConfig(mean=8, distribution="normal", variance=4) + cfg = RVConfig(mean=8, distribution=Distribution.NORMAL, variance=4) assert cfg.variance == 4.0 def test_mean_must_be_numeric() -> None: """A non-numeric mean raises a ValidationError with our custom message.""" with pytest.raises(ValidationError) as excinfo: - RVConfig(mean="not a number", distribution="poisson") + RVConfig(mean="not a number", distribution=Distribution.POISSON) # Check that at least one error refers to the 'mean' field assert any(err["loc"] == ("mean",) for err in excinfo.value.errors()) @@ -40,7 +40,7 @@ def test_missing_mean_field() -> None: """Omitting the mean field raises a 'field required' ValidationError.""" with pytest.raises(ValidationError) as excinfo: # Using model_validate avoids the constructor signature check - RVConfig.model_validate({"distribution": "normal"}) + RVConfig.model_validate({"distribution": Distribution.NORMAL}) assert any( err["loc"] == ("mean",) and err["type"] == "missing" @@ -49,7 +49,7 @@ def test_missing_mean_field() -> None: def test_gaussian_sets_variance_to_mean() -> None: """When distribution='gaussian' and variance is omitted, variance == mean.""" - cfg = RVConfig(mean=12.5, distribution="gaussian") + cfg = RVConfig(mean=12.5, distribution=Distribution.NORMAL) assert cfg.variance == pytest.approx(12.5) @@ -59,13 +59,13 @@ def test_default_distribution_is_poisson() -> None: variance stays None. """ cfg = RVConfig(mean=3.3) - assert cfg.distribution == "poisson" + assert cfg.distribution == Distribution.POISSON assert cfg.variance is None def test_explicit_variance_kept_for_poisson() -> None: """If the user supplies variance even for poisson, it is preserved.""" - cfg = RVConfig(mean=4.0, distribution="poisson", variance=2.2) + cfg = RVConfig(mean=4.0, distribution=Distribution.POISSON, variance=2.2) assert cfg.variance == pytest.approx(2.2) @@ -85,8 +85,11 @@ def test_invalid_distribution_raises() -> None: def test_default_user_sampling_window() -> None: """When user_sampling_window is omitted, it defaults to USER_SAMPLING_WINDOW.""" inp = SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, ) assert inp.user_sampling_window == TimeDefaults.USER_SAMPLING_WINDOW @@ -95,8 +98,11 @@ def test_explicit_user_sampling_window_kept() -> None: """An explicit user_sampling_window value is preserved unchanged.""" custom_window = 30 inp = SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, user_sampling_window=custom_window, ) assert inp.user_sampling_window == custom_window @@ -107,8 +113,11 @@ def test_user_sampling_window_not_int_raises() -> None: with pytest.raises(ValidationError) as excinfo: SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, user_sampling_window="not-an-int", ) @@ -128,8 +137,11 @@ def test_user_sampling_window_above_max_raises() -> None: too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW + 1 with pytest.raises(ValidationError) as excinfo: SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, user_sampling_window=too_large, ) @@ -150,8 +162,11 @@ def test_user_sampling_window_above_max_raises() -> None: def test_default_total_simulation_time() -> None: """When total_simulation_time is omitted, it defaults to SIMULATION_TIME.""" inp = SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, ) assert inp.total_simulation_time == TimeDefaults.SIMULATION_TIME @@ -160,8 +175,11 @@ def test_explicit_total_simulation_time_kept() -> None: """An explicit total_simulation_time value is preserved unchanged.""" custom_time = 3_000 inp = SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, total_simulation_time=custom_time, ) assert inp.total_simulation_time == custom_time @@ -172,8 +190,11 @@ def test_total_simulation_time_not_int_raises() -> None: with pytest.raises(ValidationError) as excinfo: SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, total_simulation_time="three thousand", ) @@ -193,8 +214,11 @@ def test_total_simulation_time_below_minimum_raises() -> None: too_small = TimeDefaults.MIN_SIMULATION_TIME - 1 with pytest.raises(ValidationError) as excinfo: SimulationInput( - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - avg_request_per_minute_per_user={"mean": 1.0, "distribution": "poisson"}, + avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, + avg_request_per_minute_per_user={ + "mean": 1.0, + "distribution": Distribution.POISSON, + }, total_simulation_time=too_small, ) From ee5d202500dac1de0266ac6d8aa9f12177c2eaf0 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Sun, 13 Jul 2025 16:34:06 +0200 Subject: [PATCH 14/52] Features/request handler endpoint input (#3) * Introduction of the input structure for endpoints * Documentation + foundation of topology input * Improve Step validation, added unit test for the input * improved documentation for the input --- .../input_structure_for_the_simulation.md | 404 ++++++++++++++++++ .../requests_generator.md | 2 +- src/app/api/simulation.py | 4 +- src/app/config/constants.py | 179 +++++++- .../core/event_samplers/gaussian_poisson.py | 4 +- .../core/event_samplers/poisson_poisson.py | 4 +- src/app/core/simulation/requests_generator.py | 4 +- src/app/core/simulation/simulation_run.py | 4 +- src/app/schemas/full_simulation_input.py | 13 + src/app/schemas/random_variables_config.py | 31 ++ src/app/schemas/requests_generator_input.py | 32 +- src/app/schemas/simulation_output.py | 1 + .../system_topology_schema/endpoint_schema.py | 97 +++++ .../full_system_topology_schema.py | 217 ++++++++++ .../input_sructure/test_endpoint_input.py | 125 ++++++ .../test_full_topology_input.py | 170 ++++++++ .../test_requests_generator_input.py | 19 +- tests/unit/sampler/test_gaussian_poisson.py | 15 +- tests/unit/sampler/test_poisson_posson.py | 21 +- .../simulation/test_requests_generator.py | 28 +- 20 files changed, 1286 insertions(+), 88 deletions(-) create mode 100644 documentation/backend_documentation/input_structure_for_the_simulation.md create mode 100644 src/app/schemas/full_simulation_input.py create mode 100644 src/app/schemas/random_variables_config.py create mode 100644 src/app/schemas/system_topology_schema/endpoint_schema.py create mode 100644 src/app/schemas/system_topology_schema/full_system_topology_schema.py create mode 100644 tests/unit/input_sructure/test_endpoint_input.py create mode 100644 tests/unit/input_sructure/test_full_topology_input.py rename tests/unit/{simulation => input_sructure}/test_requests_generator_input.py (95%) diff --git a/documentation/backend_documentation/input_structure_for_the_simulation.md b/documentation/backend_documentation/input_structure_for_the_simulation.md new file mode 100644 index 0000000..e31c860 --- /dev/null +++ b/documentation/backend_documentation/input_structure_for_the_simulation.md @@ -0,0 +1,404 @@ +### **FastSim — Request-Generator Input Configuration** + +A **single, self-consistent contract** links three layers of the codebase: + +1. **Global Constants** – `TimeDefaults`, `Distribution` +2. **Random Variable Schema** – `RVConfig` +3. **Traffic-Generator Payload** – `RqsGeneratorInput` + +Understanding how these layers interact is key to crafting valid and predictable traffic profiles, preventing common configuration errors before the simulation begins. + +--- + +### 1. Global Constants + +| Constant Set | Purpose | Key Values | +| :--- | :--- | :--- | +| **`TimeDefaults`** (`IntEnum`) | Defines default values and validation bounds for time-based fields. | `SIMULATION_TIME = 3600 s`, `MIN_SIMULATION_TIME = 1800 s`, `USER_SAMPLING_WINDOW = 60 s`, `MIN_USER_SAMPLING_WINDOW = 1 s`, `MAX_USER_SAMPLING_WINDOW = 120 s` | +| **`Distribution`** (`StrEnum`) | Defines the canonical names of probability distributions supported by the generator. | `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"` | + +***Why use constants?*** + +* **Consistency:** They are referenced by validators; changing a value in one place updates the entire validation tree. +* **Safety:** They guarantee that a typo, such as `"Poisson"`, raises an error instead of silently failing or switching to an unintended default. + +--- + +### 2. Random Variable Schema (`RVConfig`) + +```python +class RVConfig(BaseModel): + """class to configure random variables""" + + mean: float + distribution: Distribution = Distribution.POISSON + variance: float | None = None + + @field_validator("mean", mode="before") + def ensure_mean_is_numeric( + cls, # noqa: N805 + v: object, + ) -> float: + """Ensure `mean` is numeric, then coerce to float.""" + err_msg = "mean must be a number (int or float)" + if not isinstance(v, (float, int)): + raise ValueError(err_msg) # noqa: TRY004 + return float(v) + + @model_validator(mode="after") # type: ignore[arg-type] + def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 + """Set variance = mean when distribution == 'normal' and variance is missing.""" + if model.variance is None and model.distribution == Distribution.NORMAL: + model.variance = model.mean + return model + +``` + +#### Validation Logic + +| Check | Pydantic Hook | Rule | +| :--- | :--- | :--- | +| *Mean must be numeric* | `@field_validator("mean", before)` | Rejects strings and nulls; coerces `int` to `float`. | +| *Autofill variance* | `@model_validator(after)` | If `distribution == "normal"` **and** `variance` is not provided, sets `variance = mean`. | +| *Positivity enforcement* | `PositiveFloat` / `PositiveInt` | Pydantic's constrained types are used on fields like `mean` where negative values are invalid, rejecting them before business logic runs. | + +> **Self-Consistency:** Every random draw in the simulation engine relies on a validated `RVConfig` instance. This avoids redundant checks and defensive code downstream. + +--- + +### 3. Traffic-Generator Payload (`RqsGeneratorInput`) + +| Field | Type | Validation Tied to Constants | +| :--- | :--- | :--- | +| `avg_active_users` | `RVConfig` | No extra constraints needed; the inner schema guarantees correctness. | +| `avg_request_per_minute_per_user` | `RVConfig` | Same as above. | +| `total_simulation_time` | `int` | `ge=TimeDefaults.MIN_SIMULATION_TIME`
default=`TimeDefaults.SIMULATION_TIME` | +| `user_sampling_window` | `int` | `ge=TimeDefaults.MIN_USER_SAMPLING_WINDOW`
`le=TimeDefaults.MAX_USER_SAMPLING_WINDOW`
default=`TimeDefaults.USER_SAMPLING_WINDOW` | + +#### How the Generator Uses Each Field + +The simulation evolves based on a simple, powerful loop: + +1. **Timeline Partitioning** (`user_sampling_window`): The simulation timeline is divided into fixed-length windows. For each window: +2. **Active User Sampling** (`avg_active_users`): A single value is drawn to determine the concurrent user population, `U(t)`, for that window. +3. **Request Rate Calculation** (`avg_request_per_minute_per_user`): Each of the `U(t)` users contributes to the total request rate, yielding an aggregate load for the window. +4. **Termination** (`total_simulation_time`): The loop stops once the cumulative simulated time reaches this value. + +Because every numeric input is range-checked upfront, **the runtime engine never needs to defend itself** against invalid data like zero-length windows or negative rates, making the event-loop lean and predictable. + +--- + +### 4. End-to-End Example (Fully Explicit) + +```json +{ + "avg_active_users": { + "mean": 100, + "distribution": "poisson" + }, + "avg_request_per_minute_per_user": { + "mean": 4.0, + "distribution": "normal", + "variance": null + }, + "total_simulation_time": 5400, + "user_sampling_window": 45 +}``` + +#### What the Validators Do + +1. `mean` is numeric ✔️ +2. `distribution` string matches an enum member ✔️ +3. `total_simulation_time` ≥ 1800 ✔️ +4. `user_sampling_window` is in the range ✔️ +5. `variance` is `null` with a `normal` distribution ⇒ **auto-set to 4.0** ✔️ + +The payload is accepted. The simulator will run for $5400 / 45 = 120$ simulation windows. + +--- + +### 5. Common Error Example + +```json +{ + "avg_active_users": { "mean": "many" }, + "avg_request_per_minute_per_user": { "mean": -2 }, + "total_simulation_time": 600, + "user_sampling_window": 400 +} +``` + +| # | Fails On | Error Message (Abridged) | +| :- | :--- | :--- | +| 1 | Numeric check | `Input should be a valid number` | +| 2 | Positivity check | `Input should be greater than 0` | +| 3 | Minimum time check | `Input should be at least 1800` | +| 4 | Maximum window check | `Input should be at most 120` | + +--- + +### Takeaways + +* **Single Source of Truth:** Enums centralize all literal values, eliminating magic strings. +* **Layered Validation:** The `Constants → RVConfig → Request Payload` hierarchy ensures that only well-formed traffic profiles reach the simulation engine. +* **Safe Defaults:** Omitting optional fields never leads to undefined behavior; defaults are sourced directly from the `TimeDefaults` constants. + +This robust, layered approach allows you to configure the generator with confidence, knowing that any malformed scenario will be rejected early with explicit, actionable error messages. + + +### **FastSim Topology Input Schema** + +The topology schema is the blueprint of the digital twin, defining the structure, resources, behavior, and network connections of the system you wish to simulate. It describes: + +1. **What work** each request performs (`Endpoint` → `Step`). +2. **What components** exist in the system (`Server`, `Client`). +3. **Which resources** each component possesses (`ServerResources`). +4. **How** components are interconnected (`Edge`). + +To ensure simulation integrity and prevent runtime errors, FastSim uses Pydantic to rigorously validate the entire topology upfront. Every inconsistency is rejected at load-time. The following sections detail the schema's layered design, from the most granular operation to the complete system graph. + +--- +### **A Controlled Vocabulary: The Role of Constants** + +To ensure that input configurations are unambiguous and robust, the topology schema is built upon a controlled vocabulary defined by a series of Python `Enum` classes. Instead of relying on raw strings or "magic values" (e.g., `"cpu_bound_operation"`), which are prone to typos and inconsistencies, the schema uses these enumerations to define the finite set of legal values for categories like operation kinds, metrics, and node types. + +This design choice provides three critical benefits: + +1. **Strong Type-Safety:** By using `StrEnum` and `IntEnum`, Pydantic models can validate input payloads with absolute certainty. Any value not explicitly defined in the corresponding `Enum` is immediately rejected. This prevents subtle configuration errors that would be difficult to debug at simulation time. +2. **Developer Experience and Error Prevention:** This approach provides powerful auto-completion and static analysis. IDEs, `mypy`, and linters can catch invalid values during development, providing immediate feedback long before the code is executed. +3. **Single Source of Truth:** All valid categories are centralized in the `app.config.constants` module. This makes the system easier to maintain and extend. To add a new resource type or metric, a developer only needs to update the `Enum` definition, and the change propagates consistently to validation logic, the simulation engine, and any other component that uses it. + +The key enumerations that govern the topology schema include: + +| Constant Enum | Purpose | +| :--- | :--- | +| **`EndpointStepIO`, `EndpointStepCPU`, `EndpointStepRAM`** | Define the exhaustive list of valid `kind` values for a `Step`. | +| **`Metrics`** | Specify the legal dictionary keys within a `Step`'s `step_metrics`, enforcing the one-to-one link between a `kind` and its metric. | +| **`SystemNodes` and `SystemEdges`** | Enumerate the allowed categories for nodes and their connections in the high-level `TopologyGraph`. | + +### **Design Philosophy: A "Micro-to-Macro" Approach** + +The schema is built on a compositional, "micro-to-macro" principle. We start by defining the smallest indivisible units of work (`Step`) and progressively assemble them into larger, more complex structures (`Endpoint`, `Server`, and finally the `TopologyGraph`). + +This layered approach provides several key advantages: +* **Modularity and Reusability:** An `Endpoint` is just a sequence of `Steps`. You can reorder, add, or remove steps without redefining the core operations themselves. +* **Local Reasoning, Global Safety:** Each model is responsible for its own internal consistency (e.g., a `Step` ensures its metric is valid for its kind). Parent models then enforce the integrity of the connections *between* these components (e.g., the `TopologyGraph` ensures all `Edges` connect to valid `Nodes`). +* **Clarity and Maintainability:** The hierarchy makes the system description intuitive to read and write. It’s clear how atomic operations roll up into endpoints, which are hosted on servers connected by a network. +* **Robustness:** All structural and referential errors are caught before the simulation begins, guaranteeing that the SimPy engine operates on a valid, self-consistent model. + +--- + +### **1. The Atomic Unit: `Step`** + +A `Step` represents a single, indivisible operation executed by an asynchronous coroutine within an endpoint. It is the fundamental building block of all work in the simulation. + +Each `Step` has a `kind` (the category of work) and `step_metrics` (the resources it consumes). + +```python +class Step(BaseModel): + """ + A single, indivisible operation. + It must be quantified by exactly ONE metric. + """ + kind: EndpointStepIO | EndpointStepCPU | EndpointStepRAM + step_metrics: dict[Metrics, PositiveFloat | PositiveInt] + + @model_validator(mode="after") + def ensure_coherence_kind_metrics(cls, model: "Step") -> "Step": + metrics_keys = set(model.step_metrics) + + # Enforce that a step performs one and only one type of work. + if len(metrics_keys) != 1: + raise ValueError("step_metrics must contain exactly one entry") + + # Enforce that the metric is appropriate for the kind of work. + if isinstance(model.kind, EndpointStepCPU): + if metrics_keys != {Metrics.CPU_TIME}: + raise ValueError(f"CPU step requires metric '{Metrics.CPU_TIME}'") + + elif isinstance(model.kind, EndpointStepRAM): + if metrics_keys != {Metrics.NECESSARY_RAM}: + raise ValueError(f"RAM step requires metric '{Metrics.NECESSARY_RAM}'") + + elif isinstance(model.kind, EndpointStepIO): + if metrics_keys != {Metrics.IO_WAITING_TIME}: + raise ValueError(f"I/O step requires metric '{Metrics.IO_WAITING_TIME}'") + + return model +``` + +> **Design Rationale:** The strict one-to-one mapping between a `Step` and a single metric is a core design choice. It simplifies the simulation engine immensely, as each `Step` can be deterministically routed to a request on a single SimPy resource (a CPU queue, a RAM container, or an I/O event). This avoids the complexity of modeling operations that simultaneously consume multiple resource types. + +--- + +### **2. Composing Workflows: `Endpoint`** + +An `Endpoint` defines a complete, user-facing operation (e.g., an API call like `/predict`) as an ordered sequence of `Steps`. + +```python +class Endpoint(BaseModel): + """A higher-level API call, executed as a strict sequence of steps.""" + endpoint_name: str + steps: list[Step] + + @field_validator("endpoint_name", mode="before") + def name_to_lower(cls, v: str) -> str: + """Standardize endpoint name to be lowercase for consistency.""" + return v.lower() +``` + +> **Design Rationale:** The simulation processes the `steps` list in the exact order provided. The total latency and resource consumption of an endpoint call is the sequential sum of its individual `Step` delays. This directly models the execution flow of a typical web request handler. + +--- + +### **3. Defining Components: System Nodes** + +Nodes are the macro-components of your architecture where work is performed and resources are located. + +#### **`ServerResources` and `Server`** +A `Server` node hosts endpoints and owns a set of physical resources. These resources are mapped directly to specific SimPy primitives, which govern how requests queue and contend for service. + +```python +class ServerResources(BaseModel): + """Quantifiable resources available on a server node.""" + cpu_cores: PositiveInt = Field(ge=ServerResourcesDefaults.MINIMUM_CPU_CORES) + ram_mb: PositiveInt = Field(ge=ServerResourcesDefaults.MINIMUM_RAM_MB) + db_connection_pool: PositiveInt | None = None + +class Server(BaseModel): + """A node that hosts endpoints and owns resources.""" + id: str + type: SystemNodes = SystemNodes.SERVER + server_resources: ServerResources + endpoints: list[Endpoint] +``` + +> **Design Rationale: Mapping to SimPy Primitives** +> * `cpu_cores` maps to a `simpy.Resource`. This models a classic semaphore where only `N` processes can execute concurrently, and others must wait in a queue. It perfectly represents CPU-bound tasks competing for a limited number of cores. +> * `ram_mb` maps to a `simpy.Container`. A container models a divisible resource where processes can request and return variable amounts. This is ideal for memory, as multiple requests can simultaneously hold different amounts of RAM without exclusively locking the entire memory pool. + +#### **`Client`** +The `Client` is a special, resource-less node that serves as the origin point for all requests generated during the simulation. + +#### **Node Aggregation and Validation (`TopologyNodes`)** +All `Server` and `Client` nodes are collected in the `TopologyNodes` model, which performs a critical validation check: ensuring all component IDs are unique across the entire system. + +--- + +### **4. Connecting the Components: `Edge`** + +An `Edge` represents a directed network link between two nodes, defining how requests flow through the system. + +```python +class Edge(BaseModel): + """A directed connection in the topology graph.""" + source: str + target: str + latency: RVConfig + probability: float = Field(1.0, ge=0.0, le=1.0) + edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION +``` + +> **Design Rationale:** +> * **Stochastic Latency:** Latency is not a fixed number but an `RVConfig` object. This allows you to model realistic network conditions using various probability distributions (e.g., log-normal for internet RTTs, exponential for failure retries), making the simulation far more accurate. +> * **Probabilistic Routing:** The `probability` field enables modeling of simple load balancing or A/B testing scenarios where traffic from a single `source` can be split across multiple `target` nodes. + +--- + +### **5. The Complete System: `TopologyGraph`** + +The `TopologyGraph` is the root of the configuration. It aggregates all `nodes` and `edges` and performs the final, most critical validation: ensuring referential integrity. + +```python +class TopologyGraph(BaseModel): + """The complete system definition, uniting all nodes and edges.""" + nodes: TopologyNodes + edges: list[Edge] + + @model_validator(mode="after") + def edge_refs_valid(cls, model: "TopologyGraph") -> "TopologyGraph": + """Ensure every edge connects two valid, existing nodes.""" + valid_ids = {s.id for s in model.nodes.servers} | {model.nodes.client.id} + for e in model.edges: + if e.source not in valid_ids or e.target not in valid_ids: + raise ValueError(f"Edge '{e.source}->{e.target}' references an unknown node.") + return model +``` +> **Design Rationale:** This final check guarantees that the topology is a valid, connected graph. By confirming that every `edge.source` and `edge.target` corresponds to a defined node `id`, it prevents the simulation from starting with a broken or nonsensical configuration, embodying the "fail-fast" principle. + +--- + +### **End-to-End Example** + +Here is a minimal, complete JSON configuration that defines a single client and a single API server. + +```jsonc +{ + "nodes": { + // The client node is the source of all generated requests. + "client": { + "id": "user_browser", + "type": "client" + }, + // A list of all server nodes in the system. + "servers": [ + { + "id": "api_server_node", + "type": "server", + "server_resources": { + "cpu_cores": 2, + "ram_mb": 2048 + }, + "endpoints": [ + { + "endpoint_name": "/predict", + "steps": [ + { + "kind": "initial_parsing", + "step_metrics": { "cpu_time": 0.005 } + }, + { + "kind": "io_db", + "step_metrics": { "io_waiting_time": 0.050 } + }, + { + "kind": "cpu_bound_operation", + "step_metrics": { "cpu_time": 0.015 } + } + ] + } + ] + } + ] + }, + "edges": [ + // A network link from the client to the API server. + { + "source": "user_browser", + "target": "api_server_node", + "latency": { + "distribution": "log_normal", + "mean": 0.05, + "std_dev": 0.01 + }, + "probability": 1.0 + } + ] +}``` + + + + +> **YAML friendly:** +> The topology schema is 100 % agnostic to the wire format. +> You can encode the same structure in **YAML** with identical field +> names and value types—Pydantic will parse either JSON or YAML as long +> as the keys and data types respect the schema. +> No additional changes or converters are required. +``` + + + +### **Key Takeaway** + +This rigorously validated, compositional schema is the foundation of FastSim's reliability. By defining a clear vocabulary of constants (`Metrics`, `SystemNodes`) and enforcing relationships with Pydantic validators, the schema guarantees that every simulation run starts from a **complete and self-consistent** system description. This allows you to refactor simulation logic or extend the model with new resources (e.g., GPU memory) with full confidence that existing configurations remain valid and robust. \ No newline at end of file diff --git a/documentation/backend_documentation/requests_generator.md b/documentation/backend_documentation/requests_generator.md index 556c4f4..95190fa 100644 --- a/documentation/backend_documentation/requests_generator.md +++ b/documentation/backend_documentation/requests_generator.md @@ -40,7 +40,7 @@ class RVConfig(BaseModel): distribution: Literal["poisson", "normal", "gaussian"] = "poisson" variance: float | None = None # required only for normal/gaussian -class SimulationInput(BaseModel): +class RqsGeneratorInput(BaseModel): """Define simulation inputs.""" avg_active_users: RVConfig avg_request_per_minute_per_user: RVConfig diff --git a/src/app/api/simulation.py b/src/app/api/simulation.py index 55081c6..e025ad6 100644 --- a/src/app/api/simulation.py +++ b/src/app/api/simulation.py @@ -4,13 +4,13 @@ from fastapi import APIRouter from app.core.simulation.simulation_run import run_simulation -from app.schemas.simulation_input import SimulationInput +from app.schemas.requests_generator_input import RqsGeneratorInput from app.schemas.simulation_output import SimulationOutput router = APIRouter() @router.post("/simulation") -async def event_loop_simulation(input_data: SimulationInput) -> SimulationOutput: +async def event_loop_simulation(input_data: RqsGeneratorInput) -> SimulationOutput: """Run the simulation and return aggregate KPIs.""" rng = np.random.default_rng() return run_simulation(input_data, rng=rng) diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 03448de..23c7936 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -1,23 +1,184 @@ -"""Application constants and configuration values.""" +""" +Application-wide constants and configuration values. + +This module groups all the *static* enumerations used by the FastSim backend +so that: + +* JSON / YAML payloads can be strictly validated with Pydantic. +* Front-end and simulation engine share a single source of truth. +* Ruff, mypy and IDEs can leverage the strong typing provided by Enum classes. + +**IMPORTANT:** Changing any enum *value* is a breaking-change for every +stored configuration file. Add new members whenever possible instead of +renaming existing ones. +""" from enum import IntEnum, StrEnum +# ====================================================================== +# CONSTANTS FOR THE REQUEST-GENERATOR COMPONENT +# ====================================================================== + class TimeDefaults(IntEnum): - """Default time-related constants (all in seconds).""" + """ + Default time-related constants (expressed in **seconds**). + + These values are used when the user omits an explicit parameter. They also + serve as lower / upper bounds for validation for the requests generator. + """ - MIN_TO_SEC = 60 # 1 minute → 60 s - USER_SAMPLING_WINDOW = 60 # keep U(t) constant for 60 s, default - SIMULATION_TIME = 3_600 # run 1 h if user gives no other value - MIN_SIMULATION_TIME = 1800 # min simulation time - MIN_USER_SAMPLING_WINDOW = 1 # 1 second - MAX_USER_SAMPLING_WINDOW = 120 # 2 minutes + MIN_TO_SEC = 60 # 1 minute → 60 s + USER_SAMPLING_WINDOW = 60 # keep U(t) constant for 60 s + SIMULATION_TIME = 3_600 # run 1 h if user gives no value + MIN_SIMULATION_TIME = 1_800 # enforce at least 30 min + MIN_USER_SAMPLING_WINDOW = 1 # 1 s minimum + MAX_USER_SAMPLING_WINDOW = 120 # 2 min maximum class Distribution(StrEnum): - """Allowed probability distributions for an RVConfig.""" + """ + Probability distributions accepted by :class:`~app.schemas.RVConfig`. + + The *string value* is exactly the identifier that must appear in JSON + payloads. The simulation engine will map each name to the corresponding + random sampler (e.g. ``numpy.random.poisson``). + """ POISSON = "poisson" NORMAL = "normal" + LOG_NORMAL = "log_normal" + EXPONENTIAL = "exponential" + +# ====================================================================== +# CONSTANTS FOR ENDPOINT STEP DEFINITION (REQUEST-HANDLER) +# ====================================================================== + +# The JSON received by the API for an endpoint step is expected to look like: +# +# { +# "endpoint_name": "/predict", +# "kind": "io_llm", +# "metrics": { +# "cpu_time": 0.150, +# "necessary_ram": 256 +# } +# } +# +# The Enum classes below guarantee that only valid *kind* and *metric* keys +# are accepted by the Pydantic schema. + + +class EndpointStepIO(StrEnum): + """ + I/O-bound operation categories that can occur inside an endpoint *step*. + + .. list-table:: + :header-rows: 1 + + * - Constant + - Meaning (executed by coroutine) + * - ``TASK_SPAWN`` + - Spawns an additional ``asyncio.Task`` and returns immediately. + * - ``LLM`` + - Performs a remote Large-Language-Model inference call. + * - ``WAIT`` + - Passive, *non-blocking* wait for I/O completion; no new task spawned. + * - ``DB`` + - Round-trip to a relational / NoSQL database. + * - ``CACHE`` + - Access to a local or distributed cache layer. + + The *value* of each member (``"io_llm"``, ``"io_db"``, …) is the exact + identifier expected in external JSON. + """ + + TASK_SPAWN = "io_task_spawn" + LLM = "io_llm" + WAIT = "io_wait" + DB = "io_db" + CACHE = "io_cache" + + +class EndpointStepCPU(StrEnum): + """ + CPU-bound operation categories inside an endpoint step. + + Use these when the coroutine keeps the Python interpreter busy + (GIL-bound or compute-heavy code) rather than waiting for I/O. + """ + + INITIAL_PARSING = "initial_parsing" + CPU_BOUND_OPERATION = "cpu_bound_operation" + + +class EndpointStepRAM(StrEnum): + """ + Memory-related operations inside a step. + + Currently limited to a single category, but kept as an Enum so that future + resource types (e.g. GPU memory) can be added without schema changes. + """ + + RAM = "ram" + + +class Metrics(StrEnum): + """ + Keys used inside the ``metrics`` dictionary of a *step*. + + * ``NETWORK_LATENCY`` - Mean latency (seconds) incurred on a network edge + *outside* the service (used mainly for validation when steps model + short in-service hops). + * ``CPU_TIME`` - Service time (seconds) during which the coroutine occupies + the CPU / GIL. + * ``NECESSARY_RAM`` - Peak memory (MB) required by the step. + """ + + NETWORK_LATENCY = "network_latency" + CPU_TIME = "cpu_time" + IO_WAITING_TIME = "io_waiting_time" + NECESSARY_RAM = "necessary_ram" + +# ====================================================================== +# CONSTANTS FOR THE RESOURCES OF A SERVER +# ====================================================================== + +class ServerResourcesDefaults: + """Resources available for a single server""" + + CPU_CORES = 1 + MINIMUM_CPU_CORES = 1 + RAM_MB = 1024 + MINIMUM_RAM_MB = 256 + DB_CONNECTION_POOL = None + +# ====================================================================== +# CONSTANTS FOR THE MACRO-TOPOLOGY GRAPH +# ====================================================================== + +class SystemNodes(StrEnum): + """ + High-level node categories of the system topology graph. + + Each member represents a *macro-component* that may have its own SimPy + resources (CPU cores, DB pool, etc.). + """ + + SERVER = "server" + CLIENT = "client" + LOAD_BALANCER = "load_balancer" + API_GATEWAY = "api_gateway" + DATABASE = "database" + CACHE = "cache" + + +class SystemEdges(StrEnum): + """ + Edge categories connecting different :class:`SystemNodes`. + Currently only network links are modeled; new types (IPC queue, message + bus, stream) can be added without impacting existing payloads. + """ + NETWORK_CONNECTION = "network_connection" diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/core/event_samplers/gaussian_poisson.py index 98f9121..9c83b4f 100644 --- a/src/app/core/event_samplers/gaussian_poisson.py +++ b/src/app/core/event_samplers/gaussian_poisson.py @@ -16,11 +16,11 @@ truncated_gaussian_generator, uniform_variable_generator, ) -from app.schemas.requests_generator_input import SimulationInput +from app.schemas.requests_generator_input import RqsGeneratorInput def gaussian_poisson_sampling( - input_data: SimulationInput, + input_data: RqsGeneratorInput, *, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/core/event_samplers/poisson_poisson.py index d25c4dd..ebb1970 100644 --- a/src/app/core/event_samplers/poisson_poisson.py +++ b/src/app/core/event_samplers/poisson_poisson.py @@ -13,11 +13,11 @@ poisson_variable_generator, uniform_variable_generator, ) -from app.schemas.requests_generator_input import SimulationInput +from app.schemas.requests_generator_input import RqsGeneratorInput def poisson_poisson_sampling( - input_data: SimulationInput, + input_data: RqsGeneratorInput, *, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: diff --git a/src/app/core/simulation/requests_generator.py b/src/app/core/simulation/requests_generator.py index e19d8f0..810218f 100644 --- a/src/app/core/simulation/requests_generator.py +++ b/src/app/core/simulation/requests_generator.py @@ -16,11 +16,11 @@ import numpy as np - from app.schemas.requests_generator_input import SimulationInput + from app.schemas.requests_generator_input import RqsGeneratorInput def requests_generator( - input_data: SimulationInput, + input_data: RqsGeneratorInput, *, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: diff --git a/src/app/core/simulation/simulation_run.py b/src/app/core/simulation/simulation_run.py index d3f52f6..3e1672a 100644 --- a/src/app/core/simulation/simulation_run.py +++ b/src/app/core/simulation/simulation_run.py @@ -14,13 +14,13 @@ import numpy as np - from app.schemas.simulation_input import SimulationInput + from app.schemas.requests_generator_input import RqsGeneratorInput def run_simulation( - input_data: SimulationInput, + input_data: RqsGeneratorInput, *, rng: np.random.Generator, ) -> SimulationOutput: diff --git a/src/app/schemas/full_simulation_input.py b/src/app/schemas/full_simulation_input.py new file mode 100644 index 0000000..0e1d7a9 --- /dev/null +++ b/src/app/schemas/full_simulation_input.py @@ -0,0 +1,13 @@ +"""Definition of the full input for the simulation""" + +from pydantic import BaseModel + +from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.system_topology_schema.full_system_topology_schema import TopologyGraph + + +class SimulationPayload(BaseModel): + """Full input structure to perform a simulation""" + + rqs_input: RqsGeneratorInput + topology_graph: TopologyGraph diff --git a/src/app/schemas/random_variables_config.py b/src/app/schemas/random_variables_config.py new file mode 100644 index 0000000..8d17c5f --- /dev/null +++ b/src/app/schemas/random_variables_config.py @@ -0,0 +1,31 @@ +"""Definition of the schema for a Random variable""" + +from pydantic import BaseModel, field_validator, model_validator + +from app.config.constants import Distribution + + +class RVConfig(BaseModel): + """class to configure random variables""" + + mean: float + distribution: Distribution = Distribution.POISSON + variance: float | None = None + + @field_validator("mean", mode="before") + def ensure_mean_is_numeric( + cls, # noqa: N805 + v: object, + ) -> float: + """Ensure `mean` is numeric, then coerce to float.""" + err_msg = "mean must be a number (int or float)" + if not isinstance(v, (float, int)): + raise ValueError(err_msg) # noqa: TRY004 + return float(v) + + @model_validator(mode="after") # type: ignore[arg-type] + def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 + """Set variance = mean when distribution == 'normal' and variance is missing.""" + if model.variance is None and model.distribution == Distribution.NORMAL: + model.variance = model.mean + return model diff --git a/src/app/schemas/requests_generator_input.py b/src/app/schemas/requests_generator_input.py index 9090fed..88812e0 100644 --- a/src/app/schemas/requests_generator_input.py +++ b/src/app/schemas/requests_generator_input.py @@ -1,37 +1,13 @@ """Define the schemas for the simulator""" -from pydantic import BaseModel, Field, field_validator, model_validator +from pydantic import BaseModel, Field -from app.config.constants import Distribution, TimeDefaults +from app.config.constants import TimeDefaults +from app.schemas.random_variables_config import RVConfig -class RVConfig(BaseModel): - """class to configure random variables""" - - mean: float - distribution: Distribution = Distribution.POISSON - variance: float | None = None - - @field_validator("mean", mode="before") - def ensure_mean_is_numeric( - cls, # noqa: N805 - v: object, - ) -> float: - """Ensure `mean` is numeric, then coerce to float.""" - err_msg = "mean must be a number (int or float)" - if not isinstance(v, (float, int)): - raise ValueError(err_msg) # noqa: TRY004 - return float(v) - - @model_validator(mode="after") # type: ignore[arg-type] - def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 - """Set variance = mean when distribution == 'normal' and variance is missing.""" - if model.variance is None and model.distribution == Distribution.NORMAL: - model.variance = model.mean - return model - -class SimulationInput(BaseModel): +class RqsGeneratorInput(BaseModel): """Define the expected variables for the simulation""" avg_active_users: RVConfig diff --git a/src/app/schemas/simulation_output.py b/src/app/schemas/simulation_output.py index b02c172..26d1adb 100644 --- a/src/app/schemas/simulation_output.py +++ b/src/app/schemas/simulation_output.py @@ -7,6 +7,7 @@ class SimulationOutput(BaseModel): """Define the output of the simulation""" total_requests: dict[str, int | float] + # TO DEFINE metric_2: str #...... metric_n: str diff --git a/src/app/schemas/system_topology_schema/endpoint_schema.py b/src/app/schemas/system_topology_schema/endpoint_schema.py new file mode 100644 index 0000000..abe53da --- /dev/null +++ b/src/app/schemas/system_topology_schema/endpoint_schema.py @@ -0,0 +1,97 @@ +"""Defining the input schema for the requests handler""" + +from pydantic import ( + BaseModel, + PositiveFloat, + PositiveInt, + field_validator, + model_validator, +) + +from app.config.constants import ( + EndpointStepCPU, + EndpointStepIO, + EndpointStepRAM, + Metrics, +) + + +class Step(BaseModel): + """ + Steps to be executed inside an endpoint in terms of + the resources needed to accomplish the single step + """ + + kind: EndpointStepIO | EndpointStepCPU | EndpointStepRAM + step_metrics: dict[Metrics, PositiveFloat | PositiveInt] + + @field_validator("step_metrics", mode="before") + def ensure_non_empty( + cls, # noqa: N805 + v: dict[Metrics, PositiveFloat | PositiveInt], + ) -> dict[Metrics, PositiveFloat | PositiveInt]: + """Ensure the dict step metrics exist""" + if not v: + msg = "step_metrics cannot be empty" + raise ValueError(msg) + return v + + @model_validator(mode="after") # type: ignore[arg-type] + def ensure_coherence_kind_metrics( + cls, # noqa: N805 + model: "Step", + ) -> "Step": + """ + Validation to couple kind and metrics only when they are + valid for example ram cannot have associated a cpu time + """ + metrics_keys = set(model.step_metrics) + + # Control of the length of the set to be sure only on key is passed + if len(metrics_keys) != 1: + msg = "step_metrics must contain exactly one entry" + raise ValueError(msg) + + # Coherence CPU bound operation and metric + if isinstance(model.kind, EndpointStepCPU): + if metrics_keys != {Metrics.CPU_TIME}: + msg = ( + "The metric to quantify a CPU BOUND step" + f"must be {Metrics.CPU_TIME}" + ) + raise ValueError(msg) + + # Coherence RAM operation and metric + elif isinstance(model.kind, EndpointStepRAM): + if metrics_keys != {Metrics.NECESSARY_RAM}: + msg = ( + "The metric to quantify a RAM step" + f"must be {Metrics.NECESSARY_RAM}" + ) + raise ValueError(msg) + + # Coherence I/O operation and metric + elif metrics_keys != {Metrics.IO_WAITING_TIME}: + msg = ( + "The metric to quantify an I/O step" + f"must be {Metrics.IO_WAITING_TIME}" + ) + raise ValueError(msg) + + return model + + + + +class Endpoint(BaseModel): + """full endpoint structure to be validated with pydantic""" + + endpoint_name: str + steps: list[Step] + + @field_validator("endpoint_name", mode="before") + def name_to_lower(cls, v: str) -> str: # noqa: N805 + """Standardize endpoint name to be lowercase""" + return v.lower() + + diff --git a/src/app/schemas/system_topology_schema/full_system_topology_schema.py b/src/app/schemas/system_topology_schema/full_system_topology_schema.py new file mode 100644 index 0000000..b53f08e --- /dev/null +++ b/src/app/schemas/system_topology_schema/full_system_topology_schema.py @@ -0,0 +1,217 @@ +""" +Define the topology of the system as a directed graph +where nodes represents macro structure (server, client ecc ecc) +and edges how these strcutures are connected and the network +latency necessary for the requests generated to move from +one structure to another +""" + +from pydantic import ( + BaseModel, + ConfigDict, + Field, + PositiveInt, + field_validator, + model_validator, +) + +from app.config.constants import ( + ServerResourcesDefaults, + SystemEdges, + SystemNodes, +) +from app.schemas.random_variables_config import RVConfig +from app.schemas.system_topology_schema.endpoint_schema import Endpoint + +#------------------------------------------------------------- +# Definition of the nodes structure for the graph representing +# the topoogy of the system defined for the simulation +#------------------------------------------------------------- + +# ------------------------------------------------------------- +# CLIENT +# ------------------------------------------------------------- + +class Client(BaseModel): + """Definition of the client class""" + + id: str + type: SystemNodes = SystemNodes.CLIENT + + @field_validator("type", mode="after") + def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 + """Ensure the type of the client is standard""" + if v != SystemNodes.CLIENT: + msg = f"The type should have a standard value: {SystemNodes.CLIENT}" + raise ValueError(msg) + return v + +# ------------------------------------------------------------- +# SERVER RESOURCES EXAMPLE +# ------------------------------------------------------------- + +class ServerResources(BaseModel): + """ + Defines the quantifiable resources available on a server node. + Each attribute maps directly to a SimPy resource primitive. + """ + + cpu_cores: PositiveInt = Field( + ServerResourcesDefaults.CPU_CORES, + ge = ServerResourcesDefaults.MINIMUM_CPU_CORES, + description="Number of CPU cores available for processing.", + ) + db_connection_pool: PositiveInt | None = Field( + ServerResourcesDefaults.DB_CONNECTION_POOL, + description="Size of the database connection pool, if applicable.", + ) + + # Risorse modellate come simpy.Container (livello) + ram_mb: PositiveInt = Field( + ServerResourcesDefaults.RAM_MB, + ge = ServerResourcesDefaults.MINIMUM_RAM_MB, + description="Total available RAM in Megabytes.") + + # for the future + # disk_iops_limit: PositiveInt | None = None + # network_throughput_mbps: PositiveInt | None = None + +# ------------------------------------------------------------- +# SERVER +# ------------------------------------------------------------- + +class Server(BaseModel): + """ + definition of the server class: + - id: is the server identifier + - type: is the type of node in the structure + - server resources: is a dictionary to define the resources + of the machine where the server is living + - endpoints: is the list of all endpoints in a server + """ + + id: str + type: SystemNodes = SystemNodes.SERVER + #Later define a valide structure for the keys of server resources + server_resources : ServerResources + endpoints : list[Endpoint] + + @field_validator("type", mode="after") + def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 + """Ensure the type of the server is standard""" + if v != SystemNodes.SERVER: + msg = f"The type should have a standard value: {SystemNodes.SERVER}" + raise ValueError(msg) + return v + +# ------------------------------------------------------------- +# NODES CLASS WITH ALL POSSIBLE OBJECTS REPRESENTED BY A NODE +# ------------------------------------------------------------- + +class TopologyNodes(BaseModel): + """ + Definition of the nodes class: + - server: represent all servers implemented in the system + - client: is a simple object with just a name representing + the origin of the graph + """ + + servers: list[Server] + client: Client + + @model_validator(mode="after") # type: ignore[arg-type] + def unique_ids( + cls, # noqa: N805 + model: "TopologyNodes", + ) -> "TopologyNodes": + """Check that all id are unique""" + ids = [server.id for server in model.servers] + [model.client.id] + if len(ids) != len(set(ids)): + msg = "Node ids must be unique" + raise ValueError(msg) + return model + + model_config = ConfigDict(extra="forbid") + + +#------------------------------------------------------------- +# Definition of the edges structure for the graph representing +# the topoogy of the system defined for the simulation +#------------------------------------------------------------- + +class Edge(BaseModel): + """ + A directed connection in the topology graph. + + Attributes + ---------- + source : str + Identifier of the source node (where the request comes from). + target : str + Identifier of the destination node (where the request goes to). + latency : RVConfig + Random-variable configuration for network latency on this link. + probability : float + Probability of taking this edge when there are multiple outgoing links. + Must be in [0.0, 1.0]. Defaults to 1.0 (always taken). + edge_type : SystemEdges + Category of the link (e.g. network, queue, stream). + + """ + + source: str + target: str + latency: RVConfig + probability: float = Field(1.0, ge=0.0, le=1.0) + edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION + + @model_validator(mode="after") # type: ignore[arg-type] + def check_src_trgt_different(cls, model: "Edge") -> "Edge": # noqa: N805 + """Ensure source is different from target""" + if model.source == model.target: + msg = "source and target must be different nodes" + raise ValueError(msg) + return model + + +#------------------------------------------------------------- +# Definition of the Graph structure representing +# the topogy of the system defined for the simulation +#------------------------------------------------------------- + +class TopologyGraph(BaseModel): + """ + data collection for the whole graph representing + the full system + """ + + nodes: TopologyNodes + edges: list[Edge] + + @model_validator(mode="after") # type: ignore[arg-type] + def edge_refs_valid(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 + """ + Ensure that **every** edge points to valid nodes. + + The validator is executed *after* the entire ``TopologyGraph`` model has + been built, so all servers and the client already exist in ``m.nodes``. + + Steps + ----- + 1. Build the set ``valid_ids`` containing: + * all ``Server.id`` values, **plus** + * the single ``Client.id``. + 2. Iterate through each ``Edge`` in ``m.edges`` and raise + :class:`ValueError` if either ``edge.source`` or ``edge.target`` is + **not** present in ``valid_ids``. + + Returning the (unchanged) model signals that the integrity check passed. + """ + valid_ids = {s.id for s in model.nodes.servers} | {model.nodes.client.id} + for e in model.edges: + if e.source not in valid_ids or e.target not in valid_ids: + msg = f"Edge {e.source}->{e.target} references unknown node" + raise ValueError(msg) + return model + + diff --git a/tests/unit/input_sructure/test_endpoint_input.py b/tests/unit/input_sructure/test_endpoint_input.py new file mode 100644 index 0000000..7e166dd --- /dev/null +++ b/tests/unit/input_sructure/test_endpoint_input.py @@ -0,0 +1,125 @@ +"""Unit tests for the Endpoint and Step Pydantic schemas.""" + +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from app.config.constants import ( + EndpointStepCPU, + EndpointStepIO, + EndpointStepRAM, + Metrics, +) +from app.schemas.system_topology_schema.endpoint_schema import Endpoint, Step + + +# --------------------------------------------------------------------------- # +# Helper functions to build minimal valid Step objects +# --------------------------------------------------------------------------- # +def cpu_step(value: float = 0.1) -> Step: + """Return a minimal valid CPU-bound Step.""" + return Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_metrics={Metrics.CPU_TIME: value}, + ) + + +def ram_step(value: int = 128) -> Step: + """Return a minimal valid RAM Step.""" + return Step( + kind=EndpointStepRAM.RAM, + step_metrics={Metrics.NECESSARY_RAM: value}, + ) + + +def io_step(value: float = 0.05) -> Step: + """Return a minimal valid I/O Step.""" + return Step( + kind=EndpointStepIO.WAIT, + step_metrics={Metrics.IO_WAITING_TIME: value}, + ) + + +# --------------------------------------------------------------------------- # +# Positive test cases +# --------------------------------------------------------------------------- # +def test_valid_cpu_step() -> None: + """Test that a CPU step with correct 'cpu_time' metric passes validation.""" + step = cpu_step() + # The metric value must match the input + assert step.step_metrics[Metrics.CPU_TIME] == 0.1 + + +def test_valid_ram_step() -> None: + """Test that a RAM step with correct 'necessary_ram' metric passes validation.""" + step = ram_step() + assert step.step_metrics[Metrics.NECESSARY_RAM] == 128 + + +def test_valid_io_step() -> None: + """Test that an I/O step with correct 'io_waiting_time' metric passes validation.""" + step = io_step() + assert step.step_metrics[Metrics.IO_WAITING_TIME] == 0.05 + + +def test_endpoint_with_mixed_steps() -> None: + """Test that an Endpoint with multiple valid Step instances normalizes the name.""" + ep = Endpoint( + endpoint_name="/Predict", + steps=[cpu_step(), ram_step(), io_step()], + ) + # endpoint_name should be lowercased by the validator + assert ep.endpoint_name == "/predict" + # All steps should be present in the list + assert len(ep.steps) == 3 + + +# --------------------------------------------------------------------------- # +# Negative test cases +# --------------------------------------------------------------------------- # +@pytest.mark.parametrize( + ("kind", "bad_metrics"), + [ + # CPU step with RAM metric + (EndpointStepCPU.CPU_BOUND_OPERATION, {Metrics.NECESSARY_RAM: 64}), + # RAM step with CPU metric + (EndpointStepRAM.RAM, {Metrics.CPU_TIME: 0.2}), + # I/O step with CPU metric + (EndpointStepIO.DB, {Metrics.CPU_TIME: 0.05}), + ], +) +def test_incoherent_kind_metric_pair( + kind: EndpointStepCPU | EndpointStepRAM | EndpointStepIO, + bad_metrics: dict[Metrics, float | int], +) -> None: + """Test that mismatched kind and metric combinations raise ValidationError.""" + with pytest.raises(ValidationError): + Step(kind=kind, step_metrics=bad_metrics) + + +def test_multiple_metrics_not_allowed() -> None: + """Test that providing multiple metrics in a single Step raises ValidationError.""" + with pytest.raises(ValidationError): + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_metrics={ + Metrics.CPU_TIME: 0.1, + Metrics.NECESSARY_RAM: 64, + }, + ) + + +def test_empty_metrics_rejected() -> None: + """Test that an empty metrics dict is rejected by the validator.""" + with pytest.raises(ValidationError): + Step(kind=EndpointStepCPU.CPU_BOUND_OPERATION, step_metrics={}) + + +def test_wrong_metric_name_for_io() -> None: + """Test that an I/O step with a non-I/O metric key is rejected.""" + with pytest.raises(ValidationError): + Step( + kind=EndpointStepIO.CACHE, + step_metrics={Metrics.NECESSARY_RAM: 64}, + ) diff --git a/tests/unit/input_sructure/test_full_topology_input.py b/tests/unit/input_sructure/test_full_topology_input.py new file mode 100644 index 0000000..9e17573 --- /dev/null +++ b/tests/unit/input_sructure/test_full_topology_input.py @@ -0,0 +1,170 @@ +"""Unit-tests for the **topology schemas** (Client, ServerResources, …). + +Every section below is grouped by the object under test, separated by +clear comment banners so that long files remain navigable. + +The tests aim for: +* 100 % branch-coverage on custom validators. +* mypy strict-compatibility (full type hints, no Any). +* ruff compliance (imports ordered, no unused vars, ≤ 88-char lines). +""" + +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from app.config.constants import ( + EndpointStepCPU, + Metrics, + ServerResourcesDefaults, + SystemEdges, + SystemNodes, +) +from app.schemas.random_variables_config import RVConfig +from app.schemas.system_topology_schema.endpoint_schema import Endpoint, Step +from app.schemas.system_topology_schema.full_system_topology_schema import ( + Client, + Edge, + Server, + ServerResources, + TopologyGraph, + TopologyNodes, +) + + +# --------------------------------------------------------------------------- # +# Client +# --------------------------------------------------------------------------- # +def test_valid_client() -> None: + """A client with correct `type` should validate.""" + cli = Client(id="frontend", type=SystemNodes.CLIENT) + assert cli.type is SystemNodes.CLIENT + + +def test_invalid_client_type() -> None: + """Wrong `type` enum on Client must raise ValidationError.""" + with pytest.raises(ValidationError): + Client(id="wrong", type=SystemNodes.SERVER) + + +# --------------------------------------------------------------------------- # +# ServerResources +# --------------------------------------------------------------------------- # +def test_server_resources_defaults() -> None: + """Default values must match the constant table.""" + res = ServerResources() # all defaults + assert res.cpu_cores == ServerResourcesDefaults.CPU_CORES + assert res.ram_mb == ServerResourcesDefaults.RAM_MB + assert res.db_connection_pool is ServerResourcesDefaults.DB_CONNECTION_POOL + + +def test_server_resources_min_constraints() -> None: + """cpu_cores and ram_mb < minimum should fail validation.""" + with pytest.raises(ValidationError): + ServerResources(cpu_cores=0, ram_mb=128) # too small + + +# --------------------------------------------------------------------------- # +# Server +# --------------------------------------------------------------------------- # +def _dummy_endpoint() -> Endpoint: + """Return a minimal valid Endpoint needed to build a Server.""" + step = Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_metrics={Metrics.CPU_TIME: 0.1}, + ) + return Endpoint(endpoint_name="/ping", steps=[step]) + + +def test_valid_server() -> None: + """Server with correct type, resources and endpoint list.""" + srv = Server( + id="api-1", + type=SystemNodes.SERVER, + server_resources=ServerResources(cpu_cores=2, ram_mb=1024), + endpoints=[_dummy_endpoint()], + ) + assert srv.id == "api-1" + + +def test_invalid_server_type() -> None: + """Server with wrong `type` enum must be rejected.""" + with pytest.raises(ValidationError): + Server( + id="oops", + type=SystemNodes.CLIENT, + server_resources=ServerResources(), + endpoints=[_dummy_endpoint()], + ) + + +# --------------------------------------------------------------------------- # +# TopologyNodes +# --------------------------------------------------------------------------- # +def _single_node_topology() -> TopologyNodes: + """Helper that returns a valid TopologyNodes with one server and one client.""" + srv = Server( + id="svc-A", + server_resources=ServerResources(), + endpoints=[_dummy_endpoint()], + ) + cli = Client(id="browser") + return TopologyNodes(servers=[srv], client=cli) + + +def test_unique_ids_validator() -> None: + """Duplicate node IDs should trigger the unique_ids validator.""" + nodes = _single_node_topology() + # duplicate client ID + dup_srv = nodes.servers[0].model_copy(update={"id": "browser"}) + with pytest.raises(ValidationError): + TopologyNodes(servers=[dup_srv], client=nodes.client) + + +# --------------------------------------------------------------------------- # +# Edge +# --------------------------------------------------------------------------- # +def test_edge_source_equals_target_fails() -> None: + """Edge with identical source/target must raise.""" + latency_cfg = RVConfig(mean=0.05) + with pytest.raises(ValidationError): + Edge( + source="same", + target="same", + latency=latency_cfg, + edge_type=SystemEdges.NETWORK_CONNECTION, + ) + + +# --------------------------------------------------------------------------- # +# TopologyGraph +# --------------------------------------------------------------------------- # +def _latency() -> RVConfig: + """A tiny helper for RVConfig latency objects.""" + return RVConfig(mean=0.02) + + +def test_valid_topology_graph() -> None: + """End-to-end happy-path graph passes validation.""" + nodes = _single_node_topology() + edge = Edge( + source="browser", + target="svc-A", + latency=_latency(), + probability=1.0, + ) + graph = TopologyGraph(nodes=nodes, edges=[edge]) + assert len(graph.edges) == 1 + + +def test_edge_refers_unknown_node() -> None: + """Edge pointing to a non-existent node ID must fail.""" + nodes = _single_node_topology() + bad_edge = Edge( + source="browser", + target="ghost-srv", + latency=_latency(), + ) + with pytest.raises(ValidationError): + TopologyGraph(nodes=nodes, edges=[bad_edge]) diff --git a/tests/unit/simulation/test_requests_generator_input.py b/tests/unit/input_sructure/test_requests_generator_input.py similarity index 95% rename from tests/unit/simulation/test_requests_generator_input.py rename to tests/unit/input_sructure/test_requests_generator_input.py index bbd84ae..9fb49a5 100644 --- a/tests/unit/simulation/test_requests_generator_input.py +++ b/tests/unit/input_sructure/test_requests_generator_input.py @@ -2,7 +2,8 @@ from pydantic import ValidationError from app.config.constants import Distribution, TimeDefaults -from app.schemas.requests_generator_input import RVConfig, SimulationInput +from app.schemas.random_variables_config import RVConfig +from app.schemas.requests_generator_input import RqsGeneratorInput # -------------------------------------------------------------------------- # TEST RANDOM VARIABLE CONFIGURATION @@ -84,7 +85,7 @@ def test_invalid_distribution_raises() -> None: def test_default_user_sampling_window() -> None: """When user_sampling_window is omitted, it defaults to USER_SAMPLING_WINDOW.""" - inp = SimulationInput( + inp = RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, @@ -97,7 +98,7 @@ def test_default_user_sampling_window() -> None: def test_explicit_user_sampling_window_kept() -> None: """An explicit user_sampling_window value is preserved unchanged.""" custom_window = 30 - inp = SimulationInput( + inp = RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, @@ -112,7 +113,7 @@ def test_user_sampling_window_not_int_raises() -> None: """A non-integer user_sampling_window raises a ValidationError.""" with pytest.raises(ValidationError) as excinfo: - SimulationInput( + RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, @@ -136,7 +137,7 @@ def test_user_sampling_window_above_max_raises() -> None: """ too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW + 1 with pytest.raises(ValidationError) as excinfo: - SimulationInput( + RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, @@ -161,7 +162,7 @@ def test_user_sampling_window_above_max_raises() -> None: def test_default_total_simulation_time() -> None: """When total_simulation_time is omitted, it defaults to SIMULATION_TIME.""" - inp = SimulationInput( + inp = RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, @@ -174,7 +175,7 @@ def test_default_total_simulation_time() -> None: def test_explicit_total_simulation_time_kept() -> None: """An explicit total_simulation_time value is preserved unchanged.""" custom_time = 3_000 - inp = SimulationInput( + inp = RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, @@ -189,7 +190,7 @@ def test_total_simulation_time_not_int_raises() -> None: """A non-integer total_simulation_time raises a ValidationError.""" with pytest.raises(ValidationError) as excinfo: - SimulationInput( + RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, @@ -213,7 +214,7 @@ def test_total_simulation_time_below_minimum_raises() -> None: """ too_small = TimeDefaults.MIN_SIMULATION_TIME - 1 with pytest.raises(ValidationError) as excinfo: - SimulationInput( + RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, diff --git a/tests/unit/sampler/test_gaussian_poisson.py b/tests/unit/sampler/test_gaussian_poisson.py index f0abbf8..b464007 100644 --- a/tests/unit/sampler/test_gaussian_poisson.py +++ b/tests/unit/sampler/test_gaussian_poisson.py @@ -10,7 +10,8 @@ from app.config.constants import TimeDefaults from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling -from app.schemas.requests_generator_input import RVConfig, SimulationInput +from app.schemas.random_variables_config import RVConfig +from app.schemas.requests_generator_input import RqsGeneratorInput # --------------------------------------------------------------------------- # Fixture @@ -18,9 +19,9 @@ @pytest.fixture -def base_input() -> SimulationInput: - """Return a minimal, valid SimulationInput for the Gaussian-Poisson sampler.""" - return SimulationInput( +def base_input() -> RqsGeneratorInput: + """Return a minimal, valid RqsGeneratorInput for the Gaussian-Poisson sampler.""" + return RqsGeneratorInput( avg_active_users=RVConfig( mean=10.0, variance=4.0, distribution="normal", ), @@ -35,14 +36,14 @@ def base_input() -> SimulationInput: # --------------------------------------------------------------------------- -def test_returns_generator_type(base_input: SimulationInput) -> None: +def test_returns_generator_type(base_input: RqsGeneratorInput) -> None: """The function must return a generator object.""" rng = np.random.default_rng(0) gen = gaussian_poisson_sampling(base_input, rng=rng) assert isinstance(gen, GeneratorType) -def test_generates_positive_gaps(base_input: SimulationInput) -> None: +def test_generates_positive_gaps(base_input: RqsGeneratorInput) -> None: """ With nominal parameters the sampler should emit at least a few positive gaps and no gap must be non-positive. @@ -67,7 +68,7 @@ def test_generates_positive_gaps(base_input: SimulationInput) -> None: def test_zero_users_produces_no_events( monkeypatch: pytest.MonkeyPatch, - base_input: SimulationInput, + base_input: RqsGeneratorInput, ) -> None: """ If every Gaussian draw returns 0 users, Λ == 0, diff --git a/tests/unit/sampler/test_poisson_posson.py b/tests/unit/sampler/test_poisson_posson.py index 5d89686..48d4f9a 100644 --- a/tests/unit/sampler/test_poisson_posson.py +++ b/tests/unit/sampler/test_poisson_posson.py @@ -11,13 +11,14 @@ from app.config.constants import TimeDefaults from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling -from app.schemas.requests_generator_input import RVConfig, SimulationInput +from app.schemas.random_variables_config import RVConfig +from app.schemas.requests_generator_input import RqsGeneratorInput @pytest.fixture -def base_input() -> SimulationInput: - """Return a minimal-valid SimulationInput for the sampler tests.""" - return SimulationInput( +def base_input() -> RqsGeneratorInput: + """Return a minimal-valid RqsGeneratorInput for the sampler tests.""" + return RqsGeneratorInput( # 1 average concurrent user … avg_active_users={"mean": 1.0, "distribution": "poisson"}, # … sending on average 60 req/min → 1 req/s @@ -32,7 +33,7 @@ def base_input() -> SimulationInput: # --------------------------------------------------------------------- -def test_sampler_returns_generator(base_input: SimulationInput) -> None: +def test_sampler_returns_generator(base_input: RqsGeneratorInput) -> None: """The function must return a real generator object.""" rng = np.random.default_rng(0) gen = poisson_poisson_sampling(base_input, rng=rng) @@ -40,7 +41,7 @@ def test_sampler_returns_generator(base_input: SimulationInput) -> None: assert isinstance(gen, GeneratorType) -def test_all_gaps_are_positive(base_input: SimulationInput) -> None: +def test_all_gaps_are_positive(base_input: RqsGeneratorInput) -> None: """Every yielded inter-arrival gap Δt must be > 0.""" rng = np.random.default_rng(1) gaps: list[float] = list( @@ -56,7 +57,7 @@ def test_all_gaps_are_positive(base_input: SimulationInput) -> None: # --------------------------------------------------------------------- -def test_sampler_is_reproducible_with_fixed_seed(base_input: SimulationInput) -> None: +def test_sampler_is_reproducible_with_fixed_seed(base_input: RqsGeneratorInput) -> None: """Same seed ⇒ identical first N gaps.""" seed = 42 n_samples = 15 @@ -86,12 +87,12 @@ def test_sampler_is_reproducible_with_fixed_seed(base_input: SimulationInput) -> # --------------------------------------------------------------------- -def test_zero_users_produces_no_events(base_input: SimulationInput) -> None: +def test_zero_users_produces_no_events(base_input: RqsGeneratorInput) -> None: """ With mean concurrent users == 0 the Poisson draw is almost surely 0, so Λ = 0 and the generator should yield no events. """ - input_data = SimulationInput( + input_data = RqsGeneratorInput( avg_active_users=RVConfig(mean=0.0, distribution="poisson"), avg_request_per_minute_per_user=RVConfig(mean=60.0, distribution="poisson"), total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, @@ -108,7 +109,7 @@ def test_zero_users_produces_no_events(base_input: SimulationInput) -> None: # --------------------------------------------------------------------- -def test_cumulative_time_never_exceeds_horizon(base_input: SimulationInput) -> None: +def test_cumulative_time_never_exceeds_horizon(base_input: RqsGeneratorInput) -> None: """ΣΔt (virtual clock) must stay strictly below total_simulation_time.""" rng = np.random.default_rng(7) gaps = list(poisson_poisson_sampling(base_input, rng=rng)) diff --git a/tests/unit/simulation/test_requests_generator.py b/tests/unit/simulation/test_requests_generator.py index 5164413..9b77baf 100644 --- a/tests/unit/simulation/test_requests_generator.py +++ b/tests/unit/simulation/test_requests_generator.py @@ -11,7 +11,7 @@ from app.config.constants import TimeDefaults from app.core.simulation.requests_generator import requests_generator from app.core.simulation.simulation_run import run_simulation -from app.schemas.requests_generator_input import SimulationInput +from app.schemas.requests_generator_input import RqsGeneratorInput if TYPE_CHECKING: @@ -24,9 +24,9 @@ # -------------------------------------------------------------- @pytest.fixture -def base_input() -> SimulationInput: - """Return a SimulationInput with a 120-second simulation horizon.""" - return SimulationInput( +def base_input() -> RqsGeneratorInput: + """Return a RqsGeneratorInput with a 120-second simulation horizon.""" + return RqsGeneratorInput( avg_active_users={"mean": 1.0}, avg_request_per_minute_per_user={"mean": 2.0}, total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, @@ -37,7 +37,7 @@ def base_input() -> SimulationInput: # -------------------------------------------------------------- def test_default_requests_generator_uses_poisson_poisson_sampling( - base_input: SimulationInput, + base_input: RqsGeneratorInput, ) -> None: """ Verify that when avg_active_users.distribution is the default 'poisson', @@ -69,7 +69,7 @@ def test_requests_generator_dispatches_to_correct_sampler( - 'poisson' → poisson_poisson_sampling - 'normal' → gaussian_poisson_sampling """ - input_data = SimulationInput( + input_data = RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": dist}, avg_request_per_minute_per_user={"mean": 1.0}, total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, @@ -87,7 +87,7 @@ def test_requests_generator_dispatches_to_correct_sampler( # -------------------------------------------------------------- def test_run_simulation_counts_events_up_to_horizon( - monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, + monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, ) -> None: """ Verify that all events whose cumulative inter-arrival times @@ -96,7 +96,7 @@ def test_run_simulation_counts_events_up_to_horizon( yield 4 events by t=10. """ def fake_requests_generator_fixed( - data: SimulationInput, *, rng: np.random.Generator, + data: RqsGeneratorInput, *, rng: np.random.Generator, ) -> Iterator[float]: # Replace the complex Poisson-Poisson sampler with a deterministic sequence. yield from [1.0, 2.0, 3.0, 4.0] @@ -118,14 +118,14 @@ def fake_requests_generator_fixed( def test_run_simulation_includes_event_at_exact_horizon( - monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, + monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, ) -> None: """ Confirm that an event scheduled exactly at the simulation horizon is not processed, since SimPy stops at t == horizon. """ def fake_generator_at_horizon( - data: SimulationInput, *, rng: np.random.Generator, + data: RqsGeneratorInput, *, rng: np.random.Generator, ) -> Iterator[float]: # mypy assertion, pydantic guaranteed @@ -146,14 +146,14 @@ def fake_generator_at_horizon( def test_run_simulation_excludes_event_beyond_horizon( - monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, + monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, ) -> None: """ Ensure that events scheduled after the simulation horizon are not counted. """ def fake_generator_beyond_horizon( - data: SimulationInput, *, rng: np.random.Generator, + data: RqsGeneratorInput, *, rng: np.random.Generator, ) -> Iterator[float]: # mypy assertion, pydantic guaranteed @@ -173,14 +173,14 @@ def fake_generator_beyond_horizon( def test_run_simulation_zero_events_when_generator_empty( - monkeypatch: pytest.MonkeyPatch, base_input: SimulationInput, + monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, ) -> None: """ Check that run_simulation reports zero requests when no inter-arrival times are yielded. """ def fake_generator_empty( - data: SimulationInput, *, rng: np.random.Generator, + data: RqsGeneratorInput, *, rng: np.random.Generator, ) -> Iterator[float]: # Empty generator yields nothing. if False: From 953e31814c0c9094c869d80a26b16021ef49f3af Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Tue, 15 Jul 2025 19:39:09 +0200 Subject: [PATCH 15/52] Features/definition full payload simulation (#4) * Improved input structure and pytest * Improved pytest structure accordingly to the new schema * definition of the metrics to be measured and update of the simulation input * improved documentations added rationale behind metrics * improved pytest logic and code coherence * Update src/app/core/helpers.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../input_structure_for_the_simulation.md | 514 ++++++++---------- .../metrics_to_measure.md | 49 ++ src/app/api/simulation.py | 4 +- src/app/config/constants.py | 40 ++ .../core/event_samplers/gaussian_poisson.py | 6 +- .../core/event_samplers/poisson_poisson.py | 6 +- src/app/core/helpers.py | 38 ++ src/app/core/simulation/requests_generator.py | 4 + src/app/core/simulation/simulation_run.py | 28 +- src/app/schemas/full_simulation_input.py | 2 + src/app/schemas/requests_generator_input.py | 7 - src/app/schemas/simulation_settings_input.py | 31 ++ tests/conftest.py | 115 ++++ .../test_requests_generator_input.py | 175 ++---- tests/unit/sampler/test_gaussian_poisson.py | 75 ++- tests/unit/sampler/test_poisson_poisson.py | 124 +++++ tests/unit/sampler/test_poisson_posson.py | 120 ---- .../simulation/test_requests_generator.py | 212 +++----- 18 files changed, 818 insertions(+), 732 deletions(-) create mode 100644 documentation/backend_documentation/metrics_to_measure.md create mode 100644 src/app/core/helpers.py create mode 100644 src/app/schemas/simulation_settings_input.py create mode 100644 tests/unit/sampler/test_poisson_poisson.py delete mode 100644 tests/unit/sampler/test_poisson_posson.py diff --git a/documentation/backend_documentation/input_structure_for_the_simulation.md b/documentation/backend_documentation/input_structure_for_the_simulation.md index e31c860..d28b090 100644 --- a/documentation/backend_documentation/input_structure_for_the_simulation.md +++ b/documentation/backend_documentation/input_structure_for_the_simulation.md @@ -1,404 +1,320 @@ -### **FastSim — Request-Generator Input Configuration** +### **FastSim — Simulation Input Schema** -A **single, self-consistent contract** links three layers of the codebase: +The `SimulationPayload` is the single, self-contained contract that defines an entire simulation run. Its architecture is guided by a core philosophy: to achieve maximum control over input data through robust, upfront validation. To implement this, we extensively leverage Pydantic's powerful validation capabilities and Python's `Enum` classes. This approach creates a strictly-typed and self-consistent schema that guarantees any configuration is validated *before* the simulation engine starts. -1. **Global Constants** – `TimeDefaults`, `Distribution` -2. **Random Variable Schema** – `RVConfig` -3. **Traffic-Generator Payload** – `RqsGeneratorInput` +This contract brings together three distinct but interconnected layers of configuration into one cohesive structure: -Understanding how these layers interact is key to crafting valid and predictable traffic profiles, preventing common configuration errors before the simulation begins. +1. **`rqs_input` (`RqsGeneratorInput`)**: Defines the **workload profile**—how many users are active and how frequently they generate requests. +2. **`topology_graph` (`TopologyGraph`)**: Describes the **system's architecture**—its components, resources, and the network connections between them. +3. **`sim_settings` (`SimulationSettings`)**: Configures **global simulation parameters**, such as total runtime and which metrics to collect. + +This layered design decouples the *what* (the system topology) from the *how* (the traffic pattern and simulation control), allowing for modular and reusable configurations. Adherence to our validation-first philosophy means every payload is rigorously parsed against this schema. By using a controlled vocabulary of `Enums` and the power of Pydantic, we guarantee that any malformed or logically inconsistent input is rejected upfront with clear, actionable errors, ensuring the simulation engine operates only on perfectly valid data. --- -### 1. Global Constants +### **1. Component: Traffic Profile (`RqsGeneratorInput`)** -| Constant Set | Purpose | Key Values | -| :--- | :--- | :--- | -| **`TimeDefaults`** (`IntEnum`) | Defines default values and validation bounds for time-based fields. | `SIMULATION_TIME = 3600 s`, `MIN_SIMULATION_TIME = 1800 s`, `USER_SAMPLING_WINDOW = 60 s`, `MIN_USER_SAMPLING_WINDOW = 1 s`, `MAX_USER_SAMPLING_WINDOW = 120 s` | -| **`Distribution`** (`StrEnum`) | Defines the canonical names of probability distributions supported by the generator. | `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"` | +This component specifies the dynamic behavior of users interacting with the system. It is built upon a foundation of shared constants and a reusable, rigorously validated random variable schema. This design ensures that any traffic profile is not only structurally correct but also logically sound before the simulation begins. -***Why use constants?*** +#### **Global Constants** -* **Consistency:** They are referenced by validators; changing a value in one place updates the entire validation tree. -* **Safety:** They guarantee that a typo, such as `"Poisson"`, raises an error instead of silently failing or switching to an unintended default. +These enums provide a single source of truth for validation and default values, eliminating "magic strings" and ensuring consistency. + +| Constant Set | Purpose | Key Values | +| :--- | :--- | :--- | +| **`TimeDefaults`** (`IntEnum`) | Defines default values and validation bounds for time-based fields. | `USER_SAMPLING_WINDOW = 60`, `MIN_USER_SAMPLING_WINDOW = 1`, `MAX_USER_SAMPLING_WINDOW = 120` | +| **`Distribution`** (`StrEnum`) | Defines the canonical names of supported probability distributions. | `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"` | --- -### 2. Random Variable Schema (`RVConfig`) +#### **Random Variable Schema (`RVConfig`)** + +At the core of the traffic generator is the `RVConfig`, a schema for defining stochastic variables. This allows critical parameters like user population and request rates to be modeled not as fixed numbers, but as draws from a probability distribution. Pydantic validators are used extensively to enforce correctness. ```python class RVConfig(BaseModel): """class to configure random variables""" - mean: float distribution: Distribution = Distribution.POISSON variance: float | None = None @field_validator("mean", mode="before") - def ensure_mean_is_numeric( - cls, # noqa: N805 - v: object, - ) -> float: - """Ensure `mean` is numeric, then coerce to float.""" - err_msg = "mean must be a number (int or float)" - if not isinstance(v, (float, int)): - raise ValueError(err_msg) # noqa: TRY004 - return float(v) - - @model_validator(mode="after") # type: ignore[arg-type] - def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 - """Set variance = mean when distribution == 'normal' and variance is missing.""" - if model.variance is None and model.distribution == Distribution.NORMAL: - model.variance = model.mean - return model + def ensure_mean_is_numeric(cls, v: object) -> float: + # ... implementation ... + @model_validator(mode="after") + def default_variance(cls, model: "RVConfig") -> "RVConfig": + # ... implementation ... ``` -#### Validation Logic - -| Check | Pydantic Hook | Rule | -| :--- | :--- | :--- | -| *Mean must be numeric* | `@field_validator("mean", before)` | Rejects strings and nulls; coerces `int` to `float`. | -| *Autofill variance* | `@model_validator(after)` | If `distribution == "normal"` **and** `variance` is not provided, sets `variance = mean`. | -| *Positivity enforcement* | `PositiveFloat` / `PositiveInt` | Pydantic's constrained types are used on fields like `mean` where negative values are invalid, rejecting them before business logic runs. | - -> **Self-Consistency:** Every random draw in the simulation engine relies on a validated `RVConfig` instance. This avoids redundant checks and defensive code downstream. +##### **Built-in Validation Logic** ---- +Pydantic's validation system is leveraged to enforce several layers of correctness directly within the schema: -### 3. Traffic-Generator Payload (`RqsGeneratorInput`) - -| Field | Type | Validation Tied to Constants | +| Check | Pydantic Hook | Rule & Rationale | | :--- | :--- | :--- | -| `avg_active_users` | `RVConfig` | No extra constraints needed; the inner schema guarantees correctness. | -| `avg_request_per_minute_per_user` | `RVConfig` | Same as above. | -| `total_simulation_time` | `int` | `ge=TimeDefaults.MIN_SIMULATION_TIME`
default=`TimeDefaults.SIMULATION_TIME` | -| `user_sampling_window` | `int` | `ge=TimeDefaults.MIN_USER_SAMPLING_WINDOW`
`le=TimeDefaults.MAX_USER_SAMPLING_WINDOW`
default=`TimeDefaults.USER_SAMPLING_WINDOW` | - -#### How the Generator Uses Each Field - -The simulation evolves based on a simple, powerful loop: - -1. **Timeline Partitioning** (`user_sampling_window`): The simulation timeline is divided into fixed-length windows. For each window: -2. **Active User Sampling** (`avg_active_users`): A single value is drawn to determine the concurrent user population, `U(t)`, for that window. -3. **Request Rate Calculation** (`avg_request_per_minute_per_user`): Each of the `U(t)` users contributes to the total request rate, yielding an aggregate load for the window. -4. **Termination** (`total_simulation_time`): The loop stops once the cumulative simulated time reaches this value. - -Because every numeric input is range-checked upfront, **the runtime engine never needs to defend itself** against invalid data like zero-length windows or negative rates, making the event-loop lean and predictable. +| **Numeric `mean` Enforcement** | `@field_validator("mean", mode="before")` | This validator intercepts the `mean` field *before* any type casting. It ensures the provided value is an `int` or `float`, raising an explicit `ValueError` for invalid types like strings (`"100"`) or nulls. This prevents common configuration errors and guarantees a valid numeric type for all downstream logic. | +| **Valid `distribution` Name** | `Distribution` (`StrEnum`) type hint | By type-hinting the `distribution` field with the `Distribution` enum, Pydantic automatically ensures that its value must be one of the predefined members (e.g., `"poisson"`, `"normal"`). Any typo or unsupported value (like `"Poisson"` with a capital 'P') results in an immediate validation error. | +| **Intelligent `variance` Defaulting** | `@model_validator(mode="after")` | This powerful validator runs *after* all individual fields have been validated. It enforces a crucial business rule: if `distribution` is `"normal"` **and** `variance` is not provided, the schema automatically sets `variance = mean`. This provides a safe, logical default and simplifies configuration for the user, while ensuring the model is always self-consistent. | --- -### 4. End-to-End Example (Fully Explicit) - -```json -{ - "avg_active_users": { - "mean": 100, - "distribution": "poisson" - }, - "avg_request_per_minute_per_user": { - "mean": 4.0, - "distribution": "normal", - "variance": null - }, - "total_simulation_time": 5400, - "user_sampling_window": 45 -}``` - -#### What the Validators Do +#### **Payload Structure (`RqsGeneratorInput`)** -1. `mean` is numeric ✔️ -2. `distribution` string matches an enum member ✔️ -3. `total_simulation_time` ≥ 1800 ✔️ -4. `user_sampling_window` is in the range ✔️ -5. `variance` is `null` with a `normal` distribution ⇒ **auto-set to 4.0** ✔️ +This is the main payload for configuring the traffic workload. It composes the `RVConfig` schema and adds its own validation rules. -The payload is accepted. The simulator will run for $5400 / 45 = 120$ simulation windows. - ---- +| Field | Type | Validation & Purpose | +| :--- | :--- | :--- | +| `avg_active_users` | `RVConfig` | A random variable defining concurrent users. **Inherits all `RVConfig` validation**, ensuring its `mean`, `distribution`, and `variance` are valid. | +| `avg_request_per_minute_per_user` | `RVConfig` | A random variable for the user request rate. Also **inherits all `RVConfig` validation**. | +| `user_sampling_window` | `int` | The time duration (in seconds) for which the number of active users is held constant. Its value is **strictly bounded** by Pydantic's `Field` to be between `MIN_USER_SAMPLING_WINDOW` (1) and `MAX_USER_SAMPLING_WINDOW` (120). | -### 5. Common Error Example +##### **How the Generator Uses Each Field** -```json -{ - "avg_active_users": { "mean": "many" }, - "avg_request_per_minute_per_user": { "mean": -2 }, - "total_simulation_time": 600, - "user_sampling_window": 400 -} -``` +The simulation evolves based on this robustly validated input: -| # | Fails On | Error Message (Abridged) | -| :- | :--- | :--- | -| 1 | Numeric check | `Input should be a valid number` | -| 2 | Positivity check | `Input should be greater than 0` | -| 3 | Minimum time check | `Input should be at least 1800` | -| 4 | Maximum window check | `Input should be at most 120` | +1. The timeline is divided into windows of `user_sampling_window` seconds. Because this value is range-checked upfront by Pydantic, the simulation is protected from invalid configurations like zero-length or excessively long windows. +2. At the start of each window, a number of active users, `U(t)`, is drawn from the `avg_active_users` distribution. The embedded `RVConfig` guarantees this distribution is well-defined. +3. Each of the `U(t)` users generates requests according to a rate drawn from `avg_request_per_minute_per_user`. ---- +Because every numeric input is type-checked and range-checked by Pydantic before the simulation begins, **the runtime engine never needs to defend itself** against invalid data. This makes the core simulation loop leaner, more predictable, and free from redundant error-handling logic. -### Takeaways +### **2. Component: System Blueprint (`TopologyGraph`)** -* **Single Source of Truth:** Enums centralize all literal values, eliminating magic strings. -* **Layered Validation:** The `Constants → RVConfig → Request Payload` hierarchy ensures that only well-formed traffic profiles reach the simulation engine. -* **Safe Defaults:** Omitting optional fields never leads to undefined behavior; defaults are sourced directly from the `TimeDefaults` constants. +The topology schema is the static blueprint of the digital twin you wish to simulate. It describes the system's components, their resources, their behavior, and how they are interconnected. To ensure simulation integrity, FastSim uses this schema to rigorously validate the entire system description upfront, rejecting any inconsistencies before the simulation begins. -This robust, layered approach allows you to configure the generator with confidence, knowing that any malformed scenario will be rejected early with explicit, actionable error messages. +Of course. Here is the complete, consolidated, and highly detailed documentation for the `TopologyGraph` component, with all duplications removed and explanations expanded as requested. +--- -### **FastSim Topology Input Schema** +### **2. Component: System Blueprint (`TopologyGraph`)** -The topology schema is the blueprint of the digital twin, defining the structure, resources, behavior, and network connections of the system you wish to simulate. It describes: +The topology schema is the static blueprint of the digital twin you wish to simulate. It describes the system's components, their resources, their behavior, and how they are interconnected. To ensure simulation integrity, FastSim uses this schema to rigorously validate the entire system description upfront, rejecting any inconsistencies before the simulation begins. -1. **What work** each request performs (`Endpoint` → `Step`). -2. **What components** exist in the system (`Server`, `Client`). -3. **Which resources** each component possesses (`ServerResources`). -4. **How** components are interconnected (`Edge`). +#### **Design Philosophy: A "Micro-to-Macro" Approach** -To ensure simulation integrity and prevent runtime errors, FastSim uses Pydantic to rigorously validate the entire topology upfront. Every inconsistency is rejected at load-time. The following sections detail the schema's layered design, from the most granular operation to the complete system graph. +The schema is built on a compositional, "micro-to-macro" principle. We start by defining the smallest indivisible units of work (`Step`) and progressively assemble them into larger, more complex structures (`Endpoint`, `Server`, and finally the `TopologyGraph`). ---- -### **A Controlled Vocabulary: The Role of Constants** +This layered approach provides several key advantages that enhance the convenience and reliability of crafting simulations: -To ensure that input configurations are unambiguous and robust, the topology schema is built upon a controlled vocabulary defined by a series of Python `Enum` classes. Instead of relying on raw strings or "magic values" (e.g., `"cpu_bound_operation"`), which are prone to typos and inconsistencies, the schema uses these enumerations to define the finite set of legal values for categories like operation kinds, metrics, and node types. +* **Modularity and Reusability:** Core operations are defined once as `Steps` and can be reused across multiple `Endpoints`. This modularity simplifies configuration, as complex workflows can be built from a library of simple, well-defined blocks. +* **Local Reasoning, Global Safety:** Each model is responsible for its own internal consistency (e.g., a `Step` ensures its metric is valid for its kind). Parent models then enforce the integrity of the connections *between* these components (e.g., the `TopologyGraph` ensures all `Edges` connect to valid `Nodes`). This allows you to focus on one part of the configuration at a time, confident that the overall structure will be validated globally. +* **Clarity and Maintainability:** The hierarchy is intuitive and mirrors how developers conceptualize system architecture. It is clear how atomic operations roll up into endpoints, which are hosted on servers connected by a network. This makes configuration files easy to read, write, and maintain over time. +* **Guaranteed Robustness:** By catching all structural and referential errors before the simulation begins, this approach embodies the "fail-fast" principle. It guarantees that the SimPy engine operates on a valid, self-consistent model, eliminating a whole class of potential runtime bugs. -This design choice provides three critical benefits: +#### **A Controlled Vocabulary: Topology Constants** -1. **Strong Type-Safety:** By using `StrEnum` and `IntEnum`, Pydantic models can validate input payloads with absolute certainty. Any value not explicitly defined in the corresponding `Enum` is immediately rejected. This prevents subtle configuration errors that would be difficult to debug at simulation time. -2. **Developer Experience and Error Prevention:** This approach provides powerful auto-completion and static analysis. IDEs, `mypy`, and linters can catch invalid values during development, providing immediate feedback long before the code is executed. -3. **Single Source of Truth:** All valid categories are centralized in the `app.config.constants` module. This makes the system easier to maintain and extend. To add a new resource type or metric, a developer only needs to update the `Enum` definition, and the change propagates consistently to validation logic, the simulation engine, and any other component that uses it. +The schema's robustness is founded on a controlled vocabulary defined by Python `Enum` classes. Instead of error-prone "magic strings" (e.g., `"cpu_operation"`), the schema uses these enums to define the finite set of legal values for categories like operation kinds, metrics, and node types. This design choice is critical for several reasons: -The key enumerations that govern the topology schema include: +* **Absolute Type-Safety:** Pydantic can validate input with certainty. Any value not explicitly defined in the corresponding `Enum` is immediately rejected, preventing subtle typos or incorrect values from causing difficult-to-debug runtime failures. +* **Enhanced Developer Experience:** IDEs and static analysis tools like `mypy` can provide auto-completion and catch invalid values during development, offering immediate feedback long before the simulation is run. +* **Single Source of Truth:** All valid categories are centralized. To add a new resource type or metric, a developer only needs to update the `Enum` definition, and the change propagates consistently throughout the validation logic. | Constant Enum | Purpose | | :--- | :--- | -| **`EndpointStepIO`, `EndpointStepCPU`, `EndpointStepRAM`** | Define the exhaustive list of valid `kind` values for a `Step`. | -| **`Metrics`** | Specify the legal dictionary keys within a `Step`'s `step_metrics`, enforcing the one-to-one link between a `kind` and its metric. | -| **`SystemNodes` and `SystemEdges`** | Enumerate the allowed categories for nodes and their connections in the high-level `TopologyGraph`. | - -### **Design Philosophy: A "Micro-to-Macro" Approach** - -The schema is built on a compositional, "micro-to-macro" principle. We start by defining the smallest indivisible units of work (`Step`) and progressively assemble them into larger, more complex structures (`Endpoint`, `Server`, and finally the `TopologyGraph`). - -This layered approach provides several key advantages: -* **Modularity and Reusability:** An `Endpoint` is just a sequence of `Steps`. You can reorder, add, or remove steps without redefining the core operations themselves. -* **Local Reasoning, Global Safety:** Each model is responsible for its own internal consistency (e.g., a `Step` ensures its metric is valid for its kind). Parent models then enforce the integrity of the connections *between* these components (e.g., the `TopologyGraph` ensures all `Edges` connect to valid `Nodes`). -* **Clarity and Maintainability:** The hierarchy makes the system description intuitive to read and write. It’s clear how atomic operations roll up into endpoints, which are hosted on servers connected by a network. -* **Robustness:** All structural and referential errors are caught before the simulation begins, guaranteeing that the SimPy engine operates on a valid, self-consistent model. +| **`EndpointStepIO`, `EndpointStepCPU`, `EndpointStepRAM`** | Defines the exhaustive list of valid `kind` values for a `Step`. | +| **`Metrics`** | Specifies the legal dictionary keys within a `Step`'s `step_metrics`. | +| **`SystemNodes`** | Enumerate the allowed `type` for nodes (e.g., `"server"`, `"client"`). | +| **`SystemEdges`** | Enumerate the allowed categories for connections between nodes. | --- -### **1. The Atomic Unit: `Step`** +### **Schema Hierarchy and In-Depth Validation** -A `Step` represents a single, indivisible operation executed by an asynchronous coroutine within an endpoint. It is the fundamental building block of all work in the simulation. +Here we break down each component of the topology, highlighting the specific Pydantic validators that enforce its correctness and the deep rationale behind these choices. -Each `Step` has a `kind` (the category of work) and `step_metrics` (the resources it consumes). +#### **1. `Step`**: The Atomic Unit of Work +A `Step` represents a single, indivisible operation. Its validation is the cornerstone of ensuring that all work performed in the simulation is logical and well-defined. -```python -class Step(BaseModel): - """ - A single, indivisible operation. - It must be quantified by exactly ONE metric. - """ - kind: EndpointStepIO | EndpointStepCPU | EndpointStepRAM - step_metrics: dict[Metrics, PositiveFloat | PositiveInt] +| Validation Check | Pydantic Hook | Rule & Rationale | +| :--- | :--- | :--- | +| **Coherence of `kind` and `metric`** | `@model_validator` | **Rule:** The `step_metrics` dictionary must contain *exactly one* entry, and its key must be the correct metric for the `Step`'s `kind`.

**Rationale:** This is the most critical validation on a `Step`. The one-to-one mapping is a deliberate design choice for simplicity and robustness. It allows the simulation engine to be deterministic: a `cpu_bound_operation` step is routed to the CPU resource, an `io_wait` step to an I/O event, etc. This avoids the immense complexity of modeling operations that simultaneously contend for multiple resource types (e.g., CPU and RAM). This validator enforces that clear, unambiguous contract, preventing illogical pairings like a RAM allocation step being measured in `cpu_time`. | +| **Positive Metric Values** | `PositiveFloat` / `PositiveInt` | **Rule:** All numeric values in `step_metrics` must be greater than zero.

**Rationale:** It is physically impossible to spend negative or zero time on an operation or allocate negative RAM. This validation uses Pydantic's constrained types to offload this fundamental sanity check, ensuring that only plausible, positive resource requests enter the system and keeping the core simulation logic free of defensive checks against nonsensical data. | - @model_validator(mode="after") - def ensure_coherence_kind_metrics(cls, model: "Step") -> "Step": - metrics_keys = set(model.step_metrics) +#### **2. `Endpoint`**: Composing Workflows +An `Endpoint` defines a complete, user-facing operation (e.g., an API call like `/predict`) as an ordered sequence of `Steps`. - # Enforce that a step performs one and only one type of work. - if len(metrics_keys) != 1: - raise ValueError("step_metrics must contain exactly one entry") +| Validation Check | Pydantic Hook | Rule & Rationale | +| :--- | :--- | :--- | +| **Consistent Naming** | `@field_validator("endpoint_name")` | **Rule:** Automatically converts the `endpoint_name` to lowercase.

**Rationale:** This enforces a canonical representation for all endpoint identifiers. It eliminates ambiguity and potential bugs that could arise from inconsistent capitalization (e.g., treating `/predict` and `/Predict` as different endpoints). This simple normalization makes the configuration more robust and simplifies endpoint lookups within the simulation engine. | - # Enforce that the metric is appropriate for the kind of work. - if isinstance(model.kind, EndpointStepCPU): - if metrics_keys != {Metrics.CPU_TIME}: - raise ValueError(f"CPU step requires metric '{Metrics.CPU_TIME}'") +#### **3. System Nodes**: `Server` & `Client` +These models define the macro-components of your architecture where work is performed and resources are located. - elif isinstance(model.kind, EndpointStepRAM): - if metrics_keys != {Metrics.NECESSARY_RAM}: - raise ValueError(f"RAM step requires metric '{Metrics.NECESSARY_RAM}'") +| Validation Check | Pydantic Hook | Rule & Rationale | +| :--- | :--- | :--- | +| **Standardized Node `type`** | `@field_validator("type")` | **Rule:** The `type` field must strictly match the expected `SystemNodes` enum member (e.g., a `Server` object must have `type: "server"`).

**Rationale:** This provides a "belt-and-suspenders" check. Even if a default is provided, this validation prevents a user from explicitly overriding a node's type to a conflicting value. It enforces a strict contract: a `Server` object is always and only a server. This prevents object state confusion and simplifies pattern matching in the simulation engine. | +| **Unique Node IDs** | `@model_validator` in `TopologyNodes` | **Rule:** All `id` fields across all `Server` nodes and the `Client` node must be unique.

**Rationale:** This is fundamental to creating a valid graph. Node IDs are the primary keys used to address components. If two nodes shared the same ID, any `Edge` pointing to that ID would be ambiguous. This global validator prevents such ambiguity, guaranteeing that every node in the system is uniquely identifiable, which is a precondition for the final referential integrity check. | - elif isinstance(model.kind, EndpointStepIO): - if metrics_keys != {Metrics.IO_WAITING_TIME}: - raise ValueError(f"I/O step requires metric '{Metrics.IO_WAITING_TIME}'") +#### **4. `Edge`**: Connecting the Components +An `Edge` represents a directed network link between two nodes, defining how requests flow through the system. - return model -``` +| Validation Check | Pydantic Hook | Rule & Rationale | +| :--- | :--- | :--- | +| **No Self-Loops** | `@model_validator` | **Rule:** An edge's `source` ID cannot be the same as its `target` ID.

**Rationale:** In the context of a distributed system topology, a network call from a service to itself is a logical anti-pattern. Such an operation would typically be modeled as an internal process (i.e., another `Step`), not a network hop. This validator prevents this common configuration error and simplifies the routing logic by disallowing trivial cycles. | -> **Design Rationale:** The strict one-to-one mapping between a `Step` and a single metric is a core design choice. It simplifies the simulation engine immensely, as each `Step` can be deterministically routed to a request on a single SimPy resource (a CPU queue, a RAM container, or an I/O event). This avoids the complexity of modeling operations that simultaneously consume multiple resource types. +#### **5. `TopologyGraph`**: The Complete System +This is the root model that aggregates all `nodes` and `edges` and performs the final, most critical validation: ensuring referential integrity. ---- +| Validation Check | Pydantic Hook | Rule & Rationale | +| :--- | :--- | :--- | +| **Referential Integrity** | `@model_validator` | **Rule:** Every `edge.source` and `edge.target` ID must correspond to an actual node ID defined in `TopologyNodes`.

**Rationale:** This is the capstone validation that guarantees the structural integrity of the entire system graph. It prevents "dangling edges"—connections that point to non-existent nodes. Without this check, the simulation could start with a broken topology and crash unexpectedly at runtime when a request attempts to traverse a broken link. By performing this check *after* all nodes and edges have been parsed, we ensure that the system described is a complete and validly connected graph, fully embodying the "fail-fast" principle. | -### **2. Composing Workflows: `Endpoint`** +### **3. Component: Global Simulation Control (`SimulationSettings`)** -An `Endpoint` defines a complete, user-facing operation (e.g., an API call like `/predict`) as an ordered sequence of `Steps`. +This final component configures the simulation's execution parameters and, critically, determines what data is collected. It acts as the master control panel for the simulation run, governing both its duration and the scope of its output. + +#### **Payload Structure (`SimulationSettings`)** ```python -class Endpoint(BaseModel): - """A higher-level API call, executed as a strict sequence of steps.""" - endpoint_name: str - steps: list[Step] - - @field_validator("endpoint_name", mode="before") - def name_to_lower(cls, v: str) -> str: - """Standardize endpoint name to be lowercase for consistency.""" - return v.lower() +class SimulationSettings(BaseModel): + """Global parameters that apply to the whole run.""" + total_simulation_time: int = Field(...) + enabled_sample_metrics: set[SampledMetricName] = Field( + default_factory=lambda: { + SampledMetricName.READY_QUEUE_LEN, + SampledMetricName.CORE_BUSY, + SampledMetricName.RAM_IN_USE, + }, + description="Which time‑series KPIs to collect by default.", + ) + enabled_event_metrics: set[EventMetricName] = Field( + default_factory=lambda: { + EventMetricName.RQS_LATENCY, + }, + description="Which per‑event KPIs to collect by default.", + ) ``` -> **Design Rationale:** The simulation processes the `steps` list in the exact order provided. The total latency and resource consumption of an endpoint call is the sequential sum of its individual `Step` delays. This directly models the execution flow of a typical web request handler. +| Field | Type | Purpose & Validation | +| :--- | :--- | :--- | +| `total_simulation_time` | `int` | The total simulation horizon in seconds. Must be `>= MIN_SIMULATION_TIME` (1800s). Defaults to `3600`. | +| `enabled_sample_metrics` | `set[SampledMetricName]` | A set of metrics to be sampled at fixed intervals, creating a time-series (e.g., `"ready_queue_len"`, `"ram_in_use"`). | +| `enabled_event_metrics` | `set[EventMetricName]` | A set of metrics recorded only when specific events occur, with no time-series (e.g., `"rqs_latency"`, `"llm_cost"`). | +We add standard default value for the metrics in case they will be omitted --- -### **3. Defining Components: System Nodes** +#### **Design Rationale: Pre-validated, On-Demand Metrics for Robust and Efficient Collection** -Nodes are the macro-components of your architecture where work is performed and resources are located. +The design of the `settings` component, particularly the `enabled_*_metrics` fields, is centered on two core principles: **user-driven selectivity** and **ironclad validation**. The rationale behind this approach is to create a system that is both flexible and fundamentally reliable. -#### **`ServerResources` and `Server`** -A `Server` node hosts endpoints and owns a set of physical resources. These resources are mapped directly to specific SimPy primitives, which govern how requests queue and contend for service. +##### **1. The Principle of User-Driven Selectivity** -```python -class ServerResources(BaseModel): - """Quantifiable resources available on a server node.""" - cpu_cores: PositiveInt = Field(ge=ServerResourcesDefaults.MINIMUM_CPU_CORES) - ram_mb: PositiveInt = Field(ge=ServerResourcesDefaults.MINIMUM_RAM_MB) - db_connection_pool: PositiveInt | None = None - -class Server(BaseModel): - """A node that hosts endpoints and owns resources.""" - id: str - type: SystemNodes = SystemNodes.SERVER - server_resources: ServerResources - endpoints: list[Endpoint] -``` +We recognize that data collection is not free; it incurs performance overhead in terms of both memory (to store the data) and CPU cycles (to record it). Not every simulation requires every possible metric. For instance: +* A simulation focused on CPU contention may not need detailed LLM cost tracking. +* A high-level analysis of end-to-end latency might not require fine-grained data on event loop queue lengths. -> **Design Rationale: Mapping to SimPy Primitives** -> * `cpu_cores` maps to a `simpy.Resource`. This models a classic semaphore where only `N` processes can execute concurrently, and others must wait in a queue. It perfectly represents CPU-bound tasks competing for a limited number of cores. -> * `ram_mb` maps to a `simpy.Container`. A container models a divisible resource where processes can request and return variable amounts. This is ideal for memory, as multiple requests can simultaneously hold different amounts of RAM without exclusively locking the entire memory pool. +By allowing the user to explicitly select only the metrics they need, we empower them to tailor the simulation to their specific analytical goals. This on-demand approach makes the simulator more efficient and versatile, avoiding the waste of collecting and processing irrelevant data. -#### **`Client`** -The `Client` is a special, resource-less node that serves as the origin point for all requests generated during the simulation. +##### **2. The Power of Ironclad, Upfront Validation** -#### **Node Aggregation and Validation (`TopologyNodes`)** -All `Server` and `Client` nodes are collected in the `TopologyNodes` model, which performs a critical validation check: ensuring all component IDs are unique across the entire system. +This is where the design choice becomes critical for robustness. Simply allowing users to provide a list of strings is inherently risky due to potential typos or misunderstandings of metric names. Our schema mitigates this risk entirely through a strict, upfront validation contract. ---- +* **A Strict Contract via Enums:** The `enabled_sample_metrics` and `enabled_event_metrics` fields are not just sets of strings; they are sets of `SampledMetricName` and `EventMetricName` enum members. When Pydantic parses the input payload, it validates every single metric name provided by the user against these canonical `Enum` definitions. -### **4. Connecting the Components: `Edge`** +* **Immediate Rejection of Invalid Input:** If a user provides a metric name that is not a valid member of the corresponding enum (e.g., a typo like `"request_latncy"` or a misunderstanding like `"cpu_usage"` instead of `"core_busy"`), Pydantic immediately rejects the entire payload with a clear `ValidationError`. This happens *before* a single line of the simulation engine code is executed. -An `Edge` represents a directed network link between two nodes, defining how requests flow through the system. +##### **3. The Benefit: Guaranteed Runtime Integrity** -```python -class Edge(BaseModel): - """A directed connection in the topology graph.""" - source: str - target: str - latency: RVConfig - probability: float = Field(1.0, ge=0.0, le=1.0) - edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION -``` +This pre-validation provides a crucial and powerful guarantee to the simulation engine, leading to a safer and more efficient runtime: -> **Design Rationale:** -> * **Stochastic Latency:** Latency is not a fixed number but an `RVConfig` object. This allows you to model realistic network conditions using various probability distributions (e.g., log-normal for internet RTTs, exponential for failure retries), making the simulation far more accurate. -> * **Probabilistic Routing:** The `probability` field enables modeling of simple load balancing or A/B testing scenarios where traffic from a single `source` can be split across multiple `target` nodes. +* **Safe, Error-Free Initialization:** At the very beginning of the simulation, the engine receives the *validated* set of metric names. It knows with absolute certainty the complete and exact set of metrics it needs to track. This allows it to safely initialize all necessary data collection structures (like dictionaries) at the start of the run. For example: + ```python + # This is safe because every key is guaranteed to be valid. + event_results = {metric_name: [] for metric_name in settings.enabled_event_metrics} + ``` ---- - -### **5. The Complete System: `TopologyGraph`** - -The `TopologyGraph` is the root of the configuration. It aggregates all `nodes` and `edges` and performs the final, most critical validation: ensuring referential integrity. - -```python -class TopologyGraph(BaseModel): - """The complete system definition, uniting all nodes and edges.""" - nodes: TopologyNodes - edges: list[Edge] - - @model_validator(mode="after") - def edge_refs_valid(cls, model: "TopologyGraph") -> "TopologyGraph": - """Ensure every edge connects two valid, existing nodes.""" - valid_ids = {s.id for s in model.nodes.servers} | {model.nodes.client.id} - for e in model.edges: - if e.source not in valid_ids or e.target not in valid_ids: - raise ValueError(f"Edge '{e.source}->{e.target}' references an unknown node.") - return model -``` -> **Design Rationale:** This final check guarantees that the topology is a valid, connected graph. By confirming that every `edge.source` and `edge.target` corresponds to a defined node `id`, it prevents the simulation from starting with a broken or nonsensical configuration, embodying the "fail-fast" principle. +* **Elimination of Runtime KeyErrors:** Because all dictionary keys are guaranteed to exist from the start, the core data collection logic within the simulation's tight event loop becomes incredibly lean and robust. The engine never needs to perform defensive, conditional checks like `if metric_name in event_results: ...`. It can directly and safely access the key: `event_results[metric_name].append(value)`. This completely eliminates an entire class of potential `KeyError` exceptions, which are notoriously difficult to debug in complex, asynchronous simulations. +In summary, the design of `SimulationSettings` is a perfect example of the "fail-fast" philosophy. By forcing a clear and validated contract with the user upfront, we ensure that the data collection process is not only tailored and efficient but also fundamentally reliable. The engine operates with the confidence that the output data structures will perfectly and safely match the user's validated request, leading to a predictable and robust simulation from start to finish. --- -### **End-to-End Example** +### **End-to-End Example (`SimulationPayload`)** -Here is a minimal, complete JSON configuration that defines a single client and a single API server. +The following JSON object shows how these three components combine into a single, complete `SimulationPayload` for a minimal client-server setup. ```jsonc { - "nodes": { - // The client node is the source of all generated requests. - "client": { - "id": "user_browser", - "type": "client" + // Defines the traffic workload profile. + "rqs_input": { + "avg_active_users": { + "mean": 50, + "distribution": "poisson" + }, + "avg_request_per_minute_per_user": { + "mean": 5.0, + "distribution": "normal", + "variance": 1.0 }, - // A list of all server nodes in the system. - "servers": [ + "user_sampling_window": 60 + }, + // Describes the system's architectural blueprint. + "topology_graph": { + "nodes": { + "client": { + "id": "mobile_client", + "type": "client" + }, + "servers": [ + { + "id": "api_server", + "type": "server", + "server_resources": { + "cpu_cores": 4, + "ram_mb": 4096 + }, + "endpoints": [ + { + "endpoint_name": "/predict", + "steps": [ + { + "kind": "initial_parsing", + "step_metrics": { "cpu_time": 0.005 } + }, + { + "kind": "io_db", + "step_metrics": { "io_waiting_time": 0.050 } + } + ] + } + ] + } + ] + }, + "edges": [ { - "id": "api_server_node", - "type": "server", - "server_resources": { - "cpu_cores": 2, - "ram_mb": 2048 - }, - "endpoints": [ - { - "endpoint_name": "/predict", - "steps": [ - { - "kind": "initial_parsing", - "step_metrics": { "cpu_time": 0.005 } - }, - { - "kind": "io_db", - "step_metrics": { "io_waiting_time": 0.050 } - }, - { - "kind": "cpu_bound_operation", - "step_metrics": { "cpu_time": 0.015 } - } - ] - } - ] + "source": "mobile_client", + "target": "api_server", + "latency": { + "distribution": "log_normal", + "mean": 0.04, + "variance": 0.01 + } } ] }, - "edges": [ - // A network link from the client to the API server. - { - "source": "user_browser", - "target": "api_server_node", - "latency": { - "distribution": "log_normal", - "mean": 0.05, - "std_dev": 0.01 - }, - "probability": 1.0 - } - ] -}``` - - + // Configures the simulation run and metric collection. + "settings": { + "total_simulation_time": 3600, + "enabled_sample_metrics": [ + "ready_queue_len", + "ram_in_use", + "throughput_rps" + ], + "enabled_event_metrics": [ + "rqs_latency" + ] + } +} +``` +### **Key Takeaways** -> **YAML friendly:** -> The topology schema is 100 % agnostic to the wire format. -> You can encode the same structure in **YAML** with identical field -> names and value types—Pydantic will parse either JSON or YAML as long -> as the keys and data types respect the schema. -> No additional changes or converters are required. -``` +* **Single Source of Truth**: `Enum` classes centralize all valid string literals, providing robust, type-safe validation across the entire schema. +* **Layered Validation**: The `Constants → Component Schemas → SimulationPayload` hierarchy ensures that only well-formed and self-consistent configurations reach the simulation engine. +* **Separation of Concerns**: The three top-level keys (`rqs_input`, `topology_graph`, `settings`) clearly separate the workload, the system architecture, and simulation control, making configurations easier to read, write, and reuse. -### **Key Takeaway** -This rigorously validated, compositional schema is the foundation of FastSim's reliability. By defining a clear vocabulary of constants (`Metrics`, `SystemNodes`) and enforcing relationships with Pydantic validators, the schema guarantees that every simulation run starts from a **complete and self-consistent** system description. This allows you to refactor simulation logic or extend the model with new resources (e.g., GPU memory) with full confidence that existing configurations remain valid and robust. \ No newline at end of file diff --git a/documentation/backend_documentation/metrics_to_measure.md b/documentation/backend_documentation/metrics_to_measure.md new file mode 100644 index 0000000..4de1a1a --- /dev/null +++ b/documentation/backend_documentation/metrics_to_measure.md @@ -0,0 +1,49 @@ +### **FastSim — simulation's metrics** + +Metrics are the lifeblood of any simulation, transforming a series of abstract events into concrete, actionable insights about system performance, resource utilization, and potential bottlenecks. FastSim provides a flexible and robust metrics collection system designed to give you a multi-faceted view of your system's behavior under load. + +To achieve this, FastSim categorizes metrics into three distinct types based on their collection methodology: + +1. **Sampled Metrics (`SampledMetricName`):** These metrics provide a **time-series view** of the system's state. They are captured at fixed, regular intervals throughout the simulation's duration (e.g., every second). This methodology is ideal for understanding trends, observing oscillations, and measuring the continuous utilization of finite resources like CPU and RAM. Think of them as periodic snapshots of your system's health. + +2. **Event-based Metrics (`EventMetricName`):** These metrics are recorded **only when a specific event occurs**. Their collection is asynchronous and irregular, triggered by discrete happenings within the simulation, such as the completion of a request. This methodology is perfect for measuring the properties of individual transactions, such as end-to-end latency, where an average value is less important than understanding the full distribution of outcomes. + +3. **Aggregated Metrics (`AggregatedMetricName`):** These are not collected directly during the simulation but are **calculated after the simulation ends**. They provide high-level statistical summaries (like mean, median, and percentiles) derived from the raw data collected by Event-based metrics. They distill thousands of individual data points into a handful of key performance indicators (KPIs) that are easy to interpret. + +The following sections provide a detailed breakdown of each metric within these categories, explaining what they measure and the rationale for their importance. + +--- + +### **1. Sampled Metrics: A Time-Series Perspective** + +Sampled metrics are configured in the `SimulationSettings` payload. Enabling them allows you to plot the evolution of system resources over time, which is crucial for identifying saturation points and transient performance issues. + +| Metric Name (`SampledMetricName`) | Description & Rationale | +| :--- | :--- | +| **`READY_QUEUE_LEN`** | **What it is:** The number of tasks in the `asyncio` event loop's "ready" queue waiting for their turn to run on the CPU.

**Rationale:** This is arguably the most critical indicator of **CPU saturation**. In a single-threaded Python process, only one coroutine can run at a time (held by the GIL). If this queue length is consistently greater than zero, it means tasks are ready to do work but are forced to wait because the CPU is busy. A long or growing queue is a definitive sign that your application is CPU-bound and that the CPU is a primary bottleneck. | +| **`CORE_BUSY`** | **What it is:** The number of server CPU cores that are currently executing a task.

**Rationale:** This provides a direct measure of **CPU utilization**. When plotted over time, it shows how effectively you are using your provisioned processing power. If `CORE_BUSY` is consistently at its maximum value (equal to `server_resources.cpu_cores`), the system is CPU-saturated. Conversely, if it's consistently low while latency is high, the bottleneck is likely elsewhere (e.g., I/O). It perfectly complements `READY_QUEUE_LEN` to form a complete picture of CPU health. | +| **`EVENT_LOOP_IO_SLEEP`** | **What it is:** A measure indicating if the event loop is idle, polling for I/O operations to complete.

**Rationale:** This metric helps you determine if your system is **I/O-bound**. If the event loop spends a significant amount of time in this state, it means the CPU is underutilized because it has no ready tasks to run and is instead waiting for external systems (like databases, caches, or downstream APIs) to respond. High values for this metric coupled with low CPU utilization are a clear signal to investigate and optimize the performance of your I/O operations. | +| **`RAM_IN_USE`** | **What it is:** The total amount of memory (in MB) currently allocated by all active requests within a server.

**Rationale:** Essential for **capacity planning and stability analysis**. This metric allows you to visualize your system's memory footprint under load. You can identify which endpoints cause memory spikes and ensure your provisioned RAM is sufficient. A steadily increasing `RAM_IN_USE` value that never returns to a baseline is the classic signature of a **memory leak**, a critical bug this metric helps you detect. | +| **`THROUGHPUT_RPS`** | **What it is:** The number of requests successfully completed per second, calculated over the last sampling window.

**Rationale:** This is a fundamental measure of **system performance and capacity**. It answers the question: "How much work is my system actually doing?" Plotting throughput against user load or other resource metrics is key to understanding your system's scaling characteristics. A drop in throughput often correlates with a spike in latency or resource saturation, helping you pinpoint the exact moment a bottleneck began to affect performance. | + +--- + +### **2. Event-based Metrics: A Per-Transaction Perspective** + +Event-based metrics are also enabled in the `SimulationSettings` payload. They generate a collection of raw data points, one for each relevant event, which is ideal for statistical analysis of transactional performance. + +| Metric Name (`EventMetricName`) | Description & Rationale | +| :--- | :--- | +| **`RQS_LATENCY`** | **What it is:** The total end-to-end duration, in seconds, for a single request to be fully processed.

**Rationale:** This is the **primary user-facing performance metric**. Users directly experience latency. While a simple average can be useful, it often hides critical problems. By collecting the latency for *every single request*, FastSim allows for the calculation of statistical distributions and, most importantly, **tail-latency percentiles (p95, p99)**. These percentiles represent the worst-case experience for your users and are crucial for evaluating Service Level Objectives (SLOs) and ensuring a consistent user experience. | +| **`LLM_COST`** | **What it is:** The estimated monetary cost (e.g., in USD) incurred by a single call to an external Large Language Model (LLM) API during a request.

**Rationale:** In modern AI-powered applications, API calls to third-party services like LLMs can be a major operational expense. This metric moves beyond technical performance to measure **financial performance**. By tracking cost on a per-event basis, you can attribute expenses to specific endpoints or user behaviors, identify unnecessarily costly operations, and make informed decisions to optimize your application's cost-effectiveness. | + +--- + +### **3. Aggregated Metrics: High-Level Summaries** + +**Important:** Aggregated metrics are **not configured in the input payload**. They are automatically calculated by the FastSim engine at the end of a simulation run, based on the raw data collected from the enabled Event-based metrics. + +| Metric Name (`AggregatedMetricName`) | Description & Rationale | +| :--- | :--- | +| **`LATENCY_STATS`** | **What it is:** A statistical summary of the entire collection of `RQS_LATENCY` data points. This typically includes the mean, median (p50), standard deviation, and high-end percentiles (p95, p99, p99.9).

**Rationale:** This provides a comprehensive and easily digestible summary of your system's latency profile. While the raw data is essential, these summary statistics answer high-level questions quickly. The mean tells you the average experience, the median protects against outliers, and the p95/p99 values tell you the latency that 95% or 99% of your users will beat—a critical KPI for reliability and user satisfaction. | +| **`LLM_STATS`** | **What it is:** A statistical summary of the `LLM_COST` data points. This can include total cost over the simulation, average cost per request, and cost distribution.

**Rationale:** This gives you a bird's-eye view of the financial implications of your system's design. Instead of looking at individual transaction costs, `LLM_STATS` provides the bottom line: the total operational cost during the simulation period. This is invaluable for budgeting, forecasting, and validating the financial viability of new features. | \ No newline at end of file diff --git a/src/app/api/simulation.py b/src/app/api/simulation.py index e025ad6..73984d5 100644 --- a/src/app/api/simulation.py +++ b/src/app/api/simulation.py @@ -4,13 +4,13 @@ from fastapi import APIRouter from app.core.simulation.simulation_run import run_simulation -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.full_simulation_input import SimulationPayload from app.schemas.simulation_output import SimulationOutput router = APIRouter() @router.post("/simulation") -async def event_loop_simulation(input_data: RqsGeneratorInput) -> SimulationOutput: +async def event_loop_simulation(input_data: SimulationPayload) -> SimulationOutput: """Run the simulation and return aggregate KPIs.""" rng = np.random.default_rng() return run_simulation(input_data, rng=rng) diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 23c7936..498e8b7 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -182,3 +182,43 @@ class SystemEdges(StrEnum): """ NETWORK_CONNECTION = "network_connection" + +# ====================================================================== +# CONSTANTS FOR SAMPLED METRICS +# ====================================================================== + +class SampledMetricName(StrEnum): + """ + define the metrics sampled every fixed amount of + time to create a time series + """ + + READY_QUEUE_LEN = "ready_queue_len" #length of the event loop ready q + CORE_BUSY = "core_busy" + EVENT_LOOP_IO_SLEEP = "event_loop_io_sleep" + RAM_IN_USE = "ram_in_use" + THROUGHPUT_RPS = "throughput_rps" + +# ====================================================================== +# CONSTANTS FOR EVENT METRICS +# ====================================================================== + +class EventMetricName(StrEnum): + """ + define the metrics triggered by event with no + time series + """ + + RQS_LATENCY = "rqs_latency" + LLM_COST = "llm_cost" + + +# ====================================================================== +# CONSTANTS FOR AGGREGATED METRICS +# ====================================================================== + +class AggregatedMetricName(StrEnum): + """aggregated metrics to calculate at the end of simulation""" + + LATENCY_STATS = "latency_stats" + LLM_STATS = "llm_stats" diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/core/event_samplers/gaussian_poisson.py index 9c83b4f..0b7818c 100644 --- a/src/app/core/event_samplers/gaussian_poisson.py +++ b/src/app/core/event_samplers/gaussian_poisson.py @@ -17,10 +17,12 @@ uniform_variable_generator, ) from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings def gaussian_poisson_sampling( input_data: RqsGeneratorInput, + sim_settings: SimulationSettings, *, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: @@ -35,11 +37,11 @@ def gaussian_poisson_sampling( Λ = U * (mean_req_per_minute_per_user / 60) [req/s]. 3. While inside the current window, draw gaps Δt ~ Exponential(Λ) using inverse-CDF. - 4. Stop once the virtual clock exceeds *simulation_time*. + 4. Stop once the virtual clock exceeds *total_simulation_time*. """ rng = rng or np.random.default_rng() - simulation_time = input_data.total_simulation_time + simulation_time = sim_settings.total_simulation_time user_sampling_window = input_data.user_sampling_window # λ_u : mean concurrent users per window diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/core/event_samplers/poisson_poisson.py index ebb1970..1566f90 100644 --- a/src/app/core/event_samplers/poisson_poisson.py +++ b/src/app/core/event_samplers/poisson_poisson.py @@ -14,10 +14,12 @@ uniform_variable_generator, ) from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings def poisson_poisson_sampling( input_data: RqsGeneratorInput, + sim_settings: SimulationSettings, *, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: @@ -32,11 +34,11 @@ def poisson_poisson_sampling( Λ = U * (mean_req_per_minute_per_user / 60) [req/s]. 3. While inside the current window, draw gaps Δt ~ Exponential(Λ) using inverse-CDF. - 4. Stop once the virtual clock exceeds *simulation_time*. + 4. Stop once the virtual clock exceeds *total_simulation_time*. """ rng = rng or np.random.default_rng() - simulation_time = input_data.total_simulation_time + simulation_time = sim_settings.total_simulation_time user_sampling_window = input_data.user_sampling_window # λ_u : mean concurrent users per window diff --git a/src/app/core/helpers.py b/src/app/core/helpers.py new file mode 100644 index 0000000..189af47 --- /dev/null +++ b/src/app/core/helpers.py @@ -0,0 +1,38 @@ +"""helpers for the simulation""" + +from collections.abc import Iterable + +from app.config.constants import EventMetricName, SampledMetricName + + +def alloc_sample_metric( + enabled_sample_metrics: Iterable[SampledMetricName], + ) -> dict[str, list[float | int]]: + """ + After the pydantic validation of the whole input we + instantiate a dictionary to collect the sampled metrics the + user want to measure + """ + # t is the alignment parameter for example assume + # the snapshot for the sampled metrics are done every 10ms + # t = [10,20,30,40....] to each t will correspond a measured + # metric corresponding to that time interval + + dict_sampled_metrics: dict[str, list[float | int]] = {"t": []} + for key in enabled_sample_metrics: + dict_sampled_metrics[key] = [] + return dict_sampled_metrics + + +def alloc_event_metric( + enabled_event_metrics: Iterable[EventMetricName], + ) -> dict[str, list[float | int]]: + """ + After the pydantic validation of the whole input we + instantiate a dictionary to collect the event metrics the + user want to measure + """ + dict_event_metrics: dict[str, list[float | int]] = {} + for key in enabled_event_metrics: + dict_event_metrics[key] = [] + return dict_event_metrics diff --git a/src/app/core/simulation/requests_generator.py b/src/app/core/simulation/requests_generator.py index 810218f..1b177e7 100644 --- a/src/app/core/simulation/requests_generator.py +++ b/src/app/core/simulation/requests_generator.py @@ -17,10 +17,12 @@ import numpy as np from app.schemas.requests_generator_input import RqsGeneratorInput + from app.schemas.simulation_settings_input import SimulationSettings def requests_generator( input_data: RqsGeneratorInput, + sim_settings: SimulationSettings, *, rng: np.random.Generator | None = None, ) -> Generator[float, None, None]: @@ -41,6 +43,7 @@ def requests_generator( #Gaussian-Poisson model return gaussian_poisson_sampling( input_data=input_data, + sim_settings=sim_settings, rng=rng, ) @@ -48,5 +51,6 @@ def requests_generator( # Poisson + Poisson return poisson_poisson_sampling( input_data=input_data, + sim_settings=sim_settings, rng=rng, ) diff --git a/src/app/core/simulation/simulation_run.py b/src/app/core/simulation/simulation_run.py index 3e1672a..1aea997 100644 --- a/src/app/core/simulation/simulation_run.py +++ b/src/app/core/simulation/simulation_run.py @@ -14,28 +14,32 @@ import numpy as np - from app.schemas.requests_generator_input import RqsGeneratorInput + from app.schemas.full_simulation_input import SimulationPayload + + def run_simulation( - input_data: RqsGeneratorInput, + input_data: SimulationPayload, *, rng: np.random.Generator, ) -> SimulationOutput: """Simulation executor in Simpy""" - gaps: Generator[float, None, None] = requests_generator(input_data, rng=rng) + sim_settings = input_data.sim_settings + + requests_generator_input = input_data.rqs_input + + gaps: Generator[float, None, None] = requests_generator( + requests_generator_input, + sim_settings, + rng=rng) env = simpy.Environment() - simulation_time = input_data.total_simulation_time - # pydantic in the validation assign a value and mypy is not - # complaining because a None cannot be compared in the loop - # to a float - assert simulation_time is not None total_request_per_time_period = { - "simulation_time": simulation_time, + "simulation_time": sim_settings.total_simulation_time, "total_requests": 0, } @@ -47,10 +51,10 @@ def arrival_process( total_request_per_time_period["total_requests"] += 1 env.process(arrival_process(env)) - env.run(until=simulation_time) + env.run(until=sim_settings.total_simulation_time) return SimulationOutput( total_requests=total_request_per_time_period, - metric_2=str(input_data.avg_request_per_minute_per_user.mean), - metric_n=str(input_data.avg_active_users.mean), + metric_2=str(requests_generator_input.avg_request_per_minute_per_user.mean), + metric_n=str(requests_generator_input.avg_active_users.mean), ) diff --git a/src/app/schemas/full_simulation_input.py b/src/app/schemas/full_simulation_input.py index 0e1d7a9..b745fee 100644 --- a/src/app/schemas/full_simulation_input.py +++ b/src/app/schemas/full_simulation_input.py @@ -3,6 +3,7 @@ from pydantic import BaseModel from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings from app.schemas.system_topology_schema.full_system_topology_schema import TopologyGraph @@ -11,3 +12,4 @@ class SimulationPayload(BaseModel): rqs_input: RqsGeneratorInput topology_graph: TopologyGraph + sim_settings: SimulationSettings diff --git a/src/app/schemas/requests_generator_input.py b/src/app/schemas/requests_generator_input.py index 88812e0..f56a4f0 100644 --- a/src/app/schemas/requests_generator_input.py +++ b/src/app/schemas/requests_generator_input.py @@ -12,13 +12,6 @@ class RqsGeneratorInput(BaseModel): avg_active_users: RVConfig avg_request_per_minute_per_user: RVConfig - total_simulation_time: int = Field( - default=TimeDefaults.SIMULATION_TIME, - ge=TimeDefaults.MIN_SIMULATION_TIME, - description=( - f"Simulation time in seconds (>= {TimeDefaults.MIN_SIMULATION_TIME})." - ), - ) user_sampling_window: int = Field( default=TimeDefaults.USER_SAMPLING_WINDOW, diff --git a/src/app/schemas/simulation_settings_input.py b/src/app/schemas/simulation_settings_input.py new file mode 100644 index 0000000..5d0ac6a --- /dev/null +++ b/src/app/schemas/simulation_settings_input.py @@ -0,0 +1,31 @@ +"""define a class with the global settings for the simulation""" + +from pydantic import BaseModel, Field + +from app.config.constants import EventMetricName, SampledMetricName, TimeDefaults + + +class SimulationSettings(BaseModel): + """Global parameters that apply to the whole run.""" + + total_simulation_time: int = Field( + default=TimeDefaults.SIMULATION_TIME, + ge=TimeDefaults.MIN_SIMULATION_TIME, + description="Simulation horizon in seconds.", + ) + + enabled_sample_metrics: set[SampledMetricName] = Field( + default_factory=lambda: { + SampledMetricName.READY_QUEUE_LEN, + SampledMetricName.CORE_BUSY, + SampledMetricName.RAM_IN_USE, + }, + description="Which time-series KPIs to collect by default.", + ) + enabled_event_metrics: set[EventMetricName] = Field( + default_factory=lambda: { + EventMetricName.RQS_LATENCY, + }, + description="Which per-event KPIs to collect by default.", + ) + diff --git a/tests/conftest.py b/tests/conftest.py index 5fcd4bc..e6d7b75 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -10,6 +10,8 @@ from alembic.config import Config from dotenv import load_dotenv from fastapi.testclient import TestClient +from numpy.random import Generator as NpGenerator +from numpy.random import default_rng from sqlalchemy.ext.asyncio import ( AsyncEngine, AsyncSession, @@ -18,9 +20,23 @@ ) from sqlalchemy_utils import create_database, database_exists, drop_database +from app.config.constants import ( + EventMetricName, + SampledMetricName, + TimeDefaults, +) from app.config.settings import settings from app.db.session import get_db from app.main import app +from app.schemas.full_simulation_input import SimulationPayload +from app.schemas.random_variables_config import RVConfig +from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings +from app.schemas.system_topology_schema.full_system_topology_schema import ( + Client, + TopologyGraph, + TopologyNodes, +) # Load test environment variables from .env.test ENV_PATH = Path(__file__).resolve().parents[1] / "docker_fs" / ".env.test" @@ -121,3 +137,102 @@ async def db_session(async_engine: AsyncEngine) -> AsyncGenerator[AsyncSession, await transaction.rollback() # Close the connection await connection.close() + +# ============================================================================ +# STANDARD CONFIGURATION FOR INPUT VARIABLES +# ============================================================================ + +# --------------------------------------------------------------------------- +# RNG +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="session") +def rng() -> NpGenerator: + """Deterministic NumPy RNG shared across tests (seed=0).""" + return default_rng(0) + + +# --------------------------------------------------------------------------- +# Metrics sets +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="session") +def enabled_sample_metrics() -> set[SampledMetricName]: + """Default sample-level KPIs tracked in most tests.""" + return { + SampledMetricName.READY_QUEUE_LEN, + SampledMetricName.RAM_IN_USE, + } + + +@pytest.fixture(scope="session") +def enabled_event_metrics() -> set[EventMetricName]: + """Default event-level KPIs tracked in most tests.""" + return {EventMetricName.RQS_LATENCY} + + +# --------------------------------------------------------------------------- +# Global simulation settings +# --------------------------------------------------------------------------- + + +@pytest.fixture +def sim_settings( + enabled_sample_metrics: set[SampledMetricName], + enabled_event_metrics: set[EventMetricName], +) -> SimulationSettings: + """A minimal `SimulationSettings` instance for unit tests.""" + return SimulationSettings( + total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, + enabled_sample_metrics=enabled_sample_metrics, + enabled_event_metrics=enabled_event_metrics, + ) + + +# --------------------------------------------------------------------------- +# Traffic profile +# --------------------------------------------------------------------------- + + +@pytest.fixture +def rqs_input() -> RqsGeneratorInput: + """`RqsGeneratorInput` with 1 user and 2 req/min for quick tests.""" + return RqsGeneratorInput( + avg_active_users=RVConfig(mean=1.0), + avg_request_per_minute_per_user=RVConfig(mean=2.0), + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, + ) + + +# --------------------------------------------------------------------------- +# Minimal topology (one client, no servers, no edges) +# --------------------------------------------------------------------------- + + +@pytest.fixture +def topology_minimal() -> TopologyGraph: + """Valid topology with a single client and zero servers/edges.""" + client = Client(id="client-1") + nodes = TopologyNodes(servers=[], client=client) + return TopologyGraph(nodes=nodes, edges=[]) + + +# --------------------------------------------------------------------------- +# Full simulation payload +# --------------------------------------------------------------------------- + + +@pytest.fixture +def payload_base( + rqs_input: RqsGeneratorInput, + sim_settings: SimulationSettings, + topology_minimal: TopologyGraph, +) -> SimulationPayload: + """End-to-end payload used by high-level simulation tests.""" + return SimulationPayload( + rqs_input=rqs_input, + topology_graph=topology_minimal, + sim_settings=sim_settings, + ) diff --git a/tests/unit/input_sructure/test_requests_generator_input.py b/tests/unit/input_sructure/test_requests_generator_input.py index 9fb49a5..39a0b2d 100644 --- a/tests/unit/input_sructure/test_requests_generator_input.py +++ b/tests/unit/input_sructure/test_requests_generator_input.py @@ -1,90 +1,82 @@ +"""Validation tests for RVConfig, RqsGeneratorInput and SimulationSettings.""" + +from __future__ import annotations + import pytest from pydantic import ValidationError from app.config.constants import Distribution, TimeDefaults from app.schemas.random_variables_config import RVConfig from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings + +# --------------------------------------------------------------------------- +# RVCONFIG +# --------------------------------------------------------------------------- -# -------------------------------------------------------------------------- -# TEST RANDOM VARIABLE CONFIGURATION -# -------------------------------------------------------------------------- def test_normal_sets_variance_to_mean() -> None: - """When distribution='normal' and variance is omitted, variance == mean.""" + """If variance is omitted with 'normal', it defaults to mean.""" cfg = RVConfig(mean=10, distribution=Distribution.NORMAL) assert cfg.variance == 10.0 def test_poisson_keeps_variance_none() -> None: - """When distribution='poisson' and variance is omitted, variance stays None.""" + """If variance is omitted with 'poisson', it remains None.""" cfg = RVConfig(mean=5, distribution=Distribution.POISSON) assert cfg.variance is None def test_explicit_variance_is_preserved() -> None: - """If the user supplies variance explicitly, it is preserved unchanged.""" + """An explicit variance value is not modified.""" cfg = RVConfig(mean=8, distribution=Distribution.NORMAL, variance=4) assert cfg.variance == 4.0 def test_mean_must_be_numeric() -> None: - """A non-numeric mean raises a ValidationError with our custom message.""" - with pytest.raises(ValidationError) as excinfo: + """A non numeric mean triggers a ValidationError.""" + with pytest.raises(ValidationError) as exc: RVConfig(mean="not a number", distribution=Distribution.POISSON) - # Check that at least one error refers to the 'mean' field - assert any(err["loc"] == ("mean",) for err in excinfo.value.errors()) - assert "mean must be a number" in excinfo.value.errors()[0]["msg"] + assert any(err["loc"] == ("mean",) for err in exc.value.errors()) def test_missing_mean_field() -> None: - """Omitting the mean field raises a 'field required' ValidationError.""" - with pytest.raises(ValidationError) as excinfo: - # Using model_validate avoids the constructor signature check + """Omitting mean raises a 'field required' ValidationError.""" + with pytest.raises(ValidationError) as exc: RVConfig.model_validate({"distribution": Distribution.NORMAL}) assert any( err["loc"] == ("mean",) and err["type"] == "missing" - for err in excinfo.value.errors() + for err in exc.value.errors() ) -def test_gaussian_sets_variance_to_mean() -> None: - """When distribution='gaussian' and variance is omitted, variance == mean.""" - cfg = RVConfig(mean=12.5, distribution=Distribution.NORMAL) - assert cfg.variance == pytest.approx(12.5) - def test_default_distribution_is_poisson() -> None: - """ - When distribution is omitted, it defaults to 'poisson' and - variance stays None. - """ + """If distribution is missing, it defaults to 'poisson'.""" cfg = RVConfig(mean=3.3) assert cfg.distribution == Distribution.POISSON assert cfg.variance is None def test_explicit_variance_kept_for_poisson() -> None: - """If the user supplies variance even for poisson, it is preserved.""" + """Variance is kept even when distribution is poisson.""" cfg = RVConfig(mean=4.0, distribution=Distribution.POISSON, variance=2.2) assert cfg.variance == pytest.approx(2.2) def test_invalid_distribution_raises() -> None: - """Supplying a non-supported distribution literal raises ValidationError.""" - with pytest.raises(ValidationError) as excinfo: + """An unsupported distribution literal raises ValidationError.""" + with pytest.raises(ValidationError): RVConfig(mean=5.0, distribution="not_a_dist") - errors = excinfo.value.errors() - # Only assert there is at least one error for the 'distribution' field: - assert any(e["loc"] == ("distribution",) for e in errors) +# --------------------------------------------------------------------------- +# RQSGENERATORINPUT - USER_SAMPLING_WINDOW +# --------------------------------------------------------------------------- -# -------------------------------------------------------------------------- -# TEST FIELD VALIDATOR USER SAMPLING WINDOW -# -------------------------------------------------------------------------- def test_default_user_sampling_window() -> None: - """When user_sampling_window is omitted, it defaults to USER_SAMPLING_WINDOW.""" + """If user_sampling_window is missing it defaults to the constant.""" inp = RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ @@ -96,47 +88,35 @@ def test_default_user_sampling_window() -> None: def test_explicit_user_sampling_window_kept() -> None: - """An explicit user_sampling_window value is preserved unchanged.""" - custom_window = 30 + """An explicit user_sampling_window is preserved.""" inp = RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, "distribution": Distribution.POISSON, }, - user_sampling_window=custom_window, + user_sampling_window=30, ) - assert inp.user_sampling_window == custom_window + assert inp.user_sampling_window == 30 def test_user_sampling_window_not_int_raises() -> None: - """A non-integer user_sampling_window raises a ValidationError.""" - with pytest.raises(ValidationError) as excinfo: - + """A non integer user_sampling_window raises ValidationError.""" + with pytest.raises(ValidationError): RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ "mean": 1.0, "distribution": Distribution.POISSON, }, - user_sampling_window="not-an-int", + user_sampling_window="not-int", ) - errors = excinfo.value.errors() - assert any(err["loc"] == ("user_sampling_window",) for err in errors) - - # Pydantic v2 wording - assert any("valid integer" in err["msg"] for err in errors) - - def test_user_sampling_window_above_max_raises() -> None: - """ - Passing user_sampling_window > MAX_USER_SAMPLING_WINDOW - must raise a ValidationError. - """ + """user_sampling_window above the max constant raises ValidationError.""" too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW + 1 - with pytest.raises(ValidationError) as excinfo: + with pytest.raises(ValidationError): RqsGeneratorInput( avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, avg_request_per_minute_per_user={ @@ -146,90 +126,33 @@ def test_user_sampling_window_above_max_raises() -> None: user_sampling_window=too_large, ) - errors = excinfo.value.errors() - assert any(err["loc"] == ("user_sampling_window",) for err in errors) - - expected_snippet = ( - f"less than or equal to {TimeDefaults.MAX_USER_SAMPLING_WINDOW}" - ) - assert any(expected_snippet in err["msg"] for err in errors) +# --------------------------------------------------------------------------- +# SIMULATIONSETTINGS - TOTAL_SIMULATION_TIME +# --------------------------------------------------------------------------- -# -------------------------------------------------------------------------- -# TEST FIELD VALIDATOR TOTAL SIMULATION TIME -# -------------------------------------------------------------------------- def test_default_total_simulation_time() -> None: - """When total_simulation_time is omitted, it defaults to SIMULATION_TIME.""" - inp = RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, - ) - assert inp.total_simulation_time == TimeDefaults.SIMULATION_TIME + """If total_simulation_time is missing it defaults to the constant.""" + settings = SimulationSettings() + assert settings.total_simulation_time == TimeDefaults.SIMULATION_TIME def test_explicit_total_simulation_time_kept() -> None: - """An explicit total_simulation_time value is preserved unchanged.""" - custom_time = 3_000 - inp = RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, - total_simulation_time=custom_time, - ) - assert inp.total_simulation_time == custom_time + """An explicit total_simulation_time is preserved.""" + settings = SimulationSettings(total_simulation_time=3_000) + assert settings.total_simulation_time == 3_000 def test_total_simulation_time_not_int_raises() -> None: - """A non-integer total_simulation_time raises a ValidationError.""" - with pytest.raises(ValidationError) as excinfo: - - RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, - total_simulation_time="three thousand", - ) - - errors = excinfo.value.errors() - assert any(err["loc"] == ("total_simulation_time",) for err in errors) - - # Pydantic v2 wording: “Input should be a valid integer” - assert any("valid integer" in err["msg"] for err in errors) - + """A non integer total_simulation_time raises ValidationError.""" + with pytest.raises(ValidationError): + SimulationSettings(total_simulation_time="three thousand") def test_total_simulation_time_below_minimum_raises() -> None: - """ - Passing total_simulation_time < MIN_SIMULATION_TIME - must raise a ValidationError. - """ + """A total_simulation_time below the minimum constant raises ValidationError.""" too_small = TimeDefaults.MIN_SIMULATION_TIME - 1 - with pytest.raises(ValidationError) as excinfo: - RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, - total_simulation_time=too_small, - ) - - errors = excinfo.value.errors() - # c'è almeno un errore sul campo giusto - assert any(err["loc"] == ("total_simulation_time",) for err in errors) - - expected_snippet = ( - f"greater than or equal to {TimeDefaults.MIN_SIMULATION_TIME}" - ) - assert any(expected_snippet in err["msg"] for err in errors) - - + with pytest.raises(ValidationError): + SimulationSettings(total_simulation_time=too_small) diff --git a/tests/unit/sampler/test_gaussian_poisson.py b/tests/unit/sampler/test_gaussian_poisson.py index b464007..c182376 100644 --- a/tests/unit/sampler/test_gaussian_poisson.py +++ b/tests/unit/sampler/test_gaussian_poisson.py @@ -1,94 +1,109 @@ -"""Unit tests for gaussian_poisson_sampling.""" +"""Unit-tests for `gaussian_poisson_sampling`.""" from __future__ import annotations import itertools from types import GeneratorType +from typing import TYPE_CHECKING -import numpy as np import pytest +from numpy.random import Generator, default_rng from app.config.constants import TimeDefaults -from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling +from app.core.event_samplers.gaussian_poisson import ( + gaussian_poisson_sampling, +) from app.schemas.random_variables_config import RVConfig from app.schemas.requests_generator_input import RqsGeneratorInput +if TYPE_CHECKING: + + from app.schemas.simulation_settings_input import SimulationSettings + # --------------------------------------------------------------------------- -# Fixture +# FIXTURES # --------------------------------------------------------------------------- @pytest.fixture -def base_input() -> RqsGeneratorInput: - """Return a minimal, valid RqsGeneratorInput for the Gaussian-Poisson sampler.""" +def rqs_cfg() -> RqsGeneratorInput: + """Minimal, valid RqsGeneratorInput for Gaussian-Poisson tests.""" return RqsGeneratorInput( avg_active_users=RVConfig( - mean=10.0, variance=4.0, distribution="normal", + mean=10.0, + variance=4.0, + distribution="normal", ), avg_request_per_minute_per_user=RVConfig(mean=30.0), - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, ) + # --------------------------------------------------------------------------- -# Basic behaviour +# BASIC BEHAVIOUR # --------------------------------------------------------------------------- -def test_returns_generator_type(base_input: RqsGeneratorInput) -> None: +def test_returns_generator_type( + rqs_cfg: RqsGeneratorInput, + sim_settings: SimulationSettings, + rng: Generator, +) -> None: """The function must return a generator object.""" - rng = np.random.default_rng(0) - gen = gaussian_poisson_sampling(base_input, rng=rng) + gen = gaussian_poisson_sampling(rqs_cfg, sim_settings, rng=rng) assert isinstance(gen, GeneratorType) -def test_generates_positive_gaps(base_input: RqsGeneratorInput) -> None: +def test_generates_positive_gaps( + rqs_cfg: RqsGeneratorInput, + sim_settings: SimulationSettings, +) -> None: """ With nominal parameters the sampler should emit at least a few positive - gaps and no gap must be non-positive. + gaps, and the cumulative time must stay below the horizon. """ - rng = np.random.default_rng(42) gaps: list[float] = list( - itertools.islice(gaussian_poisson_sampling(base_input, rng=rng), 1000), + itertools.islice( + gaussian_poisson_sampling(rqs_cfg, sim_settings, rng=default_rng(42)), + 1000, + ), ) - # At least one event is expected. - assert gaps - # No gap may be negative or zero. - assert all(gap > 0.0 for gap in gaps) - # The cumulative time of gaps must stay below the horizon. - assert sum(gaps) < base_input.total_simulation_time + assert gaps, "Expected at least one event" + assert all(g > 0.0 for g in gaps), "No gap may be ≤ 0" + assert sum(gaps) < sim_settings.total_simulation_time # --------------------------------------------------------------------------- -# Edge-case: zero users ⇒ no events +# EDGE CASE: ZERO USERS # --------------------------------------------------------------------------- def test_zero_users_produces_no_events( monkeypatch: pytest.MonkeyPatch, - base_input: RqsGeneratorInput, + rqs_cfg: RqsGeneratorInput, + sim_settings: SimulationSettings, ) -> None: """ - If every Gaussian draw returns 0 users, Λ == 0, - hence the generator must yield no events at all. + If every Gaussian draw returns 0 users, Λ == 0 and the generator must + yield no events at all. """ def fake_truncated_gaussian( mean: float, var: float, - rng: np.random.Generator, + rng: Generator, ) -> float: return 0.0 # force U = 0 - # Patch the helper so that it always returns 0 users. monkeypatch.setattr( "app.core.event_samplers.gaussian_poisson.truncated_gaussian_generator", fake_truncated_gaussian, ) - rng = np.random.default_rng(123) - gaps = list(gaussian_poisson_sampling(base_input, rng=rng)) + gaps: list[float] = list( + gaussian_poisson_sampling(rqs_cfg, sim_settings, rng=default_rng(123)), + ) assert gaps == [] # no events should be generated diff --git a/tests/unit/sampler/test_poisson_poisson.py b/tests/unit/sampler/test_poisson_poisson.py new file mode 100644 index 0000000..2fbbb9e --- /dev/null +++ b/tests/unit/sampler/test_poisson_poisson.py @@ -0,0 +1,124 @@ +"""Unit tests for `poisson_poisson_sampling`.""" + +from __future__ import annotations + +import itertools +import math +from types import GeneratorType +from typing import TYPE_CHECKING + +import pytest +from numpy.random import Generator, default_rng + +from app.config.constants import TimeDefaults +from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling +from app.schemas.random_variables_config import RVConfig +from app.schemas.requests_generator_input import RqsGeneratorInput + +if TYPE_CHECKING: + + from app.schemas.simulation_settings_input import SimulationSettings + + +@pytest.fixture +def rqs_cfg() -> RqsGeneratorInput: + """Return a minimal, valid RqsGeneratorInput for the sampler tests.""" + return RqsGeneratorInput( + avg_active_users={"mean": 1.0, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 60.0, "distribution": "poisson"}, + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, + ) + +# -------------------------------------------------------- +# BASIC SHAPE AND TYPE TESTS +# -------------------------------------------------------- + + +def test_sampler_returns_generator( + rqs_cfg: RqsGeneratorInput, + sim_settings: SimulationSettings, + rng: Generator, +) -> None: + """Function must return a generator object.""" + gen = poisson_poisson_sampling(rqs_cfg, sim_settings, rng=rng) + assert isinstance(gen, GeneratorType) + + +def test_all_gaps_are_positive( + rqs_cfg: RqsGeneratorInput, + sim_settings: SimulationSettings, +) -> None: + """Every yielded gap must be strictly positive.""" + gaps = list( + itertools.islice( + poisson_poisson_sampling(rqs_cfg, sim_settings, rng=default_rng(1)), + 1_000, + ), + ) + assert all(g > 0.0 for g in gaps) + + +# --------------------------------------------------------------------------- +# REPRODUCIBILITY WITH FIXED SEED +# --------------------------------------------------------------------------- + + +def test_sampler_is_reproducible_with_fixed_seed( + rqs_cfg: RqsGeneratorInput, + sim_settings: SimulationSettings, +) -> None: + """Same RNG seed must produce identical first N gaps.""" + seed = 42 + n_samples = 15 + + gaps_1 = list( + itertools.islice( + poisson_poisson_sampling(rqs_cfg, sim_settings, rng=default_rng(seed)), + n_samples, + ), + ) + gaps_2 = list( + itertools.islice( + poisson_poisson_sampling(rqs_cfg, sim_settings, rng=default_rng(seed)), + n_samples, + ), + ) + assert gaps_1 == gaps_2 + + +# --------------------------------------------------------------------------- +# EDGE CASE: ZERO USERS +# --------------------------------------------------------------------------- + + +def test_zero_users_produces_no_events( + sim_settings: SimulationSettings, +) -> None: + """If the mean user count is zero the generator must yield no events.""" + cfg_zero = RqsGeneratorInput( + avg_active_users=RVConfig(mean=0.0, distribution="poisson"), + avg_request_per_minute_per_user=RVConfig(mean=60.0, distribution="poisson"), + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, + ) + + gaps: list[float] = list( + poisson_poisson_sampling(cfg_zero, sim_settings, rng=default_rng(123)), + ) + assert gaps == [] + + +# --------------------------------------------------------------------------- +# CUMULATIVE TIME NEVER EXCEEDS THE HORIZON +# --------------------------------------------------------------------------- + + +def test_cumulative_time_never_exceeds_horizon( + rqs_cfg: RqsGeneratorInput, + sim_settings: SimulationSettings, +) -> None: + """Sum of gaps must stay below the simulation horizon.""" + gaps: list[float] = list( + poisson_poisson_sampling(rqs_cfg, sim_settings, rng=default_rng(7)), + ) + cum_time = math.fsum(gaps) + assert cum_time < sim_settings.total_simulation_time diff --git a/tests/unit/sampler/test_poisson_posson.py b/tests/unit/sampler/test_poisson_posson.py deleted file mode 100644 index 48d4f9a..0000000 --- a/tests/unit/sampler/test_poisson_posson.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Unit tests for the poisson_poisson_sampling generator.""" - -from __future__ import annotations - -import itertools -import math -from types import GeneratorType - -import numpy as np -import pytest - -from app.config.constants import TimeDefaults -from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling -from app.schemas.random_variables_config import RVConfig -from app.schemas.requests_generator_input import RqsGeneratorInput - - -@pytest.fixture -def base_input() -> RqsGeneratorInput: - """Return a minimal-valid RqsGeneratorInput for the sampler tests.""" - return RqsGeneratorInput( - # 1 average concurrent user … - avg_active_users={"mean": 1.0, "distribution": "poisson"}, - # … sending on average 60 req/min → 1 req/s - avg_request_per_minute_per_user={"mean": 60.0, "distribution": "poisson"}, - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, # 30 min - user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, # 60 s - ) - - -# --------------------------------------------------------------------- -# BASIC SHAPE / TYPE TESTS -# --------------------------------------------------------------------- - - -def test_sampler_returns_generator(base_input: RqsGeneratorInput) -> None: - """The function must return a real generator object.""" - rng = np.random.default_rng(0) - gen = poisson_poisson_sampling(base_input, rng=rng) - - assert isinstance(gen, GeneratorType) - - -def test_all_gaps_are_positive(base_input: RqsGeneratorInput) -> None: - """Every yielded inter-arrival gap Δt must be > 0.""" - rng = np.random.default_rng(1) - gaps: list[float] = list( - itertools.islice(poisson_poisson_sampling(base_input, rng=rng), 1_000), - ) - - # None of the first 1 000 gaps (if any) can be negative or zero - assert all(gap > 0.0 for gap in gaps) - - -# --------------------------------------------------------------------- -# REPRODUCIBILITY WITH FIXED RNG SEED -# --------------------------------------------------------------------- - - -def test_sampler_is_reproducible_with_fixed_seed(base_input: RqsGeneratorInput) -> None: - """Same seed ⇒ identical first N gaps.""" - seed = 42 - n_samples = 15 - - gaps_1 = list( - itertools.islice( - poisson_poisson_sampling( - base_input, rng=np.random.default_rng(seed), - ), - n_samples, - ), - ) - gaps_2 = list( - itertools.islice( - poisson_poisson_sampling( - base_input, rng=np.random.default_rng(seed), - ), - n_samples, - ), - ) - - assert gaps_1 == gaps_2 - - -# --------------------------------------------------------------------- -# EDGE-CASE: ZERO USERS ⇒ NO EVENTS -# --------------------------------------------------------------------- - - -def test_zero_users_produces_no_events(base_input: RqsGeneratorInput) -> None: - """ - With mean concurrent users == 0 the Poisson draw is almost surely 0, - so Λ = 0 and the generator should yield no events. - """ - input_data = RqsGeneratorInput( - avg_active_users=RVConfig(mean=0.0, distribution="poisson"), - avg_request_per_minute_per_user=RVConfig(mean=60.0, distribution="poisson"), - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, - user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, - ) - - rng = np.random.default_rng(123) - gaps = list(poisson_poisson_sampling(input_data, rng=rng)) - - assert gaps == [] # no events expected - -# --------------------------------------------------------------------- -# CUMULATIVE TIME ALWAYS < SIMULATION HORIZON -# --------------------------------------------------------------------- - - -def test_cumulative_time_never_exceeds_horizon(base_input: RqsGeneratorInput) -> None: - """ΣΔt (virtual clock) must stay strictly below total_simulation_time.""" - rng = np.random.default_rng(7) - gaps = list(poisson_poisson_sampling(base_input, rng=rng)) - - cum_time = math.fsum(gaps) - # Even if the virtual clock can jump when λ == 0, - # the summed gaps must never exceed the horizon. - assert cum_time < base_input.total_simulation_time diff --git a/tests/unit/simulation/test_requests_generator.py b/tests/unit/simulation/test_requests_generator.py index 9b77baf..fe72f2e 100644 --- a/tests/unit/simulation/test_requests_generator.py +++ b/tests/unit/simulation/test_requests_generator.py @@ -1,57 +1,48 @@ -"""Unit test to verify the behaviour of the rqs generator""" +"""Unit-tests for the requests generator and the SimPy runner. + +All common fixtures (`rng`, `rqs_input`, `sim_settings`, `payload_base`, …) +are defined once in *tests/conftest.py*. +This module focuses purely on behavioural checks. +""" from __future__ import annotations from types import GeneratorType from typing import TYPE_CHECKING -import numpy as np import pytest -from app.config.constants import TimeDefaults from app.core.simulation.requests_generator import requests_generator from app.core.simulation.simulation_run import run_simulation -from app.schemas.requests_generator_input import RqsGeneratorInput - -if TYPE_CHECKING: +if TYPE_CHECKING: # static-typing only from collections.abc import Iterator + from numpy.random import Generator + + from app.schemas.full_simulation_input import SimulationPayload + from app.schemas.requests_generator_input import RqsGeneratorInput from app.schemas.simulation_output import SimulationOutput + from app.schemas.simulation_settings_input import SimulationSettings -# -------------------------------------------------------------- -# TESTS INPUT -# -------------------------------------------------------------- - -@pytest.fixture -def base_input() -> RqsGeneratorInput: - """Return a RqsGeneratorInput with a 120-second simulation horizon.""" - return RqsGeneratorInput( - avg_active_users={"mean": 1.0}, - avg_request_per_minute_per_user={"mean": 2.0}, - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, - ) -# -------------------------------------------------------------- -# REQUESTS GENERATOR FUNCTION TESTS -# -------------------------------------------------------------- +# --------------------------------------------------------------------------- +# REQUESTS-GENERATOR - dispatcher tests +# --------------------------------------------------------------------------- + def test_default_requests_generator_uses_poisson_poisson_sampling( - base_input: RqsGeneratorInput, + rqs_input: RqsGeneratorInput, + sim_settings: SimulationSettings, + rng: Generator, ) -> None: - """ - Verify that when avg_active_users.distribution is the default 'poisson', - requests_generator returns an iterator whose code object is from - poisson_poisson_sampling. - """ - rng = np.random.default_rng(0) - gen = requests_generator(base_input, rng=rng) - # It must be a generator. - assert isinstance(gen, GeneratorType) + """Default distribution must map to *poisson_poisson_sampling*.""" + gen = requests_generator(rqs_input, sim_settings, rng=rng) - # Internally, it should call poisson_poisson_sampling. + assert isinstance(gen, GeneratorType) assert gen.gi_code.co_name == "poisson_poisson_sampling" + @pytest.mark.parametrize( ("dist", "expected_sampler"), [ @@ -62,136 +53,93 @@ def test_default_requests_generator_uses_poisson_poisson_sampling( def test_requests_generator_dispatches_to_correct_sampler( dist: str, expected_sampler: str, + rqs_input: RqsGeneratorInput, + sim_settings: SimulationSettings, + rng: Generator, ) -> None: - """ - Verify that requests_generator returns a generator whose code object - comes from the appropriate sampler function based on distribution: - - 'poisson' → poisson_poisson_sampling - - 'normal' → gaussian_poisson_sampling - """ - input_data = RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": dist}, - avg_request_per_minute_per_user={"mean": 1.0}, - total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, - ) - rng = np.random.default_rng(0) - gen = requests_generator(input_data, rng=rng) + """Dispatcher must select the sampler matching *dist*.""" + rqs_input.avg_active_users.distribution = dist # type: ignore[assignment] + gen = requests_generator(rqs_input, sim_settings, rng=rng) - # It must be a generator object. assert isinstance(gen, GeneratorType) - # Check which underlying sampler function produced it. assert gen.gi_code.co_name == expected_sampler -# -------------------------------------------------------------- -# REQUESTS GENERATOR INSIDE SIMULATION TESTS -# -------------------------------------------------------------- -def test_run_simulation_counts_events_up_to_horizon( - monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, +# --------------------------------------------------------------------------- +# SIMULATION-RUNNER - horizon handling +# --------------------------------------------------------------------------- + + +def _patch_generator( + monkeypatch: pytest.MonkeyPatch, + gaps: list[float], ) -> None: - """ - Verify that all events whose cumulative inter-arrival times - fall within the simulation horizon are counted. - For gaps [1, 2, 3, 4], cumulative times [1, 3, 6, 10] - yield 4 events by t=10. - """ - def fake_requests_generator_fixed( - data: RqsGeneratorInput, *, rng: np.random.Generator, + """Monkey-patch *requests_generator* with a deterministic gap sequence.""" + + def _fake( + data: RqsGeneratorInput, + config: SimulationSettings, # unused, keeps signature + *, + rng: Generator | None = None, ) -> Iterator[float]: - # Replace the complex Poisson-Poisson sampler with a deterministic sequence. - yield from [1.0, 2.0, 3.0, 4.0] + yield from gaps - # Monkeypatch the internal requests_generator to use our simple generator. monkeypatch.setattr( "app.core.simulation.simulation_run.requests_generator", - fake_requests_generator_fixed, + _fake, ) - # The rng argument is unused in this deterministic test. - rng = np.random.default_rng(42) - output: SimulationOutput = run_simulation(base_input, rng=rng) - assert output.total_requests["total_requests"] == 4 - # The returned metrics should reflect the input means as strings. - assert output.metric_2 == str(base_input.avg_request_per_minute_per_user.mean) - assert output.metric_n == str(base_input.avg_active_users.mean) - - -def test_run_simulation_includes_event_at_exact_horizon( - monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, +def test_run_simulation_counts_events_up_to_horizon( + monkeypatch: pytest.MonkeyPatch, + payload_base: SimulationPayload, + rng: Generator, ) -> None: - """ - Confirm that an event scheduled exactly at the simulation horizon - is not processed, since SimPy stops at t == horizon. - """ - def fake_generator_at_horizon( - data: RqsGeneratorInput, *, rng: np.random.Generator, - ) -> Iterator[float]: + """All events with cumulative time ≤ horizon must be counted.""" + _patch_generator(monkeypatch, gaps=[1.0, 2.0, 3.0, 4.0]) - # mypy assertion, pydantic guaranteed - assert base_input.total_simulation_time is not None - # Yield a single event at exactly t == simulation_time. - yield float(base_input.total_simulation_time) + output: SimulationOutput = run_simulation(payload_base, rng=rng) - monkeypatch.setattr( - "app.core.simulation.simulation_run.requests_generator", - fake_generator_at_horizon, + assert output.total_requests["total_requests"] == 4 + assert output.metric_2 == str( + payload_base.rqs_input.avg_request_per_minute_per_user.mean, ) + assert output.metric_n == str(payload_base.rqs_input.avg_active_users.mean) - rng = np.random.default_rng(123) - output: SimulationOutput = run_simulation(base_input, rng=rng) - # SimPy does not execute events scheduled exactly at the stop time. +def test_run_simulation_skips_event_at_exact_horizon( + monkeypatch: pytest.MonkeyPatch, + payload_base: SimulationPayload, + rng: Generator, +) -> None: + """An event scheduled exactly at *t == horizon* is ignored.""" + horizon = payload_base.sim_settings.total_simulation_time + _patch_generator(monkeypatch, gaps=[float(horizon)]) + + output: SimulationOutput = run_simulation(payload_base, rng=rng) assert output.total_requests["total_requests"] == 0 def test_run_simulation_excludes_event_beyond_horizon( - monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, + monkeypatch: pytest.MonkeyPatch, + payload_base: SimulationPayload, + rng: Generator, ) -> None: - """ - Ensure that events scheduled after the simulation horizon - are not counted. - """ - def fake_generator_beyond_horizon( - data: RqsGeneratorInput, *, rng: np.random.Generator, - ) -> Iterator[float]: - - # mypy assertion, pydantic guaranteed - assert base_input.total_simulation_time is not None - # Yield a single event just beyond the horizon. - yield float(base_input.total_simulation_time) + 0.1 - - monkeypatch.setattr( - "app.core.simulation.simulation_run.requests_generator", - fake_generator_beyond_horizon, - ) - - rng = np.random.default_rng(999) - output: SimulationOutput = run_simulation(base_input, rng=rng) + """Events strictly after the horizon must not be counted.""" + horizon = payload_base.sim_settings.total_simulation_time + _patch_generator(monkeypatch, gaps=[float(horizon) + 0.1]) + output: SimulationOutput = run_simulation(payload_base, rng=rng) assert output.total_requests["total_requests"] == 0 def test_run_simulation_zero_events_when_generator_empty( - monkeypatch: pytest.MonkeyPatch, base_input: RqsGeneratorInput, + monkeypatch: pytest.MonkeyPatch, + payload_base: SimulationPayload, + rng: Generator, ) -> None: - """ - Check that run_simulation reports zero requests when no - inter-arrival times are yielded. - """ - def fake_generator_empty( - data: RqsGeneratorInput, *, rng: np.random.Generator, - ) -> Iterator[float]: - # Empty generator yields nothing. - if False: - yield # pragma: no cover - - monkeypatch.setattr( - "app.core.simulation.simulation_run.requests_generator", - fake_generator_empty, - ) - - rng = np.random.default_rng(2025) - output: SimulationOutput = run_simulation(base_input, rng=rng) + """No gaps => no requests counted.""" + _patch_generator(monkeypatch, gaps=[]) + output: SimulationOutput = run_simulation(payload_base, rng=rng) assert output.total_requests["total_requests"] == 0 From 35d507eb6b683f91aff06032efbad1f5c57a4947 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 18 Jul 2025 14:56:11 +0200 Subject: [PATCH 16/52] Features/rqs generator runtime (#5) * definition of state and RqsGeneratorRuntime * defined edge runtime and more central logic for sampler * minor changes * minor changes * minor bug fixed * pytest adapted to the new structure, added pytest for rqs_state * Update src/app/core/runtime/rqs_generator.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update src/app/core/runtime/rqs_generator.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update src/app/core/runtime/edge.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update src/app/core/runtime/edge.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update tests/unit/runtime/test_requests_generator.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update src/app/core/event_samplers/common_helpers.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update src/app/core/event_samplers/common_helpers.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update src/app/core/event_samplers/common_helpers.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * minor changes --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- src/app/api/simulation.py | 14 -- src/app/config/constants.py | 15 +- src/app/config/rqs_state.py | 50 +++++ src/app/core/event_samplers/common_helpers.py | 67 +++++- .../core/event_samplers/gaussian_poisson.py | 4 +- .../core/event_samplers/poisson_poisson.py | 4 +- .../dictionary_metrics.py} | 0 .../requests_generator.py | 10 +- src/app/core/runtime/__init__.py | 1 + src/app/core/runtime/edge.py | 69 ++++++ src/app/core/runtime/rqs_generator.py | 117 ++++++++++ .../core/{simulation => }/simulation_run.py | 2 +- src/app/schemas/random_variables_config.py | 11 +- src/app/schemas/requests_generator_input.py | 34 ++- .../system_topology_schema/endpoint_schema.py | 48 ++-- .../full_system_topology_schema.py | 33 ++- .../input_sructure/test_endpoint_input.py | 72 +++--- .../test_full_topology_input.py | 104 ++++++--- .../test_requests_generator_input.py | 132 ++++++++--- tests/unit/runtime/test_edge | 136 ++++++++++++ tests/unit/runtime/test_requests_generator.py | 151 +++++++++++++ tests/unit/sampler/test_sampler_helper.py | 208 ++++++++++++------ .../simulation/test_requests_generator.py | 145 ------------ tests/unit/test_state.py | 47 ++++ 24 files changed, 1085 insertions(+), 389 deletions(-) create mode 100644 src/app/config/rqs_state.py rename src/app/core/{helpers.py => helpers/dictionary_metrics.py} (100%) rename src/app/core/{simulation => helpers}/requests_generator.py (79%) create mode 100644 src/app/core/runtime/__init__.py create mode 100644 src/app/core/runtime/edge.py create mode 100644 src/app/core/runtime/rqs_generator.py rename src/app/core/{simulation => }/simulation_run.py (95%) create mode 100644 tests/unit/runtime/test_edge create mode 100644 tests/unit/runtime/test_requests_generator.py delete mode 100644 tests/unit/simulation/test_requests_generator.py create mode 100644 tests/unit/test_state.py diff --git a/src/app/api/simulation.py b/src/app/api/simulation.py index 73984d5..2775dcc 100644 --- a/src/app/api/simulation.py +++ b/src/app/api/simulation.py @@ -1,18 +1,4 @@ """"Api to simulate the process""" -import numpy as np -from fastapi import APIRouter - -from app.core.simulation.simulation_run import run_simulation -from app.schemas.full_simulation_input import SimulationPayload -from app.schemas.simulation_output import SimulationOutput - -router = APIRouter() - -@router.post("/simulation") -async def event_loop_simulation(input_data: SimulationPayload) -> SimulationOutput: - """Run the simulation and return aggregate KPIs.""" - rng = np.random.default_rng() - return run_simulation(input_data, rng=rng) diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 498e8b7..c611697 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -49,6 +49,7 @@ class Distribution(StrEnum): NORMAL = "normal" LOG_NORMAL = "log_normal" EXPONENTIAL = "exponential" + UNIFORM = "uniform" # ====================================================================== # CONSTANTS FOR ENDPOINT STEP DEFINITION (REQUEST-HANDLER) @@ -123,7 +124,7 @@ class EndpointStepRAM(StrEnum): RAM = "ram" -class Metrics(StrEnum): +class StepOperation(StrEnum): """ Keys used inside the ``metrics`` dictionary of a *step*. @@ -153,6 +154,17 @@ class ServerResourcesDefaults: MINIMUM_RAM_MB = 256 DB_CONNECTION_POOL = None +# ====================================================================== +# CONSTANTS FOR NETWORK PARAMETERS +# ====================================================================== + +class NetworkParameters: + """parameters for the network""" + + MIN_DROPOUT_RATE = 0.0 + DROPOUT_RATE = 0.01 + MAX_DROPOUT_RATE = 1.0 + # ====================================================================== # CONSTANTS FOR THE MACRO-TOPOLOGY GRAPH # ====================================================================== @@ -165,6 +177,7 @@ class SystemNodes(StrEnum): resources (CPU cores, DB pool, etc.). """ + GENERATOR = "generator" SERVER = "server" CLIENT = "client" LOAD_BALANCER = "load_balancer" diff --git a/src/app/config/rqs_state.py b/src/app/config/rqs_state.py new file mode 100644 index 0000000..dec9200 --- /dev/null +++ b/src/app/config/rqs_state.py @@ -0,0 +1,50 @@ +""" +defining a state in a one to one correspondence +with the requests generated that will go through +all the node necessary to accomplish the user request +""" + +from __future__ import annotations + +from dataclasses import dataclass, field + + +@dataclass +class RequestState: + """ + State object carried by each request through the simulation. + + Attributes: + id: Unique identifier of the request. + t0: Timestamp (simulated env.now) when the request was generated. + history: List of hop records, each noting a node/edge visit. + finish_time: Timestamp when the requests is satisfied + + """ + + id: int # Unique request identifier + initial_time: float # Generation timestamp (env.now) + finish_time: float | None = None # a requests might be dropped + history: list[str] = field(default_factory=list) # Trace of hops + + def record_hop(self, node_name: str, now: float) -> None: + """ + Append a record of visiting a node or edge. + + Args: + node_name: Name of the node or edge being recorded. + now: register the time of the operation + + """ + # Record hop as "NodeName@Timestamp" + self.history.append(f"{node_name}@{now:.3f}") + + @property + def latency(self) -> float | None: + """ + Return the total time in the system (finish_time - initial_time), + or None if the request hasn't completed yet. + """ + if self.finish_time is None: + return None + return self.finish_time - self.initial_time diff --git a/src/app/core/event_samplers/common_helpers.py b/src/app/core/event_samplers/common_helpers.py index 0f80870..e565a20 100644 --- a/src/app/core/event_samplers/common_helpers.py +++ b/src/app/core/event_samplers/common_helpers.py @@ -3,31 +3,74 @@ import numpy as np +from app.config.constants import Distribution +from app.schemas.random_variables_config import RVConfig -def uniform_variable_generator(rng: np.random.Generator | None = None) -> float: - """Return U~Uniform(0, 1).""" - rng = rng or np.random.default_rng() - return float(rng.random()) +def uniform_variable_generator(rng: np.random.Generator) -> float: + """Return U~Uniform(0, 1).""" + # rng is guaranteed to be a valid np.random.Generator due to the type signature. + return rng.random() def poisson_variable_generator( mean: float, - rng: np.random.Generator | None = None, -) -> int: + rng: np.random.Generator, +) -> float: """Return a Poisson-distributed integer with expectation *mean*.""" - rng = rng or np.random.default_rng() - return int(rng.poisson(mean)) - + return rng.poisson(mean) def truncated_gaussian_generator( mean: float, variance: float, rng: np.random.Generator, -) -> int: +) -> float: """ Generate a Normal-distributed variable with mean and variance """ - rng = rng or np.random.default_rng() value = rng.normal(mean, variance) - return max(0, int(value)) + return max(0.0, value) + +def lognormal_variable_generator( + mean: float, + variance: float, + rng: np.random.Generator, +) -> float: + """Return a Poisson-distributed floateger with expectation *mean*.""" + return rng.lognormal(mean, variance) + +def exponential_variable_generator( + mean: float, + rng: np.random.Generator, +) -> float: + """Return an exponentially-distributed float with mean *mean*.""" + return float(rng.exponential(mean)) + +def general_sampler(random_variable: RVConfig, rng: np.random.Generator) -> float: + """Sample a number according to the distribution described in `random_variable`.""" + dist = random_variable.distribution + mean = random_variable.mean + + match dist: + case Distribution.UNIFORM: + + assert random_variable.variance is None + return uniform_variable_generator(rng) + + case _: + + variance = random_variable.variance + assert variance is not None + + match dist: + case Distribution.NORMAL: + return truncated_gaussian_generator(mean, variance, rng) + case Distribution.LOG_NORMAL: + return lognormal_variable_generator(mean, variance, rng) + case Distribution.POISSON: + return float(poisson_variable_generator(mean, rng)) + case Distribution.EXPONENTIAL: + return exponential_variable_generator(mean, rng) + case _: + msg = f"Unsupported distribution: {dist}" + raise ValueError(msg) diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/core/event_samplers/gaussian_poisson.py index 0b7818c..f9cc401 100644 --- a/src/app/core/event_samplers/gaussian_poisson.py +++ b/src/app/core/event_samplers/gaussian_poisson.py @@ -24,7 +24,7 @@ def gaussian_poisson_sampling( input_data: RqsGeneratorInput, sim_settings: SimulationSettings, *, - rng: np.random.Generator | None = None, + rng: np.random.Generator, ) -> Generator[float, None, None]: """ Yield inter-arrival gaps (seconds) for the compound Gaussian-Poisson process. @@ -39,8 +39,6 @@ def gaussian_poisson_sampling( Δt ~ Exponential(Λ) using inverse-CDF. 4. Stop once the virtual clock exceeds *total_simulation_time*. """ - rng = rng or np.random.default_rng() - simulation_time = sim_settings.total_simulation_time user_sampling_window = input_data.user_sampling_window diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/core/event_samplers/poisson_poisson.py index 1566f90..1d4787f 100644 --- a/src/app/core/event_samplers/poisson_poisson.py +++ b/src/app/core/event_samplers/poisson_poisson.py @@ -21,7 +21,7 @@ def poisson_poisson_sampling( input_data: RqsGeneratorInput, sim_settings: SimulationSettings, *, - rng: np.random.Generator | None = None, + rng: np.random.Generator, ) -> Generator[float, None, None]: """ Yield inter-arrival gaps (seconds) for the compound Poisson-Poisson process. @@ -36,8 +36,6 @@ def poisson_poisson_sampling( Δt ~ Exponential(Λ) using inverse-CDF. 4. Stop once the virtual clock exceeds *total_simulation_time*. """ - rng = rng or np.random.default_rng() - simulation_time = sim_settings.total_simulation_time user_sampling_window = input_data.user_sampling_window diff --git a/src/app/core/helpers.py b/src/app/core/helpers/dictionary_metrics.py similarity index 100% rename from src/app/core/helpers.py rename to src/app/core/helpers/dictionary_metrics.py diff --git a/src/app/core/simulation/requests_generator.py b/src/app/core/helpers/requests_generator.py similarity index 79% rename from src/app/core/simulation/requests_generator.py rename to src/app/core/helpers/requests_generator.py index 1b177e7..2d04bef 100644 --- a/src/app/core/simulation/requests_generator.py +++ b/src/app/core/helpers/requests_generator.py @@ -1,6 +1,10 @@ """ -Continuous-time event sampling for the Poisson-Poisson -and Gaussian-Poisson workload model. +SimPy process that generates user requests at stochastic intervals. + +This node samples inter-arrival times according to the configured +distribution (Gaussian-Poisson or Poisson-Poisson), constructs a +RequestState for each new request, records its origin hop, and +immediately pushes it into the next pipeline stage via an EdgeRuntime. """ from __future__ import annotations @@ -24,7 +28,7 @@ def requests_generator( input_data: RqsGeneratorInput, sim_settings: SimulationSettings, *, - rng: np.random.Generator | None = None, + rng: np.random.Generator, ) -> Generator[float, None, None]: """ Return an iterator of inter-arrival gaps (seconds) according to the model diff --git a/src/app/core/runtime/__init__.py b/src/app/core/runtime/__init__.py new file mode 100644 index 0000000..fdc562a --- /dev/null +++ b/src/app/core/runtime/__init__.py @@ -0,0 +1 @@ +"""module for the runtime folder""" diff --git a/src/app/core/runtime/edge.py b/src/app/core/runtime/edge.py new file mode 100644 index 0000000..4f22608 --- /dev/null +++ b/src/app/core/runtime/edge.py @@ -0,0 +1,69 @@ +""" +Unidirectional link that simulates message transmission between nodes. +Encapsulates network behavior—latency sampling (LogNormal, Exponential, etc.), +drop probability, and optional connection-pool contention—by exposing a +`send(msg)` method. Each `send` call schedules a SimPy subprocess that +waits the sampled delay (and any resource wait) before delivering the +message to the target node's inbox. +""" +from collections.abc import Generator +from typing import TYPE_CHECKING + +import numpy as np +import simpy + +from app.config.rqs_state import RequestState +from app.core.event_samplers.common_helpers import general_sampler +from app.schemas.system_topology_schema.full_system_topology_schema import Edge + +if TYPE_CHECKING: + from app.schemas.random_variables_config import RVConfig + + + +class EdgeRuntime: + """definining the logic to handle the edges during the simulation""" + + def __init__( + self, + *, + env: simpy.Environment, + edge_config: Edge, + rng: np.random.Generator | None = None, + target_box: simpy.Store, + ) -> None: + """Definition of the instance attributes""" + self.env = env + self.edge_config = edge_config + self.target_box = target_box + self.rng = rng or np.random.default_rng() + + def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: + """Function to deliver the state to the next node""" + # extract the random variables defining the latency of the edge + random_variable: RVConfig = self.edge_config.latency + + uniform_variable = self.rng.uniform() + if uniform_variable < self.edge_config.dropout_rate: + state.finish_time = self.env.now + state.record_hop(f"{self.edge_config.id}-dropped", state.finish_time) + return + + transit_time = general_sampler(random_variable, self.rng) + yield self.env.timeout(transit_time) + state.record_hop(self.edge_config.id, self.env.now) + yield self.target_box.put(state) + + + def transport(self, state: RequestState) -> simpy.Process: + """ + Called by the upstream node. Immediately spins off a SimPy process + that will handle drop + delay + delivery of `state`. + """ + return self.env.process(self._deliver(state)) + + + + + + diff --git a/src/app/core/runtime/rqs_generator.py b/src/app/core/runtime/rqs_generator.py new file mode 100644 index 0000000..5611c85 --- /dev/null +++ b/src/app/core/runtime/rqs_generator.py @@ -0,0 +1,117 @@ +""" +definition of the class representing the rqs generator +that will be passed as a process in the simpy simulation +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from app.config.constants import Distribution, SystemNodes +from app.config.rqs_state import RequestState +from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling +from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling + +if TYPE_CHECKING: + + from collections.abc import Generator + + import simpy + + from app.core.runtime.edge import EdgeRuntime + from app.schemas.requests_generator_input import RqsGeneratorInput + from app.schemas.simulation_settings_input import SimulationSettings + + +class RqsGeneratorRuntime: + """ + A “node” that produces request contexts at stochastic inter-arrival times + and immediately pushes them down the pipeline via an EdgeRuntime. + """ + + def __init__( + self, + env: simpy.Environment, + out_edge: EdgeRuntime, + rqs_generator_data: RqsGeneratorInput, + sim_settings: SimulationSettings, + *, + rng: np.random.Generator | None = None, + ) -> None: + """ + Definition of the instance attributes for the RqsGeneratorRuntime + + Args: + env (simpy.Environment): environment for the simulation + out_edge (EdgeRuntime): edge connecting this node with the next one + rqs_generator_data (RqsGeneratorInput): data do define the sampler + sim_settings (SimulationSettings): settings to start the simulation + rng (np.random.Generator | None, optional): random variable generator. + + """ + self.rqs_generator_data = rqs_generator_data + self.sim_settings = sim_settings + self.rng = rng or np.random.default_rng() + self.out_edge = out_edge + self.env = env + self.id_counter = 0 + + + def _next_id(self) -> int: + self.id_counter += 1 + return self.id_counter + + + def _requests_generator(self) -> Generator[float, None, None]: + """ + Return an iterator of inter-arrival gaps (seconds) according to the model + chosen in *input_data*. + + Notes + ----- + * If ``avg_active_users.distribution`` is ``"gaussian"`` or ``"normal"``, + the Gaussian-Poisson sampler is used. + * Otherwise the default Poisson-Poisson sampler is returned. + + """ + dist = self.rqs_generator_data.avg_active_users.distribution + + if dist == Distribution.NORMAL: + #Gaussian-Poisson model + return gaussian_poisson_sampling( + input_data=self.rqs_generator_data, + sim_settings=self.sim_settings, + rng=self.rng, + + ) + + # Poisson + Poisson + return poisson_poisson_sampling( + input_data=self.rqs_generator_data, + sim_settings=self.sim_settings, + rng=self.rng, + ) + + def _event_arrival(self) -> Generator[simpy.Event, None, None]: + """Simulating the process of event generation""" + time_gaps = self._requests_generator() + + for gap in time_gaps: + yield self.env.timeout(gap) + + state = RequestState( + id=self._next_id(), + initial_time=self.env.now, + + ) + state.record_hop(SystemNodes.GENERATOR, self.env.now) + # transport is a method of the edge runtime + # which define the step of how the state is moving + # from one node to another + self.out_edge.transport(state) + + def run(self) -> simpy.Process: + """Passing the structure as a simpy process""" + return self.env.process(self._event_arrival()) diff --git a/src/app/core/simulation/simulation_run.py b/src/app/core/simulation_run.py similarity index 95% rename from src/app/core/simulation/simulation_run.py rename to src/app/core/simulation_run.py index 1aea997..c6159ca 100644 --- a/src/app/core/simulation/simulation_run.py +++ b/src/app/core/simulation_run.py @@ -6,7 +6,7 @@ import simpy -from app.core.simulation.requests_generator import requests_generator +from app.core.helpers.requests_generator import requests_generator from app.schemas.simulation_output import SimulationOutput if TYPE_CHECKING: diff --git a/src/app/schemas/random_variables_config.py b/src/app/schemas/random_variables_config.py index 8d17c5f..b09ed13 100644 --- a/src/app/schemas/random_variables_config.py +++ b/src/app/schemas/random_variables_config.py @@ -15,7 +15,7 @@ class RVConfig(BaseModel): @field_validator("mean", mode="before") def ensure_mean_is_numeric( cls, # noqa: N805 - v: object, + v: float, ) -> float: """Ensure `mean` is numeric, then coerce to float.""" err_msg = "mean must be a number (int or float)" @@ -25,7 +25,12 @@ def ensure_mean_is_numeric( @model_validator(mode="after") # type: ignore[arg-type] def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 - """Set variance = mean when distribution == 'normal' and variance is missing.""" - if model.variance is None and model.distribution == Distribution.NORMAL: + """Set variance = mean when distribution require and variance is missing.""" + needs_variance: set[Distribution] = { + Distribution.NORMAL, + Distribution.LOG_NORMAL, + } + + if model.variance is None and model.distribution in needs_variance: model.variance = model.mean return model diff --git a/src/app/schemas/requests_generator_input.py b/src/app/schemas/requests_generator_input.py index f56a4f0..35ff361 100644 --- a/src/app/schemas/requests_generator_input.py +++ b/src/app/schemas/requests_generator_input.py @@ -1,9 +1,9 @@ """Define the schemas for the simulator""" -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator -from app.config.constants import TimeDefaults +from app.config.constants import Distribution, TimeDefaults from app.schemas.random_variables_config import RVConfig @@ -24,4 +24,34 @@ class RqsGeneratorInput(BaseModel): ), ) + @field_validator("avg_request_per_minute_per_user", mode="after") + def ensure_avg_request_is_poisson( + cls, # noqa: N805 + v: RVConfig, + ) -> RVConfig: + """ + Force the distribution for the rqs generator to be poisson + at the moment we have a joint sampler just for the poisson-poisson + and gaussian-poisson case + """ + if v.distribution != Distribution.POISSON: + msg = "At the moment the variable avg request must be Poisson" + raise ValueError(msg) + return v + + @field_validator("avg_active_users", mode="after") + def ensure_avg_user_is_poisson_or_gaussian( + cls, # noqa: N805 + v: RVConfig, + ) -> RVConfig: + """ + Force the distribution for the rqs generator to be poisson + at the moment we have a joint sampler just for the poisson-poisson + and gaussian-poisson case + """ + if v.distribution not in {Distribution.POISSON, Distribution.NORMAL}: + msg = "At the moment the variable active user must be Poisson or Gaussian" + raise ValueError(msg) + return v + diff --git a/src/app/schemas/system_topology_schema/endpoint_schema.py b/src/app/schemas/system_topology_schema/endpoint_schema.py index abe53da..8933dab 100644 --- a/src/app/schemas/system_topology_schema/endpoint_schema.py +++ b/src/app/schemas/system_topology_schema/endpoint_schema.py @@ -12,7 +12,7 @@ EndpointStepCPU, EndpointStepIO, EndpointStepRAM, - Metrics, + StepOperation, ) @@ -23,58 +23,58 @@ class Step(BaseModel): """ kind: EndpointStepIO | EndpointStepCPU | EndpointStepRAM - step_metrics: dict[Metrics, PositiveFloat | PositiveInt] + step_operation: dict[StepOperation, PositiveFloat | PositiveInt] - @field_validator("step_metrics", mode="before") + @field_validator("step_operation", mode="before") def ensure_non_empty( cls, # noqa: N805 - v: dict[Metrics, PositiveFloat | PositiveInt], - ) -> dict[Metrics, PositiveFloat | PositiveInt]: - """Ensure the dict step metrics exist""" + v: dict[StepOperation, PositiveFloat | PositiveInt], + ) -> dict[StepOperation, PositiveFloat | PositiveInt]: + """Ensure the dict step operation exist""" if not v: - msg = "step_metrics cannot be empty" + msg = "step_operation cannot be empty" raise ValueError(msg) return v @model_validator(mode="after") # type: ignore[arg-type] - def ensure_coherence_kind_metrics( + def ensure_coherence_type_operation( cls, # noqa: N805 model: "Step", ) -> "Step": """ - Validation to couple kind and metrics only when they are + Validation to couple kind and operation only when they are valid for example ram cannot have associated a cpu time """ - metrics_keys = set(model.step_metrics) + operation_keys = set(model.step_operation) # Control of the length of the set to be sure only on key is passed - if len(metrics_keys) != 1: - msg = "step_metrics must contain exactly one entry" + if len(operation_keys) != 1: + msg = "step_operation must contain exactly one entry" raise ValueError(msg) - # Coherence CPU bound operation and metric + # Coherence CPU bound operation and operation if isinstance(model.kind, EndpointStepCPU): - if metrics_keys != {Metrics.CPU_TIME}: + if operation_keys != {StepOperation.CPU_TIME}: msg = ( - "The metric to quantify a CPU BOUND step" - f"must be {Metrics.CPU_TIME}" + "The operation to quantify a CPU BOUND step" + f"must be {StepOperation.CPU_TIME}" ) raise ValueError(msg) - # Coherence RAM operation and metric + # Coherence RAM operation and operation elif isinstance(model.kind, EndpointStepRAM): - if metrics_keys != {Metrics.NECESSARY_RAM}: + if operation_keys != {StepOperation.NECESSARY_RAM}: msg = ( - "The metric to quantify a RAM step" - f"must be {Metrics.NECESSARY_RAM}" + "The operation to quantify a RAM step" + f"must be {StepOperation.NECESSARY_RAM}" ) raise ValueError(msg) - # Coherence I/O operation and metric - elif metrics_keys != {Metrics.IO_WAITING_TIME}: + # Coherence I/O operation and operation + elif operation_keys != {StepOperation.IO_WAITING_TIME}: msg = ( - "The metric to quantify an I/O step" - f"must be {Metrics.IO_WAITING_TIME}" + "The operation to quantify an I/O step" + f"must be {StepOperation.IO_WAITING_TIME}" ) raise ValueError(msg) diff --git a/src/app/schemas/system_topology_schema/full_system_topology_schema.py b/src/app/schemas/system_topology_schema/full_system_topology_schema.py index b53f08e..ddbac36 100644 --- a/src/app/schemas/system_topology_schema/full_system_topology_schema.py +++ b/src/app/schemas/system_topology_schema/full_system_topology_schema.py @@ -6,6 +6,8 @@ one structure to another """ +from collections import Counter + from pydantic import ( BaseModel, ConfigDict, @@ -16,6 +18,7 @@ ) from app.config.constants import ( + NetworkParameters, ServerResourcesDefaults, SystemEdges, SystemNodes, @@ -126,8 +129,10 @@ def unique_ids( ) -> "TopologyNodes": """Check that all id are unique""" ids = [server.id for server in model.servers] + [model.client.id] - if len(ids) != len(set(ids)): - msg = "Node ids must be unique" + counter = Counter(ids) + duplicate = [node_id for node_id, value in counter.items() if value > 1] + if duplicate: + msg = f"The following node ids are duplicate {duplicate}" raise ValueError(msg) return model @@ -159,11 +164,21 @@ class Edge(BaseModel): """ + id: str source: str target: str latency: RVConfig probability: float = Field(1.0, ge=0.0, le=1.0) edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION + dropout_rate: float = Field( + NetworkParameters.DROPOUT_RATE, + ge = NetworkParameters.MIN_DROPOUT_RATE, + le = NetworkParameters.MAX_DROPOUT_RATE, + description=( + "for each nodes representing a network we define" + "a probability to drop the request" + ), + ) @model_validator(mode="after") # type: ignore[arg-type] def check_src_trgt_different(cls, model: "Edge") -> "Edge": # noqa: N805 @@ -188,6 +203,20 @@ class TopologyGraph(BaseModel): nodes: TopologyNodes edges: list[Edge] + @model_validator(mode="after") # type: ignore[arg-type] + def unique_ids( + cls, # noqa: N805 + model: "TopologyGraph", + ) -> "TopologyGraph": + """Check that all id are unique""" + counter = Counter(edge.id for edge in model.edges) + duplicate = [edge_id for edge_id, value in counter.items() if value > 1] + if duplicate: + msg = f"There are multiple edges with the following ids {duplicate}" + raise ValueError(msg) + return model + + @model_validator(mode="after") # type: ignore[arg-type] def edge_refs_valid(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 """ diff --git a/tests/unit/input_sructure/test_endpoint_input.py b/tests/unit/input_sructure/test_endpoint_input.py index 7e166dd..fc08b5e 100644 --- a/tests/unit/input_sructure/test_endpoint_input.py +++ b/tests/unit/input_sructure/test_endpoint_input.py @@ -9,7 +9,7 @@ EndpointStepCPU, EndpointStepIO, EndpointStepRAM, - Metrics, + StepOperation, ) from app.schemas.system_topology_schema.endpoint_schema import Endpoint, Step @@ -21,7 +21,7 @@ def cpu_step(value: float = 0.1) -> Step: """Return a minimal valid CPU-bound Step.""" return Step( kind=EndpointStepCPU.CPU_BOUND_OPERATION, - step_metrics={Metrics.CPU_TIME: value}, + step_operation={StepOperation.CPU_TIME: value}, ) @@ -29,7 +29,7 @@ def ram_step(value: int = 128) -> Step: """Return a minimal valid RAM Step.""" return Step( kind=EndpointStepRAM.RAM, - step_metrics={Metrics.NECESSARY_RAM: value}, + step_operation={StepOperation.NECESSARY_RAM: value}, ) @@ -37,7 +37,7 @@ def io_step(value: float = 0.05) -> Step: """Return a minimal valid I/O Step.""" return Step( kind=EndpointStepIO.WAIT, - step_metrics={Metrics.IO_WAITING_TIME: value}, + step_operation={StepOperation.IO_WAITING_TIME: value}, ) @@ -45,22 +45,25 @@ def io_step(value: float = 0.05) -> Step: # Positive test cases # --------------------------------------------------------------------------- # def test_valid_cpu_step() -> None: - """Test that a CPU step with correct 'cpu_time' metric passes validation.""" + """Test that a CPU step with correct 'cpu_time' operation passes validation.""" step = cpu_step() - # The metric value must match the input - assert step.step_metrics[Metrics.CPU_TIME] == 0.1 + # The operation value must match the input + assert step.step_operation[StepOperation.CPU_TIME] == 0.1 def test_valid_ram_step() -> None: - """Test that a RAM step with correct 'necessary_ram' metric passes validation.""" + """Test that a RAM step with correct 'necessary_ram' operation passes validation.""" step = ram_step() - assert step.step_metrics[Metrics.NECESSARY_RAM] == 128 + assert step.step_operation[StepOperation.NECESSARY_RAM] == 128 def test_valid_io_step() -> None: - """Test that an I/O step with correct 'io_waiting_time' metric passes validation.""" + """ + Test that an I/O step with correct 'io_waiting_time' + operation passes validation. + """ step = io_step() - assert step.step_metrics[Metrics.IO_WAITING_TIME] == 0.05 + assert step.step_operation[StepOperation.IO_WAITING_TIME] == 0.05 def test_endpoint_with_mixed_steps() -> None: @@ -79,47 +82,50 @@ def test_endpoint_with_mixed_steps() -> None: # Negative test cases # --------------------------------------------------------------------------- # @pytest.mark.parametrize( - ("kind", "bad_metrics"), + ("kind", "bad_operation"), [ - # CPU step with RAM metric - (EndpointStepCPU.CPU_BOUND_OPERATION, {Metrics.NECESSARY_RAM: 64}), - # RAM step with CPU metric - (EndpointStepRAM.RAM, {Metrics.CPU_TIME: 0.2}), - # I/O step with CPU metric - (EndpointStepIO.DB, {Metrics.CPU_TIME: 0.05}), + # CPU step with RAM operation + (EndpointStepCPU.CPU_BOUND_OPERATION, {StepOperation.NECESSARY_RAM: 64}), + # RAM step with CPU operation + (EndpointStepRAM.RAM, {StepOperation.CPU_TIME: 0.2}), + # I/O step with CPU operation + (EndpointStepIO.DB, {StepOperation.CPU_TIME: 0.05}), ], ) -def test_incoherent_kind_metric_pair( +def test_incoherent_kind_operation_pair( kind: EndpointStepCPU | EndpointStepRAM | EndpointStepIO, - bad_metrics: dict[Metrics, float | int], + bad_operation: dict[StepOperation, float | int], ) -> None: - """Test that mismatched kind and metric combinations raise ValidationError.""" + """Test that mismatched kind and operation combinations raise ValidationError.""" with pytest.raises(ValidationError): - Step(kind=kind, step_metrics=bad_metrics) + Step(kind=kind, step_operation=bad_operation) -def test_multiple_metrics_not_allowed() -> None: - """Test that providing multiple metrics in a single Step raises ValidationError.""" +def test_multiple_operation_not_allowed() -> None: + """ + Test that providing multiple operation in a single Step + raises ValidationError. + """ with pytest.raises(ValidationError): Step( kind=EndpointStepCPU.CPU_BOUND_OPERATION, - step_metrics={ - Metrics.CPU_TIME: 0.1, - Metrics.NECESSARY_RAM: 64, + step_operation={ + StepOperation.CPU_TIME: 0.1, + StepOperation.NECESSARY_RAM: 64, }, ) -def test_empty_metrics_rejected() -> None: - """Test that an empty metrics dict is rejected by the validator.""" +def test_empty_operation_rejected() -> None: + """Test that an empty operation dict is rejected by the validator.""" with pytest.raises(ValidationError): - Step(kind=EndpointStepCPU.CPU_BOUND_OPERATION, step_metrics={}) + Step(kind=EndpointStepCPU.CPU_BOUND_OPERATION, step_operation={}) -def test_wrong_metric_name_for_io() -> None: - """Test that an I/O step with a non-I/O metric key is rejected.""" +def test_wrong_operation_name_for_io() -> None: + """Test that an I/O step with a non-I/O operation key is rejected.""" with pytest.raises(ValidationError): Step( kind=EndpointStepIO.CACHE, - step_metrics={Metrics.NECESSARY_RAM: 64}, + step_operation={StepOperation.NECESSARY_RAM: 64}, ) diff --git a/tests/unit/input_sructure/test_full_topology_input.py b/tests/unit/input_sructure/test_full_topology_input.py index 9e17573..7f0d864 100644 --- a/tests/unit/input_sructure/test_full_topology_input.py +++ b/tests/unit/input_sructure/test_full_topology_input.py @@ -1,13 +1,4 @@ -"""Unit-tests for the **topology schemas** (Client, ServerResources, …). - -Every section below is grouped by the object under test, separated by -clear comment banners so that long files remain navigable. - -The tests aim for: -* 100 % branch-coverage on custom validators. -* mypy strict-compatibility (full type hints, no Any). -* ruff compliance (imports ordered, no unused vars, ≤ 88-char lines). -""" +"""Unit-tests for topology schemas (Client, ServerResources, Edge, …)""" from __future__ import annotations @@ -16,8 +7,9 @@ from app.config.constants import ( EndpointStepCPU, - Metrics, + NetworkParameters, ServerResourcesDefaults, + StepOperation, SystemEdges, SystemNodes, ) @@ -32,53 +24,58 @@ TopologyNodes, ) - # --------------------------------------------------------------------------- # -# Client +# Client # # --------------------------------------------------------------------------- # + + def test_valid_client() -> None: - """A client with correct `type` should validate.""" + """A client with correct ``type`` validates.""" cli = Client(id="frontend", type=SystemNodes.CLIENT) assert cli.type is SystemNodes.CLIENT def test_invalid_client_type() -> None: - """Wrong `type` enum on Client must raise ValidationError.""" + """Wrong ``type`` enumeration on Client raises ValidationError.""" with pytest.raises(ValidationError): - Client(id="wrong", type=SystemNodes.SERVER) + Client(id="oops", type=SystemNodes.SERVER) # --------------------------------------------------------------------------- # -# ServerResources +# ServerResources # # --------------------------------------------------------------------------- # + + def test_server_resources_defaults() -> None: - """Default values must match the constant table.""" - res = ServerResources() # all defaults + """All defaults match constant table.""" + res = ServerResources() assert res.cpu_cores == ServerResourcesDefaults.CPU_CORES assert res.ram_mb == ServerResourcesDefaults.RAM_MB assert res.db_connection_pool is ServerResourcesDefaults.DB_CONNECTION_POOL def test_server_resources_min_constraints() -> None: - """cpu_cores and ram_mb < minimum should fail validation.""" + """Values below minimum trigger validation failure.""" with pytest.raises(ValidationError): ServerResources(cpu_cores=0, ram_mb=128) # too small # --------------------------------------------------------------------------- # -# Server +# Server # # --------------------------------------------------------------------------- # + + def _dummy_endpoint() -> Endpoint: - """Return a minimal valid Endpoint needed to build a Server.""" + """Return a minimal valid Endpoint for Server construction.""" step = Step( kind=EndpointStepCPU.CPU_BOUND_OPERATION, - step_metrics={Metrics.CPU_TIME: 0.1}, + step_operation={StepOperation.CPU_TIME: 0.1}, ) return Endpoint(endpoint_name="/ping", steps=[step]) def test_valid_server() -> None: - """Server with correct type, resources and endpoint list.""" + """Server with correct ``type`` and resources passes validation.""" srv = Server( id="api-1", type=SystemNodes.SERVER, @@ -89,10 +86,10 @@ def test_valid_server() -> None: def test_invalid_server_type() -> None: - """Server with wrong `type` enum must be rejected.""" + """Server with wrong ``type`` raises ValidationError.""" with pytest.raises(ValidationError): Server( - id="oops", + id="bad-srv", type=SystemNodes.CLIENT, server_resources=ServerResources(), endpoints=[_dummy_endpoint()], @@ -100,10 +97,12 @@ def test_invalid_server_type() -> None: # --------------------------------------------------------------------------- # -# TopologyNodes +# TopologyNodes # # --------------------------------------------------------------------------- # + + def _single_node_topology() -> TopologyNodes: - """Helper that returns a valid TopologyNodes with one server and one client.""" + """Helper returning one server + one client topology.""" srv = Server( id="svc-A", server_resources=ServerResources(), @@ -114,22 +113,24 @@ def _single_node_topology() -> TopologyNodes: def test_unique_ids_validator() -> None: - """Duplicate node IDs should trigger the unique_ids validator.""" + """Duplicate node IDs trigger the ``unique_ids`` validator.""" nodes = _single_node_topology() - # duplicate client ID dup_srv = nodes.servers[0].model_copy(update={"id": "browser"}) with pytest.raises(ValidationError): TopologyNodes(servers=[dup_srv], client=nodes.client) # --------------------------------------------------------------------------- # -# Edge +# Edge # # --------------------------------------------------------------------------- # + + def test_edge_source_equals_target_fails() -> None: - """Edge with identical source/target must raise.""" + """Edge with identical source/target raises ValidationError.""" latency_cfg = RVConfig(mean=0.05) with pytest.raises(ValidationError): Edge( + id="edge-dup", source="same", target="same", latency=latency_cfg, @@ -137,18 +138,48 @@ def test_edge_source_equals_target_fails() -> None: ) +def test_edge_missing_id_raises() -> None: + """Omitting mandatory ``id`` field raises ValidationError.""" + latency_cfg = RVConfig(mean=0.01) + with pytest.raises(ValidationError): + Edge( # type: ignore[call-arg] + source="a", + target="b", + latency=latency_cfg, + ) + + +@pytest.mark.parametrize( + "bad_rate", + [-0.1, NetworkParameters.MAX_DROPOUT_RATE + 0.1], +) +def test_edge_dropout_rate_bounds(bad_rate: float) -> None: + """Drop-out rate outside valid range triggers ValidationError.""" + with pytest.raises(ValidationError): + Edge( + id="edge-bad-drop", + source="n1", + target="n2", + latency=RVConfig(mean=0.01), + dropout_rate=bad_rate, + ) + + # --------------------------------------------------------------------------- # -# TopologyGraph +# TopologyGraph # # --------------------------------------------------------------------------- # + + def _latency() -> RVConfig: - """A tiny helper for RVConfig latency objects.""" + """Tiny helper for latency objects.""" return RVConfig(mean=0.02) def test_valid_topology_graph() -> None: - """End-to-end happy-path graph passes validation.""" + """Happy-path graph passes validation.""" nodes = _single_node_topology() edge = Edge( + id="edge-1", source="browser", target="svc-A", latency=_latency(), @@ -159,9 +190,10 @@ def test_valid_topology_graph() -> None: def test_edge_refers_unknown_node() -> None: - """Edge pointing to a non-existent node ID must fail.""" + """Edge pointing to a non-existent node fails validation.""" nodes = _single_node_topology() bad_edge = Edge( + id="edge-ghost", source="browser", target="ghost-srv", latency=_latency(), diff --git a/tests/unit/input_sructure/test_requests_generator_input.py b/tests/unit/input_sructure/test_requests_generator_input.py index 39a0b2d..1ca9562 100644 --- a/tests/unit/input_sructure/test_requests_generator_input.py +++ b/tests/unit/input_sructure/test_requests_generator_input.py @@ -1,5 +1,4 @@ """Validation tests for RVConfig, RqsGeneratorInput and SimulationSettings.""" - from __future__ import annotations import pytest @@ -10,23 +9,41 @@ from app.schemas.requests_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings -# --------------------------------------------------------------------------- -# RVCONFIG -# --------------------------------------------------------------------------- +# --------------------------------------------------------------------------- # +# RVCONFIG # +# --------------------------------------------------------------------------- # def test_normal_sets_variance_to_mean() -> None: - """If variance is omitted with 'normal', it defaults to mean.""" + """If variance is omitted for 'normal', it defaults to mean.""" cfg = RVConfig(mean=10, distribution=Distribution.NORMAL) assert cfg.variance == 10.0 +def test_log_normal_sets_variance_to_mean() -> None: + """If variance is omitted for 'log_normal', it defaults to mean.""" + cfg = RVConfig(mean=5, distribution=Distribution.LOG_NORMAL) + assert cfg.variance == 5.0 + + def test_poisson_keeps_variance_none() -> None: - """If variance is omitted with 'poisson', it remains None.""" + """If variance is omitted for 'poisson', it remains None.""" cfg = RVConfig(mean=5, distribution=Distribution.POISSON) assert cfg.variance is None +def test_uniform_keeps_variance_none() -> None: + """If variance is omitted for 'uniform', it remains None.""" + cfg = RVConfig(mean=1, distribution=Distribution.UNIFORM) + assert cfg.variance is None + + +def test_exponential_keeps_variance_none() -> None: + """If variance is omitted for 'exponential', it remains None.""" + cfg = RVConfig(mean=2.5, distribution=Distribution.EXPONENTIAL) + assert cfg.variance is None + + def test_explicit_variance_is_preserved() -> None: """An explicit variance value is not modified.""" cfg = RVConfig(mean=8, distribution=Distribution.NORMAL, variance=4) @@ -34,7 +51,7 @@ def test_explicit_variance_is_preserved() -> None: def test_mean_must_be_numeric() -> None: - """A non numeric mean triggers a ValidationError.""" + """A non-numeric mean triggers a ValidationError.""" with pytest.raises(ValidationError) as exc: RVConfig(mean="not a number", distribution=Distribution.POISSON) @@ -65,24 +82,32 @@ def test_explicit_variance_kept_for_poisson() -> None: assert cfg.variance == pytest.approx(2.2) -def test_invalid_distribution_raises() -> None: +def test_invalid_distribution_literal_raises() -> None: """An unsupported distribution literal raises ValidationError.""" with pytest.raises(ValidationError): RVConfig(mean=5.0, distribution="not_a_dist") -# --------------------------------------------------------------------------- -# RQSGENERATORINPUT - USER_SAMPLING_WINDOW -# --------------------------------------------------------------------------- + +# --------------------------------------------------------------------------- # +# RQSGENERATORINPUT - USER_SAMPLING_WINDOW & DISTRIBUTION CONSTRAINTS # +# --------------------------------------------------------------------------- # + + +def _valid_poisson_cfg(mean: float = 1.0) -> dict[str, float | str]: + """Helper: minimal Poisson config for JSON-style input.""" + return {"mean": mean, "distribution": Distribution.POISSON} + + +def _valid_normal_cfg(mean: float = 1.0) -> dict[str, float | str]: + """Helper: minimal Normal config for JSON-style input.""" + return {"mean": mean, "distribution": Distribution.NORMAL} def test_default_user_sampling_window() -> None: """If user_sampling_window is missing it defaults to the constant.""" inp = RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, + avg_active_users=_valid_poisson_cfg(), + avg_request_per_minute_per_user=_valid_poisson_cfg(), ) assert inp.user_sampling_window == TimeDefaults.USER_SAMPLING_WINDOW @@ -90,25 +115,19 @@ def test_default_user_sampling_window() -> None: def test_explicit_user_sampling_window_kept() -> None: """An explicit user_sampling_window is preserved.""" inp = RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, + avg_active_users=_valid_poisson_cfg(), + avg_request_per_minute_per_user=_valid_poisson_cfg(), user_sampling_window=30, ) assert inp.user_sampling_window == 30 def test_user_sampling_window_not_int_raises() -> None: - """A non integer user_sampling_window raises ValidationError.""" + """A non-integer user_sampling_window raises ValidationError.""" with pytest.raises(ValidationError): RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, + avg_active_users=_valid_poisson_cfg(), + avg_request_per_minute_per_user=_valid_poisson_cfg(), user_sampling_window="not-int", ) @@ -118,19 +137,60 @@ def test_user_sampling_window_above_max_raises() -> None: too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW + 1 with pytest.raises(ValidationError): RqsGeneratorInput( - avg_active_users={"mean": 1.0, "distribution": Distribution.POISSON}, - avg_request_per_minute_per_user={ - "mean": 1.0, - "distribution": Distribution.POISSON, - }, + avg_active_users=_valid_poisson_cfg(), + avg_request_per_minute_per_user=_valid_poisson_cfg(), user_sampling_window=too_large, ) +def test_avg_request_must_be_poisson() -> None: + """avg_request_per_minute_per_user must be Poisson; Normal raises.""" + with pytest.raises(ValidationError): + RqsGeneratorInput( + avg_active_users=_valid_poisson_cfg(), + avg_request_per_minute_per_user=_valid_normal_cfg(), + ) + + +def test_avg_active_users_invalid_distribution_raises() -> None: + """avg_active_users cannot be Exponential; only Poisson or Normal allowed.""" + bad_cfg = {"mean": 1.0, "distribution": Distribution.EXPONENTIAL} + with pytest.raises(ValidationError): + RqsGeneratorInput( + avg_active_users=bad_cfg, + avg_request_per_minute_per_user=_valid_poisson_cfg(), + ) + + +def test_valid_poisson_poisson_configuration() -> None: + """Poisson-Poisson combo is accepted.""" + cfg = RqsGeneratorInput( + avg_active_users=_valid_poisson_cfg(), + avg_request_per_minute_per_user=_valid_poisson_cfg(), + ) + assert cfg.avg_active_users.distribution is Distribution.POISSON + assert ( + cfg.avg_request_per_minute_per_user.distribution + is Distribution.POISSON + ) + + +def test_valid_normal_poisson_configuration() -> None: + """Normal-Poisson combo is accepted.""" + cfg = RqsGeneratorInput( + avg_active_users=_valid_normal_cfg(), + avg_request_per_minute_per_user=_valid_poisson_cfg(), + ) + assert cfg.avg_active_users.distribution is Distribution.NORMAL + assert ( + cfg.avg_request_per_minute_per_user.distribution + is Distribution.POISSON + ) + -# --------------------------------------------------------------------------- -# SIMULATIONSETTINGS - TOTAL_SIMULATION_TIME -# --------------------------------------------------------------------------- +# --------------------------------------------------------------------------- # +# SIMULATIONSETTINGS - TOTAL_SIMULATION_TIME # +# --------------------------------------------------------------------------- # def test_default_total_simulation_time() -> None: @@ -146,7 +206,7 @@ def test_explicit_total_simulation_time_kept() -> None: def test_total_simulation_time_not_int_raises() -> None: - """A non integer total_simulation_time raises ValidationError.""" + """A non-integer total_simulation_time raises ValidationError.""" with pytest.raises(ValidationError): SimulationSettings(total_simulation_time="three thousand") diff --git a/tests/unit/runtime/test_edge b/tests/unit/runtime/test_edge new file mode 100644 index 0000000..8daaf01 --- /dev/null +++ b/tests/unit/runtime/test_edge @@ -0,0 +1,136 @@ +"""Unit-tests for :class:`EdgeRuntime`. + +The tests cover: + +* correct delivery when the random 'uniform' draw exceeds the drop rate; +* correct drop behaviour when the draw is below the drop rate; +* that the latency sampler is invoked exactly once per message. +""" +from __future__ import annotations + +from collections.abc import Iterator +from typing import TYPE_CHECKING, cast + +import numpy as np +import pytest +import simpy + +from app.config.constants import NetworkParameters, SystemNodes +from app.config.rqs_state import RequestState +from app.core.event_samplers.common_helpers import general_sampler +from app.core.runtime.edge import EdgeRuntime +from app.schemas.random_variables_config import RVConfig +from app.schemas.system_topology_schema.full_system_topology_schema import Edge + +if TYPE_CHECKING: + from collections.abc import Generator + + +pytestmark = pytest.mark.unit # module-level marker + + +# --------------------------------------------------------------------------- # +# Dummy RNG # +# --------------------------------------------------------------------------- # + + +class DummyRNG: + """RNG stub returning preset values for `uniform()` and `normal()`.""" + + def __init__( + self, + *, + uniform_value: float, + sampler_value: float = 0.0, + ) -> None: + self.uniform_value = uniform_value + self.sampler_value = sampler_value + self.uniform_called = False + self.sampler_called = False + + def uniform(self) -> float: # noqa: D401 + self.uniform_called = True + return self.uniform_value + + # EdgeRuntime passes `self` to `general_sampler`; wrap the call + def normal(self, mean: float, sigma: float) -> float: # noqa: D401 + self.sampler_called = True + return self.sampler_value + + +# --------------------------------------------------------------------------- # +# Helper to create a minimal EdgeRuntime # +# --------------------------------------------------------------------------- # + + +def _make_edge( + env: simpy.Environment, + *, + uniform_value: float, + sampler_value: float = 0.0, +) -> tuple[EdgeRuntime, DummyRNG, simpy.Store]: + rng = DummyRNG(uniform_value=uniform_value, sampler_value=sampler_value) + store: simpy.Store = simpy.Store(env) + + edge_cfg = Edge( + id="edge-1", + source="src", + target="dst", + latency=RVConfig(mean=0.0, distribution="uniform"), # value ignored in test + ) + + edge_rt = EdgeRuntime( + env=env, + edge_config=edge_cfg, + rng=cast("np.random.Generator", rng), + target_box=store, + ) + return edge_rt, rng, store + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # + + +def test_edge_delivers_message_when_not_dropped() -> None: + """Message is delivered and latency sampler is called once.""" + env = simpy.Environment() + edge, rng, store = _make_edge(env, uniform_value=0.9, sampler_value=0.5) + + # prepare request state + state = RequestState(id=1, initial_time=0.0) + state.record_hop(SystemNodes.GENERATOR, env.now) + + edge.transport(state) + env.run() + + # exactly one message delivered + assert len(store.items) == 1 + delivered: RequestState = store.items[0] + assert delivered.hops[-1].node == "edge-1" # last hop is the edge id + assert rng.uniform_called is True + assert rng.sampler_called is True + + +def test_edge_drops_message_when_uniform_below_threshold( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Message is dropped and never placed in the target store.""" + # override global drop rate to deterministic 0.5 for the test + monkeypatch.setattr(NetworkParameters, "DROPOUT_RATE", 0.5, raising=False) + + env = simpy.Environment() + edge, rng, store = _make_edge(env, uniform_value=0.1) # below 0.5 ⇒ drop + + state = RequestState(id=2, initial_time=0.0) + state.record_hop(SystemNodes.GENERATOR, env.now) + + edge.transport(state) + env.run() + + assert len(store.items) == 0 # nothing delivered + assert state.hops[-1].node.endswith("dropped") + assert rng.uniform_called is True + # sampler must not be invoked when dropped + assert rng.sampler_called is False diff --git a/tests/unit/runtime/test_requests_generator.py b/tests/unit/runtime/test_requests_generator.py new file mode 100644 index 0000000..be03583 --- /dev/null +++ b/tests/unit/runtime/test_requests_generator.py @@ -0,0 +1,151 @@ +"""Unit-tests for the :class:`RqsGeneratorRuntime` dispatcher and event flow.""" +from __future__ import annotations + +from collections.abc import Iterator +from typing import TYPE_CHECKING, cast + +import numpy as np +import simpy + +from app.config.constants import Distribution +from app.core.runtime.rqs_generator import RqsGeneratorRuntime + +if TYPE_CHECKING: + + import pytest + from numpy.random import Generator + + from app.config.rqs_state import RequestState + from app.core.runtime.edge import EdgeRuntime + from app.schemas.requests_generator_input import RqsGeneratorInput + from app.schemas.simulation_settings_input import SimulationSettings + +import importlib + +# --------------------------------------------------------------------------- # +# Helpers # +# --------------------------------------------------------------------------- # + + +class DummyEdgeRuntime: + """Minimal stub capturing transported :class:`RequestState`.""" + + def __init__(self) -> None: + """Definition of the attributes""" + self.received: list[RequestState] = [] + + def transport(self, state: RequestState) -> None: + """Collect every state passed through the edge.""" + self.received.append(state) + + +def _make_runtime( + env: simpy.Environment, + edge: DummyEdgeRuntime, + rqs_input: RqsGeneratorInput, + sim_settings: SimulationSettings, + *, + seed: int = 0, +) -> RqsGeneratorRuntime: + """Factory returning a fully wired :class:`RqsGeneratorRuntime`.""" + rng: Generator = np.random.default_rng(seed) + return RqsGeneratorRuntime( + env=env, + out_edge=cast("EdgeRuntime", edge), + rqs_generator_data=rqs_input, + sim_settings=sim_settings, + rng=rng, + ) + + +# --------------------------------------------------------------------------- # +# Dispatcher behaviour # +# --------------------------------------------------------------------------- # + + +RGR_MODULE = importlib.import_module("app.core.runtime.rqs_generator") + +def test_dispatcher_selects_poisson_poisson( + monkeypatch: pytest.MonkeyPatch, + rqs_input: RqsGeneratorInput, + sim_settings: SimulationSettings, +) -> None: + """Default (Poisson) distribution must invoke *poisson_poisson_sampling*.""" + called = {"pp": False} + + def _fake_pp(*args: object, **kwargs: object) -> Iterator[float]: + called["pp"] = True + return iter(()) # iterator already exhausted + + monkeypatch.setattr(RGR_MODULE, "poisson_poisson_sampling", _fake_pp) + + env = simpy.Environment() + edge = DummyEdgeRuntime() + runtime = _make_runtime(env, edge, rqs_input, sim_settings) + + gen = runtime._requests_generator() # noqa: SLF001 + for _ in gen: + pass + + assert called["pp"] is True + assert isinstance(gen, Iterator) + + +def test_dispatcher_selects_gaussian_poisson( + monkeypatch: pytest.MonkeyPatch, + rqs_input: RqsGeneratorInput, + sim_settings: SimulationSettings, +) -> None: + """Normal distribution must invoke *gaussian_poisson_sampling*.""" + rqs_input.avg_active_users.distribution = Distribution.NORMAL + called = {"gp": False} + + def _fake_gp(*args: object, **kwargs: object) -> Iterator[float]: + called["gp"] = True + return iter(()) + + monkeypatch.setattr(RGR_MODULE, "gaussian_poisson_sampling", _fake_gp) + + env = simpy.Environment() + edge = DummyEdgeRuntime() + runtime = _make_runtime(env, edge, rqs_input, sim_settings) + + gen = runtime._requests_generator() # noqa: SLF001 + for _ in gen: + pass + + assert called["gp"] is True + assert isinstance(gen, Iterator) + +# --------------------------------------------------------------------------- # +# Event-arrival flow # +# --------------------------------------------------------------------------- # + + +def test_event_arrival_generates_expected_number_of_requests( + monkeypatch: pytest.MonkeyPatch, + rqs_input: RqsGeneratorInput, + sim_settings: SimulationSettings, +) -> None: + """Given a deterministic gap list, exactly that many requests are sent.""" + gaps = [1.0, 2.0, 3.0] + + def _fake_gen(self: object) -> Iterator[float]: + yield from gaps + + monkeypatch.setattr( + RqsGeneratorRuntime, + "_requests_generator", + _fake_gen, + ) + + env = simpy.Environment() + edge = DummyEdgeRuntime() + runtime = _make_runtime(env, edge, rqs_input, sim_settings) + + env.process(runtime._event_arrival()) # noqa: SLF001 + env.run(until=sum(gaps) + 0.1) # run slightly past the last gap + + assert len(edge.received) == len(gaps) + ids = [s.id for s in edge.received] + assert ids == [1, 2, 3] diff --git a/tests/unit/sampler/test_sampler_helper.py b/tests/unit/sampler/test_sampler_helper.py index 0222196..7d34990 100644 --- a/tests/unit/sampler/test_sampler_helper.py +++ b/tests/unit/sampler/test_sampler_helper.py @@ -1,130 +1,196 @@ +"""Unit-tests for helper-functions in +`app.core.event_samplers.common_helpers`. +""" +from __future__ import annotations + from typing import cast import numpy as np +import pytest +from app.config.constants import Distribution from app.core.event_samplers.common_helpers import ( + exponential_variable_generator, + general_sampler, + lognormal_variable_generator, poisson_variable_generator, truncated_gaussian_generator, uniform_variable_generator, ) +from app.schemas.random_variables_config import RVConfig + +# --------------------------------------------------------------------------- # +# Dummy RNG # +# --------------------------------------------------------------------------- # class DummyRNG: - """Dummy RNG for testing: returns fixed values for random(), poisson(), normal().""" + """Minimal stub mimicking the subset of the NumPy RNG API used in tests.""" - def __init__( + def __init__( # noqa: D107 self, + *, uniform_value: float | None = None, poisson_value: int | None = None, normal_value: float | None = None, + lognormal_value: float | None = None, + exponential_value: float | None = None, ) -> None: - """ - Initialize the dummy RNG with optional preset outputs. - - Args: - uniform_value: value to return from random(), if not None. - poisson_value: value to return from poisson(), if not None. - normal_value: value to return from normal(), if not None. - - """ self.uniform_value = uniform_value self.poisson_value = poisson_value self.normal_value = normal_value + self.lognormal_value = lognormal_value + self.exponential_value = exponential_value - def random(self) -> float: - """ - Return the preset uniform_value or fall back to a real RNG. + # --- uniform ----------------------------------------------------------- # - Returns: - A float in [0.0, 1.0). - - """ + def random(self) -> float: + """Return the preset ``uniform_value`` or fall back to a real RNG.""" if self.uniform_value is not None: return self.uniform_value - return np.random.default_rng().random() - - def poisson(self, mean: float) -> int: - """ - Return the preset poisson_value or fall back to a real RNG. - - Args: - mean: the λ parameter for a Poisson draw (ignored if poisson_value is set). + return float(np.random.default_rng().random()) - Returns: - An integer sample from a Poisson distribution. + # --- Poisson ----------------------------------------------------------- # - """ + def poisson(self, lam: float) -> int: + """Return the preset ``poisson_value`` or draw from a real Poisson.""" if self.poisson_value is not None: return self.poisson_value - return int(np.random.default_rng().poisson(mean)) + return int(np.random.default_rng().poisson(lam)) - def normal(self, mean: float, sigma: float) -> float: - """ - Return the preset normal_value or fall back to a real RNG. - - Args: - mean: the mean of the Normal distribution. - sigma: the standard deviation of the Normal distribution. - - Returns: - A float sample from a Normal distribution. + # --- Normal ------------------------------------------------------------ # - """ + def normal(self, mean: float, sigma: float) -> float: + """Return the preset ``normal_value`` or draw from a real Normal.""" if self.normal_value is not None: return self.normal_value return float(np.random.default_rng().normal(mean, sigma)) + # --- Log-normal -------------------------------------------------------- # + + def lognormal(self, mean: float, sigma: float) -> float: + """Return the preset ``lognormal_value`` or draw from a real LogNormal.""" + if self.lognormal_value is not None: + return self.lognormal_value + return float(np.random.default_rng().lognormal(mean, sigma)) + + # --- Exponential ------------------------------------------------------- # + + def exponential(self, scale: float) -> float: + """Return the preset ``exponential_value`` or draw from a real Exponential.""" + if self.exponential_value is not None: + return self.exponential_value + return float(np.random.default_rng().exponential(scale)) + + +# --------------------------------------------------------------------------- # +# Tests for low-level generators # +# --------------------------------------------------------------------------- # + def test_uniform_variable_generator_with_dummy_rng() -> None: - """Ensure uniform_variable_generator returns the dummy RNGs uniform_value.""" + """`uniform_variable_generator` returns the dummy's ``uniform_value``.""" dummy = cast("np.random.Generator", DummyRNG(uniform_value=0.75)) assert uniform_variable_generator(dummy) == 0.75 -def test_uniform_variable_generator_default_rng_range() -> None: - """Ensure the default RNG produces a float in [0.0, 1.0).""" - for _ in range(100): - val = uniform_variable_generator() - assert isinstance(val, float) - assert 0.0 <= val < 1.0 +def test_uniform_variable_generator_bounds() -> None: + """Calling with a real RNG yields a value in the half-open interval [0, 1).""" + rng = np.random.default_rng(1_234) + val = uniform_variable_generator(rng) + assert 0.0 <= val < 1.0 def test_poisson_variable_generator_with_dummy_rng() -> None: - """Ensure poisson_variable_generator returns the dummy RNGs poisson_value.""" + """`poisson_variable_generator` returns the dummy's ``poisson_value``.""" dummy = cast("np.random.Generator", DummyRNG(poisson_value=3)) assert poisson_variable_generator(mean=5.0, rng=dummy) == 3 def test_poisson_variable_generator_reproducible() -> None: - """Ensure two generators with the same seed produce the same Poisson sample.""" - rng1 = np.random.default_rng(12345) - rng2 = np.random.default_rng(12345) - v1 = poisson_variable_generator(mean=10.0, rng=rng1) - v2 = poisson_variable_generator(mean=10.0, rng=rng2) + """Two RNGs with the same seed produce identical Poisson draws.""" + rng1 = np.random.default_rng(42) + rng2 = np.random.default_rng(42) + v1 = poisson_variable_generator(7.0, rng1) + v2 = poisson_variable_generator(7.0, rng2) assert v1 == v2 -def test_truncated_gaussian_generator_truncates_negative() -> None: - """Ensure truncated_gaussian_generator clamps negative draws to zero.""" +def test_truncated_gaussian_generator_negative_clamped() -> None: + """Negative Normal draws are clamped to zero.""" dummy = cast("np.random.Generator", DummyRNG(normal_value=-2.7)) - result = truncated_gaussian_generator(mean=10.0, variance=5.0, rng=dummy) - assert result == 0 + assert truncated_gaussian_generator(10.0, 5.0, dummy) == 0.0 -def test_truncated_gaussian_generator_truncates_toward_zero() -> None: - """Ensure truncated_gaussian_generator rounds toward zero for positive draws.""" +def test_truncated_gaussian_generator_positive_passthrough() -> None: + """Positive Normal draws pass through unchanged.""" dummy = cast("np.random.Generator", DummyRNG(normal_value=3.9)) - result = truncated_gaussian_generator(mean=10.0, variance=5.0, rng=dummy) - assert isinstance(result, int) - assert result == 3 + val = truncated_gaussian_generator(10.0, 5.0, dummy) + assert isinstance(val, float) + assert val == 3.9 -def test_truncated_gaussian_generator_default_rng_non_negative_int() -> None: - """ - Ensure the default RNG produces - a non-negative integer from the truncated Gaussian. - """ +def test_truncated_gaussian_generator_default_rng_non_negative() -> None: + """Real RNG always yields a non-negative float after truncation.""" rng = np.random.default_rng(321) - val = truncated_gaussian_generator(mean=10.0, variance=2.0, rng=rng) - assert isinstance(val, int) - assert val >= 0 + assert truncated_gaussian_generator(10.0, 2.0, rng) >= 0.0 + + +def test_lognormal_variable_generator_reproducible() -> None: + """`lognormal_variable_generator` is reproducible with a fixed seed.""" + rng1 = np.random.default_rng(99) + rng2 = np.random.default_rng(99) + v1 = lognormal_variable_generator(1.0, 0.5, rng1) + v2 = lognormal_variable_generator(1.0, 0.5, rng2) + assert v1 == pytest.approx(v2) + + +def test_exponential_variable_generator_reproducible() -> None: + """`exponential_variable_generator` is reproducible with a fixed seed.""" + rng1 = np.random.default_rng(54_321) + rng2 = np.random.default_rng(54_321) + v1 = exponential_variable_generator(2.0, rng1) + v2 = exponential_variable_generator(2.0, rng2) + assert v1 == pytest.approx(v2) + + +# --------------------------------------------------------------------------- # +# Tests for `general_sampler` # +# --------------------------------------------------------------------------- # + + +def test_general_sampler_uniform_path() -> None: + """Uniform branch returns the dummy's preset value.""" + dummy = cast("np.random.Generator", DummyRNG(uniform_value=0.42)) + cfg = RVConfig(mean=1.0, distribution=Distribution.UNIFORM) + assert general_sampler(cfg, dummy) == 0.42 + + +def test_general_sampler_normal_path() -> None: + """Normal branch applies truncation logic (negative → 0).""" + dummy = cast("np.random.Generator", DummyRNG(normal_value=-1.2)) + cfg = RVConfig(mean=0.0, variance=1.0, distribution=Distribution.NORMAL) + assert general_sampler(cfg, dummy) == 0.0 + + +def test_general_sampler_poisson_path() -> None: + """Poisson branch returns the dummy's preset integer as *float*.""" + dummy = cast("np.random.Generator", DummyRNG(poisson_value=4)) + cfg = RVConfig(mean=5.0, variance=5.0, distribution=Distribution.POISSON) + result = general_sampler(cfg, dummy) + assert isinstance(result, float) + assert result == 4.0 + + +def test_general_sampler_lognormal_path() -> None: + """Log-normal branch produces a strictly positive float.""" + rng = np.random.default_rng(2_025) + cfg = RVConfig(mean=0.0, variance=0.5, distribution=Distribution.LOG_NORMAL) + assert general_sampler(cfg, rng) > 0.0 + + +def test_general_sampler_exponential_path() -> None: + """Exponential branch produces a strictly positive float.""" + rng = np.random.default_rng(7) + cfg = RVConfig(mean=1.5, variance=1.5, distribution=Distribution.EXPONENTIAL) + assert general_sampler(cfg, rng) > 0.0 diff --git a/tests/unit/simulation/test_requests_generator.py b/tests/unit/simulation/test_requests_generator.py deleted file mode 100644 index fe72f2e..0000000 --- a/tests/unit/simulation/test_requests_generator.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Unit-tests for the requests generator and the SimPy runner. - -All common fixtures (`rng`, `rqs_input`, `sim_settings`, `payload_base`, …) -are defined once in *tests/conftest.py*. -This module focuses purely on behavioural checks. -""" - -from __future__ import annotations - -from types import GeneratorType -from typing import TYPE_CHECKING - -import pytest - -from app.core.simulation.requests_generator import requests_generator -from app.core.simulation.simulation_run import run_simulation - -if TYPE_CHECKING: # static-typing only - from collections.abc import Iterator - - from numpy.random import Generator - - from app.schemas.full_simulation_input import SimulationPayload - from app.schemas.requests_generator_input import RqsGeneratorInput - from app.schemas.simulation_output import SimulationOutput - from app.schemas.simulation_settings_input import SimulationSettings - - -# --------------------------------------------------------------------------- -# REQUESTS-GENERATOR - dispatcher tests -# --------------------------------------------------------------------------- - - -def test_default_requests_generator_uses_poisson_poisson_sampling( - rqs_input: RqsGeneratorInput, - sim_settings: SimulationSettings, - rng: Generator, -) -> None: - """Default distribution must map to *poisson_poisson_sampling*.""" - gen = requests_generator(rqs_input, sim_settings, rng=rng) - - assert isinstance(gen, GeneratorType) - assert gen.gi_code.co_name == "poisson_poisson_sampling" - - -@pytest.mark.parametrize( - ("dist", "expected_sampler"), - [ - ("poisson", "poisson_poisson_sampling"), - ("normal", "gaussian_poisson_sampling"), - ], -) -def test_requests_generator_dispatches_to_correct_sampler( - dist: str, - expected_sampler: str, - rqs_input: RqsGeneratorInput, - sim_settings: SimulationSettings, - rng: Generator, -) -> None: - """Dispatcher must select the sampler matching *dist*.""" - rqs_input.avg_active_users.distribution = dist # type: ignore[assignment] - gen = requests_generator(rqs_input, sim_settings, rng=rng) - - assert isinstance(gen, GeneratorType) - assert gen.gi_code.co_name == expected_sampler - - -# --------------------------------------------------------------------------- -# SIMULATION-RUNNER - horizon handling -# --------------------------------------------------------------------------- - - -def _patch_generator( - monkeypatch: pytest.MonkeyPatch, - gaps: list[float], -) -> None: - """Monkey-patch *requests_generator* with a deterministic gap sequence.""" - - def _fake( - data: RqsGeneratorInput, - config: SimulationSettings, # unused, keeps signature - *, - rng: Generator | None = None, - ) -> Iterator[float]: - yield from gaps - - monkeypatch.setattr( - "app.core.simulation.simulation_run.requests_generator", - _fake, - ) - - -def test_run_simulation_counts_events_up_to_horizon( - monkeypatch: pytest.MonkeyPatch, - payload_base: SimulationPayload, - rng: Generator, -) -> None: - """All events with cumulative time ≤ horizon must be counted.""" - _patch_generator(monkeypatch, gaps=[1.0, 2.0, 3.0, 4.0]) - - output: SimulationOutput = run_simulation(payload_base, rng=rng) - - assert output.total_requests["total_requests"] == 4 - assert output.metric_2 == str( - payload_base.rqs_input.avg_request_per_minute_per_user.mean, - ) - assert output.metric_n == str(payload_base.rqs_input.avg_active_users.mean) - - -def test_run_simulation_skips_event_at_exact_horizon( - monkeypatch: pytest.MonkeyPatch, - payload_base: SimulationPayload, - rng: Generator, -) -> None: - """An event scheduled exactly at *t == horizon* is ignored.""" - horizon = payload_base.sim_settings.total_simulation_time - _patch_generator(monkeypatch, gaps=[float(horizon)]) - - output: SimulationOutput = run_simulation(payload_base, rng=rng) - assert output.total_requests["total_requests"] == 0 - - -def test_run_simulation_excludes_event_beyond_horizon( - monkeypatch: pytest.MonkeyPatch, - payload_base: SimulationPayload, - rng: Generator, -) -> None: - """Events strictly after the horizon must not be counted.""" - horizon = payload_base.sim_settings.total_simulation_time - _patch_generator(monkeypatch, gaps=[float(horizon) + 0.1]) - - output: SimulationOutput = run_simulation(payload_base, rng=rng) - assert output.total_requests["total_requests"] == 0 - - -def test_run_simulation_zero_events_when_generator_empty( - monkeypatch: pytest.MonkeyPatch, - payload_base: SimulationPayload, - rng: Generator, -) -> None: - """No gaps => no requests counted.""" - _patch_generator(monkeypatch, gaps=[]) - - output: SimulationOutput = run_simulation(payload_base, rng=rng) - assert output.total_requests["total_requests"] == 0 diff --git a/tests/unit/test_state.py b/tests/unit/test_state.py new file mode 100644 index 0000000..4612f1b --- /dev/null +++ b/tests/unit/test_state.py @@ -0,0 +1,47 @@ +"""Unit-tests for :class:`RequestState`.""" +from __future__ import annotations + +from app.config.rqs_state import RequestState + +# --------------------------------------------------------------------------- # +# Helpers # +# --------------------------------------------------------------------------- # + + +def _state() -> RequestState: + """Return a fresh RequestState with id=42 and t0=0.0.""" + return RequestState(id=42, initial_time=0.0) + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # + + +def test_record_hop_appends_formatted_entry() -> None: + """Calling *record_hop* stores 'node@timestamp' with 3-dec precision.""" + st = _state() + st.record_hop("generator", now=1.23456) + assert st.history == ["generator@1.235"] # rounded to 3 decimals + + +def test_multiple_hops_preserve_order() -> None: + """History keeps insertion order for consecutive hops.""" + st = _state() + st.record_hop("A", 0.1) + st.record_hop("B", 0.2) + st.record_hop("C", 0.3) + assert st.history == ["A@0.100", "B@0.200", "C@0.300"] + + +def test_latency_none_until_finish_time_set() -> None: + """Latency is None if *finish_time* not assigned.""" + st = _state() + assert st.latency is None + + +def test_latency_returns_difference() -> None: + """Latency equals finish_time - initial_time once completed.""" + st = _state() + st.finish_time = 5.5 + assert st.latency == 5.5 # 5.5 - 0.0 From 13d31d67d9bcb9048f7b69a6d468458ec189eb93 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Thu, 24 Jul 2025 17:48:21 +0200 Subject: [PATCH 17/52] Features/client server runtime (#6) * defined client runtime + tests * initiated the server runtime plus correction of tghe structure * code refactor + test added for runtime + documentation * Update tests/conftest.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- README.md | 226 +++++++++---- .../input_structure_for_the_simulation.md | 320 ------------------ .../runtime_and_resources.md | 283 ++++++++++++++++ .../backend_documentation/simulation_input.md | 228 +++++++++++++ ...ORKFLOW_GUIDE.md => dev_workflow_guide.md} | 90 +++-- .../{FASTSIM_VISION.md => fastsim_vision.md} | 0 src/app/config/constants.py | 15 +- src/app/config/rqs_state.py | 50 --- src/app/core/helpers/requests_generator.py | 60 ---- .../helpers => metrics}/dictionary_metrics.py | 0 src/app/resources/__init__.py | 1 + src/app/resources/registry.py | 38 +++ src/app/resources/server_containers.py | 73 ++++ src/app/{core => }/runtime/__init__.py | 0 src/app/runtime/actors/client.py | 59 ++++ .../{core/runtime => runtime/actors}/edge.py | 19 +- .../actors}/rqs_generator.py | 18 +- src/app/runtime/actors/server.py | 136 ++++++++ src/app/runtime/rqs_state.py | 51 +++ .../common_helpers.py | 0 .../gaussian_poisson.py | 4 +- .../poisson_poisson.py | 4 +- src/app/schemas/full_simulation_input.py | 4 +- ...erator_input.py => rqs_generator_input.py} | 4 +- .../endpoint.py} | 0 .../full_system_topology.py} | 3 +- src/app/{core => }/simulation_run.py | 2 +- tests/conftest.py | 69 ++-- tests/unit/{ => db}/test_settings.py | 0 tests/unit/resources/test_registry.py | 60 ++++ .../unit/resources/test_server_containers.py | 19 ++ tests/unit/runtime/engine/test_client.py | 96 ++++++ tests/unit/runtime/engine/test_edge.py | 127 +++++++ .../test_rqs_generator.py} | 10 +- tests/unit/runtime/engine/test_server.py | 181 ++++++++++ tests/unit/runtime/test_edge | 136 -------- tests/unit/runtime/test_rqs_state.py | 66 ++++ .../test_gaussian_poisson.py | 7 +- .../test_poisson_poisson.py | 6 +- .../test_sampler_helper.py | 2 +- .../test_endpoint_input.py | 2 +- .../test_full_topology_input.py | 4 +- .../test_requests_generator_input.py | 21 +- tests/unit/test_state.py | 47 --- 44 files changed, 1750 insertions(+), 791 deletions(-) delete mode 100644 documentation/backend_documentation/input_structure_for_the_simulation.md create mode 100644 documentation/backend_documentation/runtime_and_resources.md create mode 100644 documentation/backend_documentation/simulation_input.md rename documentation/{DEV_WORKFLOW_GUIDE.md => dev_workflow_guide.md} (57%) rename documentation/{FASTSIM_VISION.md => fastsim_vision.md} (100%) delete mode 100644 src/app/config/rqs_state.py delete mode 100644 src/app/core/helpers/requests_generator.py rename src/app/{core/helpers => metrics}/dictionary_metrics.py (100%) create mode 100644 src/app/resources/__init__.py create mode 100644 src/app/resources/registry.py create mode 100644 src/app/resources/server_containers.py rename src/app/{core => }/runtime/__init__.py (100%) create mode 100644 src/app/runtime/actors/client.py rename src/app/{core/runtime => runtime/actors}/edge.py (78%) rename src/app/{core/runtime => runtime/actors}/rqs_generator.py (87%) create mode 100644 src/app/runtime/actors/server.py create mode 100644 src/app/runtime/rqs_state.py rename src/app/{core/event_samplers => samplers}/common_helpers.py (100%) rename src/app/{core/event_samplers => samplers}/gaussian_poisson.py (96%) rename src/app/{core/event_samplers => samplers}/poisson_poisson.py (95%) rename src/app/schemas/{requests_generator_input.py => rqs_generator_input.py} (93%) rename src/app/schemas/{system_topology_schema/endpoint_schema.py => system_topology/endpoint.py} (100%) rename src/app/schemas/{system_topology_schema/full_system_topology_schema.py => system_topology/full_system_topology.py} (99%) rename src/app/{core => }/simulation_run.py (97%) rename tests/unit/{ => db}/test_settings.py (100%) create mode 100644 tests/unit/resources/test_registry.py create mode 100644 tests/unit/resources/test_server_containers.py create mode 100644 tests/unit/runtime/engine/test_client.py create mode 100644 tests/unit/runtime/engine/test_edge.py rename tests/unit/runtime/{test_requests_generator.py => engine/test_rqs_generator.py} (93%) create mode 100644 tests/unit/runtime/engine/test_server.py delete mode 100644 tests/unit/runtime/test_edge create mode 100644 tests/unit/runtime/test_rqs_state.py rename tests/unit/{sampler => samplers}/test_gaussian_poisson.py (93%) rename tests/unit/{sampler => samplers}/test_poisson_poisson.py (95%) rename tests/unit/{sampler => samplers}/test_sampler_helper.py (99%) rename tests/unit/{input_sructure => schemas}/test_endpoint_input.py (98%) rename tests/unit/{input_sructure => schemas}/test_full_topology_input.py (97%) rename tests/unit/{input_sructure => schemas}/test_requests_generator_input.py (95%) delete mode 100644 tests/unit/test_state.py diff --git a/README.md b/README.md index 8180e20..a9448e5 100644 --- a/README.md +++ b/README.md @@ -1,86 +1,176 @@ -## How to Start the Backend with Docker (Development) +Certamente. Ecco il contenuto del `README.md` visualizzato direttamente qui. -To spin up the backend and its supporting services in development mode: - -1. **Install & run Docker** on your machine. -2. **Clone** the repository and `cd` into its root. -3. Execute: - - ```bash - bash ./scripts/init-docker-dev.sh - ``` - - This will launch: - - * A **PostgreSQL** container - * A **Backend** container that mounts your local `src/` folder with live-reload - ---- - -## Development Architecture & Philosophy - -We split responsibilities between Docker-managed services and local workflows: +----- + +# **FastSim Project Overview** + +## **1. Why FastSim?** + +FastAPI + Uvicorn gives Python teams a lightning-fast async stack, yet sizing it for production still means guesswork, costly cloud load-tests, or late surprises. **FastSim** fills that gap by becoming a **digital twin** of your actual service: + + * It **replicates** your FastAPI + Uvicorn event-loop behavior in SimPy, generating the same kinds of asynchronous steps (parsing, CPU work, I/O, LLM calls) that happen in real code. + * It **models** your infrastructure primitives—CPU cores (via a SimPy `Resource`), database pools, rate-limiters, and even GPU inference quotas—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency. + * It **outputs** the very metrics you would scrape in production (p50/p95/p99 latency, ready-queue lag, concurrency, throughput, cost per LLM call), but entirely offline, in seconds. + +With FastSim you can ask, *“What happens if traffic doubles on Black Friday?”*, *“How many cores are needed to keep p95 latency below 100 ms?”*, or *“Is our LLM-driven endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. + +**Outcome:** Data-driven capacity planning, early performance tuning, and far fewer surprises in production. + +## **2. Project Goals** + +| \# | Goal | Practical Outcome | +| :--- | :--- | :--- | +| 1 | **Pre-production sizing** | Know the required core count, pool size, and replica count to meet your SLA. | +| 2 | **Scenario analysis** | Explore various traffic models, endpoint mixes, latency distributions, and RTT. | +| 3 | **Twin metrics** | Produce the same metrics you’ll scrape in production (latency, queue length, CPU utilization). | +| 4 | **Rapid iteration** | A single YAML/JSON configuration or REST call generates a full performance report. | +| 5 | **Educational value** | Visualize how GIL contention, queue length, and concurrency react to load. | + +## **3. Who Benefits & Why** + +| Audience | Pain-Point Solved | FastSim Value | +| :--- | :--- | :--- | +| **Backend Engineers** | Unsure if a 4-vCPU container can survive a marketing traffic spike. | Run *what-if* scenarios, tweak CPU cores or pool sizes, and get p95 latency and max-concurrency metrics before merging code. | +| **DevOps / SRE** | Guesswork in capacity planning; high cost of over-provisioning. | Simulate 1 to N replicas, autoscaler thresholds, and database pool sizes to find the most cost-effective configuration that meets the SLA. | +| **ML / LLM Product Teams** | LLM inference cost and latency are difficult to predict. | Model the LLM step with a price and latency distribution to estimate cost-per-request and the benefits of GPU batching without needing real GPUs. | +| **Educators / Trainers** | Students struggle to visualize event-loop internals. | Visualize GIL ready-queue lag, CPU vs. I/O steps, and the effect of blocking code—perfect for live demos and labs. | +| **Consultants / Architects** | Need a quick proof-of-concept for new client designs. | Define endpoints in YAML and demonstrate throughput and latency under projected load in minutes. | +| **Open-Source Community** | Lacks a lightweight Python simulator for ASGI workloads. | An extensible codebase makes it easy to plug in new resources (e.g., rate-limiters, caches) or traffic models (e.g., spike, uniform ramp). | +| **System-Design Interviewees** | Hard to quantify trade-offs in whiteboard interviews. | Prototype real-time metrics—queue lengths, concurrency, latency distributions—to demonstrate how your design scales and where bottlenecks lie. | + +## **4. About This Documentation** + +This project contains extensive documentation covering its vision, architecture, and technical implementation. The documents are designed to be read in sequence to build a comprehensive understanding of the project. + +### **How to Read This Documentation** + +For the best understanding of FastSim, we recommend reading the documentation in the following order: + +1. **README.md (This Document)**: Start here for a high-level overview of the project's purpose, goals, target audience, and development workflow. It provides the essential context for all other documents. +2. **dev_worflow_guide**: This document details the github workflow for the development +3. **simulation_input**: This document details the technical contract for configuring a simulation. It explains the `SimulationPayload` and its components (`rqs_input`, `topology_graph`, `sim_settings`). This is essential reading for anyone who will be creating or modifying simulation configurations. +4. **runtime_and_resources**: A deep dive into the simulation's internal engine. It explains how the validated input is transformed into live SimPy processes (Actors, Resources, State). This is intended for advanced users or contributors who want to understand *how* the simulation works under the hood. +5. **requests_generator**: This document covers the mathematical and algorithmic details behind the traffic generation model. It is for those interested in the statistical foundations of the simulator. +6. **Simulation Metrics**: A comprehensive guide to all output metrics. It explains what each metric measures, how it's collected, and why it's important for performance analysis. + +Optional **fastsim_vision**: a more detailed document about the project vision + +you can find the documentation at the root of the project in the folder `documentation/` + +## **5. Development Workflow & Architecture Guide** + +This section outlines the standardized development workflow, repository architecture, and branching strategy for the FastSim backend. + +### **Technology Stack** + + * **Backend**: FastAPI + * **Backend Package Manager**: Poetry + * **Frontend**: React + JavaScript + * **Database**: PostgreSQL + * **Caching**: Redis + * **Containerization**: Docker + +### **Backend Service (`FastSim-backend`)** + +The repository hosts the entire FastAPI backend, which exposes the REST API, runs the discrete-event simulation, communicates with the database, and provides metrics. + +``` +fastsim-backend/ +├── Dockerfile +├── docker_fs/ +│ ├── docker-compose.dev.yml +│ └── docker-compose.prod.yml +├── scripts/ +│ ├── init-docker-dev.sh +│ └── quality-check.sh +├── alembic/ +│ ├── env.py +│ └── versions/ +├── documentation/ +│ └── backend_documentation/ +├── tests/ +│ ├── unit/ +│ └── integration/ +├── src/ +│ └── app/ +│ ├── api/ +│ ├── config/ +│ ├── db/ +│ ├── metrics/ +│ ├── resources/ +│ ├── runtime/ +│ │ ├── rqs_state.py +│ │ └── actors/ +│ ├── samplers/ +│ ├── schemas/ +│ ├── main.py +│ └── simulation_run.py +├── poetry.lock +├── pyproject.toml +└── README.md +``` + +### **How to Start the Backend with Docker (Development)** -### 🐳 Docker-Compose Dev - -* **Containers** host external services (PostgreSQL) and run the FastAPI app. -* Your **local `src/` directory** is mounted into the backend container for hot-reload. -* **No tests, migrations, linting, or type checks** run inside these containers during development. - -**Why?** - -* **Fater feedback** on code changes -* **Full IDE support** (debugging, autocomplete, refactoring) -* **Speed**—no rebuilding images on every change - ---- - -### Local Quality & Testing Workflow - -All code quality tools, migrations, and tests execute on your host machine: - -| Task | Command | Notes | -| --------------------- | ---------------------------------------- | ------------------------------------------------- | -| **Lint & format** | `poetry run ruff check src tests` | Style and best-practice validations | -| **Type checking** | `poetry run mypy src tests` | Static type enforcement | -| **Unit tests** | `poetry run pytest -m "not integration"` | Fast, isolated tests—no DB required | -| **Integration tests** | `poetry run pytest -m integration` | Real-DB tests against Docker’s PostgreSQL | -| **DB migrations** | `poetry run alembic upgrade head` | Applies migrations to your local Docker-hosted DB | +To spin up the backend and its supporting services in development mode: -> **Rationale:** -> Running tests or Alembic migrations inside Docker images would force you to mount the full source tree, install dev dependencies in each build, and copy over configs—**slowing down** your feedback loop and **limiting** IDE features. +1. **Install & run Docker** on your machine. +2. **Clone** the repository and `cd` into its root. +3. Execute: + ```bash + bash ./scripts/init-docker-dev.sh + ``` + This will launch a **PostgreSQL** container and a **Backend** container that mounts your local `src/` folder with live-reload enabled. ---- +### **Development Architecture & Philosophy** -## CI/CD with GitHub Actions +We split responsibilities between Docker-managed services and local workflows. -We maintain two jobs on the `develop` branch: + * **Docker-Compose for Development**: Containers host external services (PostgreSQL) and run the FastAPI app. Your local `src/` directory is mounted into the backend container for hot-reloading. No tests, migrations, or linting run inside these containers during development. + * **Local Quality & Testing Workflow**: All code quality tools, migrations, and tests are executed on your host machine for faster feedback and full IDE support. -### 🔍 Quick (on Pull Requests) +| Task | Command | Notes | +| :--- | :--- | :--- | +| **Lint & format** | `poetry run ruff check src tests` | Style and best-practice validations | +| **Type checking** | `poetry run mypy src tests` | Static type enforcement | +| **Unit tests** | `poetry run pytest -m "not integration"` | Fast, isolated tests—no DB required | +| **Integration tests** | `poetry run pytest -m integration` | Real-DB tests against Docker’s PostgreSQL | +| **DB migrations** | `poetry run alembic upgrade head` | Applies migrations to your local Docker-hosted DB | -* Ruff & MyPy -* Unit tests only -* **No database** +**Rationale**: Running tests or Alembic migrations inside Docker images would slow down your feedback loop and limit IDE features by requiring you to mount the full source tree and install dev dependencies in each build. -### 🛠️ Full (on pushes to `develop`) +## **6. CI/CD with GitHub Actions** -* All **Quick** checks -* Start a **PostgreSQL** service container -* Run **Alembic** migrations -* Execute **unit + integration** tests -* Build the **Docker** image -* **Smoke-test** the `/health` endpoint +We maintain two jobs on the `develop` branch to ensure code quality and stability. -> **Guarantee:** Every commit in `develop` is style-checked, type-safe, DB-tested, and Docker-ready. +### **Quick (on Pull Requests)** ---- + * Ruff & MyPy checks + * Unit tests only + * **No database required** -## Summary +### **Full (on pushes to `develop`)** -1. **Docker-Compose** for services & hot-reload of the app code -2. **Local** execution of migrations, tests, and QA for speed and IDE integration -3. **CI pipeline** split into quick PR checks and full develop-branch validation + * All checks from the "Quick" suite + * Starts a **PostgreSQL** service container + * Runs **Alembic** migrations + * Executes the **full test suite** (unit + integration) + * Builds the **Docker** image + * **Smoke-tests** the `/health` endpoint of the built container +**Guarantee**: Every commit in `develop` is style-checked, type-safe, database-tested, and Docker-ready. +## **7. Limitations – v0.1 (First Public Release)** +1. **Network Delay Model** + * Only pure transport latency is simulated. + * Bandwidth-related effects (e.g., payload size, link speed, congestion) are NOT accounted for. +2. **Concurrency Model** + * The service exposes **async-only endpoints**. + * Execution runs on a single `asyncio` event-loop thread. + * No thread-pool workers or multi-process setups are supported yet; therefore, concurrency is limited to coroutine scheduling (cooperative, single-thread). +3. **CPU Core Allocation** + * Every server instance is pinned to **one physical CPU core**. + * Horizontal scaling must be achieved via multiple containers/VMs, not via multi-core utilization inside a single process. +These constraints will be revisited in future milestones once kernel-level context-switching costs, I/O bandwidth modeling, and multi-process orchestration are integrated. \ No newline at end of file diff --git a/documentation/backend_documentation/input_structure_for_the_simulation.md b/documentation/backend_documentation/input_structure_for_the_simulation.md deleted file mode 100644 index d28b090..0000000 --- a/documentation/backend_documentation/input_structure_for_the_simulation.md +++ /dev/null @@ -1,320 +0,0 @@ -### **FastSim — Simulation Input Schema** - -The `SimulationPayload` is the single, self-contained contract that defines an entire simulation run. Its architecture is guided by a core philosophy: to achieve maximum control over input data through robust, upfront validation. To implement this, we extensively leverage Pydantic's powerful validation capabilities and Python's `Enum` classes. This approach creates a strictly-typed and self-consistent schema that guarantees any configuration is validated *before* the simulation engine starts. - -This contract brings together three distinct but interconnected layers of configuration into one cohesive structure: - -1. **`rqs_input` (`RqsGeneratorInput`)**: Defines the **workload profile**—how many users are active and how frequently they generate requests. -2. **`topology_graph` (`TopologyGraph`)**: Describes the **system's architecture**—its components, resources, and the network connections between them. -3. **`sim_settings` (`SimulationSettings`)**: Configures **global simulation parameters**, such as total runtime and which metrics to collect. - -This layered design decouples the *what* (the system topology) from the *how* (the traffic pattern and simulation control), allowing for modular and reusable configurations. Adherence to our validation-first philosophy means every payload is rigorously parsed against this schema. By using a controlled vocabulary of `Enums` and the power of Pydantic, we guarantee that any malformed or logically inconsistent input is rejected upfront with clear, actionable errors, ensuring the simulation engine operates only on perfectly valid data. - ---- - -### **1. Component: Traffic Profile (`RqsGeneratorInput`)** - -This component specifies the dynamic behavior of users interacting with the system. It is built upon a foundation of shared constants and a reusable, rigorously validated random variable schema. This design ensures that any traffic profile is not only structurally correct but also logically sound before the simulation begins. - -#### **Global Constants** - -These enums provide a single source of truth for validation and default values, eliminating "magic strings" and ensuring consistency. - -| Constant Set | Purpose | Key Values | -| :--- | :--- | :--- | -| **`TimeDefaults`** (`IntEnum`) | Defines default values and validation bounds for time-based fields. | `USER_SAMPLING_WINDOW = 60`, `MIN_USER_SAMPLING_WINDOW = 1`, `MAX_USER_SAMPLING_WINDOW = 120` | -| **`Distribution`** (`StrEnum`) | Defines the canonical names of supported probability distributions. | `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"` | - ---- - -#### **Random Variable Schema (`RVConfig`)** - -At the core of the traffic generator is the `RVConfig`, a schema for defining stochastic variables. This allows critical parameters like user population and request rates to be modeled not as fixed numbers, but as draws from a probability distribution. Pydantic validators are used extensively to enforce correctness. - -```python -class RVConfig(BaseModel): - """class to configure random variables""" - mean: float - distribution: Distribution = Distribution.POISSON - variance: float | None = None - - @field_validator("mean", mode="before") - def ensure_mean_is_numeric(cls, v: object) -> float: - # ... implementation ... - - @model_validator(mode="after") - def default_variance(cls, model: "RVConfig") -> "RVConfig": - # ... implementation ... -``` - -##### **Built-in Validation Logic** - -Pydantic's validation system is leveraged to enforce several layers of correctness directly within the schema: - -| Check | Pydantic Hook | Rule & Rationale | -| :--- | :--- | :--- | -| **Numeric `mean` Enforcement** | `@field_validator("mean", mode="before")` | This validator intercepts the `mean` field *before* any type casting. It ensures the provided value is an `int` or `float`, raising an explicit `ValueError` for invalid types like strings (`"100"`) or nulls. This prevents common configuration errors and guarantees a valid numeric type for all downstream logic. | -| **Valid `distribution` Name** | `Distribution` (`StrEnum`) type hint | By type-hinting the `distribution` field with the `Distribution` enum, Pydantic automatically ensures that its value must be one of the predefined members (e.g., `"poisson"`, `"normal"`). Any typo or unsupported value (like `"Poisson"` with a capital 'P') results in an immediate validation error. | -| **Intelligent `variance` Defaulting** | `@model_validator(mode="after")` | This powerful validator runs *after* all individual fields have been validated. It enforces a crucial business rule: if `distribution` is `"normal"` **and** `variance` is not provided, the schema automatically sets `variance = mean`. This provides a safe, logical default and simplifies configuration for the user, while ensuring the model is always self-consistent. | - ---- - -#### **Payload Structure (`RqsGeneratorInput`)** - -This is the main payload for configuring the traffic workload. It composes the `RVConfig` schema and adds its own validation rules. - -| Field | Type | Validation & Purpose | -| :--- | :--- | :--- | -| `avg_active_users` | `RVConfig` | A random variable defining concurrent users. **Inherits all `RVConfig` validation**, ensuring its `mean`, `distribution`, and `variance` are valid. | -| `avg_request_per_minute_per_user` | `RVConfig` | A random variable for the user request rate. Also **inherits all `RVConfig` validation**. | -| `user_sampling_window` | `int` | The time duration (in seconds) for which the number of active users is held constant. Its value is **strictly bounded** by Pydantic's `Field` to be between `MIN_USER_SAMPLING_WINDOW` (1) and `MAX_USER_SAMPLING_WINDOW` (120). | - -##### **How the Generator Uses Each Field** - -The simulation evolves based on this robustly validated input: - -1. The timeline is divided into windows of `user_sampling_window` seconds. Because this value is range-checked upfront by Pydantic, the simulation is protected from invalid configurations like zero-length or excessively long windows. -2. At the start of each window, a number of active users, `U(t)`, is drawn from the `avg_active_users` distribution. The embedded `RVConfig` guarantees this distribution is well-defined. -3. Each of the `U(t)` users generates requests according to a rate drawn from `avg_request_per_minute_per_user`. - -Because every numeric input is type-checked and range-checked by Pydantic before the simulation begins, **the runtime engine never needs to defend itself** against invalid data. This makes the core simulation loop leaner, more predictable, and free from redundant error-handling logic. - -### **2. Component: System Blueprint (`TopologyGraph`)** - -The topology schema is the static blueprint of the digital twin you wish to simulate. It describes the system's components, their resources, their behavior, and how they are interconnected. To ensure simulation integrity, FastSim uses this schema to rigorously validate the entire system description upfront, rejecting any inconsistencies before the simulation begins. - -Of course. Here is the complete, consolidated, and highly detailed documentation for the `TopologyGraph` component, with all duplications removed and explanations expanded as requested. - ---- - -### **2. Component: System Blueprint (`TopologyGraph`)** - -The topology schema is the static blueprint of the digital twin you wish to simulate. It describes the system's components, their resources, their behavior, and how they are interconnected. To ensure simulation integrity, FastSim uses this schema to rigorously validate the entire system description upfront, rejecting any inconsistencies before the simulation begins. - -#### **Design Philosophy: A "Micro-to-Macro" Approach** - -The schema is built on a compositional, "micro-to-macro" principle. We start by defining the smallest indivisible units of work (`Step`) and progressively assemble them into larger, more complex structures (`Endpoint`, `Server`, and finally the `TopologyGraph`). - -This layered approach provides several key advantages that enhance the convenience and reliability of crafting simulations: - -* **Modularity and Reusability:** Core operations are defined once as `Steps` and can be reused across multiple `Endpoints`. This modularity simplifies configuration, as complex workflows can be built from a library of simple, well-defined blocks. -* **Local Reasoning, Global Safety:** Each model is responsible for its own internal consistency (e.g., a `Step` ensures its metric is valid for its kind). Parent models then enforce the integrity of the connections *between* these components (e.g., the `TopologyGraph` ensures all `Edges` connect to valid `Nodes`). This allows you to focus on one part of the configuration at a time, confident that the overall structure will be validated globally. -* **Clarity and Maintainability:** The hierarchy is intuitive and mirrors how developers conceptualize system architecture. It is clear how atomic operations roll up into endpoints, which are hosted on servers connected by a network. This makes configuration files easy to read, write, and maintain over time. -* **Guaranteed Robustness:** By catching all structural and referential errors before the simulation begins, this approach embodies the "fail-fast" principle. It guarantees that the SimPy engine operates on a valid, self-consistent model, eliminating a whole class of potential runtime bugs. - -#### **A Controlled Vocabulary: Topology Constants** - -The schema's robustness is founded on a controlled vocabulary defined by Python `Enum` classes. Instead of error-prone "magic strings" (e.g., `"cpu_operation"`), the schema uses these enums to define the finite set of legal values for categories like operation kinds, metrics, and node types. This design choice is critical for several reasons: - -* **Absolute Type-Safety:** Pydantic can validate input with certainty. Any value not explicitly defined in the corresponding `Enum` is immediately rejected, preventing subtle typos or incorrect values from causing difficult-to-debug runtime failures. -* **Enhanced Developer Experience:** IDEs and static analysis tools like `mypy` can provide auto-completion and catch invalid values during development, offering immediate feedback long before the simulation is run. -* **Single Source of Truth:** All valid categories are centralized. To add a new resource type or metric, a developer only needs to update the `Enum` definition, and the change propagates consistently throughout the validation logic. - -| Constant Enum | Purpose | -| :--- | :--- | -| **`EndpointStepIO`, `EndpointStepCPU`, `EndpointStepRAM`** | Defines the exhaustive list of valid `kind` values for a `Step`. | -| **`Metrics`** | Specifies the legal dictionary keys within a `Step`'s `step_metrics`. | -| **`SystemNodes`** | Enumerate the allowed `type` for nodes (e.g., `"server"`, `"client"`). | -| **`SystemEdges`** | Enumerate the allowed categories for connections between nodes. | - ---- - -### **Schema Hierarchy and In-Depth Validation** - -Here we break down each component of the topology, highlighting the specific Pydantic validators that enforce its correctness and the deep rationale behind these choices. - -#### **1. `Step`**: The Atomic Unit of Work -A `Step` represents a single, indivisible operation. Its validation is the cornerstone of ensuring that all work performed in the simulation is logical and well-defined. - -| Validation Check | Pydantic Hook | Rule & Rationale | -| :--- | :--- | :--- | -| **Coherence of `kind` and `metric`** | `@model_validator` | **Rule:** The `step_metrics` dictionary must contain *exactly one* entry, and its key must be the correct metric for the `Step`'s `kind`.

**Rationale:** This is the most critical validation on a `Step`. The one-to-one mapping is a deliberate design choice for simplicity and robustness. It allows the simulation engine to be deterministic: a `cpu_bound_operation` step is routed to the CPU resource, an `io_wait` step to an I/O event, etc. This avoids the immense complexity of modeling operations that simultaneously contend for multiple resource types (e.g., CPU and RAM). This validator enforces that clear, unambiguous contract, preventing illogical pairings like a RAM allocation step being measured in `cpu_time`. | -| **Positive Metric Values** | `PositiveFloat` / `PositiveInt` | **Rule:** All numeric values in `step_metrics` must be greater than zero.

**Rationale:** It is physically impossible to spend negative or zero time on an operation or allocate negative RAM. This validation uses Pydantic's constrained types to offload this fundamental sanity check, ensuring that only plausible, positive resource requests enter the system and keeping the core simulation logic free of defensive checks against nonsensical data. | - -#### **2. `Endpoint`**: Composing Workflows -An `Endpoint` defines a complete, user-facing operation (e.g., an API call like `/predict`) as an ordered sequence of `Steps`. - -| Validation Check | Pydantic Hook | Rule & Rationale | -| :--- | :--- | :--- | -| **Consistent Naming** | `@field_validator("endpoint_name")` | **Rule:** Automatically converts the `endpoint_name` to lowercase.

**Rationale:** This enforces a canonical representation for all endpoint identifiers. It eliminates ambiguity and potential bugs that could arise from inconsistent capitalization (e.g., treating `/predict` and `/Predict` as different endpoints). This simple normalization makes the configuration more robust and simplifies endpoint lookups within the simulation engine. | - -#### **3. System Nodes**: `Server` & `Client` -These models define the macro-components of your architecture where work is performed and resources are located. - -| Validation Check | Pydantic Hook | Rule & Rationale | -| :--- | :--- | :--- | -| **Standardized Node `type`** | `@field_validator("type")` | **Rule:** The `type` field must strictly match the expected `SystemNodes` enum member (e.g., a `Server` object must have `type: "server"`).

**Rationale:** This provides a "belt-and-suspenders" check. Even if a default is provided, this validation prevents a user from explicitly overriding a node's type to a conflicting value. It enforces a strict contract: a `Server` object is always and only a server. This prevents object state confusion and simplifies pattern matching in the simulation engine. | -| **Unique Node IDs** | `@model_validator` in `TopologyNodes` | **Rule:** All `id` fields across all `Server` nodes and the `Client` node must be unique.

**Rationale:** This is fundamental to creating a valid graph. Node IDs are the primary keys used to address components. If two nodes shared the same ID, any `Edge` pointing to that ID would be ambiguous. This global validator prevents such ambiguity, guaranteeing that every node in the system is uniquely identifiable, which is a precondition for the final referential integrity check. | - -#### **4. `Edge`**: Connecting the Components -An `Edge` represents a directed network link between two nodes, defining how requests flow through the system. - -| Validation Check | Pydantic Hook | Rule & Rationale | -| :--- | :--- | :--- | -| **No Self-Loops** | `@model_validator` | **Rule:** An edge's `source` ID cannot be the same as its `target` ID.

**Rationale:** In the context of a distributed system topology, a network call from a service to itself is a logical anti-pattern. Such an operation would typically be modeled as an internal process (i.e., another `Step`), not a network hop. This validator prevents this common configuration error and simplifies the routing logic by disallowing trivial cycles. | - -#### **5. `TopologyGraph`**: The Complete System -This is the root model that aggregates all `nodes` and `edges` and performs the final, most critical validation: ensuring referential integrity. - -| Validation Check | Pydantic Hook | Rule & Rationale | -| :--- | :--- | :--- | -| **Referential Integrity** | `@model_validator` | **Rule:** Every `edge.source` and `edge.target` ID must correspond to an actual node ID defined in `TopologyNodes`.

**Rationale:** This is the capstone validation that guarantees the structural integrity of the entire system graph. It prevents "dangling edges"—connections that point to non-existent nodes. Without this check, the simulation could start with a broken topology and crash unexpectedly at runtime when a request attempts to traverse a broken link. By performing this check *after* all nodes and edges have been parsed, we ensure that the system described is a complete and validly connected graph, fully embodying the "fail-fast" principle. | - -### **3. Component: Global Simulation Control (`SimulationSettings`)** - -This final component configures the simulation's execution parameters and, critically, determines what data is collected. It acts as the master control panel for the simulation run, governing both its duration and the scope of its output. - -#### **Payload Structure (`SimulationSettings`)** - -```python -class SimulationSettings(BaseModel): - """Global parameters that apply to the whole run.""" - total_simulation_time: int = Field(...) - enabled_sample_metrics: set[SampledMetricName] = Field( - default_factory=lambda: { - SampledMetricName.READY_QUEUE_LEN, - SampledMetricName.CORE_BUSY, - SampledMetricName.RAM_IN_USE, - }, - description="Which time‑series KPIs to collect by default.", - ) - enabled_event_metrics: set[EventMetricName] = Field( - default_factory=lambda: { - EventMetricName.RQS_LATENCY, - }, - description="Which per‑event KPIs to collect by default.", - ) -``` - -| Field | Type | Purpose & Validation | -| :--- | :--- | :--- | -| `total_simulation_time` | `int` | The total simulation horizon in seconds. Must be `>= MIN_SIMULATION_TIME` (1800s). Defaults to `3600`. | -| `enabled_sample_metrics` | `set[SampledMetricName]` | A set of metrics to be sampled at fixed intervals, creating a time-series (e.g., `"ready_queue_len"`, `"ram_in_use"`). | -| `enabled_event_metrics` | `set[EventMetricName]` | A set of metrics recorded only when specific events occur, with no time-series (e.g., `"rqs_latency"`, `"llm_cost"`). | - -We add standard default value for the metrics in case they will be omitted ---- - -#### **Design Rationale: Pre-validated, On-Demand Metrics for Robust and Efficient Collection** - -The design of the `settings` component, particularly the `enabled_*_metrics` fields, is centered on two core principles: **user-driven selectivity** and **ironclad validation**. The rationale behind this approach is to create a system that is both flexible and fundamentally reliable. - -##### **1. The Principle of User-Driven Selectivity** - -We recognize that data collection is not free; it incurs performance overhead in terms of both memory (to store the data) and CPU cycles (to record it). Not every simulation requires every possible metric. For instance: -* A simulation focused on CPU contention may not need detailed LLM cost tracking. -* A high-level analysis of end-to-end latency might not require fine-grained data on event loop queue lengths. - -By allowing the user to explicitly select only the metrics they need, we empower them to tailor the simulation to their specific analytical goals. This on-demand approach makes the simulator more efficient and versatile, avoiding the waste of collecting and processing irrelevant data. - -##### **2. The Power of Ironclad, Upfront Validation** - -This is where the design choice becomes critical for robustness. Simply allowing users to provide a list of strings is inherently risky due to potential typos or misunderstandings of metric names. Our schema mitigates this risk entirely through a strict, upfront validation contract. - -* **A Strict Contract via Enums:** The `enabled_sample_metrics` and `enabled_event_metrics` fields are not just sets of strings; they are sets of `SampledMetricName` and `EventMetricName` enum members. When Pydantic parses the input payload, it validates every single metric name provided by the user against these canonical `Enum` definitions. - -* **Immediate Rejection of Invalid Input:** If a user provides a metric name that is not a valid member of the corresponding enum (e.g., a typo like `"request_latncy"` or a misunderstanding like `"cpu_usage"` instead of `"core_busy"`), Pydantic immediately rejects the entire payload with a clear `ValidationError`. This happens *before* a single line of the simulation engine code is executed. - -##### **3. The Benefit: Guaranteed Runtime Integrity** - -This pre-validation provides a crucial and powerful guarantee to the simulation engine, leading to a safer and more efficient runtime: - -* **Safe, Error-Free Initialization:** At the very beginning of the simulation, the engine receives the *validated* set of metric names. It knows with absolute certainty the complete and exact set of metrics it needs to track. This allows it to safely initialize all necessary data collection structures (like dictionaries) at the start of the run. For example: - ```python - # This is safe because every key is guaranteed to be valid. - event_results = {metric_name: [] for metric_name in settings.enabled_event_metrics} - ``` - -* **Elimination of Runtime KeyErrors:** Because all dictionary keys are guaranteed to exist from the start, the core data collection logic within the simulation's tight event loop becomes incredibly lean and robust. The engine never needs to perform defensive, conditional checks like `if metric_name in event_results: ...`. It can directly and safely access the key: `event_results[metric_name].append(value)`. This completely eliminates an entire class of potential `KeyError` exceptions, which are notoriously difficult to debug in complex, asynchronous simulations. - -In summary, the design of `SimulationSettings` is a perfect example of the "fail-fast" philosophy. By forcing a clear and validated contract with the user upfront, we ensure that the data collection process is not only tailored and efficient but also fundamentally reliable. The engine operates with the confidence that the output data structures will perfectly and safely match the user's validated request, leading to a predictable and robust simulation from start to finish. ---- - -### **End-to-End Example (`SimulationPayload`)** - -The following JSON object shows how these three components combine into a single, complete `SimulationPayload` for a minimal client-server setup. - -```jsonc -{ - // Defines the traffic workload profile. - "rqs_input": { - "avg_active_users": { - "mean": 50, - "distribution": "poisson" - }, - "avg_request_per_minute_per_user": { - "mean": 5.0, - "distribution": "normal", - "variance": 1.0 - }, - "user_sampling_window": 60 - }, - // Describes the system's architectural blueprint. - "topology_graph": { - "nodes": { - "client": { - "id": "mobile_client", - "type": "client" - }, - "servers": [ - { - "id": "api_server", - "type": "server", - "server_resources": { - "cpu_cores": 4, - "ram_mb": 4096 - }, - "endpoints": [ - { - "endpoint_name": "/predict", - "steps": [ - { - "kind": "initial_parsing", - "step_metrics": { "cpu_time": 0.005 } - }, - { - "kind": "io_db", - "step_metrics": { "io_waiting_time": 0.050 } - } - ] - } - ] - } - ] - }, - "edges": [ - { - "source": "mobile_client", - "target": "api_server", - "latency": { - "distribution": "log_normal", - "mean": 0.04, - "variance": 0.01 - } - } - ] - }, - // Configures the simulation run and metric collection. - "settings": { - "total_simulation_time": 3600, - "enabled_sample_metrics": [ - "ready_queue_len", - "ram_in_use", - "throughput_rps" - ], - "enabled_event_metrics": [ - "rqs_latency" - ] - } -} -``` - -### **Key Takeaways** - -* **Single Source of Truth**: `Enum` classes centralize all valid string literals, providing robust, type-safe validation across the entire schema. -* **Layered Validation**: The `Constants → Component Schemas → SimulationPayload` hierarchy ensures that only well-formed and self-consistent configurations reach the simulation engine. -* **Separation of Concerns**: The three top-level keys (`rqs_input`, `topology_graph`, `settings`) clearly separate the workload, the system architecture, and simulation control, making configurations easier to read, write, and reuse. - - - - diff --git a/documentation/backend_documentation/runtime_and_resources.md b/documentation/backend_documentation/runtime_and_resources.md new file mode 100644 index 0000000..6c84bd2 --- /dev/null +++ b/documentation/backend_documentation/runtime_and_resources.md @@ -0,0 +1,283 @@ +Of course. This is an excellent request. A deep dive into the "why" and the real-world analogies is what makes documentation truly valuable. + +Here is the comprehensive, detailed documentation for the FastSim Runtime Layer, written in English, incorporating all your requests. + +----- + +# **FastSim — The Runtime Layer Documentation** + +*(Version July 2025 – Aligned with `app/runtime` and `app/resources`)* + +## **1. The Runtime Philosophy: From Blueprint to Living System** + +If the `SimulationPayload` is the static **blueprint** of a system, the `runtime` package is the **engine** that brings that blueprint to life. It translates a validated, declarative configuration into a dynamic, interacting set of processes within a SimPy simulation environment. The entire design is guided by a few core principles to ensure robustness, testability, and a faithful reflection of real-world systems. + +### **The Actor Model & Process Management** + +Distributed systems are, by nature, composed of independent components that communicate with each other concurrently. To model this, we've adopted an **Actor Model**. Each major component of the architecture (`Generator`, `Server`, `Client`) is implemented as an "Actor"—a self-contained object with its own internal state and behavior that communicates with other actors by sending and receiving messages (`RequestState` objects). + +SimPy's process management is a perfect fit for this model. It uses **cooperative multitasking** within a single-threaded event loop. An actor "runs" until it `yield`s control to the SimPy environment, typically to wait for a duration (`timeout`), a resource (`Container.get`), or an event (`Store.get`). This elegantly mimics modern, non-blocking I/O frameworks (like Python's `asyncio`, Node.js, or Go's goroutines) where a process performs work until it hits an I/O-bound operation, at which point it yields control, allowing the event loop to run other ready tasks. + +### **The "Validation-First" Contract** + +A crucial design decision is the strict separation between configuration and execution. The `runtime` layer operates under the assumption that the input `SimulationPayload` is **100% valid and logically consistent**. This "validation-first" contract means the runtime code is streamlined and free of defensive checks. It doesn't need to validate if a server ID exists or if a resource is defined; it can focus entirely on its core responsibility: accurately modeling the passage of time and contention for resources. + +----- + +## **2. High-Level Architecture & Data Flow** + +The simulation is a choreography of Actors passing a `RequestState` object between them. Communication and resource access are mediated exclusively by the SimPy environment, ensuring all interactions are captured on the simulation timeline. + +```text + .start() .transport(state) .start() +┌───────────┐ Starts ┌───────────┐ Forwards ┌───────────┐ Processes ┌───────────┐ +│ ├─────────►│ ├─────────────►│ ├────────────►│ │ +│ Generator │ │ Edge │ │ Server │ │ Client │ +│ ◄─────────┤ ◄─────────────┤ ◄────────────┤ │ +└───────────┘ └───────────┘ └───────────┘ └───────────┘ + ▲ Creates ▲ Delays RequestState ▲ Consumes ▲ Finishes + │ RequestState │ (Latency & Drops) │ Resources │ Request +``` + + * **Actors** (`runtime/actors/`): The active, stateful processes that perform work (`RqsGeneratorRuntime`, `ServerRuntime`, `ClientRuntime`, `EdgeRuntime`). + * **State Object** (`RequestState`): The message passed between actors. It acts as a digital passport, collecting stamps (`Hop` objects) at every stage of its journey. + * **Resource Registry** (`resources/`): A central authority that creates and allocates finite system resources (CPU cores, RAM) to the actors that need them. + +----- + +## **3. The Anatomy of a Request: State & History** + +At the heart of the simulation is the `RequestState` object, which represents a single user request flowing through the system. + +### **3.1. `Hop` – The Immutable Breadcrumb** + +A `Hop` is a `NamedTuple` that records a single, atomic event in a request's lifecycle: its arrival at a specific component at a specific time. Being an immutable `NamedTuple` makes it lightweight and safe to use in analysis. + +### **3.2. `RequestState` – The Digital Passport** + +```python +@dataclass +class RequestState: + id: int + initial_time: float + finish_time: float | None = None + history: list[Hop] = field(default_factory=list) +``` + +This mutable dataclass is the sole carrier of a request's identity and history. + + * `id`: A unique identifier for the request, assigned by the generator. + * `initial_time`: The simulation timestamp (`env.now`) when the request was created. + * `finish_time`: The timestamp when the request completes its lifecycle. It remains `None` until then. + * `history`: A chronologically ordered list of `Hop` objects, creating a complete, traceable path of the request's journey. + +#### **Real-World Analogy** + +Think of `RequestState` as a request context in a modern microservices architecture. The `id` is analogous to a **Trace ID** (like from OpenTelemetry or Jaeger). The `history` of `Hop` objects is the collection of **spans** associated with that trace, providing a detailed, end-to-end view of where the request spent its time, which is invaluable for performance analysis and debugging. + +----- + +## **4 The Resource Layer — Modelling Contention ⚙️** + +In real infrastructures every machine has a hard ceiling: only *N* CPU cores, only *M* MB of RAM. +FastSim mirrors that physical constraint through the **Resource layer**, which exposes pre-filled SimPy containers that actors must draw from. If a token is not available the coroutine simply blocks — giving you back-pressure “for free”. + +--- + +### **4.1 `ResourcesRuntime` — The Central Bank of Resources** + +| Responsibility | Implementation detail | +| --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Discover capacity** | Walks the *validated* `TopologyGraph.nodes.servers`, reading `cpu_cores` and `ram_mb` from each `ServerResources` spec. | +| **Mint containers** | Calls `build_containers(env, spec)` which returns
`{"CPU": simpy.Container(init=cpu_cores), "RAM": simpy.Container(init=ram_mb)}` — the containers start **full** so a server can immediately consume tokens. | +| **Registry map** | Stores them in a private dict `_by_server: dict[str, ServerContainers]`. | +| **Public API** | `registry[server_id] → ServerContainers` (raises `KeyError` if the ID is unknown). | + +```python +registry = ResourcesRuntime(env, topology) +cpu_bucket = registry["srv-1"]["CPU"] # simpy.Container, level == capacity at t=0 +ram_bucket = registry["srv-1"]["RAM"] +``` + +Because the schema guarantees that every `server_id` is unique and every +server referenced in an edge actually exists, `ResourcesRuntime` never needs +defensive checks beyond the simple dictionary lookup. + +--- + +### **4.2 How Contention Emerges** + +* **CPU** – Each `yield CPU.get(1)` represents “claiming one core”. + When all tokens are gone the coroutine waits, modelling a thread-pool or worker saturation. +* **RAM** – `yield RAM.get(amount)` blocks until enough memory is free. + Large requests can starve, reproducing OOM throttling or JVM heap pressure. +* **Automatic fairness** – SimPy’s event loop resumes whichever coroutine became ready first, giving a natural first-come, first-served order. + +> **No bespoke semaphore or queueing code is required** — the SimPy +> containers *are* the semaphore. + +--- + +### **Real-World Analogy** + +| Runtime Component | Real Infrastructure Counterpart | +| -------------------- | --------------------------------------------------------------------------------------------------------- | +| `ResourcesRuntime` | A **cloud provider control plane** or **Kubernetes scheduler**: single source of truth for node capacity. | +| CPU container tokens | **Worker threads / processes** in Gunicorn, uWSGI, or an OS CPU-quota. | +| RAM container tokens | **cgroup memory limit** or a pod’s allocatable memory; once exhausted new workloads must wait. | + +Just like a Kubernetes scheduler won’t place a pod if a node lacks free CPU/RAM, +FastSim won’t let an actor proceed until it obtains the necessary tokens. + +## **5. The Actors: Bringing the System to Life** + +Actors are the core drivers of the simulation. Each represents a key component of the system architecture. They all expose a consistent `.start()` method, which registers their primary behavior as a process with the SimPy environment, allowing for clean and uniform orchestration. + +### **5.1. RqsGeneratorRuntime: The Source of Load** + +This actor's sole purpose is to create `RequestState` objects according to a specified stochastic model, initiating all traffic in the system. + +| Key Parameter (`__init__`) | Meaning | +| :--- | :--- | +| `env` | The SimPy simulation environment. | +| `out_edge` | The `EdgeRuntime` instance to which newly created requests are immediately sent. | +| `rqs_generator_data` | The validated Pydantic schema containing the statistical model for traffic (e.g., user count, request rate). | +| `rng` | A NumPy random number generator instance for deterministic, reproducible randomness. | + +**Core Logic (`.start()`):** +The generator's main process uses a statistical sampler (e.g., `poisson_poisson_sampling`) to yield a series of inter-arrival time gaps. It waits for each gap (`yield self.env.timeout(gap)`), then creates a new `RequestState`, records its first `Hop`, and immediately forwards it to the outbound edge via `out_edge.transport()`. + +**Real-World Analogy:** +The `RqsGeneratorRuntime` represents the collective behavior of your entire user base or the output of an upstream service. It's equivalent to a **load-testing tool** like **k6, Locust, or JMeter**, configured to simulate a specific traffic pattern (e.g., 500 users with an average of 30 requests per minute). + +----- + +### **5.2. EdgeRuntime: The Network Fabric 🚚** + +This actor models the connection *between* two nodes. It simulates the two most important factors of network transit: latency and unreliability. + +| Key Parameter (`__init__`) | Meaning | +| :--- | :--- | +| `env` | The SimPy simulation environment. | +| `edge_config` | The Pydantic `Edge` model containing this link's configuration (latency distribution, dropout rate). | +| `target_box` | A `simpy.Store` that acts as the "inbox" for the destination node. | +| `rng` | The random number generator for sampling latency and dropout. | + +**Core Logic (`.transport()`):** +Unlike other actors, `EdgeRuntime`'s primary method is `.transport(state)`. When called, it doesn't block the caller. Instead, it spawns a new, temporary SimPy process (`_deliver`) for that specific `RequestState`. This process: + +1. Checks for a **dropout** (packet loss) based on `dropout_rate`. If dropped, the request's `finish_time` is set, and its journey ends. +2. If not dropped, it samples a **latency** value from the configured probability distribution. +3. It `yield`s a `timeout` for the sampled latency, simulating network travel time. +4. After the wait, it records a successful `Hop` and places the `RequestState` into the `target_box` of the destination node. + +**Real-World Analogy:** +An `EdgeRuntime` is a direct analog for a **physical or virtual network link**. This could be the public **internet** between a user and your server, a **LAN connection** between two services in a data center, or a **VPC link** between two cloud resources. `latency` represents round-trip time (RTT), and `dropout_rate` models packet loss. + +----- + +### **5.3 `ServerRuntime` — The Workhorse 📦** + +`ServerRuntime` models an application server that owns finite CPU/RAM resources and executes a chain of steps for every incoming request. +With the 2025 refactor it now uses a **dispatcher / handler** pattern: the dispatcher sits in an infinite loop, and each request is handled in its own SimPy subprocess. This enables many concurrent in-flight requests while keeping the code easy to reason about. + +| `__init__` parameter | Meaning | +| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **`env`** | The shared `simpy.Environment`. Every timeout and resource operation is scheduled here. | +| **`server_resources`** | A `ServerContainers` mapping (`{"CPU": Container, "RAM": Container}`) created by `ResourcesRuntime`. The containers are **pre-filled** (`level == capacity`) so the server can immediately pull tokens. | +| **`server_config`** | The validated Pydantic `Server` model: server-wide ID, resource spec, and a list of `Endpoint` objects (each endpoint is an ordered list of `Step`s). | +| **`out_edge`** | The `EdgeRuntime` (or stub) that receives the `RequestState` once processing finishes. | +| **`server_box`** | A `simpy.Store` acting as the server’s inbox. Up-stream actors drop `RequestState`s here. | +| **`rng`** | Instance of `numpy.random.Generator`; defaults to `default_rng()`. Used to pick a random endpoint. | + +--- + +#### **Public API** + +```python +def start(self) -> simpy.Process +``` + +Registers the **dispatcher** coroutine in the environment and returns the created `Process`. + +--- + +#### **Internal Workflow** + +```text +┌───────────┐ server_box.get() ┌──────────────┐ +│ dispatcher │ ────────────────────► │ handle_req N │ +└───────────┘ spawn new process └──────────────┘ + │ + ▼ + RAM get → CPU/IO steps → RAM put → out_edge.transport() +``` + +1. **Dispatcher loop** + + ```python + while True: + raw_state = yield self.server_box.get() # blocks until a request arrives + state = cast(RequestState, raw_state) + self.env.process(self._handle_request(state)) # fire-and-forget + ``` + + *Spawning a new process per request mimics worker thread concurrency.* + +2. **Handler coroutine (`_handle_request`)** + + | Stage | Implementation detail | + | ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | + | **Record arrival** | `state.record_hop(SystemNodes.SERVER, self.server_config.id, env.now)` – leaves a breadcrumb for tracing. | + | **Endpoint selection** | Uniform random index `rng.integers(0, len(endpoints))`. (Hook point for custom routing later.) | + | **Reserve RAM (back-pressure)** | Compute `total_ram` (sum of all `StepOperation.NECESSARY_RAM`). `yield RAM.get(total_ram)`. If not enough RAM is free, the coroutine blocks, creating natural memory pressure. | + | **Execute steps in order** | | + | – CPU-bound step | `yield CPU.get(1)` → `yield env.timeout(cpu_time)` → `yield CPU.put(1)` – exactly one core is busy for the duration. | + | – I/O-bound step | `yield env.timeout(io_wait)` – no core is held, modelling non-blocking I/O. | + | **Release RAM** | `yield RAM.put(total_ram)`. | + | **Forward** | `out_edge.transport(state)` – hands the request to the next hop without waiting for network latency. | + +--- + +#### **Concurrency Guarantees** + +* **CPU contention** – because CPU is a token bucket (`simpy.Container`) the maximum number of concurrent CPU-bound steps equals `cpu_cores`. +* **RAM contention** – large requests can stall entirely until enough RAM frees up, accurately modelling out-of-memory throttling. +* **Non-blocking I/O** – while a handler waits on an I/O step it releases the core, allowing other handlers to run; this mirrors an async framework where the event loop can service other sockets. + +--- + +#### **Real-World Analogy** + +| Runtime concept | Real server analogue | +| ------------------------------------- | ------------------------------------------------------------------------------------------ | +| `server_box` | A web server’s accept queue. | +| `CPU.get(1)` | Obtaining one worker thread/process in Gunicorn, uWSGI, or a Node.js “JS thread”. | +| `env.timeout(io_wait)` without a core | An `await` on a database or HTTP call; the worker is idle while the OS handles the socket. | +| RAM token bucket | Process resident set or container memory limit; requests block when heap is exhausted. | + +Thus a **CPU-bound step** is a tight Python loop holding the GIL, while an **I/O-bound step** is `await cursor.execute(...)` that frees the event loop. + +--- + + +### **5.4. ClientRuntime: The Destination** + +This actor typically represents the end-user or system that initiated the request, serving as the final destination. + +| Key Parameter (`__init__`) | Meaning | +| :--- | :--- | +| `env` | The SimPy simulation environment. | +| `out_edge` | The `EdgeRuntime` to use if the client needs to forward the request (acting as a relay). | +| `client_box` | This client's "inbox". | +| `completed_box` | A global `simpy.Store` where all finished requests are placed for final collection and analysis. | + +**Core Logic (`.start()`):** +The client pulls requests from its `client_box`. It then makes a critical decision: + + * **If the request is new** (coming directly from the `RqsGeneratorRuntime`), it acts as a **relay**, immediately forwarding the request to its `out_edge`. + * **If the request is returning** (coming from a `ServerRuntime`), it acts as the **terminus**. It sets the request's `finish_time`, completing its lifecycle, and puts it into the global `completed_box`. + +**Design Note & Real-World Analogy:** +The current logic for this decision—`if state.history[-2].component_type != SystemNodes.GENERATOR`—is **fragile**. While it works, it's not robust. A future improvement would be to add a more explicit routing mechanism. +In the real world, the `ClientRuntime` could be a user's **web browser**, a **mobile application**, or even a **Backend-For-Frontend (BFF)** service that both initiates requests and receives the final aggregated responses. \ No newline at end of file diff --git a/documentation/backend_documentation/simulation_input.md b/documentation/backend_documentation/simulation_input.md new file mode 100644 index 0000000..aa27d58 --- /dev/null +++ b/documentation/backend_documentation/simulation_input.md @@ -0,0 +1,228 @@ +Of course. Here is the complete documentation, translated into English, based on the new Pydantic schemas. + +----- + +### **FastSim — Simulation Input Schema** + +The `SimulationPayload` is the single, self-contained contract that defines an entire simulation run. Its architecture is guided by a core philosophy: to achieve maximum control over input data through robust, upfront validation. To implement this, we extensively leverage Pydantic's powerful validation capabilities and Python's `Enum` classes. This approach creates a strictly-typed and self-consistent schema that guarantees any configuration is validated *before* the simulation engine starts. + +This contract brings together three distinct but interconnected layers of configuration into one cohesive structure: + +1. **`rqs_input` (`RqsGeneratorInput`)**: Defines the **workload profile**—how many users are active and how frequently they generate requests—and acts as the **source node** in our system graph. +2. **`topology_graph` (`TopologyGraph`)**: Describes the **system's architecture**—its components, resources, and the network connections between them, represented as a directed graph. +3. **`sim_settings` (`SimulationSettings`)**: Configures **global simulation parameters**, such as total runtime and which metrics to collect. + +This layered design decouples the *what* (the system topology) from the *how* (the traffic pattern and simulation control), allowing for modular and reusable configurations. Adherence to our validation-first philosophy means every payload is rigorously parsed against this schema. By using a controlled vocabulary of `Enums` and the power of Pydantic, we guarantee that any malformed or logically inconsistent input is rejected upfront with clear, actionable errors, ensuring the simulation engine operates only on perfectly valid data. + +----- + +## **1. The System Graph (`topology_graph` and `rqs_input`)** + +At the core of FastSim is the representation of the system as a **directed graph**. The **nodes** represent the architectural components (like servers, clients, and the traffic generator itself), while the **edges** represent the directed network connections between them. This graph-based approach allows for flexible and realistic modeling of request flows through distributed systems. + +### **Design Philosophy: A "Micro-to-Macro" Approach** + +The schema is built on a compositional, "micro-to-macro" principle. We start by defining the smallest indivisible units of work (`Step`) and progressively assemble them into larger, more complex structures (`Endpoint`, `Server`, and finally the `TopologyGraph`). + +This layered approach provides several key advantages: + + * **Modularity and Reusability:** Core operations are defined once as `Steps` and can be reused across multiple `Endpoints`. + * **Local Reasoning, Global Safety:** Each model is responsible for its own internal consistency (e.g., a `Step` ensures its metric is valid for its kind). Parent models then enforce the integrity of the connections *between* these components (e.g., the `TopologyGraph` ensures all `Edges` connect to valid `Nodes`). + * **Guaranteed Robustness:** By catching all structural and referential errors before the simulation begins, this approach embodies the "fail-fast" principle. It guarantees that the SimPy engine operates on a valid, self-consistent model. + +### **A Controlled Vocabulary: Topology Constants** + +The schema's robustness is founded on a controlled vocabulary defined by Python `Enum` classes. Instead of error-prone "magic strings," the schema uses these enums to define the finite set of legal values for categories like operation kinds, metrics, and node types. + +| Enum | Purpose | +| :------------------------- | :------------------------------------------------------------------------ | +| **`EndpointStepCPU`, `EndpointStepRAM`, `EndpointStepIO`** | Defines the exhaustive list of valid `kind` values for a `Step`. | +| **`StepOperation`** | Specifies the legal dictionary keys within a `Step`'s `step_operation`. | +| **`SystemNodes`** | Enumerate the allowed `type` for nodes (e.g., `"server"`, `"client"`, `"generator"`). | +| **`SystemEdges`** | Enumerate the allowed categories for connections between nodes. | + +----- + +### **Schema Hierarchy and In-Depth Validation** + +Here we break down each component of the topology, highlighting the specific Pydantic validators that enforce its correctness. + +#### **Random Variable Schema (`RVConfig`)** + +At the core of both the traffic generator and network latencies is `RVConfig`, a schema for defining stochastic variables. This allows critical parameters to be modeled not as fixed numbers, but as draws from a probability distribution. + +| Check | Pydantic Hook | Rule & Rationale | +| :---------------------------- | :---------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Numeric `mean` Enforcement** | `@field_validator("mean", mode="before")` | Intercepts the `mean` field and ensures the provided value is an `int` or `float`, rejecting invalid types. This guarantees a valid numeric type for all downstream logic. | +| **Valid `distribution` Name** | `Distribution` (`StrEnum`) type hint | Pydantic automatically ensures that the `distribution` field's value must be one of the predefined members (e.g., `"poisson"`, `"normal"`). Any typo or unsupported value results in an immediate validation error. | +| **Intelligent `variance` Defaulting** | `@model_validator(mode="after")` | Enforces a crucial business rule: if `distribution` is `"normal"` or `"log_normal"` **and** `variance` is not provided, the schema automatically sets `variance = mean`. This provides a safe, logical default. | + +#### **1. `Step`: The Atomic Unit of Work** + +A `Step` represents a single, indivisible operation. + +| Validation Check | Pydantic Hook | Rule & Rationale | +| :------------------------------- | :--------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Coherence of `kind` and `step_operation`** | `@model_validator` | **Rule:** The `step_operation` dictionary must contain *exactly one* entry, and its key (`StepOperation`) must be the correct metric for the `Step`'s `kind`. \\ **Rationale:** This is the most critical validation on a `Step`. It prevents illogical pairings like a RAM allocation step being measured in `cpu_time`. It ensures every step has a clear, unambiguous impact on a single system resource. | +| **Positive Metric Values** | `PositiveFloat` / `PositiveInt` | **Rule:** All numeric values in `step_operation` must be greater than zero. \\ **Rationale:** It is physically impossible to spend negative or zero time on an operation. This ensures that only plausible resource requests enter the system. | + +#### **2. `Endpoint`: Composing Workflows** + +An `Endpoint` defines a complete operation (e.g., an API call like `/predict`) as an ordered sequence of `Steps`. + +| Validation Check | Pydantic Hook | Rule & Rationale | +| :-------------------- | :--------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Consistent Naming** | `@field_validator("endpoint_name")` | **Rule:** Automatically converts the `endpoint_name` to lowercase. \\ **Rationale:** This enforces a canonical representation, eliminating ambiguity from inconsistent capitalization (e.g., treating `/predict` and `/Predict` as the same). | + +#### **3. System Nodes: `Server`, `Client`, and `RqsGeneratorInput`** + +These models define the macro-components of your architecture where work is performed, resources are located, and requests originate. + +| Validation Check | Pydantic Hook | Rule & Rationale | +| :-------------------------------- | :---------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Standardized Node `type`** | `@field_validator("type")` | **Rule:** The `type` field must strictly match the expected `SystemNodes` enum member (e.g., a `Server` object must have `type: "server"`). \\ **Rationale:** This enforces a strict contract: a `Server` object is always and only a server, preventing object state confusion. | +| **Unique Node IDs** | `@model_validator` in `TopologyNodes` | **Rule:** All `id` fields across all `Server` nodes, the `Client` node, and the `RqsGeneratorInput` node must be unique. \\ **Rationale:** This is fundamental to creating a valid graph. Node IDs are the primary keys. If two nodes shared the same ID, any `Edge` pointing to that ID would be ambiguous. | +| **Workload Distribution Constraints** | `@field_validator` in `RqsGeneratorInput` | **Rule:** The `avg_request_per_minute_per_user` field must use a Poisson distribution. The `avg_active_users` field must use a Poisson or Normal distribution. \\ **Rationale:** This is a current restriction of the simulation engine, which has a joint sampler optimized only for these combinations. This validator ensures that only supported configurations are accepted. | + +#### **4. `Edge`: Connecting the Components** + +An `Edge` represents a directed network link between two nodes. + +| Validation Check | Pydantic Hook | Rule & Rationale | +| :---------------- | :----------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **No Self-Loops** | `@model_validator` | **Rule:** An edge's `source` ID cannot be the same as its `target` ID. \\ **Rationale:** A network call from a service to itself is a logical anti-pattern in a system topology. Such an operation should be modeled as an internal process (i.e., another `Step`), not a network hop. | +| **Unique Edge IDs** | `@model_validator` in `TopologyGraph` | **Rule:** All `id` fields of the `Edge`s must be unique. \\ **Rationale:** Ensures that every network connection is uniquely identifiable, which is useful for logging and debugging. | + +#### **5. `TopologyGraph`: The Complete System** + +This is the root model that aggregates all `nodes` and `edges` and performs the final, most critical validation: ensuring referential integrity. + +| Validation Check | Pydantic Hook | Rule & Rationale | +| :---------------------- | :----------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Referential Integrity** | `@model_validator` | **Rule:** Every `edge.source` and `edge.target` ID must correspond to an actual node ID defined in the topology. \\ **Rationale:** This is the capstone validation that guarantees the structural integrity of the entire system graph. It prevents "dangling edges"—connections that point to non-existent nodes—ensuring the described system is a complete and validly connected graph. | + +----- + +## **2. Global Simulation Control (`SimulationSettings`)** + +This final component configures the simulation's execution parameters and, critically, determines what data is collected. + +#### **Payload Structure (`SimulationSettings`)** + +| Field | Type | Purpose & Validation | +| :----------------------- | :------------------------- | :------------------------------------------------------------------------------------------------------ | +| `total_simulation_time` | `int` | The total simulation horizon in seconds. Must be `>=` a defined minimum (e.g., 1800s). | +| `enabled_sample_metrics` | `set[SampledMetricName]` | A set of metrics to be sampled at fixed intervals, creating a time-series (e.g., `"ready_queue_len"`, `"ram_in_use"`). | +| `enabled_event_metrics` | `set[EventMetricName]` | A set of metrics recorded only when specific events occur (e.g., `"rqs_latency"`). | + +### **Design Rationale: Pre-validated, On-Demand Metrics** + +The design of the `settings`, particularly the `enabled_*_metrics` fields, is centered on **user-driven selectivity** and **ironclad validation**. + +1. **Selectivity:** Data collection has a performance cost. By allowing the user to explicitly select only the metrics they need, we make the simulator more efficient and versatile. + +2. **Ironclad Validation:** Simply allowing users to provide a list of strings is risky. Our schema mitigates this risk by validating every metric name provided by the user against the canonical `Enum` definitions (`SampledMetricName`, `EventMetricName`). If a user provides a misspelled or invalid metric name (e.g., `"request_latncy"`), Pydantic immediately rejects the entire payload *before* the simulation engine runs. + +This guarantees that the simulation engine can safely initialize all necessary data collection structures at the start of the run, completely eliminating an entire class of potential `KeyError` exceptions at runtime. + +----- + +## **End-to-End Example (`SimulationPayload`)** + +The following JSON object shows how these components combine into a single `SimulationPayload` for a minimal client-server setup, updated according to the new schema. + +```jsonc +{ + // Defines the workload profile as a generator node. + "rqs_input": { + "id": "mobile_user_generator", + "type": "generator", + "avg_active_users": { + "mean": 50, + "distribution": "poisson" + }, + "avg_request_per_minute_per_user": { + "mean": 5.0, + "distribution": "poisson" + }, + "user_sampling_window": 60 + }, + // Describes the system's architecture as a graph. + "topology_graph": { + "nodes": { + "client": { + "id": "entry_point_client", + "type": "client" + }, + "servers": [ + { + "id": "api_server", + "type": "server", + "server_resources": { + "cpu_cores": 4, + "ram_mb": 4096, + "db_connection_pool": 10 + }, + "endpoints": [ + { + "endpoint_name": "/predict", + "steps": [ + { + "kind": "initial_parsing", + "step_operation": { "cpu_time": 0.005 } + }, + { + "kind": "io_db_query", + "step_operation": { "io_waiting_time": 0.050 } + } + ] + } + ] + } + ] + }, + "edges": [ + { + "id": "client_to_generator", + "source": "entry_point_client", + "target": "mobile_user_generator", + "latency": { + "distribution": "log_normal", + "mean": 0.001, + "variance": 0.0001 + } + }, + { + "id": "generator_to_api", + "source": "mobile_user_generator", + "target": "api_server", + "latency": { + "distribution": "log_normal", + "mean": 0.04, + "variance": 0.01 + }, + "probability": 1.0, + "dropout_rate": 0.0 + } + ] + }, + // Configures the simulation run and metric collection. + "sim_settings": { + "total_simulation_time": 3600, + "enabled_sample_metrics": [ + "ready_queue_len", + "ram_in_use", + "core_busy" + ], + "enabled_event_metrics": [ + "rqs_latency" + ] + } +} +``` + +### **Key Takeaways** + + * **Single Source of Truth**: `Enum` classes centralize all valid string literals, providing robust, type-safe validation across the entire schema. + * **Layered Validation**: The `Constants → Component Schemas → SimulationPayload` hierarchy ensures that only well-formed and self-consistent configurations reach the simulation engine. + * **Separation of Concerns**: The three top-level keys (`rqs_input`, `topology_graph`, `sim_settings`) clearly separate the workload, the system architecture, and simulation control, making configurations easier to read, write, and reuse. \ No newline at end of file diff --git a/documentation/DEV_WORKFLOW_GUIDE.md b/documentation/dev_workflow_guide.md similarity index 57% rename from documentation/DEV_WORKFLOW_GUIDE.md rename to documentation/dev_workflow_guide.md index c08cea4..b06ef1c 100644 --- a/documentation/DEV_WORKFLOW_GUIDE.md +++ b/documentation/dev_workflow_guide.md @@ -13,47 +13,65 @@ The project is built upon the following core technologies: - **Caching**: Redis - **Containerization**: Docker -### 2.1. Backend Service (`FastSim-backend`) +### 2.1 Backend Service (`FastSim-backend`) -This repository contains all code related to the FastAPI backend service. Its primary responsibility is to handle business logic, interact with the database, and expose a RESTful API. +The repository hosts the entire FastAPI backend for FastSim. +Its job is to expose the REST API, run the discrete-event simulation, talk to the database, and provide metrics. -**Folder Structure:** ``` -project-backend/ -├── .github/ # CI workflows: tests, builds, and publishes the Docker image -│ └── workflows/ -│ └── main.yml -├── src/ -│ └── app/ # Main Python package -│ ├── api/ # API routers & endpoints -│ ├── db/ # Database session management & base models -│ ├── models/ # SQLAlchemy ORM models (database table definitions) -│ ├── schemas/ # Pydantic schemas for validation/serialization -│ ├── core/ # Business logic (services, utilities, etc.) -│ ├── config/ # settings.py & constants.py for configuration -│ └── main.py # FastAPI application entrypoint -├── Alembic/ # Database migrations managed with Alembic -│ ├── versions/ # Generated migration files -│ ├── env.py # Alembic environment setup -│ └── script.py.mako # Template for new migrations -├── tests/ # Unit and integration tests -├── alembic.ini # Alembic configuration file -├── py.ini # Python tool configurations (flake8, mypy, etc.) -├── .env.example # Template for environment variables -├── .gitignore # Files and paths ignored by Git -├── docker-compose.override.yml # Local overrides (e.g., hot-reload) -├── docker-compose.test.yml # Docker Compose setup for isolated testing -├── docker-compose.yml # Base Docker Compose configuration for development -├── Dockerfile # Instructions to build the production Docker image -├── poetry.lock # Locked dependency versions for Poetry -├── pyproject.toml # Poetry project configuration (including src layout) -└── README.md # Setup instructions and project overview - +fastsim-backend/ +├── Dockerfile +├── docker_fs/ # docker-compose for dev & prod +│ ├── docker-compose.dev.yml +│ └── docker-compose.prod.yml +├── scripts/ # helper bash scripts (lint, dev-startup, …) +│ ├── init-docker-dev.sh +│ └── quality-check.sh +├── alembic/ # DB migrations (versions/ contains revision files) +│ ├── env.py +│ └── versions/ +├── documentation/ # project vision & low-level docs +│ └── backend_documentation/ +│ └── … +├── tests/ # unit & integration tests +│ ├── unit/ +│ └── integration/ +├── src/ # **application code lives here** +│ └── app/ +│ ├── api/ # FastAPI routers & endpoint handlers +│ ├── config/ # Pydantic Settings + constants +│ ├── db/ # SQLAlchemy base, sessions, initial seed utilities +│ ├── metrics/ # helpers to compute/aggregate simulation KPIs +│ ├── resources/ # SimPy resource registry (CPU/RAM containers, etc.) +│ ├── runtime/ # simulation core +│ │ ├── rqs_state.py # RequestState & Hop +│ │ └── actors/ # SimPy “actors”: Edge, Server, Client, RqsGenerator +│ ├── samplers/ # stochastic samplers (Gaussian-Poisson, etc.) +│ ├── schemas/ # Pydantic input/output models +│ ├── main.py # FastAPI application factory / ASGI entry-point +│ └── simulation_run.py # CLI utility to run a sim outside of HTTP layer +├── poetry.lock +├── pyproject.toml +└── README.md ``` -**Key Responsibilities:** -* To be testable in isolation. -* To produce a versioned Docker image (`backend:`) as its main artifact. +#### What each top-level directory in `src/app` does + +| Directory | Purpose | +| ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **`api/`** | Defines the public HTTP surface. Each module holds a router with path operations and dependency wiring. | +| **`config/`** | Centralised configuration: `settings.py` (Pydantic `BaseSettings`) reads env vars; `constants.py` stores enums and global literals. | +| **`db/`** | Persistence layer. Contains the SQLAlchemy base class, the session factory, and a thin wrapper that seeds or resets the database (Alembic migration scripts live at project root). | +| **`metrics/`** | Post-processing helpers that turn raw simulation traces into aggregated KPIs (latency percentiles, cost per request, utilisation curves, …). | +| **`resources/`** | A tiny run-time registry mapping every simulated server to its SimPy `Container`s (CPU, RAM). Keeps resource management separate from actor logic. | +| **`runtime/`** | The heart of the simulator. `rqs_state.py` holds the mutable `RequestState`; sub-package **`actors/`** contains each SimPy process class (Generator, Edge, Server, Client). | +| **`samplers/`** | Probability-distribution utilities that generate inter-arrival and service-time samples—used by the actors during simulation. | +| **`schemas/`** | All Pydantic models for validation and (de)serialisation: request DTOs, topology definitions, simulation settings, outputs. | +| **`main.py`** | Creates and returns the FastAPI app; imported by Uvicorn/Gunicorn. | +| **`simulation_run.py`** | Convenience script to launch a simulation offline (e.g. inside tests or CLI). | + +Everything under `src/` is import-safe thanks to Poetry’s `packages = [{ include = "app" }]` entry in `pyproject.toml`. + ## 3. Branching Strategy: Git Flow diff --git a/documentation/FASTSIM_VISION.md b/documentation/fastsim_vision.md similarity index 100% rename from documentation/FASTSIM_VISION.md rename to documentation/fastsim_vision.md diff --git a/src/app/config/constants.py b/src/app/config/constants.py index c611697..8eeb01e 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -128,15 +128,11 @@ class StepOperation(StrEnum): """ Keys used inside the ``metrics`` dictionary of a *step*. - * ``NETWORK_LATENCY`` - Mean latency (seconds) incurred on a network edge - *outside* the service (used mainly for validation when steps model - short in-service hops). * ``CPU_TIME`` - Service time (seconds) during which the coroutine occupies the CPU / GIL. * ``NECESSARY_RAM`` - Peak memory (MB) required by the step. """ - NETWORK_LATENCY = "network_latency" CPU_TIME = "cpu_time" IO_WAITING_TIME = "io_waiting_time" NECESSARY_RAM = "necessary_ram" @@ -165,6 +161,7 @@ class NetworkParameters: DROPOUT_RATE = 0.01 MAX_DROPOUT_RATE = 1.0 + # ====================================================================== # CONSTANTS FOR THE MACRO-TOPOLOGY GRAPH # ====================================================================== @@ -235,3 +232,13 @@ class AggregatedMetricName(StrEnum): LATENCY_STATS = "latency_stats" LLM_STATS = "llm_stats" + +# ====================================================================== +# CONSTANTS FOR SERVER RUNTIME +# ====================================================================== + +class ServerResourceName(StrEnum): + """Keys for each server resource type, used when building the container map.""" + + CPU = "CPU" + RAM = "RAM" diff --git a/src/app/config/rqs_state.py b/src/app/config/rqs_state.py deleted file mode 100644 index dec9200..0000000 --- a/src/app/config/rqs_state.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -defining a state in a one to one correspondence -with the requests generated that will go through -all the node necessary to accomplish the user request -""" - -from __future__ import annotations - -from dataclasses import dataclass, field - - -@dataclass -class RequestState: - """ - State object carried by each request through the simulation. - - Attributes: - id: Unique identifier of the request. - t0: Timestamp (simulated env.now) when the request was generated. - history: List of hop records, each noting a node/edge visit. - finish_time: Timestamp when the requests is satisfied - - """ - - id: int # Unique request identifier - initial_time: float # Generation timestamp (env.now) - finish_time: float | None = None # a requests might be dropped - history: list[str] = field(default_factory=list) # Trace of hops - - def record_hop(self, node_name: str, now: float) -> None: - """ - Append a record of visiting a node or edge. - - Args: - node_name: Name of the node or edge being recorded. - now: register the time of the operation - - """ - # Record hop as "NodeName@Timestamp" - self.history.append(f"{node_name}@{now:.3f}") - - @property - def latency(self) -> float | None: - """ - Return the total time in the system (finish_time - initial_time), - or None if the request hasn't completed yet. - """ - if self.finish_time is None: - return None - return self.finish_time - self.initial_time diff --git a/src/app/core/helpers/requests_generator.py b/src/app/core/helpers/requests_generator.py deleted file mode 100644 index 2d04bef..0000000 --- a/src/app/core/helpers/requests_generator.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -SimPy process that generates user requests at stochastic intervals. - -This node samples inter-arrival times according to the configured -distribution (Gaussian-Poisson or Poisson-Poisson), constructs a -RequestState for each new request, records its origin hop, and -immediately pushes it into the next pipeline stage via an EdgeRuntime. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -from app.config.constants import Distribution -from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling -from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling - -if TYPE_CHECKING: - from collections.abc import Generator - - import numpy as np - - from app.schemas.requests_generator_input import RqsGeneratorInput - from app.schemas.simulation_settings_input import SimulationSettings - - -def requests_generator( - input_data: RqsGeneratorInput, - sim_settings: SimulationSettings, - *, - rng: np.random.Generator, -) -> Generator[float, None, None]: - """ - Return an iterator of inter-arrival gaps (seconds) according to the model - chosen in *input_data*. - - Notes - ----- - * If ``avg_active_users.distribution`` is ``"gaussian"`` or ``"normal"``, - the Gaussian-Poisson sampler is used. - * Otherwise the default Poisson-Poisson sampler is returned. - - """ - dist = input_data.avg_active_users.distribution.lower() - - if dist == Distribution.NORMAL: - #Gaussian-Poisson model - return gaussian_poisson_sampling( - input_data=input_data, - sim_settings=sim_settings, - rng=rng, - - ) - - # Poisson + Poisson - return poisson_poisson_sampling( - input_data=input_data, - sim_settings=sim_settings, - rng=rng, - ) diff --git a/src/app/core/helpers/dictionary_metrics.py b/src/app/metrics/dictionary_metrics.py similarity index 100% rename from src/app/core/helpers/dictionary_metrics.py rename to src/app/metrics/dictionary_metrics.py diff --git a/src/app/resources/__init__.py b/src/app/resources/__init__.py new file mode 100644 index 0000000..69884c1 --- /dev/null +++ b/src/app/resources/__init__.py @@ -0,0 +1 @@ +"""python package for resource registry""" diff --git a/src/app/resources/registry.py b/src/app/resources/registry.py new file mode 100644 index 0000000..c768cef --- /dev/null +++ b/src/app/resources/registry.py @@ -0,0 +1,38 @@ +""" +Runtime resource registry for server nodes. + +This module defines the ResourcesRuntime class, which takes a validated +TopologyGraph and a SimPy environment, then builds and stores a map +from each server's unique identifier to its SimPy resource containers. +Processes can later retrieve CPU and RAM containers by indexing this registry. +""" + +import simpy + +from app.resources.server_containers import ServerContainers, build_containers +from app.schemas.system_topology.full_system_topology import TopologyGraph + + +class ResourcesRuntime: + """definition of the class to associate resources to various nodes""" + + def __init__( + self, + env: simpy.Environment, + data: TopologyGraph, + + ) -> None: + """Initialization of the attributes""" + self.env = env + self.data = data + self._by_server: dict[str, ServerContainers] = { + server.id: build_containers(env, server.server_resources) + for server in data.nodes.servers + } + + def __getitem__(self, server_id: str) -> ServerContainers: + """ + Useful map to pass to each server the resources based + on the server unique id + """ + return self._by_server[server_id] diff --git a/src/app/resources/server_containers.py b/src/app/resources/server_containers.py new file mode 100644 index 0000000..dcbef1f --- /dev/null +++ b/src/app/resources/server_containers.py @@ -0,0 +1,73 @@ +""" +Definition of support structures for the simulation runtime. + +After Pydantic validation, this module provides TypedDicts and helpers +to build SimPy Containers for each server in the topology, improving +readability and ensuring a single point of truth for resource setup. +""" + + +from typing import TypedDict + +import simpy + +from app.config.constants import ServerResourceName +from app.schemas.system_topology.full_system_topology import ( + ServerResources, +) + +# ============================================================== +# DICT FOR THE REGISTRY TO INITIALIZE RESOURCES FOR EACH SERVER +# ============================================================== + + +class ServerContainers(TypedDict): + """ + Mapping of resource names to their SimPy Container instances for a server. + + - CPU: simpy.Container for CPU cores. + - RAM: simpy.Container for RAM in megabytes. + """ + + CPU: simpy.Container + RAM: simpy.Container + +# Central funcrion to initialize the dictionary with ram and cpu container +def build_containers( + env: simpy.Environment, + spec: ServerResources, + ) -> ServerContainers: + """ + Construct and return a mapping of SimPy Containers for a server's CPU and RAM. + + Given a SimPy environment and a validated ServerResources spec, this function + initializes one simpy.Container for CPU (with capacity equal to cpu_cores) + and one for RAM (with capacity equal to ram_mb), then returns them in a + ServerContainers TypedDict keyed by "CPU" and "RAM". + + Parameters + ---------- + env : simpy.Environment + The simulation environment in which the Containers will be created. + spec : ServerResources + A Pydantic model instance defining the server's cpu_cores and ram_mb. + + Returns + ------- + ServerContainers + A TypedDict with exactly two entries: + - "CPU": simpy.Container initialized with spec.cpu_cores + - "RAM": simpy.Container initialized with spec.ram_mb + + """ + return { + ServerResourceName.CPU.value: simpy.Container( + env, capacity=spec.cpu_cores, init=spec.cpu_cores, + ), + ServerResourceName.RAM.value: simpy.Container( + env, capacity=spec.ram_mb, init=spec.ram_mb, + ), + } + + + diff --git a/src/app/core/runtime/__init__.py b/src/app/runtime/__init__.py similarity index 100% rename from src/app/core/runtime/__init__.py rename to src/app/runtime/__init__.py diff --git a/src/app/runtime/actors/client.py b/src/app/runtime/actors/client.py new file mode 100644 index 0000000..207bd71 --- /dev/null +++ b/src/app/runtime/actors/client.py @@ -0,0 +1,59 @@ +"""defining the object client for the simulation""" + +from collections.abc import Generator +from typing import TYPE_CHECKING + +import simpy + +from app.config.constants import SystemNodes +from app.runtime.actors.edge import EdgeRuntime +from app.schemas.system_topology.full_system_topology import Client + +if TYPE_CHECKING: + from app.runtime.rqs_state import RequestState + + + +class ClientRuntime: + """class to define the client runtime""" + + def __init__( + self, + env: simpy.Environment, + out_edge: EdgeRuntime, + client_box: simpy.Store, + completed_box: simpy.Store, + client_config: Client, + ) -> None: + """Definition of attributes for the client""" + self.env = env + self.out_edge = out_edge + self.client_config = client_config + self.client_box = client_box + self.completed_box = completed_box + + + def _forwarder(self) -> Generator[simpy.Event, None, None]: + """Updtate the state before passing it to another node""" + while True: + state: RequestState = yield self.client_box.get() # type: ignore[assignment] + + state.record_hop( + SystemNodes.CLIENT, + self.client_config.id, + self.env.now, + ) + + # by checking the previous node (-2 the previous component is an edge) + # we are able to understand if the request should be elaborated + # when the type is Generator, or the request is completed, in this case + # the client is the target and the previous node type is not a rqs generator + if state.history[-2].component_type != SystemNodes.GENERATOR: + state.finish_time = self.env.now + yield self.completed_box.put(state) + else: + self.out_edge.transport(state) + + def start(self) -> simpy.Process: + """Initialization of the process""" + return self.env.process(self._forwarder()) diff --git a/src/app/core/runtime/edge.py b/src/app/runtime/actors/edge.py similarity index 78% rename from src/app/core/runtime/edge.py rename to src/app/runtime/actors/edge.py index 4f22608..afef720 100644 --- a/src/app/core/runtime/edge.py +++ b/src/app/runtime/actors/edge.py @@ -12,9 +12,10 @@ import numpy as np import simpy -from app.config.rqs_state import RequestState -from app.core.event_samplers.common_helpers import general_sampler -from app.schemas.system_topology_schema.full_system_topology_schema import Edge +from app.config.constants import SystemEdges +from app.runtime.rqs_state import RequestState +from app.samplers.common_helpers import general_sampler +from app.schemas.system_topology.full_system_topology import Edge if TYPE_CHECKING: from app.schemas.random_variables_config import RVConfig @@ -46,12 +47,20 @@ def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: uniform_variable = self.rng.uniform() if uniform_variable < self.edge_config.dropout_rate: state.finish_time = self.env.now - state.record_hop(f"{self.edge_config.id}-dropped", state.finish_time) + state.record_hop( + SystemEdges.NETWORK_CONNECTION, + f"{self.edge_config.id}-dropped", + state.finish_time, + ) return transit_time = general_sampler(random_variable, self.rng) yield self.env.timeout(transit_time) - state.record_hop(self.edge_config.id, self.env.now) + state.record_hop( + SystemEdges.NETWORK_CONNECTION, + self.edge_config.id, + self.env.now, + ) yield self.target_box.put(state) diff --git a/src/app/core/runtime/rqs_generator.py b/src/app/runtime/actors/rqs_generator.py similarity index 87% rename from src/app/core/runtime/rqs_generator.py rename to src/app/runtime/actors/rqs_generator.py index 5611c85..bc114ba 100644 --- a/src/app/core/runtime/rqs_generator.py +++ b/src/app/runtime/actors/rqs_generator.py @@ -10,9 +10,9 @@ import numpy as np from app.config.constants import Distribution, SystemNodes -from app.config.rqs_state import RequestState -from app.core.event_samplers.gaussian_poisson import gaussian_poisson_sampling -from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling +from app.runtime.rqs_state import RequestState +from app.samplers.gaussian_poisson import gaussian_poisson_sampling +from app.samplers.poisson_poisson import poisson_poisson_sampling if TYPE_CHECKING: @@ -20,8 +20,8 @@ import simpy - from app.core.runtime.edge import EdgeRuntime - from app.schemas.requests_generator_input import RqsGeneratorInput + from app.runtime.actors.edge import EdgeRuntime + from app.schemas.rqs_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings @@ -106,12 +106,16 @@ def _event_arrival(self) -> Generator[simpy.Event, None, None]: initial_time=self.env.now, ) - state.record_hop(SystemNodes.GENERATOR, self.env.now) + state.record_hop( + SystemNodes.GENERATOR, + self.rqs_generator_data.id, + self.env.now, + ) # transport is a method of the edge runtime # which define the step of how the state is moving # from one node to another self.out_edge.transport(state) - def run(self) -> simpy.Process: + def start(self) -> simpy.Process: """Passing the structure as a simpy process""" return self.env.process(self._event_arrival()) diff --git a/src/app/runtime/actors/server.py b/src/app/runtime/actors/server.py new file mode 100644 index 0000000..0a0f772 --- /dev/null +++ b/src/app/runtime/actors/server.py @@ -0,0 +1,136 @@ +""" +definition of the class necessary to manage the server +during the simulation +""" + +from collections.abc import Generator +from typing import cast + +import numpy as np +import simpy + +from app.config.constants import ( + EndpointStepCPU, + EndpointStepIO, + EndpointStepRAM, + ServerResourceName, + StepOperation, + SystemNodes, +) +from app.resources.server_containers import ServerContainers +from app.runtime.actors.edge import EdgeRuntime +from app.runtime.rqs_state import RequestState +from app.schemas.system_topology.full_system_topology import Server + + +class ServerRuntime: + """class to define the server during the simulation""" + + def __init__( # noqa: PLR0913 + self, + env: simpy.Environment, + server_resources: ServerContainers, + server_config: Server, + out_edge: EdgeRuntime, + server_box: simpy.Store, + rng: np.random.Generator | None = None, + ) -> None: + """Server attributes + + Args: + env (simpy.Environment): _description_ + server_resources (ServerContainers): _description_ + server_config (Server): _description_ + out_edge (EdgeRuntime): _description_ + server_box (simpy.Store): _description_ + rng (np.random.Generator | None, optional): _description_. Defaults to None. + + """ + self.env = env + self.server_resources = server_resources + self.server_config = server_config + self.out_edge = out_edge + self.server_box = server_box + self.rng = rng or np.random.default_rng() + + + def _handle_request( + self, + state: RequestState, + ) -> Generator[simpy.Event, None, None]: + """ + Define all the step each request has to do ones reach + the server + """ + #register the history for the state: + state.record_hop( + SystemNodes.SERVER, + self.server_config.id, + self.env.now, + ) + + # Define the length of the endpoint list + endpoints_list = self.server_config.endpoints + endpoints_number = len(endpoints_list) + + # select the endpoint where the requests is directed at the moment we use + # a uniform distribution, in the future we will allow the user to define a + # custom distribution + selected_endpoint_idx = self.rng.integers(low=0, high=endpoints_number) + selected_endpoint = endpoints_list[selected_endpoint_idx] + + # RAM management: + # first calculate the ram needed + # Ask if it is available + # Release everything when the operation completed + + total_ram = sum( + step.step_operation[StepOperation.NECESSARY_RAM] + for step in selected_endpoint.steps + if isinstance(step.kind, EndpointStepRAM) + ) + + + if total_ram: + yield self.server_resources[ServerResourceName.RAM.value].get(total_ram) + + + # --- Step Execution: Process CPU and IO operations --- + for step in selected_endpoint.steps: + + if isinstance(step.kind, EndpointStepCPU): + cpu_time = step.step_operation[StepOperation.CPU_TIME] + + # Acquire one core + yield self.server_resources[ServerResourceName.CPU.value].get(1) + # Hold the core busy + yield self.env.timeout(cpu_time) + # Release the core + yield self.server_resources[ServerResourceName.CPU.value].put(1) + + elif isinstance(step.kind, EndpointStepIO): + io_time = step.step_operation[StepOperation.IO_WAITING_TIME] + yield self.env.timeout(io_time) # Wait without holding a CPU core + + # release the ram + if total_ram: + yield self.server_resources[ServerResourceName.RAM.value].put(total_ram) + + self.out_edge.transport(state) + + def _dispatcher(self) -> Generator[simpy.Event, None, None]: + """ + The main dispatcher loop. It pulls requests from the inbox and + spawns a new '_handle_request' process for each one. + """ + while True: + # Wait for a request to arrive in the server's inbox + raw_state = yield self.server_box.get() + request_state = cast("RequestState", raw_state) + # Spawn a new, independent process to handle this request + self.env.process(self._handle_request(request_state)) + + def start(self) -> simpy.Process: + """Generate the process to simulate the server inside simpy env""" + return self.env.process(self._dispatcher()) + diff --git a/src/app/runtime/rqs_state.py b/src/app/runtime/rqs_state.py new file mode 100644 index 0000000..1c953fd --- /dev/null +++ b/src/app/runtime/rqs_state.py @@ -0,0 +1,51 @@ +"""Data structures representing the life-cycle of a single request.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, NamedTuple + +if TYPE_CHECKING: + from app.config.constants import SystemEdges, SystemNodes + + +class Hop(NamedTuple): + """A single traversal of a node or edge.""" + + component_type: SystemNodes | SystemEdges + component_id: str + timestamp: float + + +@dataclass +class RequestState: + """Mutable state carried by each request throughout the simulation.""" + + id: int + initial_time: float + finish_time: float | None = None + history: list[Hop] = field(default_factory=list) + + # ------------------------------------------------------------------ # + # API # + # ------------------------------------------------------------------ # + + def record_hop( + self, + component_type: SystemNodes | SystemEdges, + component_id: str, + now: float, + ) -> None: + """Append a new hop in chronological order.""" + self.history.append(Hop(component_type, component_id, now)) + + # ------------------------------------------------------------------ # + # Derived metrics # + # ------------------------------------------------------------------ # + + @property + def latency(self) -> float | None: + """Total time inside the system or ``None`` if not yet completed.""" + if self.finish_time is None: + return None + return self.finish_time - self.initial_time diff --git a/src/app/core/event_samplers/common_helpers.py b/src/app/samplers/common_helpers.py similarity index 100% rename from src/app/core/event_samplers/common_helpers.py rename to src/app/samplers/common_helpers.py diff --git a/src/app/core/event_samplers/gaussian_poisson.py b/src/app/samplers/gaussian_poisson.py similarity index 96% rename from src/app/core/event_samplers/gaussian_poisson.py rename to src/app/samplers/gaussian_poisson.py index f9cc401..bb1f6fd 100644 --- a/src/app/core/event_samplers/gaussian_poisson.py +++ b/src/app/samplers/gaussian_poisson.py @@ -12,11 +12,11 @@ import numpy as np from app.config.constants import TimeDefaults -from app.core.event_samplers.common_helpers import ( +from app.samplers.common_helpers import ( truncated_gaussian_generator, uniform_variable_generator, ) -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.rqs_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings diff --git a/src/app/core/event_samplers/poisson_poisson.py b/src/app/samplers/poisson_poisson.py similarity index 95% rename from src/app/core/event_samplers/poisson_poisson.py rename to src/app/samplers/poisson_poisson.py index 1d4787f..7f17364 100644 --- a/src/app/core/event_samplers/poisson_poisson.py +++ b/src/app/samplers/poisson_poisson.py @@ -9,11 +9,11 @@ import numpy as np from app.config.constants import TimeDefaults -from app.core.event_samplers.common_helpers import ( +from app.samplers.common_helpers import ( poisson_variable_generator, uniform_variable_generator, ) -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.rqs_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings diff --git a/src/app/schemas/full_simulation_input.py b/src/app/schemas/full_simulation_input.py index b745fee..8be873a 100644 --- a/src/app/schemas/full_simulation_input.py +++ b/src/app/schemas/full_simulation_input.py @@ -2,9 +2,9 @@ from pydantic import BaseModel -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.rqs_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology_schema.full_system_topology_schema import TopologyGraph +from app.schemas.system_topology.full_system_topology import TopologyGraph class SimulationPayload(BaseModel): diff --git a/src/app/schemas/requests_generator_input.py b/src/app/schemas/rqs_generator_input.py similarity index 93% rename from src/app/schemas/requests_generator_input.py rename to src/app/schemas/rqs_generator_input.py index 35ff361..4f217e7 100644 --- a/src/app/schemas/requests_generator_input.py +++ b/src/app/schemas/rqs_generator_input.py @@ -3,13 +3,15 @@ from pydantic import BaseModel, Field, field_validator -from app.config.constants import Distribution, TimeDefaults +from app.config.constants import Distribution, SystemNodes, TimeDefaults from app.schemas.random_variables_config import RVConfig class RqsGeneratorInput(BaseModel): """Define the expected variables for the simulation""" + id: str + type: SystemNodes = SystemNodes.GENERATOR avg_active_users: RVConfig avg_request_per_minute_per_user: RVConfig diff --git a/src/app/schemas/system_topology_schema/endpoint_schema.py b/src/app/schemas/system_topology/endpoint.py similarity index 100% rename from src/app/schemas/system_topology_schema/endpoint_schema.py rename to src/app/schemas/system_topology/endpoint.py diff --git a/src/app/schemas/system_topology_schema/full_system_topology_schema.py b/src/app/schemas/system_topology/full_system_topology.py similarity index 99% rename from src/app/schemas/system_topology_schema/full_system_topology_schema.py rename to src/app/schemas/system_topology/full_system_topology.py index ddbac36..2f29f2a 100644 --- a/src/app/schemas/system_topology_schema/full_system_topology_schema.py +++ b/src/app/schemas/system_topology/full_system_topology.py @@ -24,7 +24,7 @@ SystemNodes, ) from app.schemas.random_variables_config import RVConfig -from app.schemas.system_topology_schema.endpoint_schema import Endpoint +from app.schemas.system_topology.endpoint import Endpoint #------------------------------------------------------------- # Definition of the nodes structure for the graph representing @@ -138,7 +138,6 @@ def unique_ids( model_config = ConfigDict(extra="forbid") - #------------------------------------------------------------- # Definition of the edges structure for the graph representing # the topoogy of the system defined for the simulation diff --git a/src/app/core/simulation_run.py b/src/app/simulation_run.py similarity index 97% rename from src/app/core/simulation_run.py rename to src/app/simulation_run.py index c6159ca..1176751 100644 --- a/src/app/core/simulation_run.py +++ b/src/app/simulation_run.py @@ -19,7 +19,7 @@ - +### TO MODIFY EVERYTHING WORK IN PROGRESS def run_simulation( input_data: SimulationPayload, diff --git a/tests/conftest.py b/tests/conftest.py index e6d7b75..7d0f587 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -30,9 +30,9 @@ from app.main import app from app.schemas.full_simulation_input import SimulationPayload from app.schemas.random_variables_config import RVConfig -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.rqs_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology_schema.full_system_topology_schema import ( +from app.schemas.system_topology.full_system_topology import ( Client, TopologyGraph, TopologyNodes, @@ -153,14 +153,14 @@ def rng() -> NpGenerator: return default_rng(0) -# --------------------------------------------------------------------------- -# Metrics sets -# --------------------------------------------------------------------------- +# --------------------------------------------------------------------------- # +# Metric sets # +# --------------------------------------------------------------------------- # @pytest.fixture(scope="session") def enabled_sample_metrics() -> set[SampledMetricName]: - """Default sample-level KPIs tracked in most tests.""" + """Default time-series KPIs collected in most tests.""" return { SampledMetricName.READY_QUEUE_LEN, SampledMetricName.RAM_IN_USE, @@ -169,13 +169,15 @@ def enabled_sample_metrics() -> set[SampledMetricName]: @pytest.fixture(scope="session") def enabled_event_metrics() -> set[EventMetricName]: - """Default event-level KPIs tracked in most tests.""" - return {EventMetricName.RQS_LATENCY} + """Default per-event KPIs collected in most tests.""" + return { + EventMetricName.RQS_LATENCY, + } -# --------------------------------------------------------------------------- -# Global simulation settings -# --------------------------------------------------------------------------- +# --------------------------------------------------------------------------- # +# Global simulation settings # +# --------------------------------------------------------------------------- # @pytest.fixture @@ -183,7 +185,12 @@ def sim_settings( enabled_sample_metrics: set[SampledMetricName], enabled_event_metrics: set[EventMetricName], ) -> SimulationSettings: - """A minimal `SimulationSettings` instance for unit tests.""" + """ + Minimal :class:`SimulationSettings` instance. + + The simulation horizon is fixed to the lowest allowed value so that unit + tests run quickly. + """ return SimulationSettings( total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, enabled_sample_metrics=enabled_sample_metrics, @@ -191,37 +198,46 @@ def sim_settings( ) -# --------------------------------------------------------------------------- -# Traffic profile -# --------------------------------------------------------------------------- +# --------------------------------------------------------------------------- # +# Traffic profile # +# --------------------------------------------------------------------------- # @pytest.fixture def rqs_input() -> RqsGeneratorInput: - """`RqsGeneratorInput` with 1 user and 2 req/min for quick tests.""" + """ + One active user issuing two requests per minute—sufficient to + exercise the entire request-generator pipeline with minimal overhead. + """ return RqsGeneratorInput( + id="rqs-1", avg_active_users=RVConfig(mean=1.0), avg_request_per_minute_per_user=RVConfig(mean=2.0), user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, ) -# --------------------------------------------------------------------------- -# Minimal topology (one client, no servers, no edges) -# --------------------------------------------------------------------------- +# --------------------------------------------------------------------------- # +# Minimal topology (one client, no servers, no edges) # +# --------------------------------------------------------------------------- # @pytest.fixture def topology_minimal() -> TopologyGraph: - """Valid topology with a single client and zero servers/edges.""" + """ + A valid topology containing a single client and **no** servers or edges. + + Suitable for low-level tests that do not need to traverse the server + layer or network graph. + """ client = Client(id="client-1") nodes = TopologyNodes(servers=[], client=client) return TopologyGraph(nodes=nodes, edges=[]) -# --------------------------------------------------------------------------- -# Full simulation payload -# --------------------------------------------------------------------------- +# --------------------------------------------------------------------------- # +# Complete simulation payload # +# --------------------------------------------------------------------------- # @pytest.fixture @@ -230,7 +246,12 @@ def payload_base( sim_settings: SimulationSettings, topology_minimal: TopologyGraph, ) -> SimulationPayload: - """End-to-end payload used by high-level simulation tests.""" + """ + End-to-end payload used by integration tests and FastAPI endpoint tests. + + It wires together the individual fixtures into the single object expected + by the simulation engine. + """ return SimulationPayload( rqs_input=rqs_input, topology_graph=topology_minimal, diff --git a/tests/unit/test_settings.py b/tests/unit/db/test_settings.py similarity index 100% rename from tests/unit/test_settings.py rename to tests/unit/db/test_settings.py diff --git a/tests/unit/resources/test_registry.py b/tests/unit/resources/test_registry.py new file mode 100644 index 0000000..fe5a693 --- /dev/null +++ b/tests/unit/resources/test_registry.py @@ -0,0 +1,60 @@ +"""Unit tests for ResourcesRuntime (resource registry).""" + +from __future__ import annotations + +import pytest +import simpy + +from app.config.constants import ServerResourceName +from app.resources.registry import ResourcesRuntime +from app.schemas.system_topology.endpoint import Endpoint +from app.schemas.system_topology.full_system_topology import ( + Client, + Server, + ServerResources, + TopologyGraph, + TopologyNodes, +) + + +def _minimal_server(server_id: str, cores: int, ram: int) -> Server: + """Create a Server with a dummy endpoint and resource spec.""" + res = ServerResources(cpu_cores=cores, ram_mb=ram) + dummy_ep = Endpoint(endpoint_name="/ping", steps=[]) + return Server(id=server_id, server_resources=res, endpoints=[dummy_ep]) + + +def _build_topology() -> TopologyGraph: + """Return a minimal but schema-valid topology with two servers.""" + servers = [ + _minimal_server("srv-A", 2, 1024), + _minimal_server("srv-B", 4, 2048), + ] + client = Client(id="clt-1") + nodes = TopologyNodes(servers=servers, client=client) + return TopologyGraph(nodes=nodes, edges=[]) + + +def test_registry_initialises_filled_containers() -> None: + """CPU and RAM containers must start full for every server.""" + env = simpy.Environment() + topo = _build_topology() + registry = ResourcesRuntime(env, topo) + + for srv in topo.nodes.servers: + containers = registry[srv.id] + + cpu = containers[ServerResourceName.CPU.value] + ram = containers[ServerResourceName.RAM.value] + + assert cpu.level == cpu.capacity == srv.server_resources.cpu_cores + assert ram.level == ram.capacity == srv.server_resources.ram_mb + + +def test_getitem_unknown_server_raises_keyerror() -> None: + """Accessing an undefined server ID should raise KeyError.""" + env = simpy.Environment() + registry = ResourcesRuntime(env, _build_topology()) + + with pytest.raises(KeyError): + _ = registry["non-existent-server"] diff --git a/tests/unit/resources/test_server_containers.py b/tests/unit/resources/test_server_containers.py new file mode 100644 index 0000000..c36f927 --- /dev/null +++ b/tests/unit/resources/test_server_containers.py @@ -0,0 +1,19 @@ +"""Unit test: build_containers must return full containers.""" + +import simpy + +from app.config.constants import ServerResourceName +from app.resources.server_containers import build_containers +from app.schemas.system_topology.full_system_topology import ServerResources + + +def test_containers_start_full() -> None: + env = simpy.Environment() + spec = ServerResources(cpu_cores=4, ram_mb=2048) + containers = build_containers(env, spec) + + cpu = containers[ServerResourceName.CPU.value] + ram = containers[ServerResourceName.RAM.value] + + assert cpu.level == cpu.capacity == 4 + assert ram.level == ram.capacity == 2048 diff --git a/tests/unit/runtime/engine/test_client.py b/tests/unit/runtime/engine/test_client.py new file mode 100644 index 0000000..bc17471 --- /dev/null +++ b/tests/unit/runtime/engine/test_client.py @@ -0,0 +1,96 @@ +"""Unit-tests for :class:`ClientRuntime` (outbound / inbound paths).""" + +from __future__ import annotations + +import simpy + +from app.config.constants import SystemEdges, SystemNodes +from app.runtime.actors.client import ClientRuntime +from app.runtime.rqs_state import RequestState +from app.schemas.system_topology.full_system_topology import ( + Client, +) + +# --------------------------------------------------------------------------- # +# Dummy edge (no real network) # +# --------------------------------------------------------------------------- # + + +class DummyEdgeRuntime: + """Collect states passed through *transport* without SimPy side-effects.""" + + def __init__(self, env: simpy.Environment) -> None: + """Init attributes""" + self.env = env + self.forwarded: list[RequestState] = [] + + # Signature compatible with EdgeRuntime.transport but returns *None* + def transport(self, state: RequestState) -> None: + """Transport state""" + self.forwarded.append(state) + + +# --------------------------------------------------------------------------- # +# Helper # +# --------------------------------------------------------------------------- # + + +def _setup( + env: simpy.Environment, +) -> tuple[simpy.Store, simpy.Store, DummyEdgeRuntime]: + inbox: simpy.Store = simpy.Store(env) + completed: simpy.Store = simpy.Store(env) + edge_rt = DummyEdgeRuntime(env) + cli_cfg = Client(id="cli-1") + + client = ClientRuntime( + env=env, + out_edge=edge_rt, # type: ignore[arg-type] + client_box=inbox, + completed_box=completed, + client_config=cli_cfg, + ) + client.start() # start the forwarder + return inbox, completed, edge_rt + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # + + +def test_outbound_is_forwarded() -> None: + """First visit ⇒ forwarded; completed store remains empty.""" + env = simpy.Environment() + inbox, completed, edge_rt = _setup(env) + + req = RequestState(id=1, initial_time=0.0) + req.record_hop(SystemNodes.GENERATOR, "gen-1", env.now) + + inbox.put(req) + env.run() + + assert len(edge_rt.forwarded) == 1 + assert len(completed.items) == 0 + assert req.history[-1].component_type is SystemNodes.CLIENT + assert req.finish_time is None + + +def test_inbound_is_completed() -> None: + """Second visit ⇒ request stored in *completed_box* and not re-forwarded.""" + env = simpy.Environment() + inbox, completed, edge_rt = _setup(env) + + req = RequestState(id=2, initial_time=0.0) + req.record_hop(SystemNodes.GENERATOR, "gen-1", env.now) + req.record_hop(SystemEdges.NETWORK_CONNECTION, "edge-X", env.now) + + inbox.put(req) + env.run() + + assert len(edge_rt.forwarded) == 0 + assert len(completed.items) == 1 + + done = completed.items[0] + assert done.finish_time is not None + assert done.history[-1].component_type is SystemNodes.CLIENT diff --git a/tests/unit/runtime/engine/test_edge.py b/tests/unit/runtime/engine/test_edge.py new file mode 100644 index 0000000..10cb758 --- /dev/null +++ b/tests/unit/runtime/engine/test_edge.py @@ -0,0 +1,127 @@ +"""Unit-tests for :class:`EdgeRuntime` (delivery / drop paths).""" +from __future__ import annotations + +from typing import TYPE_CHECKING, cast + +import simpy + +from app.config.constants import SystemEdges, SystemNodes +from app.runtime.actors.edge import EdgeRuntime +from app.runtime.rqs_state import RequestState +from app.schemas.random_variables_config import RVConfig +from app.schemas.system_topology.full_system_topology import Edge + +if TYPE_CHECKING: + + import numpy as np + +# --------------------------------------------------------------------------- # +# Dummy RNG # +# --------------------------------------------------------------------------- # + + +class DummyRNG: + """Return preset values for ``uniform`` and ``normal``.""" + + def __init__(self, *, uniform_value: float, normal_value: float = 0.0) -> None: + """Attribute init""" + self.uniform_value = uniform_value + self.normal_value = normal_value + self.uniform_called = False + self.normal_called = False + + + def uniform(self) -> float: + """Intercept ``rng.uniform`` calls.""" + self.uniform_called = True + return self.uniform_value + + def normal(self, _mean: float, _sigma: float) -> float: + """Intercept ``rng.normal`` calls.""" + self.normal_called = True + return self.normal_value + + +# --------------------------------------------------------------------------- # +# Helper to build an EdgeRuntime # +# --------------------------------------------------------------------------- # + + +def _make_edge( + env: simpy.Environment, + *, + uniform_value: float, + normal_value: float = 0.0, + dropout_rate: float = 0.0, +) -> tuple[EdgeRuntime, DummyRNG, simpy.Store]: + """Attributes init""" + rng = DummyRNG(uniform_value=uniform_value, normal_value=normal_value) + + store: simpy.Store = simpy.Store(env) + edge_cfg = Edge( + id="edge-1", + source="src", + target="dst", + latency=RVConfig(mean=0.0, variance=1.0, distribution="normal"), + dropout_rate=dropout_rate, + ) + + edge_rt = EdgeRuntime( + env=env, + edge_config=edge_cfg, + rng=cast("np.random.Generator", rng), + target_box=store, + ) + return edge_rt, rng, store + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # + + +def test_edge_delivers_message_when_not_dropped() -> None: + """A message traverses the edge and calls the latency sampler once.""" + env = simpy.Environment() + edge_rt, rng, store = _make_edge( + env, + uniform_value=0.9, + normal_value=0.5, + dropout_rate=0.2, + ) + + state = RequestState(id=1, initial_time=0.0) + state.record_hop(SystemNodes.GENERATOR, "gen-1", env.now) + + edge_rt.transport(state) + env.run() + + assert len(store.items) == 1 + delivered: RequestState = store.items[0] + last = delivered.history[-1] + assert last.component_type is SystemEdges.NETWORK_CONNECTION + assert last.component_id == "edge-1" + assert rng.uniform_called is True + assert rng.normal_called is True + + +def test_edge_drops_message_when_uniform_below_threshold() -> None: + """A message is dropped when the random draw is below *dropout_rate*.""" + env = simpy.Environment() + edge_rt, rng, store = _make_edge( + env, + uniform_value=0.1, # < dropout → drop + dropout_rate=0.5, + ) + + state = RequestState(id=1, initial_time=0.0) + state.record_hop(SystemNodes.GENERATOR, "gen-1", env.now) + + edge_rt.transport(state) + env.run() + + assert len(store.items) == 0 + last = state.history[-1] + assert last.component_id.endswith("dropped") + assert rng.uniform_called is True + assert rng.normal_called is False diff --git a/tests/unit/runtime/test_requests_generator.py b/tests/unit/runtime/engine/test_rqs_generator.py similarity index 93% rename from tests/unit/runtime/test_requests_generator.py rename to tests/unit/runtime/engine/test_rqs_generator.py index be03583..bc76f4d 100644 --- a/tests/unit/runtime/test_requests_generator.py +++ b/tests/unit/runtime/engine/test_rqs_generator.py @@ -8,16 +8,16 @@ import simpy from app.config.constants import Distribution -from app.core.runtime.rqs_generator import RqsGeneratorRuntime +from app.runtime.actors.rqs_generator import RqsGeneratorRuntime if TYPE_CHECKING: import pytest from numpy.random import Generator - from app.config.rqs_state import RequestState - from app.core.runtime.edge import EdgeRuntime - from app.schemas.requests_generator_input import RqsGeneratorInput + from app.runtime.actors.edge import EdgeRuntime + from app.runtime.rqs_state import RequestState + from app.schemas.rqs_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings import importlib @@ -63,7 +63,7 @@ def _make_runtime( # --------------------------------------------------------------------------- # -RGR_MODULE = importlib.import_module("app.core.runtime.rqs_generator") +RGR_MODULE = importlib.import_module("app.runtime.actors.rqs_generator") def test_dispatcher_selects_poisson_poisson( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/unit/runtime/engine/test_server.py b/tests/unit/runtime/engine/test_server.py new file mode 100644 index 0000000..69292ba --- /dev/null +++ b/tests/unit/runtime/engine/test_server.py @@ -0,0 +1,181 @@ +"""Unit tests for the concurrent ServerRuntime. + +The tests create an isolated SimPy environment with a test fixture that sets up: +* A single ServerRuntime instance. +* A mock "instant" edge that immediately forwards requests to a sink. +* A server configuration with 2 CPU cores and 1024 MB of RAM. +* A single endpoint with a sequence of RAM, CPU, and I/O steps. + +This setup allows for precise testing of resource acquisition/release and +the correct execution of the processing pipeline for a single request. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import simpy +from numpy.random import default_rng + +from app.config.constants import ( + EndpointStepCPU, + EndpointStepIO, + EndpointStepRAM, + StepOperation, + SystemNodes, +) +from app.resources.server_containers import build_containers +from app.runtime.actors.server import ServerRuntime +from app.runtime.rqs_state import RequestState +from app.schemas.system_topology.endpoint import Endpoint, Step +from app.schemas.system_topology.full_system_topology import Server, ServerResources + +if TYPE_CHECKING: + from collections.abc import Generator + + +# --------------------------------------------------------------------------- # +# Test Helper: A mock edge that instantly delivers requests to a sink. # +# --------------------------------------------------------------------------- # +class InstantEdge: + """A test stub for EdgeRuntime with zero latency and no drops. + + This mock allows us to test the ServerRuntime in isolation, without + introducing the complexities of network simulation. + """ + + def __init__(self, env: simpy.Environment, sink: simpy.Store) -> None: + """Initializes the mock edge.""" + self.env = env + self.sink = sink + + def transport(self, state: RequestState) -> simpy.Process: + """Immediately puts the state in the sink via a SimPy process.""" + return self.env.process(self._deliver(state)) + + def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: + """The generator function that performs the delivery.""" + yield self.sink.put(state) + + +# --------------------------------------------------------------------------- # +# Test Fixture: Creates a standardized ServerRuntime for tests. # +# --------------------------------------------------------------------------- # +def _make_server_runtime( + env: simpy.Environment, +) -> tuple[ServerRuntime, simpy.Store]: + """Create a ServerRuntime with a dummy edge and return it and the sink store.""" + # 1. Define server resources + res_spec = ServerResources(cpu_cores=2, ram_mb=1024) + containers = build_containers(env, res_spec) + + # 2. Define a single endpoint with a sequence of steps + # Order: RAM (instant) -> CPU (5ms) -> I/O (20ms) + endpoint = Endpoint( + endpoint_name="/predict", + steps=[ + Step( + kind=EndpointStepRAM.RAM, + step_operation={StepOperation.NECESSARY_RAM: 128}, + ), + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_operation={StepOperation.CPU_TIME: 0.005}, + ), + Step( + kind=EndpointStepIO.DB, + step_operation={StepOperation.IO_WAITING_TIME: 0.020}, + ), + ], + ) + + # 3. Create the full server configuration + server_cfg = Server( + id="api_srv", + endpoints=[endpoint], + server_resources=res_spec, + ) + + # 4. Set up the simulation environment with mock components + inbox: simpy.Store = simpy.Store(env) + sink: simpy.Store = simpy.Store(env) + edge = InstantEdge(env, sink) + + # 5. Instantiate the ServerRuntime + runtime = ServerRuntime( + env=env, + server_resources=containers, + server_config=server_cfg, + out_edge=edge, # type: ignore[arg-type] + server_box=inbox, + rng=default_rng(0), + ) + return runtime, sink + + +# --------------------------------------------------------------------------- # +# Unit Tests # +# --------------------------------------------------------------------------- # +def test_server_reserves_and_releases_ram() -> None: + """Verify that RAM is acquired at the start and fully released at the end.""" + env = simpy.Environment() + server, sink = _make_server_runtime(env) + + # Prepare a request and inject it into the server's inbox. + req = RequestState(id=1, initial_time=0.0) + server.server_box.put(req) + + # Start the server's dispatcher process and run until all events are processed. + server.start() + env.run() + + ram_container = server.server_resources["RAM"] + # After the request is fully processed, the RAM level must return to its capacity. + assert ram_container.level == ram_container.capacity, "RAM must be fully released" + # The request should have successfully reached the sink. + assert len(sink.items) == 1, "Request should be forwarded to the sink" + + +def test_cpu_core_held_only_during_cpu_step() -> None: + """Verify a CPU core is held exclusively during the CPU-bound step.""" + env = simpy.Environment() + server, _ = _make_server_runtime(env) + cpu_container = server.server_resources["CPU"] + + # Inject a single request and start the server. + req = RequestState(id=2, initial_time=0.0) + server.server_box.put(req) + server.start() + + # The endpoint logic is: RAM (t=0) -> CPU (t=0 to t=0.005). + # Run the simulation to a point *during* the CPU step. + env.run(until=0.004) + # The server has 2 cores. One should be busy. + assert cpu_container.level == 1, "One core should still be busy during the CPU step" + + # Now, run the simulation past the CPU step's completion. + env.run(until=0.006) + # The core should have been released immediately after the CPU step. + assert cpu_container.level == 2, "Core should be released after the CPU step" + + +def test_server_records_hop_in_history() -> None: + """Verify that the request's history correctly records its arrival at the server.""" + env = simpy.Environment() + server, sink = _make_server_runtime(env) + + # Inject a request and run the simulation to completion. + req = RequestState(id=3, initial_time=0.0) + server.server_box.put(req) + server.start() + env.run() + + # The request must be in the sink. + assert len(sink.items) == 1, "Request did not reach the sink" + finished_req = sink.items[0] + + # Check the request's history for a 'Hop' corresponding to this server. + assert any( + hop.component_type == SystemNodes.SERVER and hop.component_id == "api_srv" + for hop in finished_req.history + ), "Server hop missing in request history" diff --git a/tests/unit/runtime/test_edge b/tests/unit/runtime/test_edge deleted file mode 100644 index 8daaf01..0000000 --- a/tests/unit/runtime/test_edge +++ /dev/null @@ -1,136 +0,0 @@ -"""Unit-tests for :class:`EdgeRuntime`. - -The tests cover: - -* correct delivery when the random 'uniform' draw exceeds the drop rate; -* correct drop behaviour when the draw is below the drop rate; -* that the latency sampler is invoked exactly once per message. -""" -from __future__ import annotations - -from collections.abc import Iterator -from typing import TYPE_CHECKING, cast - -import numpy as np -import pytest -import simpy - -from app.config.constants import NetworkParameters, SystemNodes -from app.config.rqs_state import RequestState -from app.core.event_samplers.common_helpers import general_sampler -from app.core.runtime.edge import EdgeRuntime -from app.schemas.random_variables_config import RVConfig -from app.schemas.system_topology_schema.full_system_topology_schema import Edge - -if TYPE_CHECKING: - from collections.abc import Generator - - -pytestmark = pytest.mark.unit # module-level marker - - -# --------------------------------------------------------------------------- # -# Dummy RNG # -# --------------------------------------------------------------------------- # - - -class DummyRNG: - """RNG stub returning preset values for `uniform()` and `normal()`.""" - - def __init__( - self, - *, - uniform_value: float, - sampler_value: float = 0.0, - ) -> None: - self.uniform_value = uniform_value - self.sampler_value = sampler_value - self.uniform_called = False - self.sampler_called = False - - def uniform(self) -> float: # noqa: D401 - self.uniform_called = True - return self.uniform_value - - # EdgeRuntime passes `self` to `general_sampler`; wrap the call - def normal(self, mean: float, sigma: float) -> float: # noqa: D401 - self.sampler_called = True - return self.sampler_value - - -# --------------------------------------------------------------------------- # -# Helper to create a minimal EdgeRuntime # -# --------------------------------------------------------------------------- # - - -def _make_edge( - env: simpy.Environment, - *, - uniform_value: float, - sampler_value: float = 0.0, -) -> tuple[EdgeRuntime, DummyRNG, simpy.Store]: - rng = DummyRNG(uniform_value=uniform_value, sampler_value=sampler_value) - store: simpy.Store = simpy.Store(env) - - edge_cfg = Edge( - id="edge-1", - source="src", - target="dst", - latency=RVConfig(mean=0.0, distribution="uniform"), # value ignored in test - ) - - edge_rt = EdgeRuntime( - env=env, - edge_config=edge_cfg, - rng=cast("np.random.Generator", rng), - target_box=store, - ) - return edge_rt, rng, store - - -# --------------------------------------------------------------------------- # -# Tests # -# --------------------------------------------------------------------------- # - - -def test_edge_delivers_message_when_not_dropped() -> None: - """Message is delivered and latency sampler is called once.""" - env = simpy.Environment() - edge, rng, store = _make_edge(env, uniform_value=0.9, sampler_value=0.5) - - # prepare request state - state = RequestState(id=1, initial_time=0.0) - state.record_hop(SystemNodes.GENERATOR, env.now) - - edge.transport(state) - env.run() - - # exactly one message delivered - assert len(store.items) == 1 - delivered: RequestState = store.items[0] - assert delivered.hops[-1].node == "edge-1" # last hop is the edge id - assert rng.uniform_called is True - assert rng.sampler_called is True - - -def test_edge_drops_message_when_uniform_below_threshold( - monkeypatch: pytest.MonkeyPatch, -) -> None: - """Message is dropped and never placed in the target store.""" - # override global drop rate to deterministic 0.5 for the test - monkeypatch.setattr(NetworkParameters, "DROPOUT_RATE", 0.5, raising=False) - - env = simpy.Environment() - edge, rng, store = _make_edge(env, uniform_value=0.1) # below 0.5 ⇒ drop - - state = RequestState(id=2, initial_time=0.0) - state.record_hop(SystemNodes.GENERATOR, env.now) - - edge.transport(state) - env.run() - - assert len(store.items) == 0 # nothing delivered - assert state.hops[-1].node.endswith("dropped") - assert rng.uniform_called is True - # sampler must not be invoked when dropped - assert rng.sampler_called is False diff --git a/tests/unit/runtime/test_rqs_state.py b/tests/unit/runtime/test_rqs_state.py new file mode 100644 index 0000000..d07caa4 --- /dev/null +++ b/tests/unit/runtime/test_rqs_state.py @@ -0,0 +1,66 @@ +"""Unit-tests for :class:`RequestState` and :class:`Hop`.""" +from __future__ import annotations + +from app.config.constants import SystemEdges, SystemNodes +from app.runtime.rqs_state import Hop, RequestState + +# --------------------------------------------------------------------------- # +# Helpers # +# --------------------------------------------------------------------------- # + + +def _state() -> RequestState: + """Return a fresh RequestState with id='42' and t0=0.0.""" + return RequestState(id=42, initial_time=0.0) + + +def _hop( + c_type: SystemNodes | SystemEdges, + c_id: str, + ts: float, +) -> Hop: + """Shorthand to build an Hop literal in tests.""" + return Hop(c_type, c_id, ts) + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # + + +def test_record_hop_appends_tuple() -> None: + """record_hop stores a :class:`Hop` instance with all three fields.""" + st = _state() + st.record_hop(SystemNodes.GENERATOR, "gen-1", now=1.23456) + + expected = [_hop(SystemNodes.GENERATOR, "gen-1", 1.23456)] + assert st.history == expected + assert isinstance(st.history[0], Hop) + + +def test_multiple_hops_preserve_global_order() -> None: + """History keeps exact insertion order for successive hops.""" + st = _state() + st.record_hop(SystemNodes.GENERATOR, "gen-1", 0.1) + st.record_hop(SystemEdges.NETWORK_CONNECTION, "edge-7", 0.2) + st.record_hop(SystemNodes.SERVER, "api-A", 0.3) + + expected: list[Hop] = [ + _hop(SystemNodes.GENERATOR, "gen-1", 0.1), + _hop(SystemEdges.NETWORK_CONNECTION, "edge-7", 0.2), + _hop(SystemNodes.SERVER, "api-A", 0.3), + ] + assert st.history == expected + + +def test_latency_none_until_finish_time_set() -> None: + """Latency is ``None`` if *finish_time* has not been assigned.""" + st = _state() + assert st.latency is None + + +def test_latency_returns_difference() -> None: + """Latency equals ``finish_time - initial_time`` once closed.""" + st = _state() + st.finish_time = 5.5 + assert st.latency == 5.5 # 5.5 - 0.0 diff --git a/tests/unit/sampler/test_gaussian_poisson.py b/tests/unit/samplers/test_gaussian_poisson.py similarity index 93% rename from tests/unit/sampler/test_gaussian_poisson.py rename to tests/unit/samplers/test_gaussian_poisson.py index c182376..b2b3c2e 100644 --- a/tests/unit/sampler/test_gaussian_poisson.py +++ b/tests/unit/samplers/test_gaussian_poisson.py @@ -10,11 +10,11 @@ from numpy.random import Generator, default_rng from app.config.constants import TimeDefaults -from app.core.event_samplers.gaussian_poisson import ( +from app.samplers.gaussian_poisson import ( gaussian_poisson_sampling, ) from app.schemas.random_variables_config import RVConfig -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.rqs_generator_input import RqsGeneratorInput if TYPE_CHECKING: @@ -29,6 +29,7 @@ def rqs_cfg() -> RqsGeneratorInput: """Minimal, valid RqsGeneratorInput for Gaussian-Poisson tests.""" return RqsGeneratorInput( + id= "gen-1", avg_active_users=RVConfig( mean=10.0, variance=4.0, @@ -98,7 +99,7 @@ def fake_truncated_gaussian( return 0.0 # force U = 0 monkeypatch.setattr( - "app.core.event_samplers.gaussian_poisson.truncated_gaussian_generator", + "app.samplers.gaussian_poisson.truncated_gaussian_generator", fake_truncated_gaussian, ) diff --git a/tests/unit/sampler/test_poisson_poisson.py b/tests/unit/samplers/test_poisson_poisson.py similarity index 95% rename from tests/unit/sampler/test_poisson_poisson.py rename to tests/unit/samplers/test_poisson_poisson.py index 2fbbb9e..00242fa 100644 --- a/tests/unit/sampler/test_poisson_poisson.py +++ b/tests/unit/samplers/test_poisson_poisson.py @@ -11,9 +11,9 @@ from numpy.random import Generator, default_rng from app.config.constants import TimeDefaults -from app.core.event_samplers.poisson_poisson import poisson_poisson_sampling +from app.samplers.poisson_poisson import poisson_poisson_sampling from app.schemas.random_variables_config import RVConfig -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.rqs_generator_input import RqsGeneratorInput if TYPE_CHECKING: @@ -24,6 +24,7 @@ def rqs_cfg() -> RqsGeneratorInput: """Return a minimal, valid RqsGeneratorInput for the sampler tests.""" return RqsGeneratorInput( + id="gen-1", avg_active_users={"mean": 1.0, "distribution": "poisson"}, avg_request_per_minute_per_user={"mean": 60.0, "distribution": "poisson"}, user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, @@ -96,6 +97,7 @@ def test_zero_users_produces_no_events( ) -> None: """If the mean user count is zero the generator must yield no events.""" cfg_zero = RqsGeneratorInput( + id="gen-1", avg_active_users=RVConfig(mean=0.0, distribution="poisson"), avg_request_per_minute_per_user=RVConfig(mean=60.0, distribution="poisson"), user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, diff --git a/tests/unit/sampler/test_sampler_helper.py b/tests/unit/samplers/test_sampler_helper.py similarity index 99% rename from tests/unit/sampler/test_sampler_helper.py rename to tests/unit/samplers/test_sampler_helper.py index 7d34990..7e615d0 100644 --- a/tests/unit/sampler/test_sampler_helper.py +++ b/tests/unit/samplers/test_sampler_helper.py @@ -9,7 +9,7 @@ import pytest from app.config.constants import Distribution -from app.core.event_samplers.common_helpers import ( +from app.samplers.common_helpers import ( exponential_variable_generator, general_sampler, lognormal_variable_generator, diff --git a/tests/unit/input_sructure/test_endpoint_input.py b/tests/unit/schemas/test_endpoint_input.py similarity index 98% rename from tests/unit/input_sructure/test_endpoint_input.py rename to tests/unit/schemas/test_endpoint_input.py index fc08b5e..76d22a2 100644 --- a/tests/unit/input_sructure/test_endpoint_input.py +++ b/tests/unit/schemas/test_endpoint_input.py @@ -11,7 +11,7 @@ EndpointStepRAM, StepOperation, ) -from app.schemas.system_topology_schema.endpoint_schema import Endpoint, Step +from app.schemas.system_topology.endpoint import Endpoint, Step # --------------------------------------------------------------------------- # diff --git a/tests/unit/input_sructure/test_full_topology_input.py b/tests/unit/schemas/test_full_topology_input.py similarity index 97% rename from tests/unit/input_sructure/test_full_topology_input.py rename to tests/unit/schemas/test_full_topology_input.py index 7f0d864..8382ee6 100644 --- a/tests/unit/input_sructure/test_full_topology_input.py +++ b/tests/unit/schemas/test_full_topology_input.py @@ -14,8 +14,8 @@ SystemNodes, ) from app.schemas.random_variables_config import RVConfig -from app.schemas.system_topology_schema.endpoint_schema import Endpoint, Step -from app.schemas.system_topology_schema.full_system_topology_schema import ( +from app.schemas.system_topology.endpoint import Endpoint, Step +from app.schemas.system_topology.full_system_topology import ( Client, Edge, Server, diff --git a/tests/unit/input_sructure/test_requests_generator_input.py b/tests/unit/schemas/test_requests_generator_input.py similarity index 95% rename from tests/unit/input_sructure/test_requests_generator_input.py rename to tests/unit/schemas/test_requests_generator_input.py index 1ca9562..f676f4c 100644 --- a/tests/unit/input_sructure/test_requests_generator_input.py +++ b/tests/unit/schemas/test_requests_generator_input.py @@ -6,7 +6,7 @@ from app.config.constants import Distribution, TimeDefaults from app.schemas.random_variables_config import RVConfig -from app.schemas.requests_generator_input import RqsGeneratorInput +from app.schemas.rqs_generator_input import RqsGeneratorInput from app.schemas.simulation_settings_input import SimulationSettings # --------------------------------------------------------------------------- # @@ -52,22 +52,15 @@ def test_explicit_variance_is_preserved() -> None: def test_mean_must_be_numeric() -> None: """A non-numeric mean triggers a ValidationError.""" - with pytest.raises(ValidationError) as exc: + with pytest.raises(ValidationError): RVConfig(mean="not a number", distribution=Distribution.POISSON) - assert any(err["loc"] == ("mean",) for err in exc.value.errors()) - def test_missing_mean_field() -> None: """Omitting mean raises a 'field required' ValidationError.""" - with pytest.raises(ValidationError) as exc: + with pytest.raises(ValidationError): RVConfig.model_validate({"distribution": Distribution.NORMAL}) - assert any( - err["loc"] == ("mean",) and err["type"] == "missing" - for err in exc.value.errors() - ) - def test_default_distribution_is_poisson() -> None: """If distribution is missing, it defaults to 'poisson'.""" @@ -106,6 +99,7 @@ def _valid_normal_cfg(mean: float = 1.0) -> dict[str, float | str]: def test_default_user_sampling_window() -> None: """If user_sampling_window is missing it defaults to the constant.""" inp = RqsGeneratorInput( + id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), ) @@ -115,6 +109,7 @@ def test_default_user_sampling_window() -> None: def test_explicit_user_sampling_window_kept() -> None: """An explicit user_sampling_window is preserved.""" inp = RqsGeneratorInput( + id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), user_sampling_window=30, @@ -126,6 +121,7 @@ def test_user_sampling_window_not_int_raises() -> None: """A non-integer user_sampling_window raises ValidationError.""" with pytest.raises(ValidationError): RqsGeneratorInput( + id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), user_sampling_window="not-int", @@ -137,6 +133,7 @@ def test_user_sampling_window_above_max_raises() -> None: too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW + 1 with pytest.raises(ValidationError): RqsGeneratorInput( + id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), user_sampling_window=too_large, @@ -147,6 +144,7 @@ def test_avg_request_must_be_poisson() -> None: """avg_request_per_minute_per_user must be Poisson; Normal raises.""" with pytest.raises(ValidationError): RqsGeneratorInput( + id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_normal_cfg(), ) @@ -157,6 +155,7 @@ def test_avg_active_users_invalid_distribution_raises() -> None: bad_cfg = {"mean": 1.0, "distribution": Distribution.EXPONENTIAL} with pytest.raises(ValidationError): RqsGeneratorInput( + id="rqs-1", avg_active_users=bad_cfg, avg_request_per_minute_per_user=_valid_poisson_cfg(), ) @@ -165,6 +164,7 @@ def test_avg_active_users_invalid_distribution_raises() -> None: def test_valid_poisson_poisson_configuration() -> None: """Poisson-Poisson combo is accepted.""" cfg = RqsGeneratorInput( + id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), ) @@ -178,6 +178,7 @@ def test_valid_poisson_poisson_configuration() -> None: def test_valid_normal_poisson_configuration() -> None: """Normal-Poisson combo is accepted.""" cfg = RqsGeneratorInput( + id="rqs-1", avg_active_users=_valid_normal_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), ) diff --git a/tests/unit/test_state.py b/tests/unit/test_state.py deleted file mode 100644 index 4612f1b..0000000 --- a/tests/unit/test_state.py +++ /dev/null @@ -1,47 +0,0 @@ -"""Unit-tests for :class:`RequestState`.""" -from __future__ import annotations - -from app.config.rqs_state import RequestState - -# --------------------------------------------------------------------------- # -# Helpers # -# --------------------------------------------------------------------------- # - - -def _state() -> RequestState: - """Return a fresh RequestState with id=42 and t0=0.0.""" - return RequestState(id=42, initial_time=0.0) - - -# --------------------------------------------------------------------------- # -# Tests # -# --------------------------------------------------------------------------- # - - -def test_record_hop_appends_formatted_entry() -> None: - """Calling *record_hop* stores 'node@timestamp' with 3-dec precision.""" - st = _state() - st.record_hop("generator", now=1.23456) - assert st.history == ["generator@1.235"] # rounded to 3 decimals - - -def test_multiple_hops_preserve_order() -> None: - """History keeps insertion order for consecutive hops.""" - st = _state() - st.record_hop("A", 0.1) - st.record_hop("B", 0.2) - st.record_hop("C", 0.3) - assert st.history == ["A@0.100", "B@0.200", "C@0.300"] - - -def test_latency_none_until_finish_time_set() -> None: - """Latency is None if *finish_time* not assigned.""" - st = _state() - assert st.latency is None - - -def test_latency_returns_difference() -> None: - """Latency equals finish_time - initial_time once completed.""" - st = _state() - st.finish_time = 5.5 - assert st.latency == 5.5 # 5.5 - 0.0 From 26868452150ee3dfca7a149edb67467b4628a86d Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Tue, 29 Jul 2025 17:17:27 +0200 Subject: [PATCH 18/52] Features/metric sampler and collection (#7) * defined architecture for the central collector + documentation * server modification to collect metrics and updated docs * Completed metric collector for server plus test * remove web app dependencies and added metrics for rqs latency * improved docs and improved metric collection * changes to make the code compatible with new changes * improved ci against toml changes * update lock file * minor change --- .gitattributes | 4 - .github/workflows/ci-cd-main.yml | 0 .github/workflows/ci-develop.yml | 54 +- Dockerfile | 50 - README.md | 207 +-- alembic.ini | 144 -- alembic/README | 1 - alembic/env.py | 83 - alembic/script.py.mako | 28 - docker_fs/.env.example | 8 - docker_fs/docker-compose.dev.yml | 40 - docker_fs/docker-compose.prod.yml | 0 {documentation => docs}/dev_workflow_guide.md | 0 docs/fastsim-docs/metrics_to_measure.md | 49 + .../fastsim-docs}/requests_generator.md | 0 .../fastsim-docs}/runtime_and_resources.md | 158 +- .../fastsim-docs}/simulation_input.md | 0 .../time_series_metric_architecture.md | 91 ++ {documentation => docs}/fastsim_vision.md | 0 .../metrics_to_measure.md | 49 - poetry.lock | 1432 +++-------------- pyproject.toml | 11 +- scripts/init-docker-dev.sh | 98 -- src/app/api/__init__.py | 1 - src/app/api/health_check.py | 11 - src/app/api/simulation.py | 4 - src/app/config/constants.py | 22 +- src/app/config/settings.py | 43 - src/app/db/__init__.py | 1 - src/app/db/base.py | 14 - src/app/db/init_db.py | 15 - src/app/db/session.py | 37 - src/app/main.py | 33 - src/app/metrics/client.py | 18 + src/app/metrics/collector.py | 70 + src/app/metrics/dictionary_metrics.py | 38 - src/app/metrics/edge.py | 29 + src/app/metrics/server.py | 34 + src/app/runtime/actors/client.py | 31 +- src/app/runtime/actors/edge.py | 30 +- src/app/runtime/actors/server.py | 185 ++- src/app/schemas/simulation_settings_input.py | 20 +- src/app/simulation_run.py | 60 - tests/conftest.py | 122 +- .../integration/db_initialization/__init__.py | 1 - .../db_initialization/test_db_connection.py | 16 - .../db_initialization/test_init_models.py | 17 - tests/unit/db/test_settings.py | 18 - tests/unit/runtime/engine/test_edge.py | 92 +- tests/unit/runtime/engine/test_server.py | 170 +- tests/unit/test_health_route.py | 8 - 51 files changed, 1130 insertions(+), 2517 deletions(-) delete mode 100644 .gitattributes delete mode 100644 .github/workflows/ci-cd-main.yml delete mode 100644 Dockerfile delete mode 100644 alembic.ini delete mode 100644 alembic/README delete mode 100644 alembic/env.py delete mode 100644 alembic/script.py.mako delete mode 100644 docker_fs/.env.example delete mode 100644 docker_fs/docker-compose.dev.yml delete mode 100644 docker_fs/docker-compose.prod.yml rename {documentation => docs}/dev_workflow_guide.md (100%) create mode 100644 docs/fastsim-docs/metrics_to_measure.md rename {documentation/backend_documentation => docs/fastsim-docs}/requests_generator.md (100%) rename {documentation/backend_documentation => docs/fastsim-docs}/runtime_and_resources.md (69%) rename {documentation/backend_documentation => docs/fastsim-docs}/simulation_input.md (100%) create mode 100644 docs/fastsim-docs/time_series_metric_architecture.md rename {documentation => docs}/fastsim_vision.md (100%) delete mode 100644 documentation/backend_documentation/metrics_to_measure.md delete mode 100644 scripts/init-docker-dev.sh delete mode 100644 src/app/api/__init__.py delete mode 100644 src/app/api/health_check.py delete mode 100644 src/app/api/simulation.py delete mode 100644 src/app/config/settings.py delete mode 100644 src/app/db/__init__.py delete mode 100644 src/app/db/base.py delete mode 100644 src/app/db/init_db.py delete mode 100644 src/app/db/session.py delete mode 100644 src/app/main.py create mode 100644 src/app/metrics/client.py create mode 100644 src/app/metrics/collector.py delete mode 100644 src/app/metrics/dictionary_metrics.py create mode 100644 src/app/metrics/edge.py create mode 100644 src/app/metrics/server.py delete mode 100644 src/app/simulation_run.py delete mode 100644 tests/integration/db_initialization/__init__.py delete mode 100644 tests/integration/db_initialization/test_db_connection.py delete mode 100644 tests/integration/db_initialization/test_init_models.py delete mode 100644 tests/unit/db/test_settings.py delete mode 100644 tests/unit/test_health_route.py diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 06ab1ee..0000000 --- a/.gitattributes +++ /dev/null @@ -1,4 +0,0 @@ -*.sh text eol=lf -*.py text eol=lf -*.env text eol=lf -*.yml text eol=lf diff --git a/.github/workflows/ci-cd-main.yml b/.github/workflows/ci-cd-main.yml deleted file mode 100644 index e69de29..0000000 diff --git a/.github/workflows/ci-develop.yml b/.github/workflows/ci-develop.yml index 480f075..9727868 100644 --- a/.github/workflows/ci-develop.yml +++ b/.github/workflows/ci-develop.yml @@ -62,33 +62,12 @@ jobs: # Job 2 ─ Full validation (executed only on push events) -# --------------------------------------------------------------------------- # -# Includes everything from the quick job plus: -# • PostgreSQL service container -# • Alembic migrations -# • Integration tests -# • Multi-stage Docker build and health-check - full: if: | github.event_name == 'push' && github.ref == 'refs/heads/develop' runs-on: ubuntu-latest - services: - postgres: - image: postgres:17 - env: - POSTGRES_USER: ${{ secrets.DB_USER }} - POSTGRES_PASSWORD: ${{ secrets.DB_PASSWORD }} - POSTGRES_DB: ${{ secrets.DB_NAME }} - ports: ["5432:5432"] - options: >- - --health-cmd "pg_isready -U $POSTGRES_USER -d $POSTGRES_DB" - --health-interval 10s - --health-timeout 5s - --health-retries 5 - steps: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 @@ -110,41 +89,12 @@ jobs: - name: Run mypy run: poetry run mypy src - - name: Apply Alembic migrations - env: - ENVIRONMENT: test - DB_URL: postgresql+psycopg://${{ secrets.DB_USER }}:${{ secrets.DB_PASSWORD }}@localhost:5432/${{ secrets.DB_NAME }} - run: poetry run alembic upgrade head - - name: Run all tests - env: - ENVIRONMENT: test - DB_URL: postgresql+asyncpg://${{ secrets.DB_USER }}:${{ secrets.DB_PASSWORD }}@localhost:5432/${{ secrets.DB_NAME }} run: | poetry run pytest \ --cov=src --cov-report=term \ --disable-warnings - - name: Build Docker image - run: docker build --progress=plain -t backend:ci . + - - name: Smoke test container - run: | - # partiamo con --network host così il container condivide la rete del runner - docker run -d \ - --name backend_ci \ - --network host \ - -e ENVIRONMENT=test \ - -e DB_URL=postgresql+asyncpg://${{ secrets.DB_USER }}:${{ secrets.DB_PASSWORD }}@localhost:5432/${{ secrets.DB_NAME }} \ - backend:ci \ - uvicorn app.main:app --host 0.0.0.0 --port 8000 - - for i in {1..10}; do - if curl --silent --fail http://localhost:8000/health; then - echo "✔ Health OK"; break - else - echo "Waiting…"; sleep 3 - fi - done - - docker stop backend_ci \ No newline at end of file + \ No newline at end of file diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 6bf546c..0000000 --- a/Dockerfile +++ /dev/null @@ -1,50 +0,0 @@ -# ─────────────── Build stage ─────────────── -FROM python:3.12-slim AS builder - -# Install system dependencies for psycopg and build tools -RUN apt-get update \ - && apt-get install -y --no-install-recommends gcc libpq-dev curl \ - && rm -rf /var/lib/apt/lists/* - -WORKDIR /opt/app - -# Copy only pyproject.toml, poetry.lock, README so we leverage cache -COPY pyproject.toml poetry.lock* README.md ./ - -# Install Poetry (into /root/.local/bin) -RUN curl -sSL https://install.python-poetry.org | python3 - - -# Symlink Poetry into /usr/local/bin so "poetry" is on PATH -RUN ln -s /root/.local/bin/poetry /usr/local/bin/poetry - -# Tell Poetry not to create its own venv -RUN poetry config virtualenvs.create false - -# Install only the prod deps (uvicorn, fastapi, sqlalchemy, psycopg...) -RUN poetry install --no-root --without dev - -# Now copy in your application code -COPY src/ ./src - -# ─────────── Runtime stage ─────────── -FROM python:3.12-slim AS runtime - -WORKDIR /opt/app - -# 1) Copy installed libraries -COPY --from=builder /usr/local/lib/python3.12 /usr/local/lib/python3.12 - -# 2) Copy console scripts (uvicorn, alembic, etc.) -COPY --from=builder /usr/local/bin /usr/local/bin - -# 3) Copy application code -COPY --from=builder /opt/app/src ./src - -# 4) Non-root user -RUN adduser --disabled-password --gecos '' appuser -USER appuser - -WORKDIR /opt/app/src - -# 5) Default command -CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/README.md b/README.md index a9448e5..526a127 100644 --- a/README.md +++ b/README.md @@ -1,176 +1,127 @@ -Certamente. Ecco il contenuto del `README.md` visualizzato direttamente qui. - ------ - # **FastSim Project Overview** ## **1. Why FastSim?** -FastAPI + Uvicorn gives Python teams a lightning-fast async stack, yet sizing it for production still means guesswork, costly cloud load-tests, or late surprises. **FastSim** fills that gap by becoming a **digital twin** of your actual service: +Modern async Python stacks like FastAPI + Uvicorn are incredibly fast, yet sizing them for production often involves guesswork, costly cloud load-tests, or late-stage surprises. **FastSim** fills that gap by acting as a **digital twin** of your service: - * It **replicates** your FastAPI + Uvicorn event-loop behavior in SimPy, generating the same kinds of asynchronous steps (parsing, CPU work, I/O, LLM calls) that happen in real code. - * It **models** your infrastructure primitives—CPU cores (via a SimPy `Resource`), database pools, rate-limiters, and even GPU inference quotas—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency. - * It **outputs** the very metrics you would scrape in production (p50/p95/p99 latency, ready-queue lag, concurrency, throughput, cost per LLM call), but entirely offline, in seconds. + * It **replicates** the behavior of an async event-loop in SimPy, generating the same kinds of steps (parsing, CPU work, I/O waits) that happen in real code. + * It **models** your infrastructure primitives—CPU cores, connection pools, and rate-limiters—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency. + * It **outputs** the very metrics you would scrape in production (p50/p95/p99 latency, ready-queue lag, concurrency, throughput), but entirely offline, in seconds. -With FastSim you can ask, *“What happens if traffic doubles on Black Friday?”*, *“How many cores are needed to keep p95 latency below 100 ms?”*, or *“Is our LLM-driven endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. +With FastSim, you can ask, *“What happens if traffic doubles on Black Friday?”*, *“How many cores are needed to keep p95 latency below 100 ms?”*, or *“Is our new endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. **Outcome:** Data-driven capacity planning, early performance tuning, and far fewer surprises in production. -## **2. Project Goals** - -| \# | Goal | Practical Outcome | -| :--- | :--- | :--- | -| 1 | **Pre-production sizing** | Know the required core count, pool size, and replica count to meet your SLA. | -| 2 | **Scenario analysis** | Explore various traffic models, endpoint mixes, latency distributions, and RTT. | -| 3 | **Twin metrics** | Produce the same metrics you’ll scrape in production (latency, queue length, CPU utilization). | -| 4 | **Rapid iteration** | A single YAML/JSON configuration or REST call generates a full performance report. | -| 5 | **Educational value** | Visualize how GIL contention, queue length, and concurrency react to load. | - -## **3. Who Benefits & Why** - -| Audience | Pain-Point Solved | FastSim Value | -| :--- | :--- | :--- | -| **Backend Engineers** | Unsure if a 4-vCPU container can survive a marketing traffic spike. | Run *what-if* scenarios, tweak CPU cores or pool sizes, and get p95 latency and max-concurrency metrics before merging code. | -| **DevOps / SRE** | Guesswork in capacity planning; high cost of over-provisioning. | Simulate 1 to N replicas, autoscaler thresholds, and database pool sizes to find the most cost-effective configuration that meets the SLA. | -| **ML / LLM Product Teams** | LLM inference cost and latency are difficult to predict. | Model the LLM step with a price and latency distribution to estimate cost-per-request and the benefits of GPU batching without needing real GPUs. | -| **Educators / Trainers** | Students struggle to visualize event-loop internals. | Visualize GIL ready-queue lag, CPU vs. I/O steps, and the effect of blocking code—perfect for live demos and labs. | -| **Consultants / Architects** | Need a quick proof-of-concept for new client designs. | Define endpoints in YAML and demonstrate throughput and latency under projected load in minutes. | -| **Open-Source Community** | Lacks a lightweight Python simulator for ASGI workloads. | An extensible codebase makes it easy to plug in new resources (e.g., rate-limiters, caches) or traffic models (e.g., spike, uniform ramp). | -| **System-Design Interviewees** | Hard to quantify trade-offs in whiteboard interviews. | Prototype real-time metrics—queue lengths, concurrency, latency distributions—to demonstrate how your design scales and where bottlenecks lie. | - -## **4. About This Documentation** +## **2. Installation & Quick Start** -This project contains extensive documentation covering its vision, architecture, and technical implementation. The documents are designed to be read in sequence to build a comprehensive understanding of the project. +FastSim is designed to be used as a Python library. -### **How to Read This Documentation** +```bash +# Installation (coming soon to PyPI) +pip install fastsim +``` -For the best understanding of FastSim, we recommend reading the documentation in the following order: +**Example Usage:** + +1. Define your system topology in a `config.yml` file: + + ```yaml + topology: + servers: + - id: "app-server-1" + # ... server configuration ... + load_balancers: + - id: "main-lb" + backends: ["app-server-1"] + # ... lb configuration ... + settings: + duration_s: 60 + ``` -1. **README.md (This Document)**: Start here for a high-level overview of the project's purpose, goals, target audience, and development workflow. It provides the essential context for all other documents. -2. **dev_worflow_guide**: This document details the github workflow for the development -3. **simulation_input**: This document details the technical contract for configuring a simulation. It explains the `SimulationPayload` and its components (`rqs_input`, `topology_graph`, `sim_settings`). This is essential reading for anyone who will be creating or modifying simulation configurations. -4. **runtime_and_resources**: A deep dive into the simulation's internal engine. It explains how the validated input is transformed into live SimPy processes (Actors, Resources, State). This is intended for advanced users or contributors who want to understand *how* the simulation works under the hood. -5. **requests_generator**: This document covers the mathematical and algorithmic details behind the traffic generation model. It is for those interested in the statistical foundations of the simulator. -6. **Simulation Metrics**: A comprehensive guide to all output metrics. It explains what each metric measures, how it's collected, and why it's important for performance analysis. +2. Run the simulation from a Python script: -Optional **fastsim_vision**: a more detailed document about the project vision + ```python + from fastsim import run_simulation + from fastsim.schemas import SimulationPayload -you can find the documentation at the root of the project in the folder `documentation/` + # Load and validate configuration using Pydantic + payload = SimulationPayload.from_yaml("config.yml") -## **5. Development Workflow & Architecture Guide** + # Run the simulation + results = run_simulation(payload) -This section outlines the standardized development workflow, repository architecture, and branching strategy for the FastSim backend. + # Analyze and plot results + results.plot_latency_distribution() + print(results.summary()) + ``` -### **Technology Stack** +## **3. Who Benefits & Why** - * **Backend**: FastAPI - * **Backend Package Manager**: Poetry - * **Frontend**: React + JavaScript - * **Database**: PostgreSQL - * **Caching**: Redis - * **Containerization**: Docker +| Audience | Pain-Point Solved | FastSim Value | +| :--- | :--- | :--- | +| **Backend Engineers** | Unsure if a 4-vCPU container can survive a traffic spike. | Run *what-if* scenarios, tweak CPU cores, and get p95 latency metrics before merging code. | +| **DevOps / SRE** | Guesswork in capacity planning; high cost of over-provisioning. | Simulate 1 to N replicas to find the most cost-effective configuration that meets the SLA. | +| **ML / LLM Teams** | LLM inference cost and latency are difficult to predict. | Model the LLM step with a price and latency distribution to estimate cost-per-request. | +| **Educators / Trainers** | Students struggle to visualize event-loop internals. | Visualize GIL ready-queue lag, CPU vs. I/O steps, and the effect of blocking code. | +| **System-Design Interviewees** | Hard to quantify trade-offs in whiteboard interviews. | Prototype real-time metrics to demonstrate how your design scales and where bottlenecks lie. | -### **Backend Service (`FastSim-backend`)** +## **4. Project Structure** -The repository hosts the entire FastAPI backend, which exposes the REST API, runs the discrete-event simulation, communicates with the database, and provides metrics. +The project is a standard Python library managed with Poetry. ``` -fastsim-backend/ -├── Dockerfile -├── docker_fs/ -│ ├── docker-compose.dev.yml -│ └── docker-compose.prod.yml -├── scripts/ -│ ├── init-docker-dev.sh -│ └── quality-check.sh -├── alembic/ -│ ├── env.py -│ └── versions/ +fastsim/ ├── documentation/ -│ └── backend_documentation/ -├── tests/ -│ ├── unit/ -│ └── integration/ +│ └── ... ├── src/ │ └── app/ -│ ├── api/ │ ├── config/ -│ ├── db/ │ ├── metrics/ │ ├── resources/ │ ├── runtime/ -│ │ ├── rqs_state.py -│ │ └── actors/ +│ │ ├── actors/ +│ │ └── rqs_state.py │ ├── samplers/ -│ ├── schemas/ -│ ├── main.py -│ └── simulation_run.py -├── poetry.lock +│ └── schemas/ +├── tests/ +│ ├── unit/ +│ └── integration/ +├── .github/ +│ └── workflows/ +│ └── ci-develop.yml ├── pyproject.toml +├── poetry.lock └── README.md ``` -### **How to Start the Backend with Docker (Development)** +## **5. Development & Contribution** -To spin up the backend and its supporting services in development mode: - -1. **Install & run Docker** on your machine. -2. **Clone** the repository and `cd` into its root. -3. Execute: - ```bash - bash ./scripts/init-docker-dev.sh - ``` - This will launch a **PostgreSQL** container and a **Backend** container that mounts your local `src/` folder with live-reload enabled. - -### **Development Architecture & Philosophy** - -We split responsibilities between Docker-managed services and local workflows. - - * **Docker-Compose for Development**: Containers host external services (PostgreSQL) and run the FastAPI app. Your local `src/` directory is mounted into the backend container for hot-reloading. No tests, migrations, or linting run inside these containers during development. - * **Local Quality & Testing Workflow**: All code quality tools, migrations, and tests are executed on your host machine for faster feedback and full IDE support. +We welcome contributions\! The development workflow is managed by Poetry and quality is enforced by Ruff and MyPy. | Task | Command | Notes | | :--- | :--- | :--- | -| **Lint & format** | `poetry run ruff check src tests` | Style and best-practice validations | -| **Type checking** | `poetry run mypy src tests` | Static type enforcement | -| **Unit tests** | `poetry run pytest -m "not integration"` | Fast, isolated tests—no DB required | -| **Integration tests** | `poetry run pytest -m integration` | Real-DB tests against Docker’s PostgreSQL | -| **DB migrations** | `poetry run alembic upgrade head` | Applies migrations to your local Docker-hosted DB | - -**Rationale**: Running tests or Alembic migrations inside Docker images would slow down your feedback loop and limit IDE features by requiring you to mount the full source tree and install dev dependencies in each build. - -## **6. CI/CD with GitHub Actions** +| **Install dependencies** | `poetry install --with dev` | Installs main and development packages. | +| **Lint & format** | `poetry run ruff check src tests` | Style and best-practice validations. | +| **Type checking** | `poetry run mypy src tests` | Static type enforcement. | +| **Run all tests** | `poetry run pytest` | Executes the full test suite. | -We maintain two jobs on the `develop` branch to ensure code quality and stability. +### **CI with GitHub Actions** -### **Quick (on Pull Requests)** +We maintain two jobs on the `develop` branch to ensure code quality: - * Ruff & MyPy checks - * Unit tests only - * **No database required** + * **Quick (on Pull Requests):** Runs Ruff, MyPy, and unit tests for immediate feedback. + * **Full (on pushes to `develop`):** Runs the full suite, including integration tests and code coverage reports. -### **Full (on pushes to `develop`)** +This guarantees that every commit in `develop` is style-checked, type-safe, and fully tested. - * All checks from the "Quick" suite - * Starts a **PostgreSQL** service container - * Runs **Alembic** migrations - * Executes the **full test suite** (unit + integration) - * Builds the **Docker** image - * **Smoke-tests** the `/health` endpoint of the built container +## **6. Limitations – v0.1 (First Public Release)** -**Guarantee**: Every commit in `develop` is style-checked, type-safe, database-tested, and Docker-ready. +1. **Network Delay Model:** Only pure transport latency is simulated. Bandwidth-related effects (e.g., payload size, link speed) are not yet accounted for. +2. **Concurrency Model:** The simulation models a single-threaded, cooperative event-loop (like `asyncio`). Multi-process or multi-threaded parallelism is not yet supported. +3. **CPU Core Allocation:** Every server instance is pinned to one physical CPU core. Horizontal scaling is achieved by adding more server instances, not by using multiple cores within a single process. -## **7. Limitations – v0.1 (First Public Release)** +These constraints will be revisited in future milestones. -1. **Network Delay Model** - * Only pure transport latency is simulated. - * Bandwidth-related effects (e.g., payload size, link speed, congestion) are NOT accounted for. -2. **Concurrency Model** - * The service exposes **async-only endpoints**. - * Execution runs on a single `asyncio` event-loop thread. - * No thread-pool workers or multi-process setups are supported yet; therefore, concurrency is limited to coroutine scheduling (cooperative, single-thread). -3. **CPU Core Allocation** - * Every server instance is pinned to **one physical CPU core**. - * Horizontal scaling must be achieved via multiple containers/VMs, not via multi-core utilization inside a single process. +## **7. Documentation** -These constraints will be revisited in future milestones once kernel-level context-switching costs, I/O bandwidth modeling, and multi-process orchestration are integrated. \ No newline at end of file +For a deeper understanding of FastSim, we recommend reading the detailed documentation located in the `/documentation` folder at the root of the project. A guided reading path is suggested within to build a comprehensive understanding of the project's vision, architecture, and technical implementation. \ No newline at end of file diff --git a/alembic.ini b/alembic.ini deleted file mode 100644 index 24fc464..0000000 --- a/alembic.ini +++ /dev/null @@ -1,144 +0,0 @@ -# A generic, single database configuration. - -[alembic] -# path to migration scripts. -# this is typically a path given in POSIX (e.g. forward slashes) -# format, relative to the token %(here)s which refers to the location of this -# ini file -script_location = %(here)s/alembic - -# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s -# Uncomment the line below if you want the files to be prepended with date and time -# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file -# for all available tokens -# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s - -# sys.path path, will be prepended to sys.path if present. -# defaults to the current working directory. for multiple paths, the path separator -# is defined by "path_separator" below. -prepend_sys_path = . - - -# timezone to use when rendering the date within the migration file -# as well as the filename. -# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library. -# Any required deps can installed by adding `alembic[tz]` to the pip requirements -# string value is passed to ZoneInfo() -# leave blank for localtime -# timezone = - -# max length of characters to apply to the "slug" field -# truncate_slug_length = 40 - -# set to 'true' to run the environment during -# the 'revision' command, regardless of autogenerate -# revision_environment = false - -# set to 'true' to allow .pyc and .pyo files without -# a source .py file to be detected as revisions in the -# versions/ directory -# sourceless = false - -# version location specification; This defaults -# to /versions. When using multiple version -# directories, initial revisions must be specified with --version-path. -# The path separator used here should be the separator specified by "path_separator" -# below. -# version_locations = %(here)s/bar:%(here)s/bat:%(here)s/alembic/versions - -# path_separator; This indicates what character is used to split lists of file -# paths, including version_locations and prepend_sys_path within configparser -# files such as alembic.ini. -# The default rendered in new alembic.ini files is "os", which uses os.pathsep -# to provide os-dependent path splitting. -# -# Note that in order to support legacy alembic.ini files, this default does NOT -# take place if path_separator is not present in alembic.ini. If this -# option is omitted entirely, fallback logic is as follows: -# -# 1. Parsing of the version_locations option falls back to using the legacy -# "version_path_separator" key, which if absent then falls back to the legacy -# behavior of splitting on spaces and/or commas. -# 2. Parsing of the prepend_sys_path option falls back to the legacy -# behavior of splitting on spaces, commas, or colons. -# -# Valid values for path_separator are: -# -# path_separator = : -# path_separator = ; -# path_separator = space -# path_separator = newline -# -# Use os.pathsep. Default configuration used for new projects. -path_separator = os - -# set to 'true' to search source files recursively -# in each "version_locations" directory -# new in Alembic version 1.10 -# recursive_version_locations = false - -# the output encoding used when revision files -# are written from script.py.mako -# output_encoding = utf-8 - -# database URL. This is consumed by the user-maintained env.py script only. -# other means of configuring database URLs may be customized within the env.py -# file. -#sqlalchemy.url = driver://user:pass@localhost/dbname -# sqlalchemy.url is overridden at runtime in alembic/env.py -# via config.set_main_option(..., settings.db_url) -# leave this here as a placeholder or for offline mode if needed -# sqlalchemy.url = postgresql+asyncpg://user:pass@localhost:5432/dbname - -[post_write_hooks] -# post_write_hooks defines scripts or Python functions that are run -# on newly generated revision scripts. See the documentation for further -# detail and examples - -# format using "black" - use the console_scripts runner, against the "black" entrypoint -# hooks = black -# black.type = console_scripts -# black.entrypoint = black -# black.options = -l 79 REVISION_SCRIPT_FILENAME - -# lint with attempts to fix using "ruff" - use the exec runner, execute a binary -# hooks = ruff -# ruff.type = exec -# ruff.executable = %(here)s/.venv/bin/ruff -# ruff.options = check --fix REVISION_SCRIPT_FILENAME - -# Logging configuration. This is also consumed by the user-maintained -# env.py script only. -[loggers] -keys = root,sqlalchemy,alembic - -[handlers] -keys = console - -[formatters] -keys = generic - -[logger_root] -level = WARNING -handlers = console -qualname = - -[logger_sqlalchemy] -level = WARNING -handlers = -qualname = sqlalchemy.engine - -[logger_alembic] -level = INFO -handlers = -qualname = alembic - -[handler_console] -class = StreamHandler -args = (sys.stderr,) -level = NOTSET -formatter = generic - -[formatter_generic] -format = %(levelname)-5.5s [%(name)s] %(message)s -datefmt = %H:%M:%S diff --git a/alembic/README b/alembic/README deleted file mode 100644 index 98e4f9c..0000000 --- a/alembic/README +++ /dev/null @@ -1 +0,0 @@ -Generic single-database configuration. \ No newline at end of file diff --git a/alembic/env.py b/alembic/env.py deleted file mode 100644 index 2e671fd..0000000 --- a/alembic/env.py +++ /dev/null @@ -1,83 +0,0 @@ -from logging.config import fileConfig - -from alembic import context -from sqlalchemy import engine_from_config, pool - -from app.config.settings import settings -from app.db.base import Base - - -# this is the Alembic Config object, which provides -# access to the values within the .ini file in use. -config = context.config - -# replace sqlalchemy.url with the value of the url in env.dev -raw_url = settings.db_url.replace("+asyncpg", "+psycopg") -config.set_main_option("sqlalchemy.url", raw_url) - - -# Interpret the config file for Python logging. -# This line sets up loggers basically. -if config.config_file_name is not None: - fileConfig(config.config_file_name) - -# add your model's MetaData object here -# for 'autogenerate' support -# from myapp import mymodel -# target_metadata = mymodel.Base.metadata -target_metadata = Base.metadata - -# other values from the config, defined by the needs of env.py, -# can be acquired: -# my_important_option = config.get_main_option("my_important_option") -# ... etc. - - -def run_migrations_offline() -> None: - """Run migrations in 'offline' mode. - - This configures the context with just a URL - and not an Engine, though an Engine is acceptable - here as well. By skipping the Engine creation - we don't even need a DBAPI to be available. - - Calls to context.execute() here emit the given string to the - script output. - - """ - url = config.get_main_option("sqlalchemy.url") - context.configure( - url=url, - target_metadata=target_metadata, - literal_binds=True, - dialect_opts={"paramstyle": "named"}, - ) - - with context.begin_transaction(): - context.run_migrations() - - -def run_migrations_online() -> None: - """Run migrations in 'online' mode. - - In this scenario we need to create an Engine - and associate a connection with the context. - - """ - connectable = engine_from_config( - config.get_section(config.config_ini_section, {}), - prefix="sqlalchemy.", - poolclass=pool.NullPool, - ) - - with connectable.connect() as connection: - context.configure(connection=connection, target_metadata=target_metadata) - - with context.begin_transaction(): - context.run_migrations() - - -if context.is_offline_mode(): - run_migrations_offline() -else: - run_migrations_online() diff --git a/alembic/script.py.mako b/alembic/script.py.mako deleted file mode 100644 index 1101630..0000000 --- a/alembic/script.py.mako +++ /dev/null @@ -1,28 +0,0 @@ -"""${message} - -Revision ID: ${up_revision} -Revises: ${down_revision | comma,n} -Create Date: ${create_date} - -""" -from typing import Sequence, Union - -from alembic import op -import sqlalchemy as sa -${imports if imports else ""} - -# revision identifiers, used by Alembic. -revision: str = ${repr(up_revision)} -down_revision: Union[str, Sequence[str], None] = ${repr(down_revision)} -branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} -depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} - - -def upgrade() -> None: - """Upgrade schema.""" - ${upgrades if upgrades else "pass"} - - -def downgrade() -> None: - """Downgrade schema.""" - ${downgrades if downgrades else "pass"} diff --git a/docker_fs/.env.example b/docker_fs/.env.example deleted file mode 100644 index 1ee9b47..0000000 --- a/docker_fs/.env.example +++ /dev/null @@ -1,8 +0,0 @@ -DB_HOST= -DB_USER= -DB_PASSWORD= -DB_NAME= -DB_URL= -PGADMIN_DEFAULT_EMAIL= -PGADMIN_DEFAULT_PASSWORD= -ENVIRONMENT= \ No newline at end of file diff --git a/docker_fs/docker-compose.dev.yml b/docker_fs/docker-compose.dev.yml deleted file mode 100644 index cea5fa4..0000000 --- a/docker_fs/docker-compose.dev.yml +++ /dev/null @@ -1,40 +0,0 @@ -services: - db: - image: postgres:17 - ports: ["5432:5432"] - - env_file: - - .env.dev - environment: - POSTGRES_USER: ${DB_USER} - POSTGRES_PASSWORD: ${DB_PASSWORD} - POSTGRES_DB: ${DB_NAME} - - volumes: - - dev_db:/var/lib/postgresql/data - - pgadmin: - image: dpage/pgadmin4 - ports: ["8080:80"] - env_file: - - .env.dev - environment: - PGADMIN_DEFAULT_EMAIL: ${PGADMIN_DEFAULT_EMAIL} - PGADMIN_DEFAULT_PASSWORD: ${PGADMIN_DEFAULT_PASSWORD} - depends_on: - - db - - backend: - build: .. - command: uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 - volumes: - - ../src:/opt/app/src - ports: - - "8000:8000" - env_file: - - .env.dev - depends_on: - - db - -volumes: - dev_db: \ No newline at end of file diff --git a/docker_fs/docker-compose.prod.yml b/docker_fs/docker-compose.prod.yml deleted file mode 100644 index e69de29..0000000 diff --git a/documentation/dev_workflow_guide.md b/docs/dev_workflow_guide.md similarity index 100% rename from documentation/dev_workflow_guide.md rename to docs/dev_workflow_guide.md diff --git a/docs/fastsim-docs/metrics_to_measure.md b/docs/fastsim-docs/metrics_to_measure.md new file mode 100644 index 0000000..390f6d9 --- /dev/null +++ b/docs/fastsim-docs/metrics_to_measure.md @@ -0,0 +1,49 @@ +### **FastSim — Simulation Metrics** + +Metrics are the lifeblood of any simulation, transforming a series of abstract events into concrete, actionable insights about system performance, resource utilization, and potential bottlenecks. FastSim provides a flexible and robust metrics collection system designed to give you a multi-faceted view of your system's behavior under load. + +To achieve this, FastSim categorizes metrics into three distinct types: + +1. **Sampled Metrics (`SampledMetricName`):** These metrics provide a **time-series view** of the system's state. They are captured at fixed, regular intervals (e.g., every 5 milliseconds). This methodology is ideal for understanding trends and measuring the continuous utilization of finite resources. Think of them as periodic snapshots of your system's health. + +2. **Event-based Metrics (`EventMetricName`):** These metrics are raw data points recorded **only when a specific event occurs**, such as the completion of a request. Their collection is asynchronous and irregular. This approach is designed to capture the fundamental data needed for post-simulation analysis with maximum efficiency and flexibility. + +3. **Aggregated Metrics (`AggregatedMetricName`):** These are not collected directly but are **calculated after the simulation ends**. They provide high-level statistical summaries (like mean, median, and percentiles) or rate calculations derived from the raw data collected by event-based metrics. They distill thousands of individual data points into a handful of key performance indicators (KPIs). + +The following sections provide a detailed breakdown of each metric within these categories. + +----- + +### **1. Sampled Metrics: A Time-Series Perspective** + +Sampled metrics are configured in the `SimulationSettings` payload. Enabling them allows you to plot the evolution of system resources over time, which is crucial for identifying saturation points and transient performance issues. + +| Metric Name (`SampledMetricName`) | Description & Rationale | +| :--- | :--- | +| **`READY_QUEUE_LEN`** | **What it is:** The number of tasks in the event loop's "ready" queue waiting for their turn to run on a CPU core. \\ **Rationale:** This is arguably the most critical indicator of **CPU saturation**. If this queue length is consistently greater than zero, it means tasks are ready to do work but are forced to wait because the CPU is busy. A long or growing queue is a definitive sign that your application is CPU-bound. | +| **`EVENT_LOOP_IO_SLEEP`** | **What it is:** The number of tasks currently suspended and waiting for an I/O operation to complete (e.g., a database query or a network call). \\ **Rationale:** This metric helps you determine if your system is **I/O-bound**. If this queue is long, it means the CPU is potentially underutilized because it has no ready tasks to run and is instead waiting for external systems to respond. | +| **`RAM_IN_USE`** | **What it is:** The total amount of memory (in MB) currently allocated by all active requests within a server. \\ **Rationale:** Essential for **capacity planning and stability analysis**. This metric allows you to visualize your system's memory footprint under load. A steadily increasing `RAM_IN_USE` value that never returns to a baseline is the classic signature of a **memory leak**. | +| **`EDGE_CONCURRENT_CONNECTION`** | **What it is:** The number of requests currently in transit across a network edge. \\ **Rationale:** This metric helps visualize the load on your network links. A high number of concurrent connections can indicate that downstream services are slow to respond, causing requests to pile up. | + +----- + +### **2. Event-based Metrics: The Raw Data Foundation** + +The goal of event-based metrics is to collect the most fundamental data points with minimal overhead during the simulation. This raw data becomes the single source of truth for all post-simulation transactional analysis. + +| Metric Name (`EventMetricName`) | Description & Rationale | +| :--- | :--- | +| **`RQS_CLOCK`** | **What it is:** A collection of `(start_time, finish_time)` tuples, with one tuple recorded for every single request that is fully processed. \\ **Rationale:** This is the **most efficient and flexible way to capture transactional data**. Instead of storing separate lists for latencies and completion times, we store a single, cohesive data structure. This design choice is deliberate: this raw data is all that is needed to calculate both latency and throughput after the simulation, providing maximum flexibility for analysis. | +| **`LLM_COST`** | **What it is:** A collection of the estimated monetary cost (e.g., in USD) incurred by each individual call to an external Large Language Model (LLM) API. \\ **Rationale:** In modern AI-powered applications, API calls can be a major operational expense. This metric moves beyond technical performance to measure **financial performance**, allowing for cost optimization. | + +----- + +### **3. Aggregated Metrics: Post-Simulation Insights** + +These metrics are calculated by an analysis module after the simulation finishes, using the raw data from the event-based metrics. This approach provides flexibility and keeps the simulation core lean. + +| Metric Name (`AggregatedMetricName`) | Description & Rationale | +| :--- | :--- | +| **`THROUGHPUT_RPS`** | **What it is:** The number of requests successfully completed per second, calculated by aggregating the `finish_time` timestamps from the `RQS_CLOCK` data over a specific time window. \\ **Rationale:** This is a fundamental measure of **system performance and capacity**. Calculating it post-simulation is superior because the same raw data can be analyzed with different window sizes (e.g., per-second, per-minute) without re-running the simulation. | +| **`LATENCY_STATS`** | **What it is:** Statistical summaries (mean, median, standard deviation, p50, p95, p99) calculated from the `RQS_CLOCK` data by taking `finish_time - start_time` for each tuple. \\ **Rationale:** These statistics distill thousands of raw data points into key indicators. They tell you about the average user experience (`mean`/`p50`) and, more critically, the worst-case experience (`p95`/`p99`) needed to validate Service Level Objectives (SLOs). | +| **`LLM_STATS`** | **What it is:** Statistical summaries (total cost, average cost per request, etc.) calculated from the raw `LLM_COST` data. \\ **Rationale:** Provides high-level insights into the financial performance of AI-driven features, enabling strategic decisions on cost optimization. | \ No newline at end of file diff --git a/documentation/backend_documentation/requests_generator.md b/docs/fastsim-docs/requests_generator.md similarity index 100% rename from documentation/backend_documentation/requests_generator.md rename to docs/fastsim-docs/requests_generator.md diff --git a/documentation/backend_documentation/runtime_and_resources.md b/docs/fastsim-docs/runtime_and_resources.md similarity index 69% rename from documentation/backend_documentation/runtime_and_resources.md rename to docs/fastsim-docs/runtime_and_resources.md index 6c84bd2..763e0a9 100644 --- a/documentation/backend_documentation/runtime_and_resources.md +++ b/docs/fastsim-docs/runtime_and_resources.md @@ -176,19 +176,42 @@ An `EdgeRuntime` is a direct analog for a **physical or virtual network link**. ----- -### **5.3 `ServerRuntime` — The Workhorse 📦** +### **5.3  `ServerRuntime` — The Workhorse 📦 (2025 edition)** -`ServerRuntime` models an application server that owns finite CPU/RAM resources and executes a chain of steps for every incoming request. -With the 2025 refactor it now uses a **dispatcher / handler** pattern: the dispatcher sits in an infinite loop, and each request is handled in its own SimPy subprocess. This enables many concurrent in-flight requests while keeping the code easy to reason about. +`ServerRuntime` emulates an application server that owns **finite CPU / RAM containers** and executes an ordered chain of **Step** objects for every incoming request. +The 2025 refactor keeps the classic **dispatcher / handler** pattern, adds **live metric counters** (ready‑queue length, I/O‑queue length, RAM‑in‑use) and implements the **lazy‑CPU lock** algorithm described earlier. -| `__init__` parameter | Meaning | -| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **`env`** | The shared `simpy.Environment`. Every timeout and resource operation is scheduled here. | -| **`server_resources`** | A `ServerContainers` mapping (`{"CPU": Container, "RAM": Container}`) created by `ResourcesRuntime`. The containers are **pre-filled** (`level == capacity`) so the server can immediately pull tokens. | -| **`server_config`** | The validated Pydantic `Server` model: server-wide ID, resource spec, and a list of `Endpoint` objects (each endpoint is an ordered list of `Step`s). | -| **`out_edge`** | The `EdgeRuntime` (or stub) that receives the `RequestState` once processing finishes. | -| **`server_box`** | A `simpy.Store` acting as the server’s inbox. Up-stream actors drop `RequestState`s here. | -| **`rng`** | Instance of `numpy.random.Generator`; defaults to `default_rng()`. Used to pick a random endpoint. | +| `__init__` parameter | Meaning | +| ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **`env`** | Shared `simpy.Environment`. Every timeout or resource operation is scheduled here. | +| **`server_resources`** | A `ServerContainers` mapping `{"CPU": Container, "RAM": Container}` created by `ResourcesRuntime`. Containers start **full** so a server can immediately pull tokens. | +| **`server_config`** | Validated Pydantic `Server` model: ID, resource spec, list `endpoints: list[Endpoint]`. | +| **`out_edge`** | `EdgeRuntime` (or stub) that receives the `RequestState` once processing finishes. | +| **`server_box`** | `simpy.Store` acting as the server’s inbox. Up‑stream actors drop `RequestState`s here. | +| **`rng`** | `numpy.random.Generator`; defaults to `default_rng()`. Used to pick a random endpoint. | + +--- + +#### **Live metric fields** + +| Field | Unit | Updated when… | Used for… | +| --------------------- | -------- | ------------------------------------------------ | ------------------------------------------------------------------------ | +| `_el_ready_queue_len` | requests | a CPU step acquires / releases a core | **Ready queue length** (how many coroutines wait for the GIL / a worker) | +| `_el_io_queue_len` | requests | an I/O step enters / leaves the socket wait list | **I/O queue length** (awaits in progress) | +| `_ram_in_use` | MB | RAM `get` / `put` | Instant **RAM usage** per server | + +Accessor properties expose them read‑only: + +```python +@property +def ready_queue_len(self) -> int: return self._el_ready_queue_len + +@property +def io_queue_len(self) -> int: return self._el_io_queue_len + +@property +def ram_in_use(self) -> int: return self._ram_in_use +``` --- @@ -211,56 +234,111 @@ Registers the **dispatcher** coroutine in the environment and returns the create │ ▼ RAM get → CPU/IO steps → RAM put → out_edge.transport() + ▲ │ + │ └── metric counters updated here + └── lazy CPU lock (get once, put on first I/O) ``` 1. **Dispatcher loop** - ```python - while True: - raw_state = yield self.server_box.get() # blocks until a request arrives - state = cast(RequestState, raw_state) - self.env.process(self._handle_request(state)) # fire-and-forget - ``` - - *Spawning a new process per request mimics worker thread concurrency.* +```python +while True: + raw_state = yield self.server_box.get() # blocks until a request arrives + state = cast(RequestState, raw_state) + self.env.process(self._handle_request(state)) # fire‑and‑forget +``` 2. **Handler coroutine (`_handle_request`)** - | Stage | Implementation detail | - | ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | - | **Record arrival** | `state.record_hop(SystemNodes.SERVER, self.server_config.id, env.now)` – leaves a breadcrumb for tracing. | - | **Endpoint selection** | Uniform random index `rng.integers(0, len(endpoints))`. (Hook point for custom routing later.) | - | **Reserve RAM (back-pressure)** | Compute `total_ram` (sum of all `StepOperation.NECESSARY_RAM`). `yield RAM.get(total_ram)`. If not enough RAM is free, the coroutine blocks, creating natural memory pressure. | - | **Execute steps in order** | | - | – CPU-bound step | `yield CPU.get(1)` → `yield env.timeout(cpu_time)` → `yield CPU.put(1)` – exactly one core is busy for the duration. | - | – I/O-bound step | `yield env.timeout(io_wait)` – no core is held, modelling non-blocking I/O. | - | **Release RAM** | `yield RAM.put(total_ram)`. | - | **Forward** | `out_edge.transport(state)` – hands the request to the next hop without waiting for network latency. | +| Stage | Implementation detail | +| ------------------------------- | ----------------------------------------------------------------------------------------- | +| **Record arrival** | `state.record_hop(SystemNodes.SERVER, self.server_config.id, env.now)` | +| **Endpoint selection** | Uniform random index `rng.integers(0, len(endpoints))` (plug‑in point for custom routing) | +| **Reserve RAM (back‑pressure)** | compute `total_ram` → `yield RAM.get(total_ram)` → `_ram_in_use += total_ram` | +| **Execute steps** | handled in a loop with *lazy CPU lock* and metric updates (see edge‑case notes below) | +| **Release RAM** | `_ram_in_use -= total_ram` → `yield RAM.put(total_ram)` | +| **Forward** | `out_edge.transport(state)` — send to next hop without awaiting latency | + +--- + +#### **CPU / I‑O loop details** + +* **Lazy‑CPU lock** – first CPU step acquires one core; all following contiguous CPU steps reuse it. +* **Release on I/O** – on the first I/O step the core is released; it remains free until the next CPU step. +* **Metric updates** – counters are modified only on the **state transition** (CPU→I/O, I/O→CPU) so there is never double‑counting. + +```python +if isinstance(step.kind, EndpointStepCPU): + if not core_locked: + yield CPU.get(1) + core_locked = True + self._el_ready_queue_len += 1 # entered ready queue + if is_in_io_queue: + self._el_io_queue_len -= 1 + is_in_io_queue = False + yield env.timeout(cpu_time) + +elif isinstance(step.kind, EndpointStepIO): + if core_locked: + yield CPU.put(1) + core_locked = False + self._el_ready_queue_len -= 1 + if not is_in_io_queue: + self._el_io_queue_len += 1 + is_in_io_queue = True + yield env.timeout(io_time) +``` + +**Handler epilogue** + +```python +# at exit, remove ourselves from whichever queue we are in +if core_locked: # we are still in ready queue + self._el_ready_queue_len -= 1 + yield CPU.put(1) +elif is_in_io_queue: # finished while awaiting I/O + self._el_io_queue_len -= 1 +``` + +> This guarantees both queues always balance back to 0 after the last request completes. --- #### **Concurrency Guarantees** -* **CPU contention** – because CPU is a token bucket (`simpy.Container`) the maximum number of concurrent CPU-bound steps equals `cpu_cores`. -* **RAM contention** – large requests can stall entirely until enough RAM frees up, accurately modelling out-of-memory throttling. -* **Non-blocking I/O** – while a handler waits on an I/O step it releases the core, allowing other handlers to run; this mirrors an async framework where the event loop can service other sockets. +* **CPU contention** – the `CPU` container is a token bucket; max concurrent CPU‑bound steps = `cpu_cores`. +* **RAM contention** – requests block at `RAM.get()` until memory is free (models cgroup / OOM throttling). +* **Non‑blocking I/O** – while in `env.timeout(io_wait)` no core token is held, so other handlers can run; mirrors an async server where workers return to the event‑loop on each `await`. --- -#### **Real-World Analogy** +#### **Edge‑case handling (metrics)** -| Runtime concept | Real server analogue | -| ------------------------------------- | ------------------------------------------------------------------------------------------ | -| `server_box` | A web server’s accept queue. | -| `CPU.get(1)` | Obtaining one worker thread/process in Gunicorn, uWSGI, or a Node.js “JS thread”. | -| `env.timeout(io_wait)` without a core | An `await` on a database or HTTP call; the worker is idle while the OS handles the socket. | -| RAM token bucket | Process resident set or container memory limit; requests block when heap is exhausted. | +* **First‑step I/O** – counted only in I/O queue (`+1`), never touches ready queue. +* **Consecutive I/O steps** – second I/O sees `is_in_io_queue == True`, so no extra increment (no double count). +* **CPU → I/O → CPU** – +  – CPU step: `core_locked=True`, `+1` ready queue +  – I/O step: core released, `‑1` ready queue, `+1` I/O queue +  – next CPU: core reacquired, `‑1` I/O queue, `+1` ready queue +* **Endpoint finishes** – epilogue removes the request from whichever queue it still occupies, avoiding “ghost” entries. + +--- -Thus a **CPU-bound step** is a tight Python loop holding the GIL, while an **I/O-bound step** is `await cursor.execute(...)` that frees the event loop. +#### **Real‑World Analogy** + +| Runtime concept | Real server analogue | +| --------------------------------------- | --------------------------------------------------------------------------------------- | +| `server_box` | Web server accept queue (e.g., `accept()` backlog). | +| `CPU.get(1)` / `CPU.put(1)` | Claiming / releasing a worker thread or GIL slot (Gunicorn, uWSGI, Node.js event‑loop). | +| `env.timeout(io_wait)` (without a core) | `await redis.get()` – coroutine parked while the kernel handles the socket. | +| RAM token bucket | cgroup memory limit / container hard‑RSS; requests block when heap is exhausted. | + +Thus a **CPU‑bound step** models tight Python code holding the GIL, while an **I/O‑bound step** models an `await` that yields control back to the event loop, freeing the core. --- + ### **5.4. ClientRuntime: The Destination** This actor typically represents the end-user or system that initiated the request, serving as the final destination. diff --git a/documentation/backend_documentation/simulation_input.md b/docs/fastsim-docs/simulation_input.md similarity index 100% rename from documentation/backend_documentation/simulation_input.md rename to docs/fastsim-docs/simulation_input.md diff --git a/docs/fastsim-docs/time_series_metric_architecture.md b/docs/fastsim-docs/time_series_metric_architecture.md new file mode 100644 index 0000000..572ba71 --- /dev/null +++ b/docs/fastsim-docs/time_series_metric_architecture.md @@ -0,0 +1,91 @@ +## Time‑Series Metrics: Architectural Overview + +Collecting high‑frequency, time‑series metrics from a complex simulation requires an architecture that is **performant, maintainable, and extensible**. Our design meets those goals by keeping metric *declaration*, *state management,* and *data collection* in strictly separate layers. + +\### 1  Guiding Principles & Architecture + +1. **Minimal Hot‑Path Overhead** — every state update in the simulation core is `O(1)`. +2. **Single Source of Truth** — one “Registry” enumerates every metric that can exist. +3. **User‑Defined Extensibility** — advanced users can register custom metrics without touching the framework. +4. **Predictable Memory Footprint** — data structures are pre‑allocated once, never rebuilt at each sample tick. + +| Layer | Responsibility | Lifetime | +| ------------- | ---------------------------------------------------------------- | ---------------------- | +| **Registry** | Declare *which* metrics exist for each component type | Module import (once) | +| **Runtime** | Maintain the **current value** of each metric per component | Per component instance | +| **Collector** | Periodically read runtime values and append to time‑series lists | One per simulation run | + +--- + +\### 2  Case Study — Edge Metric Collection + +```mermaid +graph TD + subgraph Init + A(Registry: EDGE_METRICS) -- builds --> B{Metric Dict} + end + subgraph Loop + C[EdgeRuntime] -- inc/dec --> D(_concurrent_connections) + E[SampledMetricCollector] -->|every N ms| F{iterate} + F -->|read property| D + F -->|append| B + end + C -- owns --> B +``` + +#### Layer Walk‑through + +1. **Registry (`metrics/edge.py`)** + + ```python + EDGE_METRICS = (SampledMetricName.EDGE_CONCURRENT_CONNECTION,) + + def build_edge_metrics(enabled): + return {m: [] for m in EDGE_METRICS if m in enabled} + ``` + +2. **Runtime (`EdgeRuntime`)** + + * Updates the counter `_concurrent_connections` in `O(1)`. + * Holds the dict produced by `build_edge_metrics`. + * **New:** exposes read‑only properties so external modules never touch private fields directly. + + ```python + class EdgeRuntime: + @property + def concurrent_connections(self) -> int: + return self._concurrent_connections + + @property + def enabled_metrics(self) -> dict[SampledMetricName, list[float | int]]: + return self._edge_enabled_metrics + ``` + +3. **Collector (`SampledMetricCollector`)** + + ```python + while True: + yield env.timeout(sample_period_s) + for edge in self.edges: + key = SampledMetricName.EDGE_CONCURRENT_CONNECTION + # properties keep encapsulation intact + if key in edge.enabled_metrics: + edge.enabled_metrics[key].append(edge.concurrent_connections) + ``` + +--- + +\### 3  Why the `if key in …` Guard Still Matters + +Even with the new properties, the guard remains essential: + +* **Robustness** — prevents `KeyError` when a metric is disabled for a given edge. +* **Extensibility** — a user can add `EDGE_PACKET_LOSS` (or any custom metric) to a subset of edges; the collector automatically respects that configuration. + +This single `O(1)` check keeps the system plug‑and‑play while preserving full encapsulation: + +* Runtime internals stay protected behind properties. +* The collector never needs to know which metrics exist ahead of time. + +--- + diff --git a/documentation/fastsim_vision.md b/docs/fastsim_vision.md similarity index 100% rename from documentation/fastsim_vision.md rename to docs/fastsim_vision.md diff --git a/documentation/backend_documentation/metrics_to_measure.md b/documentation/backend_documentation/metrics_to_measure.md deleted file mode 100644 index 4de1a1a..0000000 --- a/documentation/backend_documentation/metrics_to_measure.md +++ /dev/null @@ -1,49 +0,0 @@ -### **FastSim — simulation's metrics** - -Metrics are the lifeblood of any simulation, transforming a series of abstract events into concrete, actionable insights about system performance, resource utilization, and potential bottlenecks. FastSim provides a flexible and robust metrics collection system designed to give you a multi-faceted view of your system's behavior under load. - -To achieve this, FastSim categorizes metrics into three distinct types based on their collection methodology: - -1. **Sampled Metrics (`SampledMetricName`):** These metrics provide a **time-series view** of the system's state. They are captured at fixed, regular intervals throughout the simulation's duration (e.g., every second). This methodology is ideal for understanding trends, observing oscillations, and measuring the continuous utilization of finite resources like CPU and RAM. Think of them as periodic snapshots of your system's health. - -2. **Event-based Metrics (`EventMetricName`):** These metrics are recorded **only when a specific event occurs**. Their collection is asynchronous and irregular, triggered by discrete happenings within the simulation, such as the completion of a request. This methodology is perfect for measuring the properties of individual transactions, such as end-to-end latency, where an average value is less important than understanding the full distribution of outcomes. - -3. **Aggregated Metrics (`AggregatedMetricName`):** These are not collected directly during the simulation but are **calculated after the simulation ends**. They provide high-level statistical summaries (like mean, median, and percentiles) derived from the raw data collected by Event-based metrics. They distill thousands of individual data points into a handful of key performance indicators (KPIs) that are easy to interpret. - -The following sections provide a detailed breakdown of each metric within these categories, explaining what they measure and the rationale for their importance. - ---- - -### **1. Sampled Metrics: A Time-Series Perspective** - -Sampled metrics are configured in the `SimulationSettings` payload. Enabling them allows you to plot the evolution of system resources over time, which is crucial for identifying saturation points and transient performance issues. - -| Metric Name (`SampledMetricName`) | Description & Rationale | -| :--- | :--- | -| **`READY_QUEUE_LEN`** | **What it is:** The number of tasks in the `asyncio` event loop's "ready" queue waiting for their turn to run on the CPU.

**Rationale:** This is arguably the most critical indicator of **CPU saturation**. In a single-threaded Python process, only one coroutine can run at a time (held by the GIL). If this queue length is consistently greater than zero, it means tasks are ready to do work but are forced to wait because the CPU is busy. A long or growing queue is a definitive sign that your application is CPU-bound and that the CPU is a primary bottleneck. | -| **`CORE_BUSY`** | **What it is:** The number of server CPU cores that are currently executing a task.

**Rationale:** This provides a direct measure of **CPU utilization**. When plotted over time, it shows how effectively you are using your provisioned processing power. If `CORE_BUSY` is consistently at its maximum value (equal to `server_resources.cpu_cores`), the system is CPU-saturated. Conversely, if it's consistently low while latency is high, the bottleneck is likely elsewhere (e.g., I/O). It perfectly complements `READY_QUEUE_LEN` to form a complete picture of CPU health. | -| **`EVENT_LOOP_IO_SLEEP`** | **What it is:** A measure indicating if the event loop is idle, polling for I/O operations to complete.

**Rationale:** This metric helps you determine if your system is **I/O-bound**. If the event loop spends a significant amount of time in this state, it means the CPU is underutilized because it has no ready tasks to run and is instead waiting for external systems (like databases, caches, or downstream APIs) to respond. High values for this metric coupled with low CPU utilization are a clear signal to investigate and optimize the performance of your I/O operations. | -| **`RAM_IN_USE`** | **What it is:** The total amount of memory (in MB) currently allocated by all active requests within a server.

**Rationale:** Essential for **capacity planning and stability analysis**. This metric allows you to visualize your system's memory footprint under load. You can identify which endpoints cause memory spikes and ensure your provisioned RAM is sufficient. A steadily increasing `RAM_IN_USE` value that never returns to a baseline is the classic signature of a **memory leak**, a critical bug this metric helps you detect. | -| **`THROUGHPUT_RPS`** | **What it is:** The number of requests successfully completed per second, calculated over the last sampling window.

**Rationale:** This is a fundamental measure of **system performance and capacity**. It answers the question: "How much work is my system actually doing?" Plotting throughput against user load or other resource metrics is key to understanding your system's scaling characteristics. A drop in throughput often correlates with a spike in latency or resource saturation, helping you pinpoint the exact moment a bottleneck began to affect performance. | - ---- - -### **2. Event-based Metrics: A Per-Transaction Perspective** - -Event-based metrics are also enabled in the `SimulationSettings` payload. They generate a collection of raw data points, one for each relevant event, which is ideal for statistical analysis of transactional performance. - -| Metric Name (`EventMetricName`) | Description & Rationale | -| :--- | :--- | -| **`RQS_LATENCY`** | **What it is:** The total end-to-end duration, in seconds, for a single request to be fully processed.

**Rationale:** This is the **primary user-facing performance metric**. Users directly experience latency. While a simple average can be useful, it often hides critical problems. By collecting the latency for *every single request*, FastSim allows for the calculation of statistical distributions and, most importantly, **tail-latency percentiles (p95, p99)**. These percentiles represent the worst-case experience for your users and are crucial for evaluating Service Level Objectives (SLOs) and ensuring a consistent user experience. | -| **`LLM_COST`** | **What it is:** The estimated monetary cost (e.g., in USD) incurred by a single call to an external Large Language Model (LLM) API during a request.

**Rationale:** In modern AI-powered applications, API calls to third-party services like LLMs can be a major operational expense. This metric moves beyond technical performance to measure **financial performance**. By tracking cost on a per-event basis, you can attribute expenses to specific endpoints or user behaviors, identify unnecessarily costly operations, and make informed decisions to optimize your application's cost-effectiveness. | - ---- - -### **3. Aggregated Metrics: High-Level Summaries** - -**Important:** Aggregated metrics are **not configured in the input payload**. They are automatically calculated by the FastSim engine at the end of a simulation run, based on the raw data collected from the enabled Event-based metrics. - -| Metric Name (`AggregatedMetricName`) | Description & Rationale | -| :--- | :--- | -| **`LATENCY_STATS`** | **What it is:** A statistical summary of the entire collection of `RQS_LATENCY` data points. This typically includes the mean, median (p50), standard deviation, and high-end percentiles (p95, p99, p99.9).

**Rationale:** This provides a comprehensive and easily digestible summary of your system's latency profile. While the raw data is essential, these summary statistics answer high-level questions quickly. The mean tells you the average experience, the median protects against outliers, and the p95/p99 values tell you the latency that 95% or 99% of your users will beat—a critical KPI for reliability and user satisfaction. | -| **`LLM_STATS`** | **What it is:** A statistical summary of the `LLM_COST` data points. This can include total cost over the simulation, average cost per request, and cost distribution.

**Rationale:** This gives you a bird's-eye view of the financial implications of your system's design. Instead of looking at individual transaction costs, `LLM_STATS` provides the bottom line: the total operational cost during the simulation period. 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"sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f"}, - {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"}, -] - [metadata] lock-version = "2.0" python-versions = "^3.12" -content-hash = "71f5d3bb002c33ab3bad9d5e9efadc144b033c272b4319c2686728026dd5d849" +content-hash = "ca95f91568cb076386715d183efe27addd2732eb4347c385a0e9f1479d2035d6" diff --git a/pyproject.toml b/pyproject.toml index f5fc34a..b4c46ff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [tool.poetry] -name = "FastSim-backend" +name = "FastSim" version = "0.1.0" description = "Simulate fastapi event loop to manage resources" authors = ["Gioele Botta"] @@ -11,25 +11,16 @@ packages = [ [tool.poetry.dependencies] python = "^3.12" -fastapi = "^0.115.14" -sqlalchemy = "^2.0.41" -psycopg = {extras = ["binary"], version = "^3.2.9"} -uvicorn = {extras = ["standard"], version = "^0.34.3"} -gunicorn = "^23.0.0" pydantic-settings = "^2.10.1" pydantic = {extras = ["email"], version = "^2.11.7"} -asyncpg = "^0.30.0" -sqlalchemy-utils = "^0.41.2" numpy = "^2.3.1" simpy = "^4.1.1" [tool.poetry.group.dev.dependencies] pytest = "^8.4.1" pytest-asyncio = "^1.0.0" -httpx = "^0.28.1" pytest-cov = "^6.2.1" mypy = "^1.16.1" -alembic = "^1.16.2" ruff = "^0.12.1" [build-system] diff --git a/scripts/init-docker-dev.sh b/scripts/init-docker-dev.sh deleted file mode 100644 index fa2c4ee..0000000 --- a/scripts/init-docker-dev.sh +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env bash -# scripts/init-docker-dev.sh -# Bring up local development stack using .env.dev in project root - - -set -euo pipefail - -# ────────────────────────────────────────────────────────────────────────────── -# 0. Paths -# ────────────────────────────────────────────────────────────────────────────── -SCRIPT_PATH="$(realpath "${BASH_SOURCE[0]}")" -SCRIPT_DIR="$(cd "$(dirname "$SCRIPT_PATH")" && pwd)" -PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" -COMPOSE_FILE="$PROJECT_ROOT/docker_fs/docker-compose.dev.yml" -ENV_DEV="$PROJECT_ROOT/docker_fs/.env.dev" -ENV_DOT="$PROJECT_ROOT/docker_fs/.env" - -# ────────────────────────────────────────────────────────────────────────────── -# 0.1 Make script executable -# ────────────────────────────────────────────────────────────────────────────── -if [[ ! -x "$SCRIPT_PATH" ]]; then - chmod +x "$SCRIPT_PATH" || true -fi - -# ────────────────────────────────────────────────────────────────────────────── -# 0.2 Ensure docker_fs/.env exists for Compose interpolation -# ────────────────────────────────────────────────────────────────────────────── -if [[ -f "$ENV_DEV" && ! -f "$ENV_DOT" ]]; then - echo ">>> Copying .env.dev → .env for Compose interpolation" - cp "$ENV_DEV" "$ENV_DOT" -fi - -# ────────────────────────────────────────────────────────────────────────────── -# 1. Load env vars from .env.dev into this shell -# ────────────────────────────────────────────────────────────────────────────── -if [[ -f "$ENV_DEV" ]]; then - set -o allexport - source "$ENV_DEV" - set +o allexport -else - echo "ERROR: $ENV_DEV not found. Please create it from .env.example." >&2 - exit 1 -fi - -# ────────────────────────────────────────────────────────────────────────────── -# 2. Pull remote images (only missing ones) -# ────────────────────────────────────────────────────────────────────────────── -echo ">>> Pulling external service images..." -docker compose \ - --env-file "$ENV_DEV" \ - -f "$COMPOSE_FILE" pull - -# ────────────────────────────────────────────────────────────────────────────── -# 3. Start Postgres + pgAdmin (detached) -# ────────────────────────────────────────────────────────────────────────────── -echo ">>> Starting Postgres and pgAdmin..." -docker compose \ - --env-file "$ENV_DEV" \ - -f "$COMPOSE_FILE" up -d db pgadmin - -# ────────────────────────────────────────────────────────────────────────────── -# 4. Wait for Postgres to be ready, then run Alembic migrations -# ────────────────────────────────────────────────────────────────────────────── -PGUSER="${DB_USER:-${POSTGRES_USER:-postgres}}" - -echo ">>> Waiting for Postgres to be ready..." -MAX_RETRIES=30 -RETRIES=0 -until docker exec docker_fs-db-1 pg_isready -U "$PGUSER" > /dev/null 2>&1; do - sleep 1 - RETRIES=$((RETRIES+1)) - if [ "$RETRIES" -ge "$MAX_RETRIES" ]; then - echo "ERROR: Postgres non si è avviato in tempo." >&2 - exit 1 - fi -done -echo ">>> Postgres is ready!" - -echo ">>> Applying database migrations (Alembic)…" -cd "$PROJECT_ROOT" -poetry run alembic upgrade head - -# ────────────────────────────────────────────────────────────────────────────── -# 5. Build (if needed) & start everything in background -# ────────────────────────────────────────────────────────────────────────────── -echo ">>> Building (if needed) and starting all services…" -docker compose \ - --env-file "$ENV_DEV" \ - -f "$COMPOSE_FILE" up -d --build - -echo ">>> Development stack is up!" -echo " • Backend: http://localhost:8000" -echo " • pgAdmin: http://localhost:8080" -echo -echo "To tail backend logs without warnings, run:" -echo " docker compose \\ - --env-file \"$ENV_DEV\" \\ - -f \"$COMPOSE_FILE\" logs -f backend" diff --git a/src/app/api/__init__.py b/src/app/api/__init__.py deleted file mode 100644 index 9455eaa..0000000 --- a/src/app/api/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""API endpoints and routers.""" diff --git a/src/app/api/health_check.py b/src/app/api/health_check.py deleted file mode 100644 index 74b4d4f..0000000 --- a/src/app/api/health_check.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Health check API endpoints.""" - -from fastapi import APIRouter - -router = APIRouter() - - -@router.get("/health") -def health_check() -> dict[str, str]: - """Return the health status of the application.""" - return {"status": "ok"} diff --git a/src/app/api/simulation.py b/src/app/api/simulation.py deleted file mode 100644 index 2775dcc..0000000 --- a/src/app/api/simulation.py +++ /dev/null @@ -1,4 +0,0 @@ -""""Api to simulate the process""" - - - diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 8eeb01e..0e40950 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -13,7 +13,7 @@ renaming existing ones. """ -from enum import IntEnum, StrEnum +from enum import Enum, IntEnum, StrEnum # ====================================================================== # CONSTANTS FOR THE REQUEST-GENERATOR COMPONENT @@ -203,11 +203,22 @@ class SampledMetricName(StrEnum): time to create a time series """ + # Mandatory metrics to collect READY_QUEUE_LEN = "ready_queue_len" #length of the event loop ready q - CORE_BUSY = "core_busy" EVENT_LOOP_IO_SLEEP = "event_loop_io_sleep" RAM_IN_USE = "ram_in_use" - THROUGHPUT_RPS = "throughput_rps" + EDGE_CONCURRENT_CONNECTION = "edge_concurrent_connection" + + +class SamplePeriods(float, Enum): + """ + defining the value of the sample periods for the metrics for which + we have to extract a time series + """ + + STANDARD_TIME = 0.005 # 5 MILLISECONDS + MINIMUM_TIME = 0.001 # 1 MILLISECOND + MAXIMUM_TIME = 0.1 # 10 MILLISECONDS # ====================================================================== # CONSTANTS FOR EVENT METRICS @@ -219,7 +230,9 @@ class EventMetricName(StrEnum): time series """ - RQS_LATENCY = "rqs_latency" + # Mandatory + RQS_CLOCK = "rqs_clock" + # Not mandatory LLM_COST = "llm_cost" @@ -231,6 +244,7 @@ class AggregatedMetricName(StrEnum): """aggregated metrics to calculate at the end of simulation""" LATENCY_STATS = "latency_stats" + THROUGHPUT_RPS = "throughput_rps" LLM_STATS = "llm_stats" # ====================================================================== diff --git a/src/app/config/settings.py b/src/app/config/settings.py deleted file mode 100644 index 7fc3d9d..0000000 --- a/src/app/config/settings.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Application settings and configuration.""" - -from pathlib import Path -from typing import Literal - -from dotenv import load_dotenv -from pydantic import Field -from pydantic_settings import BaseSettings, SettingsConfigDict - -env_path = Path(__file__).resolve().parents[3] / "docker_fs" / ".env.dev" -if env_path.exists(): - load_dotenv(dotenv_path=env_path) - - -class Settings(BaseSettings): - """Application settings loaded from environment variables.""" - - model_config = SettingsConfigDict(env_file=None, extra="ignore") - - app_name: str = "FastSim" - - environment: Literal["development", "staging", "production", "test"] = Field( - default="development", - description="Runtime environment", - alias="ENVIRONMENT", - ) - - db_host: str = Field(default="localhost", alias="DB_HOST") - db_user: str = Field(default="postgres", alias="DB_USER") - db_password: str = Field(default="password", alias="DB_PASSWORD") - db_name: str = Field(default="project_db", alias="DB_NAME") - db_url_env: str | None = Field(default=None, alias="DB_URL") - - @property - def db_url(self) -> str: - """Compute the full database URL from components if not explicitly set.""" - if self.db_url_env: - return self.db_url_env - return f"postgresql+asyncpg://{self.db_user}:{self.db_password}@{self.db_host}/{self.db_name}" - - - -settings = Settings() diff --git a/src/app/db/__init__.py b/src/app/db/__init__.py deleted file mode 100644 index 8e0edc2..0000000 --- a/src/app/db/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Database modules and utilities.""" diff --git a/src/app/db/base.py b/src/app/db/base.py deleted file mode 100644 index 2b59174..0000000 --- a/src/app/db/base.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Database base classes and utilities.""" - -import re - -from sqlalchemy.orm import DeclarativeBase, declared_attr - - -class Base(DeclarativeBase): - """Base class for all ORM models.""" - - @declared_attr.directive - def __tablename__(cls) -> str: - """Generate table name from class name by converting CamelCase to snake_case.""" - return re.sub(r"(? None: - """Initialize database models by creating all tables.""" - async with engine.begin() as conn: - await conn.run_sync(Base.metadata.create_all) - - -async def close_engine() -> None: - """Close the database engine and dispose of connections.""" - await engine.dispose() diff --git a/src/app/db/session.py b/src/app/db/session.py deleted file mode 100644 index 6e2924e..0000000 --- a/src/app/db/session.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Database session management and connection utilities.""" - -from collections.abc import AsyncGenerator - -from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine - -from app.config.settings import settings - -engine = create_async_engine( - settings.db_url, - echo=False, - future=True, -) - -AsyncSessionLocal = async_sessionmaker( - engine, - class_=AsyncSession, - expire_on_commit=False, -) - - -async def get_db() -> AsyncGenerator[AsyncSession, None]: - """ - Dependency that provides a transactional database session. - It commits the transaction on successful completion or rolls back on error. - """ - async with AsyncSessionLocal() as session: - try: - - yield session - - await session.commit() - except Exception: - - await session.rollback() - - raise diff --git a/src/app/main.py b/src/app/main.py deleted file mode 100644 index b5ef876..0000000 --- a/src/app/main.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Main FastAPI application module.""" - -from collections.abc import AsyncGenerator -from contextlib import asynccontextmanager - -from fastapi import FastAPI - -from app.api.health_check import router as health_router -from app.config.settings import settings -from app.db.init_db import close_engine, init_models - - -@asynccontextmanager -async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]: - """Manage application lifespan events.""" - # Startup - if settings.environment == "development": - await init_models() - yield - # Shutdown - await close_engine() - - -app = FastAPI( - title="Project Backend", - version="0.1.0", - description="Backend service with health-check endpoint", - validate_response=True, #type validation of pydantic output - lifespan=lifespan, -) - -app.include_router(health_router) - diff --git a/src/app/metrics/client.py b/src/app/metrics/client.py new file mode 100644 index 0000000..2e49638 --- /dev/null +++ b/src/app/metrics/client.py @@ -0,0 +1,18 @@ +""" +initialization of the structure to gather the metrics +for the client of the system +""" + +from typing import NamedTuple + + +class RqsClock(NamedTuple): + """ + structure to register time of generation and + time of elaboration for each request + """ + + start: float + finish: float + + diff --git a/src/app/metrics/collector.py b/src/app/metrics/collector.py new file mode 100644 index 0000000..ed85126 --- /dev/null +++ b/src/app/metrics/collector.py @@ -0,0 +1,70 @@ +"""class to centralized the the collection of time series regarding metrics""" + +from collections.abc import Generator + +import simpy + +from app.config.constants import SampledMetricName +from app.runtime.actors.edge import EdgeRuntime +from app.runtime.actors.server import ServerRuntime +from app.schemas.simulation_settings_input import SimulationSettings + +# The idea for this class is to gather list of runtime objects that +# are defined in the central class to build the simulation, in this +# way we optimize the initialization of various objects reducing +# the global overhead + + +class SampledMetricCollector: + """class to define a centralized object to collect sampled metrics""" + + def __init__( + self, + *, + edges: list[EdgeRuntime], + servers: list[ServerRuntime], + env: simpy.Environment, + sim_settings: SimulationSettings, + ) -> None: + """Docstring to complete""" + self.edges = edges + self.servers = servers + self.sim_settings = sim_settings + self.env = env + self._sample_period = sim_settings.sample_period_s + + + # enum keys instance-level for mandatory sampled metrics to collect + self._conn_key = SampledMetricName.EDGE_CONCURRENT_CONNECTION + self._ram_key = SampledMetricName.RAM_IN_USE + self._io_key = SampledMetricName.EVENT_LOOP_IO_SLEEP + self._ready_key = SampledMetricName.READY_QUEUE_LEN + + + env.process(self._build_time_series()) + + def _build_time_series(self) -> Generator[simpy.Event, None, None]: + """Function to build time series for enabled metrics""" + while True: + yield self.env.timeout(self._sample_period) + for edge in self.edges: + if self._conn_key in edge.enabled_metrics: + edge.enabled_metrics[self._conn_key].append( + edge.concurrent_connections, + ) + for server in self.servers: + if all( + k in server.enabled_metrics + for k in (self._ram_key, self._io_key, self._ready_key) + ): + server.enabled_metrics[self._ram_key].append(server.ram_in_use) + server.enabled_metrics[self._io_key].append(server.io_queue_len) + server.enabled_metrics[self._ready_key].append(server.ready_queue_len) + + + + + + + + diff --git a/src/app/metrics/dictionary_metrics.py b/src/app/metrics/dictionary_metrics.py deleted file mode 100644 index 189af47..0000000 --- a/src/app/metrics/dictionary_metrics.py +++ /dev/null @@ -1,38 +0,0 @@ -"""helpers for the simulation""" - -from collections.abc import Iterable - -from app.config.constants import EventMetricName, SampledMetricName - - -def alloc_sample_metric( - enabled_sample_metrics: Iterable[SampledMetricName], - ) -> dict[str, list[float | int]]: - """ - After the pydantic validation of the whole input we - instantiate a dictionary to collect the sampled metrics the - user want to measure - """ - # t is the alignment parameter for example assume - # the snapshot for the sampled metrics are done every 10ms - # t = [10,20,30,40....] to each t will correspond a measured - # metric corresponding to that time interval - - dict_sampled_metrics: dict[str, list[float | int]] = {"t": []} - for key in enabled_sample_metrics: - dict_sampled_metrics[key] = [] - return dict_sampled_metrics - - -def alloc_event_metric( - enabled_event_metrics: Iterable[EventMetricName], - ) -> dict[str, list[float | int]]: - """ - After the pydantic validation of the whole input we - instantiate a dictionary to collect the event metrics the - user want to measure - """ - dict_event_metrics: dict[str, list[float | int]] = {} - for key in enabled_event_metrics: - dict_event_metrics[key] = [] - return dict_event_metrics diff --git a/src/app/metrics/edge.py b/src/app/metrics/edge.py new file mode 100644 index 0000000..f714482 --- /dev/null +++ b/src/app/metrics/edge.py @@ -0,0 +1,29 @@ +"""initialization of the structure to gather the metrics for the edges of the system""" + +from collections.abc import Iterable + +from app.config.constants import SampledMetricName + +# Initialize one time outside the function all possible metrics +# related to the edges, the idea of this structure is to +# guarantee scalability in the long term if multiple metrics +# will be considered + +EDGE_METRICS = ( + SampledMetricName.EDGE_CONCURRENT_CONNECTION, +) + +def build_edge_metrics( + enabled_sample_metrics: Iterable[SampledMetricName], + ) -> dict[SampledMetricName, list[float | int]]: + """ + Function to populate a dictionary to collect values for + time series of sampled metrics related to the edges of + the system. + """ + # The edge case of the empty dict is avoided since at least + # one metric is always measured by default. + return { + metric: [] for metric in EDGE_METRICS + if metric in enabled_sample_metrics + } diff --git a/src/app/metrics/server.py b/src/app/metrics/server.py new file mode 100644 index 0000000..18810ed --- /dev/null +++ b/src/app/metrics/server.py @@ -0,0 +1,34 @@ +""" +initialization of the structure to gather the sampled metrics +for the server of the system +""" + +from collections.abc import Iterable + +from app.config.constants import SampledMetricName + +# Initialize one time outside the function all possible metrics +# related to the servers, the idea of this structure is to +# guarantee scalability in the long term if multiple metrics +# will be considered + +SERVER_METRICS = ( + SampledMetricName.READY_QUEUE_LEN, + SampledMetricName.EVENT_LOOP_IO_SLEEP, + SampledMetricName.RAM_IN_USE, +) + +def build_server_metrics( + enabled_sample_metrics: Iterable[SampledMetricName], + ) -> dict[SampledMetricName, list[float | int]]: + """ + Function to populate a dictionary to collect values for + time series of sampled metrics related to the server of + the system. + """ + # The edge case of the empty dict is avoided since at least + # one metric is always measured by default. + return { + metric: [] for metric in SERVER_METRICS + if metric in enabled_sample_metrics + } diff --git a/src/app/runtime/actors/client.py b/src/app/runtime/actors/client.py index 207bd71..f5d63ac 100644 --- a/src/app/runtime/actors/client.py +++ b/src/app/runtime/actors/client.py @@ -6,6 +6,7 @@ import simpy from app.config.constants import SystemNodes +from app.metrics.client import RqsClock from app.runtime.actors.edge import EdgeRuntime from app.schemas.system_topology.full_system_topology import Client @@ -31,6 +32,11 @@ def __init__( self.client_config = client_config self.client_box = client_box self.completed_box = completed_box + # This list will be enough to calculate at the end + # of the simulation both the throughput and the + # latency distribution + + self._rqs_clock: list[RqsClock] = [] def _forwarder(self) -> Generator[simpy.Event, None, None]: @@ -44,12 +50,19 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]: self.env.now, ) - # by checking the previous node (-2 the previous component is an edge) - # we are able to understand if the request should be elaborated - # when the type is Generator, or the request is completed, in this case - # the client is the target and the previous node type is not a rqs generator - if state.history[-2].component_type != SystemNodes.GENERATOR: + # if the length of the list is bigger than two + # it means that the state is coming back to the + # client after being elaborated, since if the value + # would be equal to two would mean that the state + # went through the mandatory path to be generated + # rqs generator and client registration + if len(state.history) > 2: state.finish_time = self.env.now + clock_data = RqsClock( + start=state.initial_time, + finish=state.finish_time, + ) + self._rqs_clock.append(clock_data) yield self.completed_box.put(state) else: self.out_edge.transport(state) @@ -57,3 +70,11 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]: def start(self) -> simpy.Process: """Initialization of the process""" return self.env.process(self._forwarder()) + + @property + def rqs_clock(self) -> list[RqsClock]: + """ + Expose the value of the private list of the starting + and arrival time for each rqs just for reading purpose + """ + return self._rqs_clock diff --git a/src/app/runtime/actors/edge.py b/src/app/runtime/actors/edge.py index afef720..4e2f549 100644 --- a/src/app/runtime/actors/edge.py +++ b/src/app/runtime/actors/edge.py @@ -12,9 +12,11 @@ import numpy as np import simpy -from app.config.constants import SystemEdges +from app.config.constants import SampledMetricName, SystemEdges +from app.metrics.edge import build_edge_metrics from app.runtime.rqs_state import RequestState from app.samplers.common_helpers import general_sampler +from app.schemas.simulation_settings_input import SimulationSettings from app.schemas.system_topology.full_system_topology import Edge if TYPE_CHECKING: @@ -32,12 +34,26 @@ def __init__( edge_config: Edge, rng: np.random.Generator | None = None, target_box: simpy.Store, + settings: SimulationSettings, ) -> None: """Definition of the instance attributes""" self.env = env self.edge_config = edge_config self.target_box = target_box self.rng = rng or np.random.default_rng() + self.setting = settings + self._edge_enabled_metrics = build_edge_metrics( + settings.enabled_sample_metrics, + ) + self._concurrent_connections: int = 0 + + # We keep a reference to `settings` because this class needs to observe but not + # persist the edge-related metrics the user has enabled. + # The actual persistence (appending snapshots to the time series lists) + # is handled centrally in metrics/collector.py,which runs every Xmilliseconds. + # Here we only expose the current metric values, guarded by a few if checks to + # verify that each optional metric is active. For deafult metric settings + # is not needed but as we will scale as explained above we will need it def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: """Function to deliver the state to the next node""" @@ -54,13 +70,17 @@ def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: ) return + self._concurrent_connections +=1 + transit_time = general_sampler(random_variable, self.rng) yield self.env.timeout(transit_time) + state.record_hop( SystemEdges.NETWORK_CONNECTION, self.edge_config.id, self.env.now, ) + self._concurrent_connections -=1 yield self.target_box.put(state) @@ -71,7 +91,15 @@ def transport(self, state: RequestState) -> simpy.Process: """ return self.env.process(self._deliver(state)) + @property + def enabled_metrics(self) -> dict[SampledMetricName, list[float | int]]: + """Read-only access to the metric store.""" + return self._edge_enabled_metrics + @property + def concurrent_connections(self) -> int: + """Current number of open connections on this edge.""" + return self._concurrent_connections diff --git a/src/app/runtime/actors/server.py b/src/app/runtime/actors/server.py index 0a0f772..94d6b24 100644 --- a/src/app/runtime/actors/server.py +++ b/src/app/runtime/actors/server.py @@ -13,13 +13,16 @@ EndpointStepCPU, EndpointStepIO, EndpointStepRAM, + SampledMetricName, ServerResourceName, StepOperation, SystemNodes, ) +from app.metrics.server import build_server_metrics from app.resources.server_containers import ServerContainers from app.runtime.actors.edge import EdgeRuntime from app.runtime.rqs_state import RequestState +from app.schemas.simulation_settings_input import SimulationSettings from app.schemas.system_topology.full_system_topology import Server @@ -33,28 +36,34 @@ def __init__( # noqa: PLR0913 server_config: Server, out_edge: EdgeRuntime, server_box: simpy.Store, + settings: SimulationSettings, rng: np.random.Generator | None = None, ) -> None: - """Server attributes - - Args: - env (simpy.Environment): _description_ - server_resources (ServerContainers): _description_ - server_config (Server): _description_ - out_edge (EdgeRuntime): _description_ - server_box (simpy.Store): _description_ - rng (np.random.Generator | None, optional): _description_. Defaults to None. - - """ + """Docstring to complete""" self.env = env self.server_resources = server_resources self.server_config = server_config self.out_edge = out_edge self.server_box = server_box self.rng = rng or np.random.default_rng() + # length of the active queue of the event loop + self._el_ready_queue_len: int = 0 + # total ram used in the server + self._ram_in_use: int | float = 0 + # length of the queue of the I/O task of the vent loop + self._el_io_queue_len: int = 0 + # Right now is not necessary but as we will introduce + # non mandatory metrics we will need this structure to + # check if we have to measure a given metric + # right now it is not necessary because we are dealing + # only with mandatory metrics + self._server_enabled_metrics = build_server_metrics( + settings.enabled_sample_metrics, + ) - def _handle_request( + # right now we disable the warnings but a refactor will be done soon + def _handle_request( # noqa: PLR0915, PLR0912, C901 self, state: RequestState, ) -> Generator[simpy.Event, None, None]: @@ -79,45 +88,175 @@ def _handle_request( selected_endpoint_idx = self.rng.integers(low=0, high=endpoints_number) selected_endpoint = endpoints_list[selected_endpoint_idx] - # RAM management: - # first calculate the ram needed - # Ask if it is available - # Release everything when the operation completed + # Extract the total ram to execute the endpoint total_ram = sum( step.step_operation[StepOperation.NECESSARY_RAM] for step in selected_endpoint.steps if isinstance(step.kind, EndpointStepRAM) ) + # ------------------------------------------------------------------ + # CPU & RAM SCHEDULING + # + # RAM FIRST, CPU LATER + # - The request reserves its full working set (total_ram) before + # acquiring any CPU core. If memory isn't available, it stays + # queued and leaves cores free for other requests. + # + # LAZY-CPU LOCK + # - A core token is acquired only at the FIRST CPU step + # (`if not core_locked`) and held for all consecutive CPU steps. + # - As soon as an I/O step is encountered, the core is released + # (`CPU.put(1)`) and remains free until the next CPU step, + # which will re-acquire it. + # + # WHY THIS IS REALISTIC + # Prevents “core-hogging” during long I/O awaits. + # Avoids redundant get/put calls for consecutive CPU steps + # (one token for the entire sequence). + # Mirrors a real Python async server: the GIL/worker thread is + # held only during CPU-bound code and released on each await. + # + # END OF HANDLER + # - If we still hold the core at the end (`core_locked == True`), + # we put it back, then release the reserved RAM. + # ------------------------------------------------------------------ + # Ask the necessary ram to the server if total_ram: yield self.server_resources[ServerResourceName.RAM.value].get(total_ram) + self._ram_in_use += total_ram + + # Initial conditions of the server a rqs a priori is not in any queue + # and it does not occupy a core until it started to be elaborated + core_locked = False + is_in_io_queue = False + is_in_ready_queue = False # --- Step Execution: Process CPU and IO operations --- + # EDGE CASE + # First-step I/O + # A request could (in theory) start with an I/O step. In that case + # it doesn't hold any core; we enter the + # `not core_locked and not is_in_io_queue` branch and add +1 + # to the I/O queue without touching the ready queue. + # + # Consecutive I/O steps + # The second I/O sees `is_in_io_queue == True`, so it does NOT + # increment again—no double counting. + # + # Transition CPU → I/O → CPU + # - CPU step: `core_locked` becomes True, +1 ready queue + # - I/O step: core is put back, -1 ready queue, +1 I/O queue + # - Next CPU step: core is acquired, -1 I/O queue, +1 ready queue + # + # Endpoint completion + # If `core_locked == True` we were in the ready queue (-1) + # Otherwise we were in the I/O queue (-1) + # In both cases we clear the local flags so no “ghost” entries + # remain in the global counters. + # ------------------------------------------------------------------ + + for step in selected_endpoint.steps: if isinstance(step.kind, EndpointStepCPU): - cpu_time = step.step_operation[StepOperation.CPU_TIME] + # with the boolean we avoid redundant operation of asking + # the core multiple time on a given step + # for example if we have two consecutive cpu bound step + # in this configuration we are asking the cpu just in the + # first one - # Acquire one core - yield self.server_resources[ServerResourceName.CPU.value].get(1) - # Hold the core busy + if not core_locked: + core_locked = True + + if is_in_io_queue: + is_in_io_queue = False + self._el_io_queue_len -= 1 + + if not is_in_ready_queue: + is_in_ready_queue = True + self._el_ready_queue_len += 1 + + + yield self.server_resources[ServerResourceName.CPU.value].get(1) + + cpu_time = step.step_operation[StepOperation.CPU_TIME] + # Execute the step giving back the control to the simpy env yield self.env.timeout(cpu_time) - # Release the core - yield self.server_resources[ServerResourceName.CPU.value].put(1) + elif isinstance(step.kind, EndpointStepIO): io_time = step.step_operation[StepOperation.IO_WAITING_TIME] + # Same here with the boolean if we have multiple I/O steps + # we release the core just the first time if the previous step + # was a cpu bound step + + if not core_locked and not is_in_io_queue: + is_in_io_queue = True + self._el_io_queue_len += 1 + + + if core_locked: + # if the core is locked in the function it means that for sure + # we had a cpu bound step so the if statement will be always + # satisfy and we have to remove one element from the ready queue + + if is_in_ready_queue: + is_in_ready_queue = False + self._el_ready_queue_len -= 1 + + if not is_in_io_queue: + is_in_io_queue = True + self._el_io_queue_len += 1 + + yield self.server_resources[ServerResourceName.CPU.value].put(1) + core_locked = False yield self.env.timeout(io_time) # Wait without holding a CPU core - # release the ram + + if core_locked: + is_in_ready_queue = False + self._el_ready_queue_len -= 1 + yield self.server_resources[ServerResourceName.CPU.value].put(1) + else: + is_in_io_queue = False + self._el_io_queue_len -= 1 + if total_ram: + + self._ram_in_use -= total_ram yield self.server_resources[ServerResourceName.RAM.value].put(total_ram) self.out_edge.transport(state) + + # we need three accessor because we need to read these private attribute + # in the sampled metric collector + @property + def ready_queue_len(self) -> int: + """Current length of the event-loop ready queue for this server.""" + return self._el_ready_queue_len + + @property + def io_queue_len(self) -> int: + """Current length of the event-loop I/O queue for this server.""" + return self._el_io_queue_len + + @property + def ram_in_use(self) -> int | float: + """Total RAM (MB) currently reserved by active requests.""" + return self._ram_in_use + + @property + def enabled_metrics(self) -> dict[SampledMetricName, list[float | int]]: + """Read-only access to the metric store.""" + return self._server_enabled_metrics + + + def _dispatcher(self) -> Generator[simpy.Event, None, None]: """ The main dispatcher loop. It pulls requests from the inbox and diff --git a/src/app/schemas/simulation_settings_input.py b/src/app/schemas/simulation_settings_input.py index 5d0ac6a..80828b5 100644 --- a/src/app/schemas/simulation_settings_input.py +++ b/src/app/schemas/simulation_settings_input.py @@ -2,7 +2,12 @@ from pydantic import BaseModel, Field -from app.config.constants import EventMetricName, SampledMetricName, TimeDefaults +from app.config.constants import ( + EventMetricName, + SampledMetricName, + SamplePeriods, + TimeDefaults, +) class SimulationSettings(BaseModel): @@ -14,18 +19,27 @@ class SimulationSettings(BaseModel): description="Simulation horizon in seconds.", ) + # These represent the mandatory metrics to collect enabled_sample_metrics: set[SampledMetricName] = Field( default_factory=lambda: { SampledMetricName.READY_QUEUE_LEN, - SampledMetricName.CORE_BUSY, + SampledMetricName.EVENT_LOOP_IO_SLEEP, SampledMetricName.RAM_IN_USE, + SampledMetricName.EDGE_CONCURRENT_CONNECTION, }, description="Which time-series KPIs to collect by default.", ) enabled_event_metrics: set[EventMetricName] = Field( default_factory=lambda: { - EventMetricName.RQS_LATENCY, + EventMetricName.RQS_CLOCK, }, description="Which per-event KPIs to collect by default.", ) + sample_period_s: float = Field( + default = SamplePeriods.STANDARD_TIME, + ge = SamplePeriods.MINIMUM_TIME, + le = SamplePeriods.MAXIMUM_TIME, + description="constant interval of time to build time series for metrics", + ) + diff --git a/src/app/simulation_run.py b/src/app/simulation_run.py deleted file mode 100644 index 1176751..0000000 --- a/src/app/simulation_run.py +++ /dev/null @@ -1,60 +0,0 @@ -"""simulation of the server""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import simpy - -from app.core.helpers.requests_generator import requests_generator -from app.schemas.simulation_output import SimulationOutput - -if TYPE_CHECKING: - from collections.abc import Generator - - import numpy as np - - from app.schemas.full_simulation_input import SimulationPayload - - - - -### TO MODIFY EVERYTHING WORK IN PROGRESS - -def run_simulation( - input_data: SimulationPayload, - *, - rng: np.random.Generator, -) -> SimulationOutput: - """Simulation executor in Simpy""" - sim_settings = input_data.sim_settings - - requests_generator_input = input_data.rqs_input - - gaps: Generator[float, None, None] = requests_generator( - requests_generator_input, - sim_settings, - rng=rng) - env = simpy.Environment() - - - total_request_per_time_period = { - "simulation_time": sim_settings.total_simulation_time, - "total_requests": 0, - } - - def arrival_process( - env: simpy.Environment, - ) -> Generator[simpy.events.Event, None, None]: - for gap in gaps: - yield env.timeout(gap) - total_request_per_time_period["total_requests"] += 1 - - env.process(arrival_process(env)) - env.run(until=sim_settings.total_simulation_time) - - return SimulationOutput( - total_requests=total_request_per_time_period, - metric_2=str(requests_generator_input.avg_request_per_minute_per_user.mean), - metric_n=str(requests_generator_input.avg_active_users.mean), - ) diff --git a/tests/conftest.py b/tests/conftest.py index 7d0f587..09a80d4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,33 +1,16 @@ """Pytest configuration file for setting up test fixtures and plugins.""" -import os -from collections.abc import AsyncGenerator, Generator -from pathlib import Path -from typing import cast import pytest -from alembic import command -from alembic.config import Config -from dotenv import load_dotenv -from fastapi.testclient import TestClient from numpy.random import Generator as NpGenerator from numpy.random import default_rng -from sqlalchemy.ext.asyncio import ( - AsyncEngine, - AsyncSession, - async_sessionmaker, - create_async_engine, -) -from sqlalchemy_utils import create_database, database_exists, drop_database from app.config.constants import ( EventMetricName, SampledMetricName, + SamplePeriods, TimeDefaults, ) -from app.config.settings import settings -from app.db.session import get_db -from app.main import app from app.schemas.full_simulation_input import SimulationPayload from app.schemas.random_variables_config import RVConfig from app.schemas.rqs_generator_input import RqsGeneratorInput @@ -38,106 +21,6 @@ TopologyNodes, ) -# Load test environment variables from .env.test -ENV_PATH = Path(__file__).resolve().parents[1] / "docker_fs" / ".env.test" -load_dotenv(dotenv_path=ENV_PATH, override=True) - - -class DummySession: - """A no-op async session substitute for unit tests.""" - - def add(self, instance: object) -> None: - """Perform a no-op add.""" - - async def commit(self) -> None: - """Perform a no-op commit.""" - - async def refresh(self, instance: object) -> None: - """Perform a no-op refresh.""" - - async def flush(self) -> None: - """Perform a no-op flush, required for unit tests.""" - - -async def override_get_db() -> AsyncGenerator[AsyncSession, None]: - """Yield a dummy async session for DB dependency override in unit tests.""" - # Cast so mypy sees an AsyncSession - yield cast("AsyncSession", DummySession()) - - -# Override the get_db dependency for all tests that use the app directly -app.dependency_overrides[get_db] = override_get_db - - -@pytest.fixture(scope="module") -def client() -> TestClient: - """Return a TestClient with the database dependency overridden.""" - return TestClient(app) - - -@pytest.fixture(scope="session", autouse=True) -def setup_test_database( - request: pytest.FixtureRequest, -) -> Generator[None, None, None]: - """Drop and recreate the test database, then apply migrations.""" - markexpr = request.config.option.markexpr or "" - is_integration_test = ( - "integration" in markexpr and "not integration" not in markexpr - ) - - if not is_integration_test: - yield - return - - # --- SETUP: This code runs before the test session starts --- - sync_db_url = settings.db_url.replace("+asyncpg", "") - - if database_exists(sync_db_url): - drop_database(sync_db_url) - create_database(sync_db_url) - - os.environ["ENVIRONMENT"] = "test" - alembic_cfg = Config("alembic.ini") - command.upgrade(alembic_cfg, "head") - - try: - # The test session runs at this point - yield - finally: - # --- TEARDOWN: This code runs after the test session ends --- - if database_exists(sync_db_url): - drop_database(sync_db_url) - - -@pytest.fixture(scope="session") -def async_engine() -> AsyncEngine: - """Create and return an async SQLAlchemy engine for the test session.""" - return create_async_engine(settings.db_url, future=True, echo=False) - - -@pytest.fixture -async def db_session(async_engine: AsyncEngine) -> AsyncGenerator[AsyncSession, None]: - """Yield an async DB session wrapped in a transaction that is always rolled back.""" - # This fixture ensures that each test is isolated from others - # within the same session. - connection = await async_engine.connect() - transaction = await connection.begin() - - session_factory = async_sessionmaker( - bind=connection, - expire_on_commit=False, - class_=AsyncSession, - ) - session = session_factory() - - try: - yield session - finally: - # Roll back the transaction to discard any changes made during the test - await transaction.rollback() - # Close the connection - await connection.close() - # ============================================================================ # STANDARD CONFIGURATION FOR INPUT VARIABLES # ============================================================================ @@ -171,7 +54,7 @@ def enabled_sample_metrics() -> set[SampledMetricName]: def enabled_event_metrics() -> set[EventMetricName]: """Default per-event KPIs collected in most tests.""" return { - EventMetricName.RQS_LATENCY, + EventMetricName.RQS_CLOCK, } @@ -195,6 +78,7 @@ def sim_settings( total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME, enabled_sample_metrics=enabled_sample_metrics, enabled_event_metrics=enabled_event_metrics, + sample_period_s=SamplePeriods.STANDARD_TIME, ) diff --git a/tests/integration/db_initialization/__init__.py b/tests/integration/db_initialization/__init__.py deleted file mode 100644 index 8c00603..0000000 --- a/tests/integration/db_initialization/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Database initialization integration tests.""" diff --git a/tests/integration/db_initialization/test_db_connection.py b/tests/integration/db_initialization/test_db_connection.py deleted file mode 100644 index 8f725ee..0000000 --- a/tests/integration/db_initialization/test_db_connection.py +++ /dev/null @@ -1,16 +0,0 @@ -import pytest -from sqlalchemy import text - -from app.db.session import engine - - -@pytest.mark.integration -async def test_db_connection() -> None: - """Verify that the SQLAlchemy engine can connect to the database. - - This test ensures that the database connection is properly configured - and can execute a simple query. - """ - async with engine.connect() as conn: - result = await conn.execute(text("SELECT 1")) - assert result.scalar_one() == 1 diff --git a/tests/integration/db_initialization/test_init_models.py b/tests/integration/db_initialization/test_init_models.py deleted file mode 100644 index 0ae8e69..0000000 --- a/tests/integration/db_initialization/test_init_models.py +++ /dev/null @@ -1,17 +0,0 @@ -import pytest -from sqlalchemy import text -from sqlalchemy.exc import SQLAlchemyError -from sqlalchemy.ext.asyncio import create_async_engine - -from app.config.settings import settings - -pytestmark = [pytest.mark.integration, pytest.mark.asyncio] - -async def test_users_table_exists_after_migrations() -> None: - engine = create_async_engine(settings.db_url, echo=False) - try: - async with engine.connect() as conn: - await conn.execute(text("SELECT 1")) - except SQLAlchemyError: - pytest.fail("Database connection or Alembic setup failed.") - diff --git a/tests/unit/db/test_settings.py b/tests/unit/db/test_settings.py deleted file mode 100644 index 9a277bc..0000000 --- a/tests/unit/db/test_settings.py +++ /dev/null @@ -1,18 +0,0 @@ -from app.config.settings import Settings - - -def test_settings_defaults() -> None: - """Ensure that default settings are applied correctly. - - This test verifies that the Settings class properly handles explicit configuration - values and applies the correct defaults for unspecified fields. - """ - s = Settings( - db_host="localhost", - db_user="x", - db_password="y", - db_name="z", - db_url="postgresql+asyncpg://x:y@localhost/z", - ) - assert s.environment == "test" - assert "postgresql" in s.db_url diff --git a/tests/unit/runtime/engine/test_edge.py b/tests/unit/runtime/engine/test_edge.py index 10cb758..37ad296 100644 --- a/tests/unit/runtime/engine/test_edge.py +++ b/tests/unit/runtime/engine/test_edge.py @@ -1,20 +1,28 @@ -"""Unit-tests for :class:`EdgeRuntime` (delivery / drop paths).""" +""" +Unit tests for :class:`EdgeRuntime`: +* delivery vs. drop paths +* connection-counter bookkeeping +* public properties (`enabled_metrics`, `concurrent_connections`) +""" + from __future__ import annotations from typing import TYPE_CHECKING, cast import simpy -from app.config.constants import SystemEdges, SystemNodes +from app.config.constants import SampledMetricName, SystemEdges, SystemNodes from app.runtime.actors.edge import EdgeRuntime from app.runtime.rqs_state import RequestState from app.schemas.random_variables_config import RVConfig from app.schemas.system_topology.full_system_topology import Edge if TYPE_CHECKING: - import numpy as np + from app.schemas.simulation_settings_input import SimulationSettings + + # --------------------------------------------------------------------------- # # Dummy RNG # # --------------------------------------------------------------------------- # @@ -24,26 +32,38 @@ class DummyRNG: """Return preset values for ``uniform`` and ``normal``.""" def __init__(self, *, uniform_value: float, normal_value: float = 0.0) -> None: - """Attribute init""" + """To complete""" self.uniform_value = uniform_value self.normal_value = normal_value self.uniform_called = False self.normal_called = False - - def uniform(self) -> float: - """Intercept ``rng.uniform`` calls.""" + def uniform(self) -> float: # called by EdgeRuntime + """To complete""" self.uniform_called = True return self.uniform_value - def normal(self, _mean: float, _sigma: float) -> float: - """Intercept ``rng.normal`` calls.""" + def normal(self, _mean: float, _sigma: float) -> float: # called by sampler + """To complete""" self.normal_called = True return self.normal_value # --------------------------------------------------------------------------- # -# Helper to build an EdgeRuntime # +# Minimal stub for SimulationSettings # +# --------------------------------------------------------------------------- # + + +class _SettingsStub: + """Only the attributes required by EdgeRuntime/build_edge_metrics.""" + + def __init__(self, enabled_sample_metrics: set[SampledMetricName]) -> None: + self.enabled_sample_metrics = enabled_sample_metrics + self.sample_period_s = 0.001 # not used in these unit tests + + +# --------------------------------------------------------------------------- # +# Helper factory # # --------------------------------------------------------------------------- # @@ -54,10 +74,10 @@ def _make_edge( normal_value: float = 0.0, dropout_rate: float = 0.0, ) -> tuple[EdgeRuntime, DummyRNG, simpy.Store]: - """Attributes init""" + """Create a fully wired :class:`EdgeRuntime` + associated objects.""" rng = DummyRNG(uniform_value=uniform_value, normal_value=normal_value) - store: simpy.Store = simpy.Store(env) + edge_cfg = Edge( id="edge-1", source="src", @@ -66,11 +86,16 @@ def _make_edge( dropout_rate=dropout_rate, ) + settings_stub = _SettingsStub( + enabled_sample_metrics={SampledMetricName.EDGE_CONCURRENT_CONNECTION}, + ) + edge_rt = EdgeRuntime( env=env, edge_config=edge_cfg, rng=cast("np.random.Generator", rng), target_box=store, + settings=cast("SimulationSettings", settings_stub), ) return edge_rt, rng, store @@ -80,8 +105,8 @@ def _make_edge( # --------------------------------------------------------------------------- # -def test_edge_delivers_message_when_not_dropped() -> None: - """A message traverses the edge and calls the latency sampler once.""" +def test_edge_delivers_message() -> None: + """A request traverses the edge when `uniform >= dropout_rate`.""" env = simpy.Environment() edge_rt, rng, store = _make_edge( env, @@ -93,24 +118,32 @@ def test_edge_delivers_message_when_not_dropped() -> None: state = RequestState(id=1, initial_time=0.0) state.record_hop(SystemNodes.GENERATOR, "gen-1", env.now) + assert edge_rt.concurrent_connections == 0 + edge_rt.transport(state) env.run() + # message delivered assert len(store.items) == 1 delivered: RequestState = store.items[0] last = delivered.history[-1] assert last.component_type is SystemEdges.NETWORK_CONNECTION assert last.component_id == "edge-1" + + # RNG calls assert rng.uniform_called is True assert rng.normal_called is True + # counter restored + assert edge_rt.concurrent_connections == 0 + -def test_edge_drops_message_when_uniform_below_threshold() -> None: - """A message is dropped when the random draw is below *dropout_rate*.""" +def test_edge_drops_message() -> None: + """A request is dropped when `uniform < dropout_rate`.""" env = simpy.Environment() edge_rt, rng, store = _make_edge( env, - uniform_value=0.1, # < dropout → drop + uniform_value=0.1, # < dropout_rate → drop dropout_rate=0.5, ) @@ -120,8 +153,33 @@ def test_edge_drops_message_when_uniform_below_threshold() -> None: edge_rt.transport(state) env.run() + # no delivery assert len(store.items) == 0 last = state.history[-1] assert last.component_id.endswith("dropped") + + # RNG calls assert rng.uniform_called is True assert rng.normal_called is False + + # counter unchanged + assert edge_rt.concurrent_connections == 0 + + +def test_metric_dict_initialised_and_mutable() -> None: + """`enabled_metrics` exposes the default key and supports list append.""" + env = simpy.Environment() + edge_rt, _rng, _store = _make_edge( + env, + uniform_value=0.9, + dropout_rate=0.0, + ) + + key = SampledMetricName.EDGE_CONCURRENT_CONNECTION + assert key in edge_rt.enabled_metrics + assert edge_rt.enabled_metrics[key] == [] + + # Simulate a collector append + edge_rt.enabled_metrics[key].append(5) + assert edge_rt.enabled_metrics[key] == [5] + diff --git a/tests/unit/runtime/engine/test_server.py b/tests/unit/runtime/engine/test_server.py index 69292ba..9e7edb1 100644 --- a/tests/unit/runtime/engine/test_server.py +++ b/tests/unit/runtime/engine/test_server.py @@ -1,13 +1,18 @@ -"""Unit tests for the concurrent ServerRuntime. +"""Unit-tests for ServerRuntime concurrency, resource handling and metrics. -The tests create an isolated SimPy environment with a test fixture that sets up: -* A single ServerRuntime instance. -* A mock "instant" edge that immediately forwards requests to a sink. -* A server configuration with 2 CPU cores and 1024 MB of RAM. -* A single endpoint with a sequence of RAM, CPU, and I/O steps. +Each test spins up an isolated SimPy environment containing: -This setup allows for precise testing of resource acquisition/release and -the correct execution of the processing pipeline for a single request. +* one ServerRuntime +* one mock edge with zero-latency delivery (InstantEdge) +* an inbox (simpy.Store) for incoming requests +* a sink (simpy.Store) that receives the request after the server + +The server exposes: + RAM = 1024 MB, CPU cores = 2 +and a single endpoint with the step sequence: + RAM(128 MB) ➜ CPU(5 ms) ➜ I/O(20 ms). + +All timings are in **seconds** because SimPy's clock is unit-agnostic. """ from __future__ import annotations @@ -21,56 +26,51 @@ EndpointStepCPU, EndpointStepIO, EndpointStepRAM, + SampledMetricName, StepOperation, - SystemNodes, ) from app.resources.server_containers import build_containers from app.runtime.actors.server import ServerRuntime from app.runtime.rqs_state import RequestState +from app.schemas.simulation_settings_input import SimulationSettings from app.schemas.system_topology.endpoint import Endpoint, Step from app.schemas.system_topology.full_system_topology import Server, ServerResources if TYPE_CHECKING: + from collections.abc import Generator -# --------------------------------------------------------------------------- # -# Test Helper: A mock edge that instantly delivers requests to a sink. # -# --------------------------------------------------------------------------- # -class InstantEdge: - """A test stub for EdgeRuntime with zero latency and no drops. - This mock allows us to test the ServerRuntime in isolation, without - introducing the complexities of network simulation. - """ +# ---------------------------------------------------------------------------# +# Helpers # +# ---------------------------------------------------------------------------# +class InstantEdge: + """Stub EdgeRuntime with zero latency and no drops.""" def __init__(self, env: simpy.Environment, sink: simpy.Store) -> None: - """Initializes the mock edge.""" - self.env = env - self.sink = sink + """Attribute""" + self._env = env + self._sink = sink def transport(self, state: RequestState) -> simpy.Process: - """Immediately puts the state in the sink via a SimPy process.""" - return self.env.process(self._deliver(state)) + """Transport function""" + return self._env.process(self._deliver(state)) def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: - """The generator function that performs the delivery.""" - yield self.sink.put(state) + """Deliver function""" + yield self._sink.put(state) -# --------------------------------------------------------------------------- # -# Test Fixture: Creates a standardized ServerRuntime for tests. # -# --------------------------------------------------------------------------- # def _make_server_runtime( env: simpy.Environment, ) -> tuple[ServerRuntime, simpy.Store]: - """Create a ServerRuntime with a dummy edge and return it and the sink store.""" - # 1. Define server resources + """Return a (ServerRuntime, sink) ready for injection tests.""" + # Resources res_spec = ServerResources(cpu_cores=2, ram_mb=1024) containers = build_containers(env, res_spec) - # 2. Define a single endpoint with a sequence of steps - # Order: RAM (instant) -> CPU (5ms) -> I/O (20ms) + # Endpoint: RAM → CPU → I/O endpoint = Endpoint( endpoint_name="/predict", steps=[ @@ -89,93 +89,95 @@ def _make_server_runtime( ], ) - # 3. Create the full server configuration - server_cfg = Server( - id="api_srv", - endpoints=[endpoint], - server_resources=res_spec, - ) + server_cfg = Server(id="api_srv", endpoints=[endpoint], server_resources=res_spec) - # 4. Set up the simulation environment with mock components inbox: simpy.Store = simpy.Store(env) sink: simpy.Store = simpy.Store(env) edge = InstantEdge(env, sink) - # 5. Instantiate the ServerRuntime + settings = SimulationSettings(total_simulation_time=1900, sample_period_s=0.1) + runtime = ServerRuntime( env=env, server_resources=containers, server_config=server_cfg, - out_edge=edge, # type: ignore[arg-type] + out_edge=edge, # type: ignore[arg-type] server_box=inbox, - rng=default_rng(0), + settings=settings, + rng=default_rng(seed=0), ) return runtime, sink -# --------------------------------------------------------------------------- # -# Unit Tests # -# --------------------------------------------------------------------------- # -def test_server_reserves_and_releases_ram() -> None: - """Verify that RAM is acquired at the start and fully released at the end.""" +# ---------------------------------------------------------------------------# +# Tests # +# ---------------------------------------------------------------------------# +def test_ram_is_released_at_end() -> None: + """RAM tokens must return to capacity once the request finishes.""" env = simpy.Environment() server, sink = _make_server_runtime(env) - # Prepare a request and inject it into the server's inbox. - req = RequestState(id=1, initial_time=0.0) - server.server_box.put(req) - - # Start the server's dispatcher process and run until all events are processed. + server.server_box.put(RequestState(id=1, initial_time=0.0)) server.start() env.run() - ram_container = server.server_resources["RAM"] - # After the request is fully processed, the RAM level must return to its capacity. - assert ram_container.level == ram_container.capacity, "RAM must be fully released" - # The request should have successfully reached the sink. - assert len(sink.items) == 1, "Request should be forwarded to the sink" + ram = server.server_resources["RAM"] + assert ram.level == ram.capacity + assert len(sink.items) == 1 def test_cpu_core_held_only_during_cpu_step() -> None: - """Verify a CPU core is held exclusively during the CPU-bound step.""" + """Exactly one core is busy during the CPU-bound window (0 5ms).""" env = simpy.Environment() server, _ = _make_server_runtime(env) - cpu_container = server.server_resources["CPU"] + cpu = server.server_resources["CPU"] - # Inject a single request and start the server. - req = RequestState(id=2, initial_time=0.0) - server.server_box.put(req) + server.server_box.put(RequestState(id=2, initial_time=0.0)) server.start() - # The endpoint logic is: RAM (t=0) -> CPU (t=0 to t=0.005). - # Run the simulation to a point *during* the CPU step. - env.run(until=0.004) - # The server has 2 cores. One should be busy. - assert cpu_container.level == 1, "One core should still be busy during the CPU step" + env.run(until=0.004) # mid-CPU step + assert cpu.level == 1 # 2-1 - # Now, run the simulation past the CPU step's completion. - env.run(until=0.006) - # The core should have been released immediately after the CPU step. - assert cpu_container.level == 2, "Core should be released after the CPU step" + env.run(until=0.006) # after CPU step + assert cpu.level == 2 # released -def test_server_records_hop_in_history() -> None: - """Verify that the request's history correctly records its arrival at the server.""" +def test_ready_and_io_queue_counters() -> None: + """ready_queue_len and io_queue_len should toggle as CPU⇄I/O phases alternate.""" env = simpy.Environment() - server, sink = _make_server_runtime(env) + server, _ = _make_server_runtime(env) - # Inject a request and run the simulation to completion. - req = RequestState(id=3, initial_time=0.0) - server.server_box.put(req) + server.server_box.put(RequestState(id=3, initial_time=0.0)) server.start() + + # 1) before start queues are empty + assert server.ready_queue_len == 0 + assert server.io_queue_len == 0 + + # 2) during CPU (0 5ms) ready queue+1 + env.run(until=0.003) + assert server.ready_queue_len == 1 + assert server.io_queue_len == 0 + + # 3) during I/O (5 25ms) ready 0, io+1 + env.run(until=0.010) + assert server.ready_queue_len == 0 + assert server.io_queue_len == 1 + + # 4) completed both back to 0 env.run() + assert server.ready_queue_len == 0 + assert server.io_queue_len == 0 + - # The request must be in the sink. - assert len(sink.items) == 1, "Request did not reach the sink" - finished_req = sink.items[0] +def test_enabled_metrics_dict_populated() -> None: + """ServerRuntime must create lists for every mandatory sampled metric.""" + env = simpy.Environment() + server, _ = _make_server_runtime(env) - # Check the request's history for a 'Hop' corresponding to this server. - assert any( - hop.component_type == SystemNodes.SERVER and hop.component_id == "api_srv" - for hop in finished_req.history - ), "Server hop missing in request history" + mandatory = { + SampledMetricName.RAM_IN_USE, + SampledMetricName.READY_QUEUE_LEN, + SampledMetricName.EVENT_LOOP_IO_SLEEP, + } + assert mandatory.issubset(server.enabled_metrics.keys()) diff --git a/tests/unit/test_health_route.py b/tests/unit/test_health_route.py deleted file mode 100644 index 57cfe1f..0000000 --- a/tests/unit/test_health_route.py +++ /dev/null @@ -1,8 +0,0 @@ -from fastapi.testclient import TestClient - - -def test_health_endpoint_returns_ok(client: TestClient) -> None: - """Ensure the /health endpoint returns HTTP 200 and the expected JSON payload.""" - response = client.get("/health") - assert response.status_code == 200 - assert response.json() == {"status": "ok"} From 7f1f7dea3e3f93cb1eaebc35087d479dabf3ea6b Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 1 Aug 2025 15:35:32 +0200 Subject: [PATCH 19/52] Features/metrics elaboration (#8) * introduction to the analyzer class + tests * refactor of the analyzer --- poetry.lock | 481 +++++++++++++++++++++++++++- pyproject.toml | 1 + src/app/config/constants.py | 28 +- src/app/config/plot_constants.py | 47 +++ src/app/metrics/analyzer.py | 225 +++++++++++++ tests/unit/metrics/test_analyzer.py | 190 +++++++++++ 6 files changed, 965 insertions(+), 7 deletions(-) create mode 100644 src/app/config/plot_constants.py create mode 100644 src/app/metrics/analyzer.py create mode 100644 tests/unit/metrics/test_analyzer.py diff --git a/poetry.lock b/poetry.lock index e657f54..543faa8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -22,6 +22,97 @@ files = [ {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] +[[package]] +name = "contourpy" +version = "1.3.3" +description = "Python library for calculating contours of 2D quadrilateral grids" 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"packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "trove-classifiers (>=2024.10.12)"] +typing = ["typing-extensions"] +xmp = ["defusedxml"] + [[package]] name = "pluggy" version = "1.6.0" @@ -536,6 +976,20 @@ files = [ [package.extras] windows-terminal = ["colorama (>=0.4.6)"] +[[package]] +name = "pyparsing" +version = "3.2.3" +description = "pyparsing module - Classes and methods to define and execute parsing grammars" +optional = false +python-versions = ">=3.9" +files = [ + {file = "pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf"}, + {file = "pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be"}, +] + +[package.extras] +diagrams = ["jinja2", "railroad-diagrams"] + [[package]] name = "pytest" version = "8.4.1" @@ -594,6 +1048,20 @@ pytest = ">=6.2.5" [package.extras] testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"] +[[package]] +name = "python-dateutil" +version = "2.9.0.post0" +description = "Extensions to the standard Python datetime module" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +files = [ + {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, + {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, +] + +[package.dependencies] +six = ">=1.5" + [[package]] name = "python-dotenv" version = "1.1.1" @@ -646,6 +1114,17 @@ files = [ {file = "simpy-4.1.1.tar.gz", hash = "sha256:06d0750a7884b11e0e8e20ce0bc7c6d4ed5f1743d456695340d13fdff95001a6"}, ] +[[package]] +name = "six" +version = "1.17.0" +description = "Python 2 and 3 compatibility utilities" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +files = [ + {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"}, + {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"}, +] + [[package]] name = "typing-extensions" version = "4.14.1" @@ -674,4 +1153,4 @@ typing-extensions = ">=4.12.0" [metadata] lock-version = "2.0" python-versions = "^3.12" -content-hash = "ca95f91568cb076386715d183efe27addd2732eb4347c385a0e9f1479d2035d6" +content-hash = "1441494d6783804e41cd06a1a630c2031b47573ba6f28619d4967e27953aff40" diff --git a/pyproject.toml b/pyproject.toml index b4c46ff..1528987 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ pydantic-settings = "^2.10.1" pydantic = {extras = ["email"], version = "^2.11.7"} numpy = "^2.3.1" simpy = "^4.1.1" +matplotlib = "^3.10.3" [tool.poetry.group.dev.dependencies] pytest = "^8.4.1" diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 0e40950..74cf0e2 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -216,9 +216,9 @@ class SamplePeriods(float, Enum): we have to extract a time series """ - STANDARD_TIME = 0.005 # 5 MILLISECONDS + STANDARD_TIME = 0.01 # 10 MILLISECONDS MINIMUM_TIME = 0.001 # 1 MILLISECOND - MAXIMUM_TIME = 0.1 # 10 MILLISECONDS + MAXIMUM_TIME = 0.1 # 100 MILLISECONDS # ====================================================================== # CONSTANTS FOR EVENT METRICS @@ -244,7 +244,7 @@ class AggregatedMetricName(StrEnum): """aggregated metrics to calculate at the end of simulation""" LATENCY_STATS = "latency_stats" - THROUGHPUT_RPS = "throughput_rps" + THROUGHPUT = "throughput_rps" LLM_STATS = "llm_stats" # ====================================================================== @@ -252,7 +252,23 @@ class AggregatedMetricName(StrEnum): # ====================================================================== class ServerResourceName(StrEnum): - """Keys for each server resource type, used when building the container map.""" + """Keys for each server resource type, used when building the container map.""" - CPU = "CPU" - RAM = "RAM" + CPU = "CPU" + RAM = "RAM" + +# ====================================================================== +# CONSTANTS FOR LATENCY STATS +# ====================================================================== + +class LatencyKey(StrEnum): + """Keys for the collection of the latency stats""" + + TOTAL_REQUESTS = "total_requests" + MEAN = "mean" + MEDIAN = "median" + STD_DEV = "std_dev" + P95 = "p95" + P99 = "p99" + MIN = "min" + MAX = "max" diff --git a/src/app/config/plot_constants.py b/src/app/config/plot_constants.py new file mode 100644 index 0000000..efe28b9 --- /dev/null +++ b/src/app/config/plot_constants.py @@ -0,0 +1,47 @@ +"""Dataclass to define a central structure to plot the metrics""" +from dataclasses import dataclass + + +@dataclass(frozen=True) +class PlotCfg: + """Dataclass for the plot of the various metrics""" + + no_data: str + title: str + x_label: str + y_label: str + ready_label: str | None = None + io_label: str | None = None + legend_label: str | None = None + +LATENCY_PLOT = PlotCfg( + no_data="No latency data", + title="Request Latency Distribution", + x_label="Latency (s)", + y_label="Frequency", +) + +THROUGHPUT_PLOT = PlotCfg( + no_data="No throughput data", + title="Throughput (RPS)", + x_label="Time (s)", + y_label="Requests/s", +) + + +SERVER_QUEUES_PLOT = PlotCfg( + no_data="No queue data", + title="Server Queues", + x_label="Time (s)", + y_label="Queue length", + ready_label="Ready queue", + io_label="I/O queue", +) + +RAM_PLOT = PlotCfg( + no_data="No RAM data", + title="RAM Usage", + x_label="Time (s)", + y_label="RAM (MB)", + legend_label="RAM", +) diff --git a/src/app/metrics/analyzer.py b/src/app/metrics/analyzer.py new file mode 100644 index 0000000..00bbd35 --- /dev/null +++ b/src/app/metrics/analyzer.py @@ -0,0 +1,225 @@ +"""Module for post-simulation analysis and visualization.""" + +from __future__ import annotations + +from collections import defaultdict +from typing import TYPE_CHECKING + +import numpy as np + +from app.config.constants import LatencyKey, SampledMetricName +from app.config.plot_constants import ( + LATENCY_PLOT, + RAM_PLOT, + SERVER_QUEUES_PLOT, + THROUGHPUT_PLOT, +) + +if TYPE_CHECKING: + + from matplotlib.axes import Axes + + from app.runtime.actors.client import ClientRuntime + from app.runtime.actors.edge import EdgeRuntime + from app.runtime.actors.server import ServerRuntime + from app.schemas.simulation_settings_input import SimulationSettings + + +class ResultsAnalyzer: + """Analyze and visualize the results of a completed simulation. + + This class holds the raw runtime objects and lazily computes: + - latency statistics + - throughput time series (RPS) + - sampled metrics from servers and edges + """ + + # Class attribute to define the period to calculate the throughput in s + _WINDOW_SIZE_S: float = 1 + + def __init__( + self, + *, + client: ClientRuntime, + servers: list[ServerRuntime], + edges: list[EdgeRuntime], + settings: SimulationSettings, + ) -> None: + """ + Args: + client: Client runtime object, containing RqsClock entries. + servers: List of server runtime objects. + edges: List of edge runtime objects. + settings: Original simulation settings. + + """ + self._client = client + self._servers = servers + self._edges = edges + self._settings = settings + + # Lazily computed caches + self.latencies: list[float] | None = None + self.latency_stats: dict[LatencyKey, float] | None = None + self.throughput_series: tuple[list[float], list[float]] | None = None + self.sampled_metrics: dict[str, dict[str, list[float]]] | None = None + + def process_all_metrics(self) -> None: + """Compute all aggregated and sampled metrics if not already done.""" + if self.latency_stats is None and self._client.rqs_clock: + self._process_event_metrics() + + if self.sampled_metrics is None: + self._extract_sampled_metrics() + + def _process_event_metrics(self) -> None: + """Calculate latency stats and throughput time series (RPS).""" + # 1) Latencies + self.latencies = [ + clock.finish - clock.start for clock in self._client.rqs_clock + ] + + # 2) Summary stats + if self.latencies: + arr = np.array(self.latencies) + self.latency_stats = { + LatencyKey.TOTAL_REQUESTS: float(arr.size), + LatencyKey.MEAN: float(np.mean(arr)), + LatencyKey.MEDIAN: float(np.median(arr)), + LatencyKey.STD_DEV: float(np.std(arr)), + LatencyKey.P95: float(np.percentile(arr, 95)), + LatencyKey.P99: float(np.percentile(arr, 99)), + LatencyKey.MIN: float(np.min(arr)), + LatencyKey.MAX: float(np.max(arr)), + } + else: + self.latency_stats = {} + + # 3) Throughput per 1s window + completion_times = sorted(clock.finish for clock in self._client.rqs_clock) + end_time = self._settings.total_simulation_time + + timestamps: list[float] = [] + rps_values: list[float] = [] + count = 0 + idx = 0 + current_end = ResultsAnalyzer._WINDOW_SIZE_S + + while current_end <= end_time: + while idx < len(completion_times) and completion_times[idx] <= current_end: + count += 1 + idx += 1 + timestamps.append(current_end) + rps_values.append(count / ResultsAnalyzer._WINDOW_SIZE_S) + current_end += ResultsAnalyzer._WINDOW_SIZE_S + count = 0 + + self.throughput_series = (timestamps, rps_values) + + def _extract_sampled_metrics(self) -> None: + """Gather sampled metrics from servers and edges into a nested dict.""" + metrics: dict[str, dict[str, list[float]]] = defaultdict(dict) + + for server in self._servers: + sid = server.server_config.id + for name, values in server.enabled_metrics.items(): + metrics[name.value][sid] = values + + for edge in self._edges: + eid = edge.edge_config.id + for name, values in edge.enabled_metrics.items(): + metrics[name.value][eid] = values + + self.sampled_metrics = metrics + + def get_latency_stats(self) -> dict[LatencyKey, float]: + """Return latency statistics, computing them if necessary.""" + self.process_all_metrics() + return self.latency_stats or {} + + def get_throughput_series(self) -> tuple[list[float], list[float]]: + """Return throughput time series (timestamps, RPS).""" + self.process_all_metrics() + assert self.throughput_series is not None + return self.throughput_series + + def get_sampled_metrics(self) -> dict[str, dict[str, list[float]]]: + """Return sampled metrics from servers and edges.""" + self.process_all_metrics() + assert self.sampled_metrics is not None + return self.sampled_metrics + + def plot_latency_distribution(self, ax: Axes) -> None: + """Draw a histogram of request latencies onto the given Axes.""" + if not self.latencies: + ax.text(0.5, 0.5, LATENCY_PLOT.no_data, ha="center", va="center") + return + + ax.hist(self.latencies, bins=50) + ax.set_title(LATENCY_PLOT.title) + ax.set_xlabel(LATENCY_PLOT.x_label) + ax.set_ylabel(LATENCY_PLOT.y_label) + ax.grid(visible=True) + + def plot_throughput(self, ax: Axes) -> None: + """Draw throughput (RPS) over time onto the given Axes.""" + timestamps, values = self.get_throughput_series() + if not timestamps: + ax.text(0.5, 0.5, THROUGHPUT_PLOT.no_data, ha="center", va="center") + return + + ax.plot(timestamps, values, marker="o", linestyle="-") + ax.set_title(THROUGHPUT_PLOT.title) + ax.set_xlabel(THROUGHPUT_PLOT.x_label) + ax.set_ylabel(THROUGHPUT_PLOT.y_label) + ax.grid(visible=True) + + def plot_server_queues(self, ax: Axes) -> None: + """Draw server queue lengths over time onto the given Axes.""" + metrics = self.get_sampled_metrics() + ready = metrics.get(SampledMetricName.READY_QUEUE_LEN, {}) + io_q = metrics.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) + + if not (ready or io_q): + ax.text(0.5, 0.5, SERVER_QUEUES_PLOT.no_data, ha="center", va="center") + return + + samples = len(next(iter(ready.values()), [])) + times = np.arange(samples) * self._settings.sample_period_s + + for sid, vals in ready.items(): + ax.plot(times, vals, label=f"{sid} {SERVER_QUEUES_PLOT.ready_label}") + for sid, vals in io_q.items(): + ax.plot( + times, + vals, + label=f"{sid} {SERVER_QUEUES_PLOT.io_label}", + linestyle="--", + ) + + ax.set_title(SERVER_QUEUES_PLOT.title) + ax.set_xlabel(SERVER_QUEUES_PLOT.x_label) + ax.set_ylabel(SERVER_QUEUES_PLOT.y_label) + ax.legend() + ax.grid(visible=True) + + def plot_ram_usage(self, ax: Axes) -> None: + """Draw RAM usage over time onto the given Axes.""" + metrics = self.get_sampled_metrics() + ram = metrics.get(SampledMetricName.RAM_IN_USE, {}) + + if not ram: + ax.text(0.5, 0.5, RAM_PLOT.no_data, ha="center", va="center") + return + + samples = len(next(iter(ram.values()))) + times = np.arange(samples) * self._settings.sample_period_s + + for sid, vals in ram.items(): + ax.plot(times, vals, label=f"{sid} {RAM_PLOT.legend_label}") + + ax.set_title(RAM_PLOT.title) + ax.set_xlabel(RAM_PLOT.x_label) + ax.set_ylabel(RAM_PLOT.y_label) + ax.legend() + ax.grid(visible=True) diff --git a/tests/unit/metrics/test_analyzer.py b/tests/unit/metrics/test_analyzer.py new file mode 100644 index 0000000..4aa33b9 --- /dev/null +++ b/tests/unit/metrics/test_analyzer.py @@ -0,0 +1,190 @@ +"""Unit-tests for ``ResultsAnalyzer`` (latency, throughput, plots).""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, cast + +import pytest +from matplotlib.figure import Figure + +from app.config.constants import LatencyKey +from app.metrics.analyzer import ResultsAnalyzer + +if TYPE_CHECKING: + from app.runtime.actors.client import ClientRuntime + from app.runtime.actors.edge import EdgeRuntime + from app.runtime.actors.server import ServerRuntime + from app.schemas.simulation_settings_input import SimulationSettings + + +# ---------------------------------------------------------------------- # +# Dummy objects (test doubles) # +# ---------------------------------------------------------------------- # +class DummyClock: + """Clock with *start* / *finish* timestamps to emulate a request.""" + + def __init__(self, start: float, finish: float) -> None: + """Save *start* and *finish* times.""" + self.start = start + self.finish = finish + + +class DummyClient: + """Emulates ``ClientRuntime`` by exposing ``rqs_clock``.""" + + def __init__(self, clocks: list[DummyClock]) -> None: + """Attach a list of dummy clocks to the stub client.""" + self.rqs_clock = clocks + + +class DummyName: + """Mimic an Enum member that carries a ``.value`` attribute.""" + + def __init__(self, value: str) -> None: + """Store the dummy string *value*.""" + self.value = value + + +class DummyServerConfig: + """Lightweight replacement for the real ``ServerConfig``.""" + + def __init__(self, identifier: str) -> None: + """Expose only the *id* field required by the analyzer.""" + self.id = identifier + + +class DummyServer: + """Stub for ``ServerRuntime`` exposing ``enabled_metrics``.""" + + def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: + """Create a fake server with the given *metrics*.""" + self.server_config = DummyServerConfig(identifier) + self.enabled_metrics = { + DummyName(name): values for name, values in metrics.items() + } + + +class DummyEdgeConfig: + """Minified replacement for the real ``EdgeConfig``.""" + + def __init__(self, identifier: str) -> None: + """Expose only the *id* property.""" + self.id = identifier + + +class DummyEdge: + """Stub for ``EdgeRuntime`` exposing ``enabled_metrics``.""" + + def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: + """Create a fake edge with the given *metrics*.""" + self.edge_config = DummyEdgeConfig(identifier) + self.enabled_metrics = { + DummyName(name): values for name, values in metrics.items() + } + + +# ---------------------------------------------------------------------- # +# Fixtures # +# ---------------------------------------------------------------------- # +@pytest.fixture +def simple_analyzer(sim_settings: SimulationSettings) -> ResultsAnalyzer: + """ + Analyzer with two synthetic requests (durations 1 s and 2 s) on a + 3-second horizon and **no** sampled metrics. + """ + sim_settings.total_simulation_time = 3 + clocks = [DummyClock(0.0, 1.0), DummyClock(0.0, 2.0)] + client = DummyClient(clocks) + return ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[], + edges=[], + settings=sim_settings, + ) + + +# ---------------------------------------------------------------------- # +# Tests for computed metrics # +# ---------------------------------------------------------------------- # +def test_latency_stats(simple_analyzer: ResultsAnalyzer) -> None: + stats = simple_analyzer.get_latency_stats() + assert stats[LatencyKey.TOTAL_REQUESTS] == 2.0 + assert stats[LatencyKey.MEAN] == pytest.approx(1.5) + assert stats[LatencyKey.MEDIAN] == pytest.approx(1.5) + assert stats[LatencyKey.MIN] == pytest.approx(1.0) + assert stats[LatencyKey.MAX] == pytest.approx(2.0) + assert stats[LatencyKey.P95] == pytest.approx(1.95, rel=1e-3) + + +def test_throughput_series(simple_analyzer: ResultsAnalyzer) -> None: + timestamps, rps = simple_analyzer.get_throughput_series() + assert timestamps == [1.0, 2.0, 3.0] + assert rps == [1.0, 1.0, 0.0] + + +def test_sampled_metrics_empty(simple_analyzer: ResultsAnalyzer) -> None: + assert simple_analyzer.get_sampled_metrics() == {} + + +# ---------------------------------------------------------------------- # +# Tests for plotting methods # +# ---------------------------------------------------------------------- # +def test_plot_latency_distribution(simple_analyzer: ResultsAnalyzer) -> None: + fig = Figure() + ax = fig.subplots() + simple_analyzer.process_all_metrics() + simple_analyzer.plot_latency_distribution(ax) + assert ax.get_title() == "Request Latency Distribution" + + +def test_plot_throughput(simple_analyzer: ResultsAnalyzer) -> None: + fig = Figure() + ax = fig.subplots() + simple_analyzer.process_all_metrics() + simple_analyzer.plot_throughput(ax) + assert ax.get_title() == "Throughput (RPS)" + + +def test_plot_server_queues_with_data(sim_settings: SimulationSettings) -> None: + sim_settings.total_simulation_time = 3 + client = DummyClient([]) + server = DummyServer("srv1", {"ready_queue_len": [1, 2, 3]}) + edge = DummyEdge("edge1", {}) + analyzer = ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[cast("ServerRuntime", server)], + edges=[cast("EdgeRuntime", edge)], + settings=sim_settings, + ) + + fig = Figure() + ax = fig.subplots() + analyzer.process_all_metrics() + analyzer.plot_server_queues(ax) + assert ax.get_title() == "Server Queues" + legend = ax.get_legend() + assert legend is not None + texts = [t.get_text() for t in legend.get_texts()] + assert "srv1 Ready queue" in texts + + +def test_plot_ram_usage_with_data(sim_settings: SimulationSettings) -> None: + sim_settings.total_simulation_time = 3 + client = DummyClient([]) + edge = DummyEdge("edgeA", {"ram_in_use": [10.0, 20.0]}) + analyzer = ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[], + edges=[cast("EdgeRuntime", edge)], + settings=sim_settings, + ) + + fig = Figure() + ax = fig.subplots() + analyzer.process_all_metrics() + analyzer.plot_ram_usage(ax) + assert ax.get_title() == "RAM Usage" + legend = ax.get_legend() + assert legend is not None + texts = [t.get_text() for t in legend.get_texts()] + assert "edgeA RAM" in texts From 84f9897d4e4bd9d529b921cba64aa92dbc1c53cb Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Sun, 3 Aug 2025 15:24:06 +0200 Subject: [PATCH 20/52] Features/load balancer node (#9) * Added LB structure in the input for the simulation + tests * introduced LB runtime + documentation * Added test for LB and small refactor to define the SimRunner --- docs/fastsim-docs/runtime_and_resources.md | 92 +++++++++++- src/app/config/constants.py | 14 +- src/app/metrics/analyzer.py | 53 ++++--- src/app/metrics/collector.py | 4 +- src/app/resources/registry.py | 1 + src/app/runtime/actors/client.py | 4 +- .../runtime/actors/helpers/lb_algorithms.py | 30 ++++ src/app/runtime/actors/load_balancer.py | 73 ++++++++++ src/app/runtime/actors/rqs_generator.py | 4 +- src/app/runtime/actors/server.py | 2 + src/app/schemas/simulation_settings_input.py | 1 + .../system_topology/full_system_topology.py | 65 ++++++++- tests/unit/resources/test_registry.py | 4 +- .../runtime/{engine => actors}/test_client.py | 0 .../runtime/{engine => actors}/test_edge.py | 0 .../unit/runtime/actors/test_load_balancer.py | 131 ++++++++++++++++++ .../{engine => actors}/test_rqs_generator.py | 0 .../runtime/{engine => actors}/test_server.py | 0 .../unit/schemas/test_full_topology_input.py | 103 ++++++++++++++ 19 files changed, 547 insertions(+), 34 deletions(-) create mode 100644 src/app/runtime/actors/helpers/lb_algorithms.py create mode 100644 src/app/runtime/actors/load_balancer.py rename tests/unit/runtime/{engine => actors}/test_client.py (100%) rename tests/unit/runtime/{engine => actors}/test_edge.py (100%) create mode 100644 tests/unit/runtime/actors/test_load_balancer.py rename tests/unit/runtime/{engine => actors}/test_rqs_generator.py (100%) rename tests/unit/runtime/{engine => actors}/test_server.py (100%) diff --git a/docs/fastsim-docs/runtime_and_resources.md b/docs/fastsim-docs/runtime_and_resources.md index 763e0a9..a5dca9c 100644 --- a/docs/fastsim-docs/runtime_and_resources.md +++ b/docs/fastsim-docs/runtime_and_resources.md @@ -358,4 +358,94 @@ The client pulls requests from its `client_box`. It then makes a critical decisi **Design Note & Real-World Analogy:** The current logic for this decision—`if state.history[-2].component_type != SystemNodes.GENERATOR`—is **fragile**. While it works, it's not robust. A future improvement would be to add a more explicit routing mechanism. -In the real world, the `ClientRuntime` could be a user's **web browser**, a **mobile application**, or even a **Backend-For-Frontend (BFF)** service that both initiates requests and receives the final aggregated responses. \ No newline at end of file +In the real world, the `ClientRuntime` could be a user's **web browser**, a **mobile application**, or even a **Backend-For-Frontend (BFF)** service that both initiates requests and receives the final aggregated responses. + +## **5.5 `LoadBalancerRuntime` — The Traffic Cop 🚦** + +The **Load Balancer** actor lives in `app/runtime/actors/load_balancer_runtime.py`. +It receives a `RequestState` from the client side, decides **which outbound +edge** should carry it to a server, and immediately forwards the request. + +```text +lb_box.get() choose edge transport(state) +┌───────────────┐ ───────────────────────────► ┌────────────────┐ +│ LoadBalancer │ │ EdgeRuntime n │ +└───────────────┘ ◄───────────────────────────┘ └────────────────┘ +``` + +### **Constructor Parameters** + +| Parameter | Meaning | +| ------------- | ----------------------------------------------------------------------- | +| `env` | Shared `simpy.Environment` | +| `lb_config` | Validated `LoadBalancer` schema (ID, chosen algorithm enum) | +| `outer_edges` | Immutable list `list[EdgeRuntime]`, one per target server | +| `lb_box` | `simpy.Store` acting as the inbox for requests arriving from the client | + +```python +self._round_robin_index: int = 0 # round-robin pointer (private state) +``` + +### **Supported Algorithms** + +| Enum value (`LbAlgorithmsName`) | Function used | Signature | +| ------------------------------- | --------------------------------------------- | --------- | +| `ROUND_ROBIN` | `round_robin(edges, idx)` → `(edge, new_idx)` | O(1) | +| `LEAST_CONNECTIONS` | `least_connections(edges)` → `edge` | O(N) scan | + +*Both helpers live in* `app/runtime/actors/helpers/lb_algorithms.py`. + +#### **Why an index and not list rotation?** + +`outer_edges` is **shared** with other components (e.g. metrics collector, +tests). Rotating it in-place would mutate a shared object and create +hard-to-trace bugs (aliasing). +Keeping `_round_robin_index` inside the LB runtime preserves immutability while +still advancing the pointer on every request. + +### **Process Workflow** + +```python +def _forwarder(self) -> Generator[simpy.Event, None, None]: + while True: + state: RequestState = yield self.lb_box.get() # ① wait for a request + + state.record_hop(SystemNodes.LOAD_BALANCER, + self.lb_config.id, + self.env.now) # ② trace + + if self.lb_config.algorithms is LbAlgorithmsName.ROUND_ROBIN: + edge, self._round_robin_index = round_robin( + self.outer_edges, + self._round_robin_index, + ) # ③a choose RR edge + else: # LEAST_CONNECTIONS + edge = least_connections(self.outer_edges) # ③b choose LC edge + + edge.transport(state) # ④ forward +``` + +| Step | What happens | Real-world analogue | +| ---- | ------------------------------------------------------------------------ | ---------------------------------------- | +| ① | Pull next `RequestState` out of `lb_box`. | Socket `accept()` on the LB front-end. | +| ② | Add a `Hop` stamped `LOAD_BALANCER`. | Trace span in NGINX/Envoy access log. | +| ③a | **Round-Robin** – pick `outer_edges[_round_robin_index]`, advance index. | Classic DNS-RR or NGINX default. | +| ③b | **Least-Connections** – `min(edges, key=concurrent_connections)`. | HAProxy `leastconn`, NGINX `least_conn`. | +| ④ | Spawn network transit via `EdgeRuntime.transport()`. | LB writes request to backend socket. | + +### **Edge-Case Safety** + +* **Empty `outer_edges`** → impossible by schema validation (LB must cover >1 server). +* **Single server** → RR degenerates to index 0; LC always returns that edge. +* **Concurrency metric** (`edge.concurrent_connections`) is updated inside + `EdgeRuntime` in real time, so `least_connections` adapts instantly to load spikes. + +### **Key Takeaways** + +1. **Stateful but side-effect-free** – `_round_robin_index` keeps per-LB state without mutating the shared edge list. +2. **Uniform API** – both algorithms integrate through a simple `if/else`; additional strategies can be added with negligible changes. +3. **Deterministic & reproducible** – no randomness inside the LB, ensuring repeatable simulations. + +With these mechanics the `LoadBalancerRuntime` faithfully emulates behaviour of +production LBs (NGINX, HAProxy, AWS ALB) while remaining lightweight and +fully deterministic inside the FastSim event loop. diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 74cf0e2..07e383c 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -161,6 +161,16 @@ class NetworkParameters: DROPOUT_RATE = 0.01 MAX_DROPOUT_RATE = 1.0 +# ====================================================================== +# NAME FOR LOAD BALANCER ALGORITHMS +# ====================================================================== + +class LbAlgorithmsName(StrEnum): + """definition of the available algortithms for the Load Balancer""" + + ROUND_ROBIN = "round_robin" + LEAST_CONNECTIONS = "least_connection" + # ====================================================================== # CONSTANTS FOR THE MACRO-TOPOLOGY GRAPH @@ -178,10 +188,6 @@ class SystemNodes(StrEnum): SERVER = "server" CLIENT = "client" LOAD_BALANCER = "load_balancer" - API_GATEWAY = "api_gateway" - DATABASE = "database" - CACHE = "cache" - class SystemEdges(StrEnum): """ diff --git a/src/app/metrics/analyzer.py b/src/app/metrics/analyzer.py index 00bbd35..07a021e 100644 --- a/src/app/metrics/analyzer.py +++ b/src/app/metrics/analyzer.py @@ -13,11 +13,15 @@ RAM_PLOT, SERVER_QUEUES_PLOT, THROUGHPUT_PLOT, + PlotCfg, ) if TYPE_CHECKING: + from collections.abc import Iterable + from matplotlib.axes import Axes + from matplotlib.lines import Line2D from app.runtime.actors.client import ClientRuntime from app.runtime.actors.edge import EdgeRuntime @@ -35,7 +39,7 @@ class ResultsAnalyzer: """ # Class attribute to define the period to calculate the throughput in s - _WINDOW_SIZE_S: float = 1 + _WINDOW_SIZE_S: float = 1.0 def __init__( self, @@ -64,6 +68,22 @@ def __init__( self.throughput_series: tuple[list[float], list[float]] | None = None self.sampled_metrics: dict[str, dict[str, list[float]]] | None = None + @staticmethod + def _apply_plot_cfg( + ax: Axes, + cfg: PlotCfg, + *, + legend_handles: Iterable[Line2D] | None = None, + ) -> None: + """Apply title / axis labels / grid and (optionally) legend to ax.""" + ax.set_title(cfg.title) + ax.set_xlabel(cfg.x_label) + ax.set_ylabel(cfg.y_label) + ax.grid(visible=True) + + if legend_handles: + ax.legend(handles=legend_handles) + def process_all_metrics(self) -> None: """Compute all aggregated and sampled metrics if not already done.""" if self.latency_stats is None and self._client.rqs_clock: @@ -150,32 +170,26 @@ def get_sampled_metrics(self) -> dict[str, dict[str, list[float]]]: return self.sampled_metrics def plot_latency_distribution(self, ax: Axes) -> None: - """Draw a histogram of request latencies onto the given Axes.""" + """Plot the distribution of the latency""" if not self.latencies: ax.text(0.5, 0.5, LATENCY_PLOT.no_data, ha="center", va="center") return ax.hist(self.latencies, bins=50) - ax.set_title(LATENCY_PLOT.title) - ax.set_xlabel(LATENCY_PLOT.x_label) - ax.set_ylabel(LATENCY_PLOT.y_label) - ax.grid(visible=True) + self._apply_plot_cfg(ax, LATENCY_PLOT) def plot_throughput(self, ax: Axes) -> None: - """Draw throughput (RPS) over time onto the given Axes.""" + """Plot the distribution of the throughput""" timestamps, values = self.get_throughput_series() if not timestamps: ax.text(0.5, 0.5, THROUGHPUT_PLOT.no_data, ha="center", va="center") return ax.plot(timestamps, values, marker="o", linestyle="-") - ax.set_title(THROUGHPUT_PLOT.title) - ax.set_xlabel(THROUGHPUT_PLOT.x_label) - ax.set_ylabel(THROUGHPUT_PLOT.y_label) - ax.grid(visible=True) + self._apply_plot_cfg(ax, THROUGHPUT_PLOT) def plot_server_queues(self, ax: Axes) -> None: - """Draw server queue lengths over time onto the given Axes.""" + """Plot the server queues""" metrics = self.get_sampled_metrics() ready = metrics.get(SampledMetricName.READY_QUEUE_LEN, {}) io_q = metrics.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) @@ -197,14 +211,11 @@ def plot_server_queues(self, ax: Axes) -> None: linestyle="--", ) - ax.set_title(SERVER_QUEUES_PLOT.title) - ax.set_xlabel(SERVER_QUEUES_PLOT.x_label) - ax.set_ylabel(SERVER_QUEUES_PLOT.y_label) - ax.legend() - ax.grid(visible=True) + self._apply_plot_cfg(ax, SERVER_QUEUES_PLOT, legend_handles=ax.lines) + def plot_ram_usage(self, ax: Axes) -> None: - """Draw RAM usage over time onto the given Axes.""" + """Plot the ram usage""" metrics = self.get_sampled_metrics() ram = metrics.get(SampledMetricName.RAM_IN_USE, {}) @@ -218,8 +229,4 @@ def plot_ram_usage(self, ax: Axes) -> None: for sid, vals in ram.items(): ax.plot(times, vals, label=f"{sid} {RAM_PLOT.legend_label}") - ax.set_title(RAM_PLOT.title) - ax.set_xlabel(RAM_PLOT.x_label) - ax.set_ylabel(RAM_PLOT.y_label) - ax.legend() - ax.grid(visible=True) + self._apply_plot_cfg(ax, RAM_PLOT, legend_handles=ax.lines) diff --git a/src/app/metrics/collector.py b/src/app/metrics/collector.py index ed85126..17339fa 100644 --- a/src/app/metrics/collector.py +++ b/src/app/metrics/collector.py @@ -61,7 +61,9 @@ def _build_time_series(self) -> Generator[simpy.Event, None, None]: server.enabled_metrics[self._io_key].append(server.io_queue_len) server.enabled_metrics[self._ready_key].append(server.ready_queue_len) - + def start(self) -> simpy.Process: + """Definition of the process to collect sampled metrics""" + return self.env.process(self._build_time_series()) diff --git a/src/app/resources/registry.py b/src/app/resources/registry.py index c768cef..02b7585 100644 --- a/src/app/resources/registry.py +++ b/src/app/resources/registry.py @@ -18,6 +18,7 @@ class ResourcesRuntime: def __init__( self, + *, env: simpy.Environment, data: TopologyGraph, diff --git a/src/app/runtime/actors/client.py b/src/app/runtime/actors/client.py index f5d63ac..42b0c3f 100644 --- a/src/app/runtime/actors/client.py +++ b/src/app/runtime/actors/client.py @@ -20,8 +20,9 @@ class ClientRuntime: def __init__( self, + *, env: simpy.Environment, - out_edge: EdgeRuntime, + out_edge: EdgeRuntime | None, client_box: simpy.Store, completed_box: simpy.Store, client_config: Client, @@ -41,6 +42,7 @@ def __init__( def _forwarder(self) -> Generator[simpy.Event, None, None]: """Updtate the state before passing it to another node""" + assert self.out_edge is not None while True: state: RequestState = yield self.client_box.get() # type: ignore[assignment] diff --git a/src/app/runtime/actors/helpers/lb_algorithms.py b/src/app/runtime/actors/helpers/lb_algorithms.py new file mode 100644 index 0000000..90c325e --- /dev/null +++ b/src/app/runtime/actors/helpers/lb_algorithms.py @@ -0,0 +1,30 @@ +"""algorithms to simulate the load balancer during the simulation""" + + + +from app.runtime.actors.edge import EdgeRuntime + + +def least_connections(list_edges: list[EdgeRuntime]) -> EdgeRuntime: + """We send the state to the edge with less concurrent connections""" + concurrent_connections = [edge.concurrent_connections for edge in list_edges] + + idx_min = concurrent_connections.index(min(concurrent_connections)) + + return list_edges[idx_min] + +def round_robin(edges: list[EdgeRuntime], idx: int) -> tuple[EdgeRuntime, int]: + """ + We send states to different server in uniform way by + rotating the list of edges that should transport the state + to the correct server, we rotate the index and not the list + to avoid aliasing since the list is shared by many components + """ + idx %= len(edges) + chosen = edges[idx] + idx = (idx + 1) % len(edges) + return chosen, idx + + + + diff --git a/src/app/runtime/actors/load_balancer.py b/src/app/runtime/actors/load_balancer.py new file mode 100644 index 0000000..b370ff3 --- /dev/null +++ b/src/app/runtime/actors/load_balancer.py @@ -0,0 +1,73 @@ +"""Definition of the node represented by the LB in the simulation""" + +from collections.abc import Generator +from typing import TYPE_CHECKING + +import simpy + +from app.config.constants import LbAlgorithmsName, SystemNodes +from app.runtime.actors.edge import EdgeRuntime +from app.runtime.actors.helpers.lb_algorithms import ( + least_connections, + round_robin, +) +from app.schemas.system_topology.full_system_topology import LoadBalancer + +if TYPE_CHECKING: + from app.runtime.rqs_state import RequestState + + + +class LoadBalancerRuntime: + """class to define the behaviour of the LB in the simulation""" + + def __init__( + self, + *, + env: simpy.Environment, + lb_config: LoadBalancer, + out_edges: list[EdgeRuntime] | None, + lb_box: simpy.Store, + ) -> None: + """ + Descriprion of the instance attributes for the class + Args: + env (simpy.Environment): env of the simulation + lb_config (LoadBalancer): input to define the lb in the runtime + rqs_state (RequestState): state of the simulation + out_edges (list[EdgeRuntime]): list of edges that connects lb with servers + lb_box (simpy.Store): store to add the state + + """ + self.env = env + self.lb_config = lb_config + self.out_edges = out_edges + self.lb_box = lb_box + self._round_robin_index: int = 0 + + + def _forwarder(self) -> Generator[simpy.Event, None, None]: + """Updtate the state before passing it to another node""" + assert self.out_edges is not None + while True: + state: RequestState = yield self.lb_box.get() # type: ignore[assignment] + + state.record_hop( + SystemNodes.LOAD_BALANCER, + self.lb_config.id, + self.env.now, + ) + + if self.lb_config.algorithms == LbAlgorithmsName.ROUND_ROBIN: + out_edge, self._round_robin_index = round_robin( + self.out_edges, + self._round_robin_index, + ) + else: + out_edge = least_connections(self.out_edges) + + out_edge.transport(state) + + def start(self) -> simpy.Process: + """Initialization of the simpy process for the LB""" + return self.env.process(self._forwarder()) diff --git a/src/app/runtime/actors/rqs_generator.py b/src/app/runtime/actors/rqs_generator.py index bc114ba..b666d5d 100644 --- a/src/app/runtime/actors/rqs_generator.py +++ b/src/app/runtime/actors/rqs_generator.py @@ -33,11 +33,11 @@ class RqsGeneratorRuntime: def __init__( self, + *, env: simpy.Environment, out_edge: EdgeRuntime, rqs_generator_data: RqsGeneratorInput, sim_settings: SimulationSettings, - *, rng: np.random.Generator | None = None, ) -> None: """ @@ -96,6 +96,8 @@ def _requests_generator(self) -> Generator[float, None, None]: def _event_arrival(self) -> Generator[simpy.Event, None, None]: """Simulating the process of event generation""" + assert self.out_edge is not None + time_gaps = self._requests_generator() for gap in time_gaps: diff --git a/src/app/runtime/actors/server.py b/src/app/runtime/actors/server.py index 94d6b24..bf6b0b6 100644 --- a/src/app/runtime/actors/server.py +++ b/src/app/runtime/actors/server.py @@ -31,6 +31,7 @@ class ServerRuntime: def __init__( # noqa: PLR0913 self, + *, env: simpy.Environment, server_resources: ServerContainers, server_config: Server, @@ -262,6 +263,7 @@ def _dispatcher(self) -> Generator[simpy.Event, None, None]: The main dispatcher loop. It pulls requests from the inbox and spawns a new '_handle_request' process for each one. """ + assert self.out_edge is not None while True: # Wait for a request to arrive in the server's inbox raw_state = yield self.server_box.get() diff --git a/src/app/schemas/simulation_settings_input.py b/src/app/schemas/simulation_settings_input.py index 80828b5..a9a90a6 100644 --- a/src/app/schemas/simulation_settings_input.py +++ b/src/app/schemas/simulation_settings_input.py @@ -43,3 +43,4 @@ class SimulationSettings(BaseModel): description="constant interval of time to build time series for metrics", ) + diff --git a/src/app/schemas/system_topology/full_system_topology.py b/src/app/schemas/system_topology/full_system_topology.py index 2f29f2a..6b49b21 100644 --- a/src/app/schemas/system_topology/full_system_topology.py +++ b/src/app/schemas/system_topology/full_system_topology.py @@ -18,6 +18,7 @@ ) from app.config.constants import ( + LbAlgorithmsName, NetworkParameters, ServerResourcesDefaults, SystemEdges, @@ -50,7 +51,7 @@ def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 return v # ------------------------------------------------------------- -# SERVER RESOURCES EXAMPLE +# SERVER RESOURCES # ------------------------------------------------------------- class ServerResources(BaseModel): @@ -107,6 +108,29 @@ def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 raise ValueError(msg) return v +class LoadBalancer(BaseModel): + """ + basemodel for the load balancer + - id: unique name associated to the lb + - type: type of the node in the structure + - server_covered: list of server id connected to the lb + """ + + id: str + type: SystemNodes = SystemNodes.LOAD_BALANCER + algorithms: LbAlgorithmsName = LbAlgorithmsName.ROUND_ROBIN + server_covered: set[str] = Field(default_factory=set) + + + + @field_validator("type", mode="after") + def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 + """Ensure the type of the server is standard""" + if v != SystemNodes.LOAD_BALANCER: + msg = f"The type should have a standard value: {SystemNodes.LOAD_BALANCER}" + raise ValueError(msg) + return v + # ------------------------------------------------------------- # NODES CLASS WITH ALL POSSIBLE OBJECTS REPRESENTED BY A NODE # ------------------------------------------------------------- @@ -121,6 +145,7 @@ class TopologyNodes(BaseModel): servers: list[Server] client: Client + load_balancer: LoadBalancer | None = None @model_validator(mode="after") # type: ignore[arg-type] def unique_ids( @@ -236,10 +261,48 @@ def edge_refs_valid(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N80 Returning the (unchanged) model signals that the integrity check passed. """ valid_ids = {s.id for s in model.nodes.servers} | {model.nodes.client.id} + if model.nodes.load_balancer is not None: + valid_ids.add(model.nodes.load_balancer.id) + for e in model.edges: if e.source not in valid_ids or e.target not in valid_ids: msg = f"Edge {e.source}->{e.target} references unknown node" raise ValueError(msg) return model + @model_validator(mode="after") # type: ignore[arg-type] + def valid_load_balancer(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 + """ + Check de validity of the load balancer: first we check + if is present in the simulation, second we check if the LB list + is a proper subset of the server sets of ids, then we check if + edge from LB to the servers are well defined + """ + lb = model.nodes.load_balancer + if lb is None: + return model + + server_ids = {s.id for s in model.nodes.servers} + + # 1) LB list ⊆ server_ids + missing = lb.server_covered - server_ids + if missing: + + msg = (f"Load balancer '{lb.id}'" + "references unknown servers: {sorted(missing)}") + raise ValueError(msg) + + # edge are well defined + targets_from_lb = {e.target for e in model.edges if e.source == lb.id} + not_linked = lb.server_covered - targets_from_lb + if not_linked: + msg = ( + f"Servers {sorted(not_linked)} are covered by LB '{lb.id}' " + "but have no outgoing edge from it." + ) + + raise ValueError(msg) + + return model + diff --git a/tests/unit/resources/test_registry.py b/tests/unit/resources/test_registry.py index fe5a693..6aef76a 100644 --- a/tests/unit/resources/test_registry.py +++ b/tests/unit/resources/test_registry.py @@ -39,7 +39,7 @@ def test_registry_initialises_filled_containers() -> None: """CPU and RAM containers must start full for every server.""" env = simpy.Environment() topo = _build_topology() - registry = ResourcesRuntime(env, topo) + registry = ResourcesRuntime(env=env, data=topo) for srv in topo.nodes.servers: containers = registry[srv.id] @@ -54,7 +54,7 @@ def test_registry_initialises_filled_containers() -> None: def test_getitem_unknown_server_raises_keyerror() -> None: """Accessing an undefined server ID should raise KeyError.""" env = simpy.Environment() - registry = ResourcesRuntime(env, _build_topology()) + registry = ResourcesRuntime(env=env, data=_build_topology()) with pytest.raises(KeyError): _ = registry["non-existent-server"] diff --git a/tests/unit/runtime/engine/test_client.py b/tests/unit/runtime/actors/test_client.py similarity index 100% rename from tests/unit/runtime/engine/test_client.py rename to tests/unit/runtime/actors/test_client.py diff --git a/tests/unit/runtime/engine/test_edge.py b/tests/unit/runtime/actors/test_edge.py similarity index 100% rename from tests/unit/runtime/engine/test_edge.py rename to tests/unit/runtime/actors/test_edge.py diff --git a/tests/unit/runtime/actors/test_load_balancer.py b/tests/unit/runtime/actors/test_load_balancer.py new file mode 100644 index 0000000..35e622a --- /dev/null +++ b/tests/unit/runtime/actors/test_load_balancer.py @@ -0,0 +1,131 @@ +"""Unit tests for ``LoadBalancerRuntime`` (round-robin & least-connections).""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, cast + +import pytest +import simpy + +from app.config.constants import LbAlgorithmsName, SystemNodes +from app.runtime.actors.load_balancer import LoadBalancerRuntime +from app.schemas.system_topology.full_system_topology import LoadBalancer + +if TYPE_CHECKING: + from app.runtime.actors.edge import EdgeRuntime + + + +# --------------------------------------------------------------------------- # +# Dummy objects (lightweight test doubles) # +# --------------------------------------------------------------------------- # +class DummyState: + """Tiny substitute for ``RequestState`` - only ``history`` is needed.""" + + def __init__(self) -> None: + """Instance of the state history""" + self.history: list[str] = [] + + def record_hop(self, comp_type: SystemNodes, comp_id: str, _: float) -> None: + """Append the hop as ``":"``.""" + self.history.append(f"{comp_type.value}:{comp_id}") + + +class DummyEdge: + """Stub that mimics just the pieces `LoadBalancerRuntime` relies on.""" + + def __init__(self, edge_id: str, concurrent: int = 0) -> None: + """Instance for the dummy edge""" + self.edge_config = type("Cfg", (), {"id": edge_id}) + self.concurrent_connections = concurrent + self.received: list[DummyState] = [] + + # Signature compatible with EdgeRuntime.transport + def transport(self, state: DummyState) -> None: + """Collect the state for later assertions.""" + self.received.append(state) + + +# --------------------------------------------------------------------------- # +# Fixtures # +# --------------------------------------------------------------------------- # +@pytest.fixture +def env() -> simpy.Environment: + """Return a fresh SimPy environment per test.""" + return simpy.Environment() + + +def _make_lb_runtime( + env: simpy.Environment, + algorithm: LbAlgorithmsName, + edges: list[DummyEdge], +) -> LoadBalancerRuntime: + """Wire LB, its inbox store and the supplied dummy edges.""" + lb_cfg = LoadBalancer( + id="lb-1", + algorithms=algorithm, + server_covered={e.edge_config.id for e in edges}, # type: ignore[attr-defined] + ) + inbox: simpy.Store = simpy.Store(env) + lb = LoadBalancerRuntime( + env=env, + lb_config=lb_cfg, + # ② cast DummyEdge list to the expected interface type + out_edges=cast("list[EdgeRuntime]", edges), + lb_box=inbox, + ) + lb.start() + return lb + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # +def test_round_robin_rotation(env: simpy.Environment) -> None: + """Three requests, two edges ⇒ order must be edge-0, edge-1, edge-0.""" + edge0, edge1 = DummyEdge("srv-A"), DummyEdge("srv-B") + lb = _make_lb_runtime(env, LbAlgorithmsName.ROUND_ROBIN, [edge0, edge1]) + + for _ in range(3): + lb.lb_box.put(DummyState()) + + env.run() + + assert len(edge0.received) == 2 + assert len(edge1.received) == 1 + + tag = SystemNodes.LOAD_BALANCER.value + assert edge0.received[0].history[0].startswith(f"{tag}:") + assert edge0.received[1].history[0].startswith(f"{tag}:") + + +def test_least_connections_picks_lowest(env: simpy.Environment) -> None: + """Edge with fewer concurrent connections must be selected.""" + busy = DummyEdge("busy", concurrent=10) + idle = DummyEdge("idle", concurrent=1) + + lb = _make_lb_runtime(env, LbAlgorithmsName.LEAST_CONNECTIONS, [busy, idle]) + lb.lb_box.put(DummyState()) + + env.run() + + assert idle.received + assert not busy.received + + +def test_start_raises_if_no_edges(env: simpy.Environment) -> None: + """`start()` followed by `env.run()` with `out_edges=None` must assert.""" + lb_cfg = LoadBalancer( + id="lb-bad", + algorithms=LbAlgorithmsName.ROUND_ROBIN, + server_covered=set(), + ) + lb = LoadBalancerRuntime( + env=env, + lb_config=lb_cfg, + out_edges=None, + lb_box=simpy.Store(env), + ) + + lb.start() + with pytest.raises(AssertionError): + env.run() diff --git a/tests/unit/runtime/engine/test_rqs_generator.py b/tests/unit/runtime/actors/test_rqs_generator.py similarity index 100% rename from tests/unit/runtime/engine/test_rqs_generator.py rename to tests/unit/runtime/actors/test_rqs_generator.py diff --git a/tests/unit/runtime/engine/test_server.py b/tests/unit/runtime/actors/test_server.py similarity index 100% rename from tests/unit/runtime/engine/test_server.py rename to tests/unit/runtime/actors/test_server.py diff --git a/tests/unit/schemas/test_full_topology_input.py b/tests/unit/schemas/test_full_topology_input.py index 8382ee6..f6bf1f9 100644 --- a/tests/unit/schemas/test_full_topology_input.py +++ b/tests/unit/schemas/test_full_topology_input.py @@ -18,6 +18,7 @@ from app.schemas.system_topology.full_system_topology import ( Client, Edge, + LoadBalancer, Server, ServerResources, TopologyGraph, @@ -95,6 +96,18 @@ def test_invalid_server_type() -> None: endpoints=[_dummy_endpoint()], ) +# --------------------------------------------------------------------------- # +# Load Balancer # +# --------------------------------------------------------------------------- # + +def test_valid_lb() -> None: + """A LB with correct ``type`` validates.""" + cli = LoadBalancer( + id="LB", + type=SystemNodes.LOAD_BALANCER, + server_covered=["s1", "s2"], + ) + assert cli.type is SystemNodes.LOAD_BALANCER # --------------------------------------------------------------------------- # # TopologyNodes # @@ -174,6 +187,37 @@ def _latency() -> RVConfig: """Tiny helper for latency objects.""" return RVConfig(mean=0.02) +def _topology_with_lb( + cover: set[str], + extra_edges: list[Edge] | None = None, +) -> TopologyGraph: + """Build a minimal graph with 1 client, 1 server and a load balancer.""" + nodes = _single_node_topology() + lb = LoadBalancer(id="lb-1", server_covered=cover) + nodes = TopologyNodes( + servers=nodes.servers, + client=nodes.client, + load_balancer=lb, + ) + + edges: list[Edge] = [ + Edge( # client -> LB + id="cli-lb", + source="browser", + target="lb-1", + latency=_latency(), + ), + Edge( # LB -> server (may be removed in invalid tests) + id="lb-srv", + source="lb-1", + target="svc-A", + latency=_latency(), + ), + ] + if extra_edges: + edges.extend(extra_edges) + return TopologyGraph(nodes=nodes, edges=edges) + def test_valid_topology_graph() -> None: """Happy-path graph passes validation.""" @@ -188,6 +232,19 @@ def test_valid_topology_graph() -> None: graph = TopologyGraph(nodes=nodes, edges=[edge]) assert len(graph.edges) == 1 +def test_topology_graph_without_lb_still_valid() -> None: + """Graph without load balancer validates just like before.""" + nodes = _single_node_topology() + edge = Edge( + id="edge-1", + source="browser", + target="svc-A", + latency=_latency(), + ) + graph = TopologyGraph(nodes=nodes, edges=[edge]) + assert graph.nodes.load_balancer is None + + def test_edge_refers_unknown_node() -> None: """Edge pointing to a non-existent node fails validation.""" @@ -200,3 +257,49 @@ def test_edge_refers_unknown_node() -> None: ) with pytest.raises(ValidationError): TopologyGraph(nodes=nodes, edges=[bad_edge]) + + +# --------------------------------------------------------------------------- # +# 2) LB is valid # +# --------------------------------------------------------------------------- # +def test_load_balancer_valid_graph() -> None: + """LB covering a server with proper edges passes validation.""" + graph = _topology_with_lb({"svc-A"}) + assert graph.nodes.load_balancer is not None + assert graph.nodes.load_balancer.server_covered == {"svc-A"} + + +# --------------------------------------------------------------------------- # +# 3) LB con server inesistente # +# --------------------------------------------------------------------------- # +def test_lb_references_unknown_server() -> None: + """LB that lists a non-existent server triggers ValidationError.""" + with pytest.raises(ValidationError): + _topology_with_lb({"ghost-srv"}) + + +# --------------------------------------------------------------------------- # +# 4) LB no edge with a server covered # +# --------------------------------------------------------------------------- # +def test_lb_missing_edge_to_covered_server() -> None: + """LB covers svc-A but edge LB→svc-A is missing → ValidationError.""" + # costruiamo il grafo senza l'edge lb-srv + nodes = _single_node_topology() + lb = LoadBalancer(id="lb-1", server_covered={"svc-A"}) + nodes = TopologyNodes( + servers=nodes.servers, + client=nodes.client, + load_balancer=lb, + ) + edges = [ + Edge( + id="cli-lb", + source="browser", + target="lb-1", + latency=_latency(), + ), + ] + with pytest.raises(ValidationError): + TopologyGraph(nodes=nodes, edges=edges) + + From 7649866b0281a077efacbda6091cdf8c0629b79f Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Wed, 6 Aug 2025 16:24:40 +0200 Subject: [PATCH 21/52] Features/simulation runner (#10) * added the first method to the simulation runner * added methods to handle nodes * completed simulation runtime + integration test --- poetry.lock | 75 +++++- pyproject.toml | 2 + src/app/metrics/analyzer.py | 6 +- src/app/runtime/actors/rqs_generator.py | 2 +- src/app/runtime/actors/server.py | 22 +- src/app/runtime/simulation_runner.py | 313 ++++++++++++++++++++++ tests/integration/conftest.py | 46 ++++ tests/integration/minimal/conftest.py | 80 ++++++ tests/integration/minimal/test_minimal.py | 100 +++++++ 9 files changed, 637 insertions(+), 9 deletions(-) create mode 100644 src/app/runtime/simulation_runner.py create mode 100644 tests/integration/conftest.py create mode 100644 tests/integration/minimal/conftest.py create mode 100644 tests/integration/minimal/test_minimal.py diff --git a/poetry.lock b/poetry.lock index 543faa8..f698f6c 100644 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"types_pyyaml-6.0.12.20250516.tar.gz", hash = "sha256:9f21a70216fc0fa1b216a8176db5f9e0af6eb35d2f2932acb87689d03a5bf6ba"}, +] + [[package]] name = "typing-extensions" version = "4.14.1" @@ -1153,4 +1226,4 @@ typing-extensions = ">=4.12.0" [metadata] lock-version = "2.0" python-versions = "^3.12" -content-hash = "1441494d6783804e41cd06a1a630c2031b47573ba6f28619d4967e27953aff40" +content-hash = "b7b303ed9df0a73da71790803bfde02b8dc243871801686bb7a3127ca3f638aa" diff --git a/pyproject.toml b/pyproject.toml index 1528987..22fcaa2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,7 @@ pydantic = {extras = ["email"], version = "^2.11.7"} numpy = "^2.3.1" simpy = "^4.1.1" matplotlib = "^3.10.3" +pyyaml = "^6.0.2" [tool.poetry.group.dev.dependencies] pytest = "^8.4.1" @@ -23,6 +24,7 @@ pytest-asyncio = "^1.0.0" pytest-cov = "^6.2.1" mypy = "^1.16.1" ruff = "^0.12.1" +types-pyyaml = "^6.0.12.20250516" [build-system] requires = ["poetry-core"] diff --git a/src/app/metrics/analyzer.py b/src/app/metrics/analyzer.py index 07a021e..762e5c6 100644 --- a/src/app/metrics/analyzer.py +++ b/src/app/metrics/analyzer.py @@ -158,9 +158,11 @@ def get_latency_stats(self) -> dict[LatencyKey, float]: return self.latency_stats or {} def get_throughput_series(self) -> tuple[list[float], list[float]]: - """Return throughput time series (timestamps, RPS).""" + """Return (timestamps, RPS). Empty lists when no traffic.""" self.process_all_metrics() - assert self.throughput_series is not None + if self.throughput_series is None: + return [], [] + return self.throughput_series def get_sampled_metrics(self) -> dict[str, dict[str, list[float]]]: diff --git a/src/app/runtime/actors/rqs_generator.py b/src/app/runtime/actors/rqs_generator.py index b666d5d..d9e7e38 100644 --- a/src/app/runtime/actors/rqs_generator.py +++ b/src/app/runtime/actors/rqs_generator.py @@ -35,7 +35,7 @@ def __init__( self, *, env: simpy.Environment, - out_edge: EdgeRuntime, + out_edge: EdgeRuntime | None, rqs_generator_data: RqsGeneratorInput, sim_settings: SimulationSettings, rng: np.random.Generator | None = None, diff --git a/src/app/runtime/actors/server.py b/src/app/runtime/actors/server.py index bf6b0b6..9051d99 100644 --- a/src/app/runtime/actors/server.py +++ b/src/app/runtime/actors/server.py @@ -29,18 +29,30 @@ class ServerRuntime: """class to define the server during the simulation""" - def __init__( # noqa: PLR0913 + def __init__( # noqa: PLR0913 self, *, env: simpy.Environment, server_resources: ServerContainers, server_config: Server, - out_edge: EdgeRuntime, + out_edge: EdgeRuntime | None, server_box: simpy.Store, settings: SimulationSettings, rng: np.random.Generator | None = None, - ) -> None: - """Docstring to complete""" + ) -> None: + """ + Definition of the instance attributes + Args: + env (simpy.Environment): simpy environment + server_resources (ServerContainers):resource defined in the + input for each server + server_config (Server): parameter to define the server from the input + out_edge (EdgeRuntime): edge connecting the server to the next node + server_box (simpy.Store): box with the states that the server + should elaborate + settings (SimulationSettings): general input settings for the simulation + rng (np.random.Generator | None, optional): random number generator. + """ self.env = env self.server_resources = server_resources self.server_config = server_config @@ -231,6 +243,7 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 self._ram_in_use -= total_ram yield self.server_resources[ServerResourceName.RAM.value].put(total_ram) + assert self.out_edge is not None self.out_edge.transport(state) @@ -263,7 +276,6 @@ def _dispatcher(self) -> Generator[simpy.Event, None, None]: The main dispatcher loop. It pulls requests from the inbox and spawns a new '_handle_request' process for each one. """ - assert self.out_edge is not None while True: # Wait for a request to arrive in the server's inbox raw_state = yield self.server_box.get() diff --git a/src/app/runtime/simulation_runner.py b/src/app/runtime/simulation_runner.py new file mode 100644 index 0000000..e2b5c6f --- /dev/null +++ b/src/app/runtime/simulation_runner.py @@ -0,0 +1,313 @@ +"""Components to run the whole simulation given specific input data""" + +from itertools import chain +from pathlib import Path +from typing import TYPE_CHECKING, Protocol, cast + +import numpy as np +import simpy +import yaml + +from app.metrics.analyzer import ResultsAnalyzer +from app.metrics.collector import SampledMetricCollector +from app.resources.registry import ResourcesRuntime +from app.runtime.actors.client import ClientRuntime +from app.runtime.actors.edge import EdgeRuntime +from app.runtime.actors.load_balancer import LoadBalancerRuntime +from app.runtime.actors.rqs_generator import RqsGeneratorRuntime +from app.runtime.actors.server import ServerRuntime +from app.schemas.full_simulation_input import SimulationPayload + +if TYPE_CHECKING: + from collections.abc import Iterable + + from app.schemas.rqs_generator_input import RqsGeneratorInput + from app.schemas.system_topology.full_system_topology import ( + Client, + Edge, + LoadBalancer, + Server, + ) + +# --- PROTOCOL DEFINITION --- +# This is the contract that all runtime actors must follow. +# it is a contract useful to communicate to mypy that object of +# startable type have all the method start +class Startable(Protocol): + """A protocol for runtime actors that can be started.""" + + def start(self) -> simpy.Process: + """Starts the main process loop for the actor.""" + ... + +class SimulationRunner: + """Class to handle the simulation""" + + def __init__( + self, + *, + env: simpy.Environment, + simulation_input: SimulationPayload, + ) -> None: + """ + Orchestrates building, wiring and running all actor runtimes. + + Args: + env (simpy.Environment): global environment for the simulation + simulation_input (SimulationPayload): full input for the simulation + + """ + self.env = env + self.simulation_input = simulation_input + + # instantiation of object needed to build nodes for the runtime phase + self.servers: list[Server] = simulation_input.topology_graph.nodes.servers + self.client: Client = simulation_input.topology_graph.nodes.client + self.rqs_generator: RqsGeneratorInput = simulation_input.rqs_input + self.lb: LoadBalancer | None = None + self.simulation_settings = simulation_input.sim_settings + self.edges: list[Edge] = simulation_input.topology_graph.edges + self.rng = np.random.default_rng() + + # Object needed to start the simualation + self._servers_runtime: dict[str, ServerRuntime] = {} + self._client_runtime: dict[str, ClientRuntime] = {} + self._rqs_runtime: dict[str, RqsGeneratorRuntime] = {} + self._lb_runtime: dict[str, LoadBalancerRuntime] = {} + self._edges_runtime: dict[tuple[str, str], EdgeRuntime] = {} + + + # ------------------------------------------------------------------ # + # Private: build phase # + # ------------------------------------------------------------------ # + + def _make_inbox(self) -> simpy.Store: # local helper + """Helper to create store for the states of the simulation""" + return simpy.Store(self.env) + + def _build_rqs_generator(self) -> None: + """ + Build the rqs generator runtime, we use a dict for one reason + In the future we might add CDN so we will need + multiple generators , one for each client + """ + self._rqs_runtime[self.rqs_generator.id] = RqsGeneratorRuntime( + env = self.env, + out_edge=None, + rqs_generator_data=self.rqs_generator, + sim_settings=self.simulation_settings, + rng=self.rng, + ) + + + def _build_client(self) -> None: + """ + Build the client runtime, we use a dict for two reasons + 1) In the future we might add CDN so we will need + multiple client + 2) When we will assign outer edges we will need a dict + with all components indexed by their id + """ + self._client_runtime[self.client.id] = ClientRuntime( + env=self.env, + out_edge=None, + completed_box=self._make_inbox(), + client_box=self._make_inbox(), + client_config=self.client, + ) + + + def _build_servers(self) -> None: + """ + Build given the input data a dict containing all server Runtime + indexed by their unique id + """ + registry = ResourcesRuntime( + env=self.env, + data=self.simulation_input.topology_graph, + ) + for server in self.servers: + container = registry[server.id] + self._servers_runtime[server.id] = ServerRuntime( + env=self.env, + server_resources=container, + server_config=server, + out_edge=None, + server_box=self._make_inbox(), + settings=self.simulation_settings, + rng= self.rng, + + ) + + + def _build_load_balancer(self) -> None: + """ + Build given the input data the load balancer runtime we will + use a dict because we may have multiple load balancer and we + will be usefull to assign outer edges + """ + # Topologies without a LB are perfectly legal (e.g. the “minimal” + # integration test). Early-return instead of asserting. + if self.simulation_input.topology_graph.nodes.load_balancer is None: + return + + self.lb = self.simulation_input.topology_graph.nodes.load_balancer + + self._lb_runtime[self.lb.id] = LoadBalancerRuntime( + env=self.env, + lb_config=self.lb, + out_edges= [], + lb_box=self._make_inbox(), + ) + + + def _build_edges(self) -> None: + """Initialization of the edges runtime dictionary from the input data""" + # We need to merge all previous dictionary for the nodes to assign + # for each edge the correct target box + all_nodes: dict[str, object] = { + **self._servers_runtime, + **self._client_runtime, + **self._lb_runtime, + **self._rqs_runtime, +} + + for edge in self.edges: + + target_object = all_nodes[edge.target] # O(1) lookup + + if isinstance(target_object, ServerRuntime): + target_box = target_object.server_box + elif isinstance(target_object, ClientRuntime): + target_box = target_object.client_box + elif isinstance(target_object, LoadBalancerRuntime): + target_box = target_object.lb_box + else: + msg = f"Unknown runtime for {edge.target!r}" + raise TypeError(msg) + + self._edges_runtime[(edge.source, edge.target)] = ( + EdgeRuntime( + env=self.env, + edge_config=edge, + rng=self.rng, + target_box= target_box, + settings=self.simulation_settings, + ) + ) + # Here we assign the outer edges to all nodes + source_object = all_nodes[edge.source] + + if isinstance(source_object, ( + ServerRuntime, + ClientRuntime, + RqsGeneratorRuntime, + )): + source_object.out_edge = self._edges_runtime[( + edge.source, + edge.target) + ] + elif isinstance(source_object, LoadBalancerRuntime): + assert source_object.out_edges is not None + source_object.out_edges.append(self._edges_runtime[( + edge.source, + edge.target, + ) + ]) + + else: + msg = f"Unknown runtime for {edge.source!r}" + raise TypeError(msg) + + + # ------------------------------------------------------------------ # + # RUN phase # + # ------------------------------------------------------------------ # + def _start_all_processes(self) -> None: + """Register every .start() in the environment.""" + # ------------------------------------------------------------------ + # Start every actor's main coroutine + # + # * itertools.chain lazily stitches together the four dict_views + # into ONE iterator. No temporary list is built, zero extra + # allocations, yet the for-loop stays single and readable. + # * Order matters only for determinism, so we keep the natural + # “generator → client → servers → LB” sequence by listing the + # dicts explicitly. + # * Alternative ( list(a)+list(b)+… ) would copy thousands of + # references just to throw them away after the loop - wasteful. + # ------------------------------------------------------------------ + + runtimes = chain( + self._rqs_runtime.values(), + self._client_runtime.values(), + self._servers_runtime.values(), + self._lb_runtime.values(), + ) + + # Here we are saying to mypy that those object are of + # the startable type and they share the start method + for rt in cast("Iterable[Startable]", runtimes): + rt.start() + + def _start_metric_collector(self) -> None: + """One coroutine that snapshots RAM / queues / connections.""" + SampledMetricCollector( + edges=list(self._edges_runtime.values()), + servers=list(self._servers_runtime.values()), + env=self.env, + sim_settings=self.simulation_settings, + ).start() + + # ------------------------------------------------------------------ # + # Public entry-point # + # ------------------------------------------------------------------ # + def run(self) -> ResultsAnalyzer: + """Build → wire → start → run the clock → return `ResultsAnalyzer`""" + # 1. BUILD + self._build_rqs_generator() + self._build_client() + self._build_servers() + self._build_load_balancer() + + # 2. WIRE + self._build_edges() + + # 3. START ALL COROUTINES + self._start_all_processes() + self._start_metric_collector() + + # 4. ADVANCE THE SIMULATION + self.env.run(until=self.simulation_settings.total_simulation_time) + + return ResultsAnalyzer( + client=next(iter(self._client_runtime.values())), + servers=list(self._servers_runtime.values()), + edges=list(self._edges_runtime.values()), + settings=self.simulation_settings, + ) + + # ------------------------------------------------------------------ # + # Convenience constructor (load from YAML) # + # ------------------------------------------------------------------ # + @classmethod + def from_yaml( + cls, + *, + env: simpy.Environment, + yaml_path: str | Path, + ) -> "SimulationRunner": + """ + Quick helper so that integration tests & CLI can do: + + ```python + runner = SimulationRunner.from_yaml(env, "scenario.yml") + results = runner.run() + ``` + """ + data = yaml.safe_load(Path(yaml_path).read_text()) + payload = SimulationPayload.model_validate(data) + return cls(env=env, simulation_input=payload) + + + diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py new file mode 100644 index 0000000..b46300b --- /dev/null +++ b/tests/integration/conftest.py @@ -0,0 +1,46 @@ +"""Shared fixtures used by several integration-test groups.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest +import simpy + +from app.runtime.simulation_runner import SimulationRunner + +if TYPE_CHECKING: + from collections.abc import Callable + from pathlib import Path + + +# --------------------------------------------------------------------------- # +# Environment # +# --------------------------------------------------------------------------- # +@pytest.fixture +def env() -> simpy.Environment: + """A fresh SimPy environment per test.""" + return simpy.Environment() + + +# --------------------------------------------------------------------------- # +# Runner factory (load YAML scenarios) # +# --------------------------------------------------------------------------- # +@pytest.fixture +def make_runner( + env: simpy.Environment, +) -> Callable[[str | Path], SimulationRunner]: + """ + Factory that loads a YAML scenario and instantiates a + :class:`SimulationRunner`. + + Usage inside a test:: + + runner = make_runner("scenarios/minimal.yml") + results = runner.run() + """ + + def _factory(yaml_path: str | Path) -> SimulationRunner: + return SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) + + return _factory diff --git a/tests/integration/minimal/conftest.py b/tests/integration/minimal/conftest.py new file mode 100644 index 0000000..1c9f81e --- /dev/null +++ b/tests/integration/minimal/conftest.py @@ -0,0 +1,80 @@ +""" +Local fixtures for the *minimal* integration scenario. + +We **do not** add any Edge to the TopologyGraph because the core schema +forbids generator-origin edges. Instead we patch the single +`RqsGeneratorRuntime` after the `SimulationRunner` is built, giving it a +*no-op* EdgeRuntime so its internal assertion passes. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest +import simpy + +from app.config.constants import TimeDefaults +from app.runtime.simulation_runner import SimulationRunner +from app.schemas.random_variables_config import RVConfig +from app.schemas.rqs_generator_input import RqsGeneratorInput + +if TYPE_CHECKING: + from app.schemas.full_simulation_input import SimulationPayload + from app.schemas.full_simulation_input import ( + SimulationPayload as _Payload, # noqa: F401 + ) + from app.schemas.rqs_generator_input import ( + RqsGeneratorInput as _RqsIn, # noqa: F401 + ) + +# ────────────────────────────────────────────────────────────────────────────── +# 0-traffic generator (shadows the project-wide fixture) +# ────────────────────────────────────────────────────────────────────────────── +@pytest.fixture(scope="session") +def rqs_input() -> RqsGeneratorInput: + """A generator that never emits any request.""" + return RqsGeneratorInput( + id="rqs-zero", + avg_active_users=RVConfig(mean=0.0), + avg_request_per_minute_per_user=RVConfig(mean=0.0), + user_sampling_window=TimeDefaults.USER_SAMPLING_WINDOW, + ) + + +# ────────────────────────────────────────────────────────────────────────────── +# SimPy env - local to this directory +# ────────────────────────────────────────────────────────────────────────────── +@pytest.fixture +def env() -> simpy.Environment: + """Fresh environment per test module.""" + return simpy.Environment() + + +class _NoOpEdge: + """EdgeRuntime stand-in that simply discards every state.""" + + def transport(self, _state: object) -> None: # ANN001: _state annotated + return # swallow the request silently + + +# ────────────────────────────────────────────────────────────────────────────── +# Runner factory - assigns the dummy edge *after* building the runner +# ────────────────────────────────────────────────────────────────────────────── +@pytest.fixture +def runner( + env: simpy.Environment, + payload_base: SimulationPayload, +) -> SimulationRunner: + """Build a `SimulationRunner` and patch the generator's `out_edge`.""" + sim_runner = SimulationRunner(env=env, simulation_input=payload_base) + + def _patch_noop_edge(r: SimulationRunner) -> None: + + gen_rt = next(iter(r._rqs_runtime.values())) # noqa: SLF001 + gen_rt.out_edge = _NoOpEdge() # type: ignore[assignment] + + + sim_runner._patch_noop_edge = _patch_noop_edge # type: ignore[attr-defined] # noqa: SLF001 + + return sim_runner diff --git a/tests/integration/minimal/test_minimal.py b/tests/integration/minimal/test_minimal.py new file mode 100644 index 0000000..0ae77c2 --- /dev/null +++ b/tests/integration/minimal/test_minimal.py @@ -0,0 +1,100 @@ +""" +Smoke-test: the **smallest** valid topology boots, ticks and +shuts down without recording any metric. + +Topology under test +------------------- +generator ──Ø── client (Ø == no real EdgeRuntime) + +The request-generator cannot emit messages because its ``out_edge`` is +replaced by a no-op stub. The client is patched the same way so its own +forwarder never attempts a network send. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest +import simpy + +from app.metrics.analyzer import ResultsAnalyzer +from app.runtime.simulation_runner import SimulationRunner + +if TYPE_CHECKING: + from app.schemas.full_simulation_input import SimulationPayload + + +# --------------------------------------------------------------------------- # +# Helpers # +# --------------------------------------------------------------------------- # + +class _NoOpEdge: + """Edge stub: swallows every transport call.""" + + def transport(self) -> None: + # Nothing to do - we just black-hole the message. + return + + +# --------------------------------------------------------------------------- # +# Local fixtures # +# --------------------------------------------------------------------------- # +@pytest.fixture +def env() -> simpy.Environment: + """Fresh SimPy environment for this test file.""" + return simpy.Environment() + + +@pytest.fixture +def runner( + env: simpy.Environment, + payload_base: SimulationPayload, # comes from project-wide conftest +) -> SimulationRunner: + """SimulationRunner already loaded with *minimal* payload.""" + return SimulationRunner(env=env, simulation_input=payload_base) + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # +def test_smoke_minimal_runs(runner: SimulationRunner) -> None: + """ + The simulation should: + + * start without any server or edge, + * execute its clock, + * leave all metric collections empty. + """ + # ── 1. Build generator + patch its edge ────────────────────────────── + runner._build_rqs_generator() # noqa: SLF001 - private builder ok in test + gen_rt = next(iter(runner._rqs_runtime.values())) # noqa: SLF001 + gen_rt.out_edge = _NoOpEdge() # type: ignore[assignment] + + # ── 2. Build client + patch its edge ───────────────────────────────── + runner._build_client() # noqa: SLF001 + cli_rt = next(iter(runner._client_runtime.values())) # noqa: SLF001 + cli_rt.out_edge = _NoOpEdge() # type: ignore[assignment] + + # ── 3. Build remaining artefacts (no servers / no LB present) ─────── + runner._start_all_processes() # noqa: SLF001 + runner._start_metric_collector() # noqa: SLF001 + + # ── 4. Run the clock ───────────────────────────────────────────────── + runner.env.run(until=runner.simulation_settings.total_simulation_time) + + # ── 5. Post-processing - everything must be empty ─────────────────── + results: ResultsAnalyzer = ResultsAnalyzer( + client=cli_rt, + servers=[], # none built + edges=[], # none built + settings=runner.simulation_settings, + ) + + # No latencies were produced + assert results.get_latency_stats() == {} + # Throughput time-series must be entirely empty + timestamps, rps = results.get_throughput_series() + assert timestamps == [] + # No sampled metrics either + assert results.get_sampled_metrics() == {} From d6ff9f6539b33566f3845f079ec364a70e34a6db Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 8 Aug 2025 17:04:47 +0200 Subject: [PATCH 22/52] Features/integration tests unit tests (#11) * new integration test + bug fixing * added integration and unit test * first tests working * minor change * first working example, upgrade docs, upgrade docstrings * improvements * path bug fixed --- docs/dev_workflow_guide.md | 92 ++-- docs/fastsim-docs/requests_generator.md | 150 +++++- docs/fastsim-docs/simulation_input.md | 495 +++++++++++------- docs/fastsim-docs/simulation_runner.md | 236 +++++++++ example/data/single_server.yml | 56 ++ example/single_server_yaml.py | 273 ++++++++++ example/single_server_yml.png | Bin 0 -> 158475 bytes src/app/config/constants.py | 96 ++-- src/app/metrics/analyzer.py | 9 +- src/app/metrics/collector.py | 14 +- src/app/runtime/actors/client.py | 3 +- src/app/runtime/actors/server.py | 8 +- src/app/samplers/common_helpers.py | 55 +- src/app/samplers/poisson_poisson.py | 2 +- src/app/schemas/random_variables_config.py | 3 +- src/app/schemas/simulation_output.py | 13 - src/app/schemas/system_topology/endpoint.py | 23 +- .../system_topology/full_system_topology.py | 98 +++- tests/conftest.py | 20 +- tests/integration/minimal/conftest.py | 7 +- .../payload/data/invalid/missing_field.yml | 17 + .../payload/data/invalid/negative_latency.yml | 15 + .../payload/data/invalid/wrong_enum.yml | 13 + .../payload/test_payload_invalid.py | 19 + tests/integration/single_server/conftest.py | 47 ++ .../single_server/data/single_server.yml | 54 ++ .../single_server/test_single_server.py | 52 ++ tests/unit/runtime/actors/test_client.py | 1 + tests/unit/runtime/actors/test_edge.py | 2 +- tests/unit/runtime/test_simulation_runner.py | 159 ++++++ tests/unit/samplers/test_sampler_helper.py | 4 +- 31 files changed, 1622 insertions(+), 414 deletions(-) create mode 100644 docs/fastsim-docs/simulation_runner.md create mode 100644 example/data/single_server.yml create mode 100644 example/single_server_yaml.py create mode 100644 example/single_server_yml.png delete mode 100644 src/app/schemas/simulation_output.py create mode 100644 tests/integration/payload/data/invalid/missing_field.yml create mode 100644 tests/integration/payload/data/invalid/negative_latency.yml create mode 100644 tests/integration/payload/data/invalid/wrong_enum.yml create mode 100644 tests/integration/payload/test_payload_invalid.py create mode 100644 tests/integration/single_server/conftest.py create mode 100644 tests/integration/single_server/data/single_server.yml create mode 100644 tests/integration/single_server/test_single_server.py create mode 100644 tests/unit/runtime/test_simulation_runner.py diff --git a/docs/dev_workflow_guide.md b/docs/dev_workflow_guide.md index b06ef1c..d1379db 100644 --- a/docs/dev_workflow_guide.md +++ b/docs/dev_workflow_guide.md @@ -20,58 +20,56 @@ Its job is to expose the REST API, run the discrete-event simulation, talk to th ``` fastsim-backend/ -├── Dockerfile -├── docker_fs/ # docker-compose for dev & prod -│ ├── docker-compose.dev.yml -│ └── docker-compose.prod.yml -├── scripts/ # helper bash scripts (lint, dev-startup, …) -│ ├── init-docker-dev.sh +├── example/ # examples of working simulations +│ ├── data +├── scripts/ # helper bash scripts (lint, dev-startup, …) │ └── quality-check.sh -├── alembic/ # DB migrations (versions/ contains revision files) -│ ├── env.py -│ └── versions/ -├── documentation/ # project vision & low-level docs -│ └── backend_documentation/ -│ └── … -├── tests/ # unit & integration tests +├── docs/ # project vision & low-level docs +│ └── fastsim-documentation/ +├── tests/ # unit & integration tests │ ├── unit/ │ └── integration/ -├── src/ # **application code lives here** +├── src/ # application code lives here │ └── app/ -│ ├── api/ # FastAPI routers & endpoint handlers -│ ├── config/ # Pydantic Settings + constants -│ ├── db/ # SQLAlchemy base, sessions, initial seed utilities -│ ├── metrics/ # helpers to compute/aggregate simulation KPIs -│ ├── resources/ # SimPy resource registry (CPU/RAM containers, etc.) -│ ├── runtime/ # simulation core -│ │ ├── rqs_state.py # RequestState & Hop -│ │ └── actors/ # SimPy “actors”: Edge, Server, Client, RqsGenerator -│ ├── samplers/ # stochastic samplers (Gaussian-Poisson, etc.) -│ ├── schemas/ # Pydantic input/output models -│ ├── main.py # FastAPI application factory / ASGI entry-point -│ └── simulation_run.py # CLI utility to run a sim outside of HTTP layer +│ ├── config/ # Pydantic Settings + constants +│ ├── metrics/ # logic to compute/aggregate simulation KPIs +│ ├── resources/ # SimPy resource registry (CPU/RAM containers, etc.) +│ ├── runtime/ # simulation core +│ │ ├── rqs_state.py # RequestState & Hop +│ │ ├── simulation_runner.py # logic to initialize the whole simulation + | └── actors/ # SimPy “actors”: Edge, Server, Client, RqsGenerator +│ ├── samplers/ # stochastic samplers (Gaussian-Poisson, etc.) +│ ├── schemas/ # Pydantic input/output models ├── poetry.lock ├── pyproject.toml └── README.md ``` +### **What each top-level directory in `src/app` does** + +| Directory | Purpose | +| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **`config/`** | Centralised configuration layer. Contains Pydantic `BaseSettings` classes for reading environment variables and constants/enums used across the simulation engine. | +| **`metrics/`** | Post-processing and analytics. Aggregates raw simulation traces into KPIs such as latency percentiles, throughput, resource utilisation, and other performance metrics. | +| **`resources/`** | Runtime resource registry for simulated hardware components (e.g., SimPy `Container`s for CPU and RAM). Decouples resource management from actor behaviour. | +| **`runtime/`** | Core simulation engine. Orchestrates SimPy execution, maintains request state, and wires together simulation components. Includes: | +| | - **`rqs_state.py`** — Defines `RequestState` and `Hop` for tracking request lifecycle. | +| | - **`simulation_runner.py`** — Entry point for initialising and running simulations. | +| | - **`actors/`** — SimPy actor classes representing system components (`RqsGenerator`, `Client`, `Server`, `Edge`) and their behaviour. | +| **`samplers/`** | Random-variable samplers for stochastic simulation. Supports Poisson, Normal, and mixed distributions for modelling inter-arrival times and service steps. | +| **`schemas/`** | Pydantic models for input/output validation and serialisation. Includes scenario definitions, topology graphs, simulation settings, and results payloads. | -#### What each top-level directory in `src/app` does +--- -| Directory | Purpose | -| ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **`api/`** | Defines the public HTTP surface. Each module holds a router with path operations and dependency wiring. | -| **`config/`** | Centralised configuration: `settings.py` (Pydantic `BaseSettings`) reads env vars; `constants.py` stores enums and global literals. | -| **`db/`** | Persistence layer. Contains the SQLAlchemy base class, the session factory, and a thin wrapper that seeds or resets the database (Alembic migration scripts live at project root). | -| **`metrics/`** | Post-processing helpers that turn raw simulation traces into aggregated KPIs (latency percentiles, cost per request, utilisation curves, …). | -| **`resources/`** | A tiny run-time registry mapping every simulated server to its SimPy `Container`s (CPU, RAM). Keeps resource management separate from actor logic. | -| **`runtime/`** | The heart of the simulator. `rqs_state.py` holds the mutable `RequestState`; sub-package **`actors/`** contains each SimPy process class (Generator, Edge, Server, Client). | -| **`samplers/`** | Probability-distribution utilities that generate inter-arrival and service-time samples—used by the actors during simulation. | -| **`schemas/`** | All Pydantic models for validation and (de)serialisation: request DTOs, topology definitions, simulation settings, outputs. | -| **`main.py`** | Creates and returns the FastAPI app; imported by Uvicorn/Gunicorn. | -| **`simulation_run.py`** | Convenience script to launch a simulation offline (e.g. inside tests or CLI). | +### **Other Top-Level Directories** -Everything under `src/` is import-safe thanks to Poetry’s `packages = [{ include = "app" }]` entry in `pyproject.toml`. +| Directory | Purpose | +| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | +| **`example/`** | Ready-to-run simulation scenarios and example configurations. Includes `data/` with YAML definitions and scripts to demonstrate engine usage. | +| **`scripts/`** | Utility shell scripts for development workflow, linting, formatting, and local startup (`quality-check.sh`, etc.). | +| **`docs/`** | Project documentation. Contains both high-level vision documents and low-level technical references (`fastsim-documentation/`). | +| **`tests/`** | Automated test suite, split into **unit** and **integration** tests to verify correctness of both individual components and end-to-end scenarios. | +--- ## 3. Branching Strategy: Git Flow @@ -182,21 +180,11 @@ We will start to describe the CI part related to push and PR in the develop bran * **Full Suite (push to `develop`)** *Runs in a few minutes; includes real services and Docker.* - - * All steps from the Quick Suite - * PostgreSQL service container started via `services:` - * Alembic migrations applied to the test database + * Full test suite, including `@pytest.mark.integration` tests - * Multi-stage Docker build of the backend image - * Smoke test: container started with Uvicorn → `curl /health` + -### 4.1.3 Key Implementation Details -* **Service containers** – PostgreSQL 17 is spun up in CI with a health-check to ensure migrations run against a live instance. -* **Test markers** – integration tests are isolated with `@pytest.mark.integration`, enabling selective execution. -* **Caching** – Poetry’s download cache is restored to cut installation time; Docker layer cache is reused between builds. -* **Smoke test logic** – after the image is built, CI launches it in detached mode, polls the `/health` endpoint, prints logs, and stops the container. The job fails if the endpoint is unreachable. -* **Secrets management** – database credentials and registry tokens are stored in GitHub Secrets and injected as environment variables only at runtime. diff --git a/docs/fastsim-docs/requests_generator.md b/docs/fastsim-docs/requests_generator.md index 95190fa..a15e241 100644 --- a/docs/fastsim-docs/requests_generator.md +++ b/docs/fastsim-docs/requests_generator.md @@ -8,43 +8,151 @@ This document describes the design of the **requests generator**, which models a Following the FastSim philosophy, we accept a small set of input parameters to drive a “what-if” analysis in a pre-production environment. These inputs let you explore reliability and cost implications under different traffic scenarios. -**Inputs** +## **Inputs** -1. **Average concurrent users** – expected number of users (or sessions) simultaneously hitting the endpoint. -2. **Average requests per minute per user** – average number of requests each user issues per minute. -3. **Simulation time** – total duration of the simulation, in seconds. +1. **Average Concurrent Users (`avg_active_users`)** + Expected number of simultaneous active users (or sessions) interacting with the system. -**Output** -A continuous sequence of timestamps (seconds) marking individual request arrivals. + * Modeled as a random variable (`RVConfig`). + * Allowed distributions: **Poisson** or **Normal**. + +2. **Average Requests per Minute per User (`avg_request_per_minute_per_user`)** + Average request rate per user, expressed in requests per minute. + + * Modeled as a random variable (`RVConfig`). + * **Must** use the **Poisson** distribution. + +3. **User Sampling Window (`user_sampling_window`)** + Time interval (in seconds) over which active users are resampled. + + * Constrained between `MIN_USER_SAMPLING_WINDOW` and `MAX_USER_SAMPLING_WINDOW`. + * Defaults to `USER_SAMPLING_WINDOW`. --- -## Model Assumptions +## **Model Assumptions** -* *Concurrent users* and *requests per minute per user* are **random variables**. -* *Simulation time* is **deterministic**. +* **Random variables**: -We model: + * *Concurrent users* and *requests per minute per user* are independent random variables. + * Each is configured via the `RVConfig` model, which specifies: -* **Requests per minute per user** as Poisson($\lambda_r$). -* **Concurrent users** as either Poisson($\lambda_u$) or truncated Normal. -* **The variables are independent** + * **mean** (mandatory, must be numeric and positive), + * **distribution** (default: Poisson), + * **variance** (optional; defaults to `mean` for Normal and Log-Normal distributions). -```python -from pydantic import BaseModel -from typing import Literal +* **Supported joint sampling cases**: + + * Poisson (users) × Poisson (requests) + * Normal (users) × Poisson (requests) + + Other combinations are currently unsupported. + +* **Variance handling**: + + * If the distribution is **Normal** or **Log-Normal** and `variance` is not provided, it is automatically set to the `mean`. + +--- + +## **Validation Rules** +* `avg_request_per_minute_per_user`: + + * **Must** be Poisson-distributed. + * Validation enforces this constraint. + +* `avg_active_users`: + + * Must be either Poisson or Normal. + * Validation enforces this constraint. + +* `mean` in `RVConfig`: + + * Must be a positive number (int or float). + * Automatically coerced to `float`. + +```python class RVConfig(BaseModel): - """Configure a random-variable parameter.""" + """class to configure random variables""" + mean: float - distribution: Literal["poisson", "normal", "gaussian"] = "poisson" - variance: float | None = None # required only for normal/gaussian + distribution: Distribution = Distribution.POISSON + variance: float | None = None + + @field_validator("mean", mode="before") + def ensure_mean_is_numeric_and_positive( + cls, # noqa: N805 + v: float, + ) -> float: + """Ensure `mean` is numeric, then coerce to float.""" + err_msg = "mean must be a number (int or float)" + if not isinstance(v, (float, int)): + raise ValueError(err_msg) # noqa: TRY004 + + return float(v) + + @model_validator(mode="after") # type: ignore[arg-type] + def default_variance(cls, model: "RVConfig") -> "RVConfig": # noqa: N805 + """Set variance = mean when distribution require and variance is missing.""" + needs_variance: set[Distribution] = { + Distribution.NORMAL, + Distribution.LOG_NORMAL, + } + + if model.variance is None and model.distribution in needs_variance: + model.variance = model.mean + return model + class RqsGeneratorInput(BaseModel): - """Define simulation inputs.""" + """Define the expected variables for the simulation""" + + id: str + type: SystemNodes = SystemNodes.GENERATOR avg_active_users: RVConfig avg_request_per_minute_per_user: RVConfig - total_simulation_time: int | None = None + + user_sampling_window: int = Field( + default=TimeDefaults.USER_SAMPLING_WINDOW, + ge=TimeDefaults.MIN_USER_SAMPLING_WINDOW, + le=TimeDefaults.MAX_USER_SAMPLING_WINDOW, + description=( + "Sampling window in seconds " + f"({TimeDefaults.MIN_USER_SAMPLING_WINDOW}-" + f"{TimeDefaults.MAX_USER_SAMPLING_WINDOW})." + ), + ) + + @field_validator("avg_request_per_minute_per_user", mode="after") + def ensure_avg_request_is_poisson( + cls, # noqa: N805 + v: RVConfig, + ) -> RVConfig: + """ + Force the distribution for the rqs generator to be poisson + at the moment we have a joint sampler just for the poisson-poisson + and gaussian-poisson case + """ + if v.distribution != Distribution.POISSON: + msg = "At the moment the variable avg request must be Poisson" + raise ValueError(msg) + return v + + @field_validator("avg_active_users", mode="after") + def ensure_avg_user_is_poisson_or_gaussian( + cls, # noqa: N805 + v: RVConfig, + ) -> RVConfig: + """ + Force the distribution for the rqs generator to be poisson + at the moment we have a joint sampler just for the poisson-poisson + and gaussian-poisson case + """ + if v.distribution not in {Distribution.POISSON, Distribution.NORMAL}: + msg = "At the moment the variable active user must be Poisson or Gaussian" + raise ValueError(msg) + return v + ``` --- diff --git a/docs/fastsim-docs/simulation_input.md b/docs/fastsim-docs/simulation_input.md index aa27d58..ad89247 100644 --- a/docs/fastsim-docs/simulation_input.md +++ b/docs/fastsim-docs/simulation_input.md @@ -1,228 +1,349 @@ -Of course. Here is the complete documentation, translated into English, based on the new Pydantic schemas. +# FastSim — Simulation Input Schema (v2) ------ +This document describes the **complete input contract** used by FastSim to run a simulation, the **design rationale** behind it, and the **guarantees** provided by the validation layer. It closes with an **end-to-end example** (YAML) you can drop into the project and run as-is. -### **FastSim — Simulation Input Schema** +The entry point is the Pydantic model: -The `SimulationPayload` is the single, self-contained contract that defines an entire simulation run. Its architecture is guided by a core philosophy: to achieve maximum control over input data through robust, upfront validation. To implement this, we extensively leverage Pydantic's powerful validation capabilities and Python's `Enum` classes. This approach creates a strictly-typed and self-consistent schema that guarantees any configuration is validated *before* the simulation engine starts. +```python +class SimulationPayload(BaseModel): + """Full input structure to perform a simulation""" + rqs_input: RqsGeneratorInput + topology_graph: TopologyGraph + sim_settings: SimulationSettings +``` + +Everything the engine needs is captured by these three components: + +* **`rqs_input`** — the workload model (how traffic is generated). +* **`topology_graph`** — the system under test, described as a directed graph (nodes & edges). +* **`sim_settings`** — global simulation controls and which metrics to collect. + +--- + +## Why this shape? (Rationale) + +### 1) **Separation of concerns** + +* **Workload** (traffic intensity & arrival process) is independent from the **topology** (architecture under test) and from **simulation control** (duration & metrics). +* This lets you reuse the same topology with different workloads, or vice versa, without touching unrelated parts. + +### 2) **Validation-first, fail-fast** + +* All inputs are **typed** and **validated** with Pydantic before the engine starts. +* Validation catches type errors, inconsistent references, and illegal combinations (e.g., an I/O step with a CPU metric). +* When a payload parses successfully, the engine can run without defensive checks scattered in runtime code. + +### 3) **Small-to-large composition** + +* The smallest unit is a **`Step`** (one resource-bound operation). +* Steps compose into an **`Endpoint`** (an ordered workflow). +* Endpoints live on a **`Server`** node with finite resources. +* Nodes and **Edges** form a **`TopologyGraph`**. +* A disciplined set of **Enums** (no magic strings) ensure a closed vocabulary. + +--- + +## 1. Workload: `RqsGeneratorInput` + +### Purpose + +Defines the traffic generator that produces request arrivals. + +```python +class RqsGeneratorInput(BaseModel): + id: str + type: SystemNodes = SystemNodes.GENERATOR + avg_active_users: RVConfig + avg_request_per_minute_per_user: RVConfig + user_sampling_window: int = Field( ... ) # seconds +``` + +### Random variables (`RVConfig`) + +```python +class RVConfig(BaseModel): + mean: float + distribution: Distribution = Distribution.POISSON + variance: float | None = None +``` + +#### Validation & guarantees -This contract brings together three distinct but interconnected layers of configuration into one cohesive structure: +* **`mean` is numeric** + `@field_validator("mean", mode="before")` coerces to `float` and rejects non-numeric values. +* **Auto variance** for Normal/LogNormal + `@model_validator(mode="after")` sets `variance = mean` if missing and the distribution is `NORMAL` or `LOG_NORMAL`. +* **Distribution constraints** on workload: -1. **`rqs_input` (`RqsGeneratorInput`)**: Defines the **workload profile**—how many users are active and how frequently they generate requests—and acts as the **source node** in our system graph. -2. **`topology_graph` (`TopologyGraph`)**: Describes the **system's architecture**—its components, resources, and the network connections between them, represented as a directed graph. -3. **`sim_settings` (`SimulationSettings`)**: Configures **global simulation parameters**, such as total runtime and which metrics to collect. + * `avg_request_per_minute_per_user` **must be Poisson** (engine currently optimised for Poisson arrivals). + * `avg_active_users` **must be Poisson or Normal**. + * Enforced via `@field_validator(..., mode="after")` with clear error messages. -This layered design decouples the *what* (the system topology) from the *how* (the traffic pattern and simulation control), allowing for modular and reusable configurations. Adherence to our validation-first philosophy means every payload is rigorously parsed against this schema. By using a controlled vocabulary of `Enums` and the power of Pydantic, we guarantee that any malformed or logically inconsistent input is rejected upfront with clear, actionable errors, ensuring the simulation engine operates only on perfectly valid data. +#### Why these constraints? ------ +* They reflect the current joint-sampling logic in the generator: **Poisson–Poisson** and **Normal–Poisson** are implemented and tested. Additional combos can be enabled later without changing the public contract. -## **1. The System Graph (`topology_graph` and `rqs_input`)** +--- -At the core of FastSim is the representation of the system as a **directed graph**. The **nodes** represent the architectural components (like servers, clients, and the traffic generator itself), while the **edges** represent the directed network connections between them. This graph-based approach allows for flexible and realistic modeling of request flows through distributed systems. +## 2. System Graph: `TopologyGraph` -### **Design Philosophy: A "Micro-to-Macro" Approach** +### Purpose -The schema is built on a compositional, "micro-to-macro" principle. We start by defining the smallest indivisible units of work (`Step`) and progressively assemble them into larger, more complex structures (`Endpoint`, `Server`, and finally the `TopologyGraph`). +Defines the architecture under test as a **directed graph**: nodes are components (client, server, optional load balancer), edges are network links with latency models. -This layered approach provides several key advantages: +```python +class TopologyGraph(BaseModel): + nodes: TopologyNodes + edges: list[Edge] +``` - * **Modularity and Reusability:** Core operations are defined once as `Steps` and can be reused across multiple `Endpoints`. - * **Local Reasoning, Global Safety:** Each model is responsible for its own internal consistency (e.g., a `Step` ensures its metric is valid for its kind). Parent models then enforce the integrity of the connections *between* these components (e.g., the `TopologyGraph` ensures all `Edges` connect to valid `Nodes`). - * **Guaranteed Robustness:** By catching all structural and referential errors before the simulation begins, this approach embodies the "fail-fast" principle. It guarantees that the SimPy engine operates on a valid, self-consistent model. +### Nodes -### **A Controlled Vocabulary: Topology Constants** +```python +class TopologyNodes(BaseModel): + servers: list[Server] + client: Client + load_balancer: LoadBalancer | None = None +``` -The schema's robustness is founded on a controlled vocabulary defined by Python `Enum` classes. Instead of error-prone "magic strings," the schema uses these enums to define the finite set of legal values for categories like operation kinds, metrics, and node types. +#### `Client` -| Enum | Purpose | -| :------------------------- | :------------------------------------------------------------------------ | -| **`EndpointStepCPU`, `EndpointStepRAM`, `EndpointStepIO`** | Defines the exhaustive list of valid `kind` values for a `Step`. | -| **`StepOperation`** | Specifies the legal dictionary keys within a `Step`'s `step_operation`. | -| **`SystemNodes`** | Enumerate the allowed `type` for nodes (e.g., `"server"`, `"client"`, `"generator"`). | -| **`SystemEdges`** | Enumerate the allowed categories for connections between nodes. | +```python +class Client(BaseModel): + id: str + type: SystemNodes = SystemNodes.CLIENT +``` ------ +* **Validator**: `type` must equal `SystemNodes.CLIENT`. -### **Schema Hierarchy and In-Depth Validation** +#### `ServerResources` -Here we break down each component of the topology, highlighting the specific Pydantic validators that enforce its correctness. +```python +class ServerResources(BaseModel): + cpu_cores: PositiveInt = Field(...) + db_connection_pool: PositiveInt | None = Field(...) + ram_mb: PositiveInt = Field(...) +``` -#### **Random Variable Schema (`RVConfig`)** +* Maps directly to SimPy containers (CPU tokens, RAM capacity, etc.). +* Bounds enforced via `Field(ge=..., ...)`. -At the core of both the traffic generator and network latencies is `RVConfig`, a schema for defining stochastic variables. This allows critical parameters to be modeled not as fixed numbers, but as draws from a probability distribution. +#### `Step` (the atomic unit) -| Check | Pydantic Hook | Rule & Rationale | -| :---------------------------- | :---------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Numeric `mean` Enforcement** | `@field_validator("mean", mode="before")` | Intercepts the `mean` field and ensures the provided value is an `int` or `float`, rejecting invalid types. This guarantees a valid numeric type for all downstream logic. | -| **Valid `distribution` Name** | `Distribution` (`StrEnum`) type hint | Pydantic automatically ensures that the `distribution` field's value must be one of the predefined members (e.g., `"poisson"`, `"normal"`). Any typo or unsupported value results in an immediate validation error. | -| **Intelligent `variance` Defaulting** | `@model_validator(mode="after")` | Enforces a crucial business rule: if `distribution` is `"normal"` or `"log_normal"` **and** `variance` is not provided, the schema automatically sets `variance = mean`. This provides a safe, logical default. | +```python +class Step(BaseModel): + kind: EndpointStepIO | EndpointStepCPU | EndpointStepRAM + step_operation: dict[StepOperation, PositiveFloat | PositiveInt] +``` -#### **1. `Step`: The Atomic Unit of Work** +**Key validator (coherence):** -A `Step` represents a single, indivisible operation. +```python +@model_validator(mode="after") +def ensure_coherence_type_operation(cls, model: "Step") -> "Step": + # exactly one operation key, and it must match the step kind +``` -| Validation Check | Pydantic Hook | Rule & Rationale | -| :------------------------------- | :--------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Coherence of `kind` and `step_operation`** | `@model_validator` | **Rule:** The `step_operation` dictionary must contain *exactly one* entry, and its key (`StepOperation`) must be the correct metric for the `Step`'s `kind`. \\ **Rationale:** This is the most critical validation on a `Step`. It prevents illogical pairings like a RAM allocation step being measured in `cpu_time`. It ensures every step has a clear, unambiguous impact on a single system resource. | -| **Positive Metric Values** | `PositiveFloat` / `PositiveInt` | **Rule:** All numeric values in `step_operation` must be greater than zero. \\ **Rationale:** It is physically impossible to spend negative or zero time on an operation. This ensures that only plausible resource requests enter the system. | +* If `kind` is CPU → the only key must be `CPU_TIME`. +* If `kind` is RAM → only `NECESSARY_RAM`. +* If `kind` is I/O → only `IO_WAITING_TIME`. +* Values must be positive (`PositiveFloat/PositiveInt`). -#### **2. `Endpoint`: Composing Workflows** +This guarantees every step is **unambiguous** and **physically meaningful**. -An `Endpoint` defines a complete operation (e.g., an API call like `/predict`) as an ordered sequence of `Steps`. +#### `Endpoint` -| Validation Check | Pydantic Hook | Rule & Rationale | -| :-------------------- | :--------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Consistent Naming** | `@field_validator("endpoint_name")` | **Rule:** Automatically converts the `endpoint_name` to lowercase. \\ **Rationale:** This enforces a canonical representation, eliminating ambiguity from inconsistent capitalization (e.g., treating `/predict` and `/Predict` as the same). | +```python +class Endpoint(BaseModel): + endpoint_name: str + steps: list[Step] -#### **3. System Nodes: `Server`, `Client`, and `RqsGeneratorInput`** + @field_validator("endpoint_name", mode="before") + def name_to_lower(cls, v: str) -> str: + return v.lower() +``` -These models define the macro-components of your architecture where work is performed, resources are located, and requests originate. +* Canonical lowercase naming avoids duplicates differing only by case. -| Validation Check | Pydantic Hook | Rule & Rationale | -| :-------------------------------- | :---------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Standardized Node `type`** | `@field_validator("type")` | **Rule:** The `type` field must strictly match the expected `SystemNodes` enum member (e.g., a `Server` object must have `type: "server"`). \\ **Rationale:** This enforces a strict contract: a `Server` object is always and only a server, preventing object state confusion. | -| **Unique Node IDs** | `@model_validator` in `TopologyNodes` | **Rule:** All `id` fields across all `Server` nodes, the `Client` node, and the `RqsGeneratorInput` node must be unique. \\ **Rationale:** This is fundamental to creating a valid graph. Node IDs are the primary keys. If two nodes shared the same ID, any `Edge` pointing to that ID would be ambiguous. | -| **Workload Distribution Constraints** | `@field_validator` in `RqsGeneratorInput` | **Rule:** The `avg_request_per_minute_per_user` field must use a Poisson distribution. The `avg_active_users` field must use a Poisson or Normal distribution. \\ **Rationale:** This is a current restriction of the simulation engine, which has a joint sampler optimized only for these combinations. This validator ensures that only supported configurations are accepted. | +#### `Server` -#### **4. `Edge`: Connecting the Components** +```python +class Server(BaseModel): + id: str + type: SystemNodes = SystemNodes.SERVER + server_resources: ServerResources + endpoints: list[Endpoint] +``` -An `Edge` represents a directed network link between two nodes. +* **Validator**: `type` must equal `SystemNodes.SERVER`. -| Validation Check | Pydantic Hook | Rule & Rationale | -| :---------------- | :----------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **No Self-Loops** | `@model_validator` | **Rule:** An edge's `source` ID cannot be the same as its `target` ID. \\ **Rationale:** A network call from a service to itself is a logical anti-pattern in a system topology. Such an operation should be modeled as an internal process (i.e., another `Step`), not a network hop. | -| **Unique Edge IDs** | `@model_validator` in `TopologyGraph` | **Rule:** All `id` fields of the `Edge`s must be unique. \\ **Rationale:** Ensures that every network connection is uniquely identifiable, which is useful for logging and debugging. | +#### `LoadBalancer` (optional) -#### **5. `TopologyGraph`: The Complete System** +```python +class LoadBalancer(BaseModel): + id: str + type: SystemNodes = SystemNodes.LOAD_BALANCER + algorithms: LbAlgorithmsName = LbAlgorithmsName.ROUND_ROBIN + server_covered: set[str] = Field(default_factory=set) +``` + +### Edges + +```python +class Edge(BaseModel): + id: str + source: str + target: str + latency: RVConfig + probability: float = Field(1.0, ge=0.0, le=1.0) + edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION + dropout_rate: float = Field(...) +``` -This is the root model that aggregates all `nodes` and `edges` and performs the final, most critical validation: ensuring referential integrity. +#### Validation & guarantees -| Validation Check | Pydantic Hook | Rule & Rationale | -| :---------------------- | :----------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Referential Integrity** | `@model_validator` | **Rule:** Every `edge.source` and `edge.target` ID must correspond to an actual node ID defined in the topology. \\ **Rationale:** This is the capstone validation that guarantees the structural integrity of the entire system graph. It prevents "dangling edges"—connections that point to non-existent nodes—ensuring the described system is a complete and validly connected graph. | +* **Latency sanity** + `@field_validator("latency", mode="after")` ensures `mean > 0` and `variance >= 0` (if provided). Error messages mention the **edge id** for clarity. +* **No self-loops** + `@model_validator(mode="after")` rejects `source == target`. +* **Unique edge IDs** + `TopologyGraph.unique_ids` enforces uniqueness across `edges`. +* **Referential integrity** + `TopologyGraph.edge_refs_valid` ensures: ------ - -## **2. Global Simulation Control (`SimulationSettings`)** - -This final component configures the simulation's execution parameters and, critically, determines what data is collected. - -#### **Payload Structure (`SimulationSettings`)** - -| Field | Type | Purpose & Validation | -| :----------------------- | :------------------------- | :------------------------------------------------------------------------------------------------------ | -| `total_simulation_time` | `int` | The total simulation horizon in seconds. Must be `>=` a defined minimum (e.g., 1800s). | -| `enabled_sample_metrics` | `set[SampledMetricName]` | A set of metrics to be sampled at fixed intervals, creating a time-series (e.g., `"ready_queue_len"`, `"ram_in_use"`). | -| `enabled_event_metrics` | `set[EventMetricName]` | A set of metrics recorded only when specific events occur (e.g., `"rqs_latency"`). | - -### **Design Rationale: Pre-validated, On-Demand Metrics** - -The design of the `settings`, particularly the `enabled_*_metrics` fields, is centered on **user-driven selectivity** and **ironclad validation**. - -1. **Selectivity:** Data collection has a performance cost. By allowing the user to explicitly select only the metrics they need, we make the simulator more efficient and versatile. - -2. **Ironclad Validation:** Simply allowing users to provide a list of strings is risky. Our schema mitigates this risk by validating every metric name provided by the user against the canonical `Enum` definitions (`SampledMetricName`, `EventMetricName`). If a user provides a misspelled or invalid metric name (e.g., `"request_latncy"`), Pydantic immediately rejects the entire payload *before* the simulation engine runs. - -This guarantees that the simulation engine can safely initialize all necessary data collection structures at the start of the run, completely eliminating an entire class of potential `KeyError` exceptions at runtime. - ------ - -## **End-to-End Example (`SimulationPayload`)** - -The following JSON object shows how these components combine into a single `SimulationPayload` for a minimal client-server setup, updated according to the new schema. - -```jsonc -{ - // Defines the workload profile as a generator node. - "rqs_input": { - "id": "mobile_user_generator", - "type": "generator", - "avg_active_users": { - "mean": 50, - "distribution": "poisson" - }, - "avg_request_per_minute_per_user": { - "mean": 5.0, - "distribution": "poisson" - }, - "user_sampling_window": 60 - }, - // Describes the system's architecture as a graph. - "topology_graph": { - "nodes": { - "client": { - "id": "entry_point_client", - "type": "client" - }, - "servers": [ - { - "id": "api_server", - "type": "server", - "server_resources": { - "cpu_cores": 4, - "ram_mb": 4096, - "db_connection_pool": 10 - }, - "endpoints": [ - { - "endpoint_name": "/predict", - "steps": [ - { - "kind": "initial_parsing", - "step_operation": { "cpu_time": 0.005 } - }, - { - "kind": "io_db_query", - "step_operation": { "io_waiting_time": 0.050 } - } - ] - } - ] - } - ] - }, - "edges": [ - { - "id": "client_to_generator", - "source": "entry_point_client", - "target": "mobile_user_generator", - "latency": { - "distribution": "log_normal", - "mean": 0.001, - "variance": 0.0001 - } - }, - { - "id": "generator_to_api", - "source": "mobile_user_generator", - "target": "api_server", - "latency": { - "distribution": "log_normal", - "mean": 0.04, - "variance": 0.01 - }, - "probability": 1.0, - "dropout_rate": 0.0 - } - ] - }, - // Configures the simulation run and metric collection. - "sim_settings": { - "total_simulation_time": 3600, - "enabled_sample_metrics": [ - "ready_queue_len", - "ram_in_use", - "core_busy" - ], - "enabled_event_metrics": [ - "rqs_latency" - ] - } -} + * Every `target` is a declared node ID. + * **External sources** (e.g., the generator id) are allowed, but **may not** appear as a `target` anywhere. +* **Load balancer integrity** (if present) + `TopologyGraph.valid_load_balancer` enforces: + + * `server_covered ⊆ {server ids}`. + * For every covered server there exists an **outgoing edge from the LB** to that server. + +These checks make the graph **closed**, **consistent**, and **wirable** without surprises at runtime. + +--- + +## 3. Simulation Control: `SimulationSettings` + +```python +class SimulationSettings(BaseModel): + total_simulation_time: int = Field(..., ge=TimeDefaults.MIN_SIMULATION_TIME) + enabled_sample_metrics: set[SampledMetricName] = Field(default_factory=...) + enabled_event_metrics: set[EventMetricName] = Field(default_factory=...) + sample_period_s: float = Field(..., ge=SamplePeriods.MINIMUM_TIME, le=SamplePeriods.MAXIMUM_TIME) ``` -### **Key Takeaways** +### What it controls + +* **Clock** — `total_simulation_time` (seconds). +* **Sampling cadence** — `sample_period_s` for time-series metrics. +* **What to collect** — two sets of enums: + + * `enabled_sample_metrics`: time-series KPIs (e.g., ready queue length, RAM in use, edge concurrency). + * `enabled_event_metrics`: per-event KPIs (e.g., request clocks/latency). + +### Why Enums matter here + +Letting users pass strings like `"ram_in_use"` is error-prone. By using **`SampledMetricName`** and **`EventMetricName`** enums, the settings are **validated upfront**, so the runtime collector knows exactly which lists to allocate and fill. No hidden `KeyError`s halfway through a run. + +--- + +## What these validations buy you + +* **Type safety** (no accidental strings where enums are expected). +* **Physical realism** (no zero/negative times or RAM). +* **Graph integrity** (no dangling edges or self-loops). +* **Operational clarity** (every step has exactly one effect). +* **Better errors** (validators point to the exact field/entity at fault). + +Together, they make the model **predictable** for the simulation engine and **pleasant** to debug. + +--- + +## End-to-End Example (YAML) + +This is a complete, valid payload you can load with `SimulationRunner.from_yaml(...)`. + +```yaml +# ─────────────────────────────────────────────────────────────── +# FastSim scenario: generator → client → server → client +# ─────────────────────────────────────────────────────────────── + +rqs_input: + id: rqs-1 + # avg_active_users can be POISSON or NORMAL; mean is required. + avg_active_users: + mean: 100 + distribution: poisson + # must be POISSON (engine constraint) + avg_request_per_minute_per_user: + mean: 20 + distribution: poisson + user_sampling_window: 60 # seconds + +topology_graph: + nodes: + client: + id: client-1 + type: client + servers: + - id: srv-1 + type: server + server_resources: + cpu_cores: 1 + ram_mb: 2048 + # db_connection_pool: 50 # optional + endpoints: + - endpoint_name: /predict + steps: + - kind: ram + step_operation: { necessary_ram: 100 } + - kind: initial_parsing # CPU step (enum in your code) + step_operation: { cpu_time: 0.001 } + - kind: io_wait # I/O step + step_operation: { io_waiting_time: 0.100 } + + edges: + - id: gen-to-client + source: rqs-1 # external source OK + target: client-1 + latency: { mean: 0.003, distribution: exponential } + + - id: client-to-server + source: client-1 + target: srv-1 + latency: { mean: 0.003, distribution: exponential } + + - id: server-to-client + source: srv-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + +sim_settings: + total_simulation_time: 500 + sample_period_s: 0.05 + enabled_sample_metrics: + - ready_queue_len + - event_loop_io_sleep + - ram_in_use + - edge_concurrent_connection + enabled_event_metrics: + - rqs_clock +``` + + Notes: +> + * `kind` uses the **EndpointStep** enums you’ve defined (e.g., `ram`, `initial_parsing`, `io_wait`). + * The coherence validator ensures that each `kind` uses the correct `step_operation` key and **exactly one** entry. + * The **edge** constraints guarantee a clean, connected, and sensible graph. + +--- + +## Summary + +* The **payload** is small but expressive: workload, topology, and settings. +* The **validators** are doing real work: they make illegal states unrepresentable. +* The **enums** keep the contract tight and maintainable. +* Together, they let you move fast **without** breaking the simulation engine. - * **Single Source of Truth**: `Enum` classes centralize all valid string literals, providing robust, type-safe validation across the entire schema. - * **Layered Validation**: The `Constants → Component Schemas → SimulationPayload` hierarchy ensures that only well-formed and self-consistent configurations reach the simulation engine. - * **Separation of Concerns**: The three top-level keys (`rqs_input`, `topology_graph`, `sim_settings`) clearly separate the workload, the system architecture, and simulation control, making configurations easier to read, write, and reuse. \ No newline at end of file +If you extend the engine (new distributions, step kinds, metrics), you can **keep the same contract** and enrich the enums & validators to preserve the same guarantees. diff --git a/docs/fastsim-docs/simulation_runner.md b/docs/fastsim-docs/simulation_runner.md new file mode 100644 index 0000000..8153e13 --- /dev/null +++ b/docs/fastsim-docs/simulation_runner.md @@ -0,0 +1,236 @@ + +# **Simulation Runner — Technical Documentation** + +## **Overview** + +The `SimulationRunner` is the **orchestrator** of the FastSim engine. +Its main responsibility is to: + +1. **Build** simulation actors from a structured input (`SimulationPayload`). +2. **Wire** actors together via `EdgeRuntime` connections. +3. **Start** all simulation processes in a SimPy environment. +4. **Run** the simulation clock for the configured duration. +5. **Collect and return** performance metrics through a `ResultsAnalyzer` instance. + +This design separates **topology definition** (data models) from **execution logic** (runtime actors), ensuring clarity, testability, and future extensibility. + +--- + +## **High-Level Flow** + +```mermaid +flowchart TD + subgraph Input + A["SimulationPayload (Pydantic)"] + end + + subgraph Build_Phase + B1[Build RqsGeneratorRuntime] + B2[Build ClientRuntime] + B3[Build ServerRuntimes] + B4["Build LoadBalancerRuntime (optional)"] + end + + subgraph Wire_Phase + C["Build EdgeRuntimes + assign target boxes"] + end + + subgraph Start_Phase + D1[Start all Startable actors] + D2[Start SampledMetricCollector] + end + + subgraph Run_Phase + E["SimPy env.run(until=total_simulation_time)"] + end + + subgraph Output + F[ResultsAnalyzer] + end + + A --> B1 & B2 & B3 & B4 + B1 & B2 & B3 & B4 --> C + C --> D1 & D2 + D1 & D2 --> E + E --> F +``` + +--- + +## **Component Responsibilities** + +### **SimulationRunner** + +* **Inputs:** + + * `env`: a `simpy.Environment` controlling discrete-event simulation time. + * `simulation_input`: a `SimulationPayload` describing topology, request generation parameters, and simulation settings. + +* **Responsibilities:** + + * Build **all runtime actors** (`RqsGeneratorRuntime`, `ClientRuntime`, `ServerRuntime`, `LoadBalancerRuntime`). + * Instantiate **EdgeRuntime** objects to connect actors. + * Start processes and the metric collector. + * Advance the simulation clock. + * Package results into a `ResultsAnalyzer`. + +--- + +### **Actors** + +All runtime actors implement the `Startable` protocol (i.e., they expose a `.start()` method returning a `simpy.Process`). + +| Actor | Responsibility | +| ----------------------- | -------------------------------------------------------------------------------------------- | +| **RqsGeneratorRuntime** | Produces incoming requests according to stochastic models (Poisson, Gaussian-Poisson, etc.). | +| **ClientRuntime** | Consumes responses, tracks completion, and stores latency samples. | +| **ServerRuntime** | Processes incoming requests, interacts with CPU/RAM containers, measures processing times. | +| **LoadBalancerRuntime** | Distributes incoming requests across available servers according to configured policy. | +| **EdgeRuntime** | Models the connection between two nodes (latency, bandwidth, loss). | + +--- + +### **ResourcesRuntime** + +* Registry mapping server IDs to their SimPy `Container` resources (CPU, RAM). +* Keeps resource allocation/consumption logic decoupled from server logic. + +--- + +### **Metrics** + +* **SampledMetricCollector**: Periodically snapshots runtime metrics (queue sizes, RAM usage, connection counts). +* **ResultsAnalyzer**: Consumes raw metrics and computes aggregated KPIs (latency distribution, throughput series, etc.). + +--- + +## **Execution Timeline** + +```mermaid +sequenceDiagram + participant Runner as SimulationRunner + participant RqsGen as RqsGeneratorRuntime + participant Client as ClientRuntime + participant Server as ServerRuntime + participant LB as LoadBalancerRuntime + participant Edge as EdgeRuntime + participant Metrics as SampledMetricCollector + + Runner->>RqsGen: Build from input + Runner->>Client: Build from input + Runner->>Server: Build each from input + Runner->>LB: Build if present + Runner->>Edge: Build + assign target_box + Runner->>RqsGen: Set out_edge + Runner->>Client: Set out_edge + Runner->>Server: Set out_edge + Runner->>LB: Append out_edges + Runner->>Metrics: Start collector + Runner->>RqsGen: start() + Runner->>Client: start() + Runner->>Server: start() + Runner->>LB: start() + Runner->>Runner: env.run(until=total_simulation_time) + Runner->>Metrics: Gather results + Runner->>Runner: Return ResultsAnalyzer +``` + +--- + +## **Detailed Build & Wire Steps** + +### 1️⃣ **Build Phase** + +* **`_build_rqs_generator()`**: Creates a single `RqsGeneratorRuntime` for now; architecture allows for multiple (future CDN scenarios). +* **`_build_client()`**: Instantiates the single client node; stored in a dict for future multi-client extensions. +* **`_build_servers()`**: Creates one `ServerRuntime` per configured server. Pulls CPU/RAM resources from `ResourcesRuntime`. +* **`_build_load_balancer()`**: Optional; created only if present in the topology. + +--- + +### 2️⃣ **Wire Phase** + +* Merges all runtime actor dictionaries into a `all_nodes` map. +* For each `Edge` in the topology: + + * Looks up **target** object and assigns the correct inbox (`simpy.Store`). + * Creates an `EdgeRuntime` and assigns it as `out_edge` (or appends to `out_edges` for LBs). + +--- + +### 3️⃣ **Start Phase** + +* Uses `itertools.chain` to lazily iterate over all runtime actors in the correct deterministic order. +* Casts to `Iterable[Startable]` to make Mypy type-checking explicit. +* Starts `SampledMetricCollector` to record periodic metrics. + +--- + +### 4️⃣ **Run Phase** + +* Advances SimPy’s event loop until `total_simulation_time` from the simulation settings. +* Returns a `ResultsAnalyzer` for downstream reporting and plotting. + +--- + +## **Extensibility Hooks** + +* **Multiple Generators / Clients**: Dictionaries keyed by node ID already prepared. +* **CDN or Multi-tier Architectures**: Easily extendable via new actor types + wiring rules. +* **Different LB Policies**: Swap `LoadBalancerRuntime` strategy without touching the runner. +* **Metric Expansion**: `SampledMetricCollector` can be extended to capture additional KPIs. + +--- + +## **Architecture Diagram** + +``` + ┌───────────────────────┐ + │ SimulationPayload │ + │ (input topology + cfg) │ + └─────────┬─────────────┘ + │ + ▼ + ┌───────────────────────┐ + │ SimulationRunner │ + └─────────┬─────────────┘ + │ build actors + ▼ + ┌─────────────────────────────────────────────────┐ + │ Runtime Actors (Startable) │ + │ ┌──────────────────┐ ┌──────────────────────┐ │ + │ │ RqsGenerator │→│ ClientRuntime │ │ + │ └──────────────────┘ └──────────────────────┘ │ + │ ↓ edges ↑ edges │ + │ ┌──────────────────┐ ┌──────────────────────┐ │ + │ │ ServerRuntime(s) │←→│ LoadBalancerRuntime │ │ + │ └──────────────────┘ └──────────────────────┘ │ + └─────────────────────────────────────────────────┘ + │ + ▼ + ┌────────────────────────────┐ + │ SampledMetricCollector │ + └──────────────┬─────────────┘ + ▼ + ┌────────────────┐ + │ ResultsAnalyzer │ + └────────────────┘ +``` + +--- + +## Architectural rationale + +✅ **Separation of concerns** — Topology definition, resource allocation, runtime behaviour, and metric processing are decoupled. + +✅ **Extensible** — Adding new node types or connection logic requires minimal changes. + +✅ **Testable** — Each phase can be tested in isolation (unit + integration). + +✅ **Deterministic order** — Startup sequence guarantees reproducibility. + +✅ **Scalable** — Supports larger topologies by design. + +--- + + diff --git a/example/data/single_server.yml b/example/data/single_server.yml new file mode 100644 index 0000000..c6e2ce6 --- /dev/null +++ b/example/data/single_server.yml @@ -0,0 +1,56 @@ +# ─────────────────────────────────────────────────────────────── +# FastSim scenario: generator ➜ client ➜ server ➜ client +# ─────────────────────────────────────────────────────────────── + +# 1. Traffic generator (light load) +rqs_input: + id: rqs-1 + avg_active_users: { mean: 100 } + avg_request_per_minute_per_user: { mean: 20 } + user_sampling_window: 60 + +# 2. Topology +topology_graph: + nodes: + client: { id: client-1 } + servers: + - id: srv-1 + server_resources: { cpu_cores: 1, ram_mb: 2048 } + endpoints: + - endpoint_name: ep-1 + probability: 1.0 + steps: + - kind: initial_parsing + step_operation: { cpu_time: 0.001 } + - kind: ram + step_operation: { necessary_ram: 100} + - kind: io_wait + step_operation: { io_waiting_time: 0.1 } + + edges: + - id: gen-to-client + source: rqs-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + + - id: client-to-server + source: client-1 + target: srv-1 + latency: { mean: 0.003, distribution: exponential } + + - id: server-to-client + source: srv-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + +# 3. Simulation settings +sim_settings: + total_simulation_time: 500 + sample_period_s: 0.05 + enabled_sample_metrics: + - ready_queue_len + - event_loop_io_sleep + - ram_in_use + - edge_concurrent_connection + enabled_event_metrics: + - rqs_clock diff --git a/example/single_server_yaml.py b/example/single_server_yaml.py new file mode 100644 index 0000000..3057a52 --- /dev/null +++ b/example/single_server_yaml.py @@ -0,0 +1,273 @@ +#!/usr/bin/env python3 +""" +Run a FastSim scenario from a YAML file and print diagnostics. + +What it does: +- Loads the simulation payload from YAML via `SimulationRunner.from_yaml`. +- Runs the simulation. +- Prints latency stats, 1s-bucket throughput, and a preview of sampled metrics. +- Saves four plots (latency histogram, throughput, server queues, RAM). +- Performs sanity checks (expected vs observed) with simple queueing heuristics. + +Usage: + python src/app/example/run_from_yaml.py \ + --yaml src/app/example/data/single_server.yml +""" + +from __future__ import annotations + +from argparse import ArgumentParser +from pathlib import Path +from typing import Dict, Iterable, List, Mapping, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import simpy +from app.config.constants import ( + EndpointStepCPU, + EndpointStepIO, + EndpointStepRAM, + LatencyKey, + StepOperation, +) +from app.metrics.analyzer import ResultsAnalyzer +from app.runtime.simulation_runner import SimulationRunner + + +# ───────────────────────────────────────────────────────────── +# Pretty printers +# ───────────────────────────────────────────────────────────── +def print_latency_stats(res: ResultsAnalyzer) -> None: + """Print latency statistics returned by the analyzer.""" + stats: Mapping[LatencyKey, float] = res.get_latency_stats() + print("\n════════ LATENCY STATS ════════") + if not stats: + print("(empty)") + return + + # Keep deterministic ordering for readability. + order: List[LatencyKey] = [ + LatencyKey.TOTAL_REQUESTS, + LatencyKey.MEAN, + LatencyKey.MEDIAN, + LatencyKey.STD_DEV, + LatencyKey.P95, + LatencyKey.P99, + LatencyKey.MIN, + LatencyKey.MAX, + ] + for key in order: + if key in stats: + print(f"{key.name:<20} = {stats[key]:.6f}") + + +def print_throughput(res: ResultsAnalyzer) -> None: + """Print 1-second throughput buckets.""" + timestamps, rps = res.get_throughput_series() + print("\n════════ THROUGHPUT (req/sec) ════════") + if not timestamps: + print("(empty)") + return + + for t, rate in zip(timestamps, rps): + print(f"t={t:4.1f}s → {rate:6.2f} rps") + + +def print_sampled_preview(res: ResultsAnalyzer) -> None: + """Print first 5 samples of each sampled metric series.""" + sampled: Dict[str, Dict[str, List[float]]] = res.get_sampled_metrics() + print("\n════════ SAMPLED METRICS ════════") + if not sampled: + print("(empty)") + return + + for metric, series in sampled.items(): + print(f"\n📈 {metric}:") + for entity, vals in series.items(): + head = list(vals[:5]) if vals else [] + print(f" - {entity}: len={len(vals)}, first={head}") + + +# ───────────────────────────────────────────────────────────── +# Plotting +# ───────────────────────────────────────────────────────────── +def save_all_plots(res: ResultsAnalyzer, out_path: Path) -> None: + """Generate the 2x2 plot figure and save it to `out_path`.""" + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + res.plot_latency_distribution(axes[0, 0]) + res.plot_throughput(axes[0, 1]) + res.plot_server_queues(axes[1, 0]) + res.plot_ram_usage(axes[1, 1]) + fig.tight_layout() + fig.savefig(out_path) + print(f"\n🖼️ Plots saved to: {out_path}") + + +# ───────────────────────────────────────────────────────────── +# Sanity checks (expected vs observed) +# ───────────────────────────────────────────────────────────── +REL_TOL = 0.30 # 30% tolerance for rough sanity checks + + +def _tick(label: str, expected: float, observed: float) -> None: + """Print a ✓ or ⚠ depending on relative error vs `REL_TOL`.""" + if expected == 0.0: + delta_pct = 0.0 + icon = "•" + else: + delta = abs(observed - expected) / abs(expected) + delta_pct = delta * 100.0 + icon = "✓" if delta <= REL_TOL else "⚠" + print(f"{icon} {label:<28} expected≈{expected:.3f} observed={observed:.3f} Δ={delta_pct:.1f}%") + + +def _endpoint_totals(runner: SimulationRunner) -> Tuple[float, float, float]: + """ + Return (CPU_seconds, IO_seconds, RAM_MB) of the first endpoint on the first server. + + This keeps the check simple. If you use multiple endpoints weighted by probability, + extend this function to compute a probability-weighted average. + """ + servers = runner.simulation_input.topology_graph.nodes.servers + if not servers or not servers[0].endpoints: + return (0.0, 0.0, 0.0) + + ep = servers[0].endpoints[0] + cpu_s = 0.0 + io_s = 0.0 + ram_mb = 0.0 + + for step in ep.steps: + if isinstance(step.kind, EndpointStepCPU): + cpu_s += float(step.step_operation[StepOperation.CPU_TIME]) + elif isinstance(step.kind, EndpointStepIO): + io_s += float(step.step_operation[StepOperation.IO_WAITING_TIME]) + elif isinstance(step.kind, EndpointStepRAM): + ram_mb += float(step.step_operation[StepOperation.NECESSARY_RAM]) + + return (cpu_s, io_s, ram_mb) + + +def _edges_mean_latency(runner: SimulationRunner) -> float: + """Sum of edge mean latencies across the graph (simple additive approximation).""" + return float(sum(e.latency.mean for e in runner.simulation_input.topology_graph.edges)) + + +def _mean(series: Iterable[float]) -> float: + """Numerically stable mean for a generic float iterable.""" + arr = np.asarray(list(series), dtype=float) + return float(np.mean(arr)) if arr.size else 0.0 + + +def run_sanity_checks(runner: SimulationRunner, res: ResultsAnalyzer) -> None: + """ + Compare expected vs observed metrics using back-of-the-envelope approximations. + + Approximations used: + - Throughput ≈ λ = users * RPM / 60 + - Mean latency ≈ CPU_s + IO_s + NET_s (ignores queueing inside the server) + - Mean RAM ≈ λ * T_srv * RAM_per_request (Little’s law approximation) + - Mean I/O queue length ≈ λ * IO_s + - Edge concurrency ≈ λ * edge_mean_latency + """ + print("\n════════ SANITY CHECKS (expected vs observed) ════════") + + # Arrival rate λ (requests per second) + w = runner.simulation_input.rqs_input + lam_rps = float(w.avg_active_users.mean) * float(w.avg_request_per_minute_per_user.mean) / 60.0 + + # Endpoint sums + cpu_s, io_s, ram_mb = _endpoint_totals(runner) + net_s = _edges_mean_latency(runner) + t_srv = cpu_s + io_s + latency_expected = cpu_s + io_s + net_s + + # Observed latency, throughput + stats = res.get_latency_stats() + latency_observed = float(stats.get(LatencyKey.MEAN, 0.0)) + _, rps_series = res.get_throughput_series() + rps_observed = _mean(rps_series) + + # Observed RAM and queues + sampled = res.get_sampled_metrics() + ram_series = sampled.get("ram_in_use", {}) + ram_means = [_mean(vals) for vals in ram_series.values()] + ram_observed = float(sum(ram_means)) if ram_means else 0.0 + + ready_series = sampled.get("ready_queue_len", {}) + ioq_series = sampled.get("event_loop_io_sleep", {}) + ready_observed = _mean([_mean(v) for v in ready_series.values()]) if ready_series else 0.0 + ioq_observed = _mean([_mean(v) for v in ioq_series.values()]) if ioq_series else 0.0 + + # Expected quantities (very rough) + rps_expected = lam_rps + ram_expected = lam_rps * t_srv * ram_mb + ioq_expected = lam_rps * io_s + + _tick("Mean throughput (rps)", rps_expected, rps_observed) + _tick("Mean latency (s)", latency_expected, latency_observed) + _tick("Mean RAM (MB)", ram_expected, ram_observed) + _tick("Mean I/O queue", ioq_expected, ioq_observed) + + # Edge concurrency + edge_conc = sampled.get("edge_concurrent_connection", {}) + if edge_conc: + print("\n— Edge concurrency —") + edge_means: Dict[str, float] = {eid: _mean(vals) for eid, vals in edge_conc.items()} + for e in runner.simulation_input.topology_graph.edges: + exp = lam_rps * float(e.latency.mean) + obs = edge_means.get(e.id, 0.0) + _tick(f"edge {e.id}", exp, obs) + + # Extra diagnostics + print("\n— Diagnostics —") + print( + "λ={:.3f} rps | CPU_s={:.3f} IO_s={:.3f} NET_s≈{:.3f} | RAM/req={:.1f} MB" + .format(lam_rps, cpu_s, io_s, net_s, ram_mb) + ) + print("T_srv={:.3f}s → RAM_expected≈λ*T_srv*RAM = {:.1f} MB".format(t_srv, ram_expected)) + + +# ───────────────────────────────────────────────────────────── +# Main +# ───────────────────────────────────────────────────────────── +def main() -> None: + """Entry-point: parse args, run simulation, print/plot, sanity-check.""" + parser = ArgumentParser(description="Run FastSim from YAML and print outputs + sanity checks.") + parser.add_argument( + "--yaml", + type=Path, + default=Path(__file__).parent / "data" /"single_server.yml", + help="Path to the simulation YAML file.", + ) + parser.add_argument( + "--out", + type=Path, + default=Path(__file__).parent / "single_server_yml.png", + help="Path to the output image (plots).", + ) + args = parser.parse_args() + + yaml_path: Path = args.yaml + if not yaml_path.exists(): + raise FileNotFoundError(f"YAML not found: {yaml_path}") + + # Build runner from YAML and execute + env = simpy.Environment() + runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) + results: ResultsAnalyzer = runner.run() + + # Prints + print_latency_stats(results) + print_throughput(results) + print_sampled_preview(results) + + # Sanity checks + run_sanity_checks(runner, results) + + # Plots + save_all_plots(results, args.out) + + +if __name__ == "__main__": + main() diff --git a/example/single_server_yml.png b/example/single_server_yml.png new file mode 100644 index 0000000000000000000000000000000000000000..89cca6d5cd245400ed7fe97b9ff0b33123794647 GIT binary patch literal 158475 zcmce;byQVt7d?7Vx=XqdR2rpI8bnG&Iu)f;x{;I;P*74aP*6g;8$<-81f*N(l!m*u zzV-d?9pnCW?-;zV3g_&z_w%f0tvTnKi%3m%B?4R;ToejLprR~)1BF5_K%veuVPnE~ zu1uz$fj`9VE9l+VcD#Gv!`#IZrDlHL$D|Gpzd;Jg{h|6G-ZwBWxl zh+rqc(fjWU8SD00pJALkgdK#*U&vUC& zn6`=|%v_%W2M5P`W#XX{&Aa*B$Iw{=6AQY2tu&{j0f&^ ze=o6Rlg^@^s#>dU`q`Jwf4dcbDlL0rAe@k%Lxqu=I?^_;0y8t8eYDw;;f9hazIsGcbq2lX##3QO28-Lb zweBZeAJ9z7wOYKJ<#mA%N4tyUP*t!JIr>w5t3X9$IS~VGy`*3-<2)?11%wy~{ zprb2jHN2y6^(sp0cv~BFRY3tmJwqaLAXj(h*N9^qys6M-?pjD_D9%q|yP-*E>blbt z@r1-g=Lx@EW)_ylb-#1x&aH3dPQ;PcRMLyOptF-5Q^-NA3J}!p&(pSV44MYS^!MLv z*=LaSo`lO0=$EL*me>wXKOmA?S8n#6i4o>=o|X>@59iGD+BB|#xX>#wLqkoDR=DoL 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diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 07e383c..0aa5f9d 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -1,15 +1,15 @@ """ Application-wide constants and configuration values. -This module groups all the *static* enumerations used by the FastSim backend +This module groups all the static enumerations used by the FastSim backend so that: -* JSON / YAML payloads can be strictly validated with Pydantic. -* Front-end and simulation engine share a single source of truth. -* Ruff, mypy and IDEs can leverage the strong typing provided by Enum classes. + JSON / YAML payloads can be strictly validated with Pydantic. + Front-end and simulation engine share a single source of truth. + Ruff, mypy and IDEs can leverage the strong typing provided by Enum classes. -**IMPORTANT:** Changing any enum *value* is a breaking-change for every -stored configuration file. Add new members whenever possible instead of +IMPORTANT: Changing any enum value is a breaking-change for every +stored configuration file. Add new members whenever possible instead of renaming existing ones. """ @@ -22,27 +22,27 @@ class TimeDefaults(IntEnum): """ - Default time-related constants (expressed in **seconds**). + Default time-related constants (expressed in seconds). - These values are used when the user omits an explicit parameter. They also + These values are used when the user omits an explicit parameter. They also serve as lower / upper bounds for validation for the requests generator. """ - MIN_TO_SEC = 60 # 1 minute → 60 s - USER_SAMPLING_WINDOW = 60 # keep U(t) constant for 60 s - SIMULATION_TIME = 3_600 # run 1 h if user gives no value - MIN_SIMULATION_TIME = 1_800 # enforce at least 30 min - MIN_USER_SAMPLING_WINDOW = 1 # 1 s minimum - MAX_USER_SAMPLING_WINDOW = 120 # 2 min maximum + MIN_TO_SEC = 60 # 1 minute → 60 s + USER_SAMPLING_WINDOW = 60 # every 60 seconds sample the number of active user + SIMULATION_TIME = 3_600 # run 1 h if user gives no value + MIN_SIMULATION_TIME = 5 # 5 seconds give a broad spectrum + MIN_USER_SAMPLING_WINDOW = 1 # 1 s minimum + MAX_USER_SAMPLING_WINDOW = 120 # 2 min maximum class Distribution(StrEnum): """ - Probability distributions accepted by :class:`~app.schemas.RVConfig`. + Probability distributions accepted by app.schemas.RVConfig. - The *string value* is exactly the identifier that must appear in JSON + The string value is exactly the identifier that must appear in JSON payloads. The simulation engine will map each name to the corresponding - random sampler (e.g. ``numpy.random.poisson``). + random sampler (e.g.numpy.random.poisson). """ POISSON = "poisson" @@ -55,43 +55,19 @@ class Distribution(StrEnum): # CONSTANTS FOR ENDPOINT STEP DEFINITION (REQUEST-HANDLER) # ====================================================================== -# The JSON received by the API for an endpoint step is expected to look like: -# -# { -# "endpoint_name": "/predict", -# "kind": "io_llm", -# "metrics": { -# "cpu_time": 0.150, -# "necessary_ram": 256 -# } -# } -# -# The Enum classes below guarantee that only valid *kind* and *metric* keys -# are accepted by the Pydantic schema. - - class EndpointStepIO(StrEnum): """ - I/O-bound operation categories that can occur inside an endpoint *step*. - - .. list-table:: - :header-rows: 1 - - * - Constant - - Meaning (executed by coroutine) - * - ``TASK_SPAWN`` - - Spawns an additional ``asyncio.Task`` and returns immediately. - * - ``LLM`` - - Performs a remote Large-Language-Model inference call. - * - ``WAIT`` - - Passive, *non-blocking* wait for I/O completion; no new task spawned. - * - ``DB`` - - Round-trip to a relational / NoSQL database. - * - ``CACHE`` - - Access to a local or distributed cache layer. - - The *value* of each member (``"io_llm"``, ``"io_db"``, …) is the exact - identifier expected in external JSON. + I/O-bound operation categories that can occur inside an endpoint step. + - TASK_SPAWN + Spawns an additional ``asyncio.Task`` and returns immediately. + - LLM + Performs a remote Large-Language-Model inference call. + - WAIT + Passive, *non-blocking* wait for I/O completion; no new task spawned. + - DB + Round-trip to a relational / NoSQL database. + - CACHE + Access to a local or distributed cache layer. """ TASK_SPAWN = "io_task_spawn" @@ -126,11 +102,11 @@ class EndpointStepRAM(StrEnum): class StepOperation(StrEnum): """ - Keys used inside the ``metrics`` dictionary of a *step*. + Keys used inside the metrics dictionary of a step. - * ``CPU_TIME`` - Service time (seconds) during which the coroutine occupies + CPU_TIME - Service time (seconds) during which the coroutine occupies the CPU / GIL. - * ``NECESSARY_RAM`` - Peak memory (MB) required by the step. + NECESSARY_RAM - Peak memory (MB) required by the step. """ CPU_TIME = "cpu_time" @@ -191,7 +167,7 @@ class SystemNodes(StrEnum): class SystemEdges(StrEnum): """ - Edge categories connecting different :class:`SystemNodes`. + Edge categories connecting different class SystemNodes. Currently only network links are modeled; new types (IPC queue, message bus, stream) can be added without impacting existing payloads. @@ -205,7 +181,7 @@ class SystemEdges(StrEnum): class SampledMetricName(StrEnum): """ - define the metrics sampled every fixed amount of + Define the metrics sampled every fixed amount of time to create a time series """ @@ -218,7 +194,7 @@ class SampledMetricName(StrEnum): class SamplePeriods(float, Enum): """ - defining the value of the sample periods for the metrics for which + Defining the value of the sample periods for the metrics for which we have to extract a time series """ @@ -232,12 +208,12 @@ class SamplePeriods(float, Enum): class EventMetricName(StrEnum): """ - define the metrics triggered by event with no + Define the metrics triggered by event with no time series """ # Mandatory - RQS_CLOCK = "rqs_clock" + RQS_CLOCK = "rqs_clock" # useful to collect starting and finishing time of rqs # Not mandatory LLM_COST = "llm_cost" diff --git a/src/app/metrics/analyzer.py b/src/app/metrics/analyzer.py index 762e5c6..e1aa50e 100644 --- a/src/app/metrics/analyzer.py +++ b/src/app/metrics/analyzer.py @@ -51,11 +51,10 @@ def __init__( ) -> None: """ Args: - client: Client runtime object, containing RqsClock entries. - servers: List of server runtime objects. - edges: List of edge runtime objects. - settings: Original simulation settings. - + client: Client runtime object, containing RqsClock entries. + servers: List of server runtime objects. + edges: List of edge runtime objects. + settings: Original simulation settings. """ self._client = client self._servers = servers diff --git a/src/app/metrics/collector.py b/src/app/metrics/collector.py index 17339fa..d2158f5 100644 --- a/src/app/metrics/collector.py +++ b/src/app/metrics/collector.py @@ -14,7 +14,6 @@ # way we optimize the initialization of various objects reducing # the global overhead - class SampledMetricCollector: """class to define a centralized object to collect sampled metrics""" @@ -26,7 +25,14 @@ def __init__( env: simpy.Environment, sim_settings: SimulationSettings, ) -> None: - """Docstring to complete""" + """ + Args: + edges (list[EdgeRuntime]): list of the class EdgeRuntime + servers (list[ServerRuntime]): list of server of the class ServerRuntime + env (simpy.Environment): environment for the simulation + sim_settings (SimulationSettings): general settings for the simulation + + """ self.edges = edges self.servers = servers self.sim_settings = sim_settings @@ -41,8 +47,6 @@ def __init__( self._ready_key = SampledMetricName.READY_QUEUE_LEN - env.process(self._build_time_series()) - def _build_time_series(self) -> Generator[simpy.Event, None, None]: """Function to build time series for enabled metrics""" while True: @@ -61,6 +65,8 @@ def _build_time_series(self) -> Generator[simpy.Event, None, None]: server.enabled_metrics[self._io_key].append(server.io_queue_len) server.enabled_metrics[self._ready_key].append(server.ready_queue_len) + + def start(self) -> simpy.Process: """Definition of the process to collect sampled metrics""" return self.env.process(self._build_time_series()) diff --git a/src/app/runtime/actors/client.py b/src/app/runtime/actors/client.py index 42b0c3f..2f2da4b 100644 --- a/src/app/runtime/actors/client.py +++ b/src/app/runtime/actors/client.py @@ -44,6 +44,7 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]: """Updtate the state before passing it to another node""" assert self.out_edge is not None while True: + state: RequestState = yield self.client_box.get() # type: ignore[assignment] state.record_hop( @@ -58,7 +59,7 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]: # would be equal to two would mean that the state # went through the mandatory path to be generated # rqs generator and client registration - if len(state.history) > 2: + if len(state.history) > 3: state.finish_time = self.env.now clock_data = RqsClock( start=state.initial_time, diff --git a/src/app/runtime/actors/server.py b/src/app/runtime/actors/server.py index 9051d99..d791bbb 100644 --- a/src/app/runtime/actors/server.py +++ b/src/app/runtime/actors/server.py @@ -141,6 +141,7 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 yield self.server_resources[ServerResourceName.RAM.value].get(total_ram) self._ram_in_use += total_ram + # Initial conditions of the server a rqs a priori is not in any queue # and it does not occupy a core until it started to be elaborated core_locked = False @@ -175,7 +176,7 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 for step in selected_endpoint.steps: - if isinstance(step.kind, EndpointStepCPU): + if step.kind in EndpointStepCPU: # with the boolean we avoid redundant operation of asking # the core multiple time on a given step # for example if we have two consecutive cpu bound step @@ -200,8 +201,9 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 # Execute the step giving back the control to the simpy env yield self.env.timeout(cpu_time) - - elif isinstance(step.kind, EndpointStepIO): + # since the object is of an Enum class we check if the step.kind + # is one member of enum + elif step.kind in EndpointStepIO: io_time = step.step_operation[StepOperation.IO_WAITING_TIME] # Same here with the boolean if we have multiple I/O steps # we release the core just the first time if the previous step diff --git a/src/app/samplers/common_helpers.py b/src/app/samplers/common_helpers.py index e565a20..d594538 100644 --- a/src/app/samplers/common_helpers.py +++ b/src/app/samplers/common_helpers.py @@ -47,30 +47,43 @@ def exponential_variable_generator( return float(rng.exponential(mean)) def general_sampler(random_variable: RVConfig, rng: np.random.Generator) -> float: - """Sample a number according to the distribution described in `random_variable`.""" - dist = random_variable.distribution - mean = random_variable.mean + """ + Draw one sample from the distribution described by *random_variable*. + + Only **Normal** and **Log-normal** require an explicit ``variance``. + For **Uniform**, **Poisson** and **Exponential** the mean is enough. + """ + dist = random_variable.distribution + mean = random_variable.mean + var = random_variable.variance match dist: + # ── No extra parameters needed ────────────────────────────────── case Distribution.UNIFORM: - - assert random_variable.variance is None + # Variance is meaningless for an ad-hoc uniform [0, 1) helper. + assert var is None return uniform_variable_generator(rng) - case _: + case Distribution.POISSON: + # λ == mean ; numpy returns ints → cast to float for consistency + assert var is None + return float(poisson_variable_generator(mean, rng)) - variance = random_variable.variance - assert variance is not None - - match dist: - case Distribution.NORMAL: - return truncated_gaussian_generator(mean, variance, rng) - case Distribution.LOG_NORMAL: - return lognormal_variable_generator(mean, variance, rng) - case Distribution.POISSON: - return float(poisson_variable_generator(mean, rng)) - case Distribution.EXPONENTIAL: - return exponential_variable_generator(mean, rng) - case _: - msg = f"Unsupported distribution: {dist}" - raise ValueError(msg) + case Distribution.EXPONENTIAL: + # β (scale) == mean ; nothing else required + assert var is None + return exponential_variable_generator(mean, rng) + + # ── Distributions that *do* need a variance parameter ─────────── + case Distribution.NORMAL: + assert var is not None + return truncated_gaussian_generator(mean, var, rng) + + case Distribution.LOG_NORMAL: + assert var is not None + return lognormal_variable_generator(mean, var, rng) + + # ── Anything else is unsupported ──────────────────────────────── + case _: + msg = f"Unsupported distribution: {dist}" + raise ValueError(msg) diff --git a/src/app/samplers/poisson_poisson.py b/src/app/samplers/poisson_poisson.py index 7f17364..725faa5 100644 --- a/src/app/samplers/poisson_poisson.py +++ b/src/app/samplers/poisson_poisson.py @@ -28,7 +28,7 @@ def poisson_poisson_sampling( Algorithm --------- - 1. Every *sampling_window_s* seconds, draw + 1. Every sampling_window_s seconds, draw U ~ Poisson(mean_concurrent_user). 2. Compute the aggregate rate Λ = U * (mean_req_per_minute_per_user / 60) [req/s]. diff --git a/src/app/schemas/random_variables_config.py b/src/app/schemas/random_variables_config.py index b09ed13..e15d509 100644 --- a/src/app/schemas/random_variables_config.py +++ b/src/app/schemas/random_variables_config.py @@ -13,7 +13,7 @@ class RVConfig(BaseModel): variance: float | None = None @field_validator("mean", mode="before") - def ensure_mean_is_numeric( + def ensure_mean_is_numeric_and_positive( cls, # noqa: N805 v: float, ) -> float: @@ -21,6 +21,7 @@ def ensure_mean_is_numeric( err_msg = "mean must be a number (int or float)" if not isinstance(v, (float, int)): raise ValueError(err_msg) # noqa: TRY004 + return float(v) @model_validator(mode="after") # type: ignore[arg-type] diff --git a/src/app/schemas/simulation_output.py b/src/app/schemas/simulation_output.py deleted file mode 100644 index 26d1adb..0000000 --- a/src/app/schemas/simulation_output.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Define the output of the simulation""" - -from pydantic import BaseModel - - -class SimulationOutput(BaseModel): - """Define the output of the simulation""" - - total_requests: dict[str, int | float] - # TO DEFINE - metric_2: str - #...... - metric_n: str diff --git a/src/app/schemas/system_topology/endpoint.py b/src/app/schemas/system_topology/endpoint.py index 8933dab..aa40e9e 100644 --- a/src/app/schemas/system_topology/endpoint.py +++ b/src/app/schemas/system_topology/endpoint.py @@ -53,8 +53,10 @@ def ensure_coherence_type_operation( raise ValueError(msg) # Coherence CPU bound operation and operation - if isinstance(model.kind, EndpointStepCPU): - if operation_keys != {StepOperation.CPU_TIME}: + if ( + isinstance(model.kind, EndpointStepCPU) + and operation_keys != {StepOperation.CPU_TIME} + ): msg = ( "The operation to quantify a CPU BOUND step" f"must be {StepOperation.CPU_TIME}" @@ -62,8 +64,10 @@ def ensure_coherence_type_operation( raise ValueError(msg) # Coherence RAM operation and operation - elif isinstance(model.kind, EndpointStepRAM): - if operation_keys != {StepOperation.NECESSARY_RAM}: + if ( + isinstance(model.kind, EndpointStepRAM) + and operation_keys != {StepOperation.NECESSARY_RAM} + ): msg = ( "The operation to quantify a RAM step" f"must be {StepOperation.NECESSARY_RAM}" @@ -71,11 +75,12 @@ def ensure_coherence_type_operation( raise ValueError(msg) # Coherence I/O operation and operation - elif operation_keys != {StepOperation.IO_WAITING_TIME}: - msg = ( - "The operation to quantify an I/O step" - f"must be {StepOperation.IO_WAITING_TIME}" - ) + if ( + isinstance(model.kind, EndpointStepIO) + and operation_keys != {StepOperation.IO_WAITING_TIME} + ): + + msg = f"An I/O step must use {StepOperation.IO_WAITING_TIME}" raise ValueError(msg) return model diff --git a/src/app/schemas/system_topology/full_system_topology.py b/src/app/schemas/system_topology/full_system_topology.py index 6b49b21..ddd4c5f 100644 --- a/src/app/schemas/system_topology/full_system_topology.py +++ b/src/app/schemas/system_topology/full_system_topology.py @@ -16,6 +16,7 @@ field_validator, model_validator, ) +from pydantic_core.core_schema import ValidationInfo from app.config.constants import ( LbAlgorithmsName, @@ -204,6 +205,37 @@ class Edge(BaseModel): ), ) + # The idea to put here the control about variance and mean about the edges + # latencies and not in RVConfig is to provide a better error handling + # providing a direct indication of the edge with the error + # The idea to put here the control about variance and mean about the edges + # latencies and not in RVConfig is to provide a better error handling + # providing a direct indication of the edge with the error + @field_validator("latency", mode="after") + def ensure_latency_is_non_negative( + cls, # noqa: N805 + v: RVConfig, + info: ValidationInfo, + ) -> RVConfig: + """Ensures that the latency's mean and variance are positive.""" + mean = v.mean + variance = v.variance + + # We can get the edge ID from the validation context for a better error message + edge_id = info.data.get("id", "unknown") + + if mean <= 0: + msg = f"The mean latency of the edge '{edge_id}' must be positive" + raise ValueError(msg) + if variance is not None and variance < 0: # Variance can be zero + msg = ( + f"The variance of the latency of the edge {edge_id}" + "must be positive" + ) + raise ValueError(msg) + return v + + @model_validator(mode="after") # type: ignore[arg-type] def check_src_trgt_different(cls, model: "Edge") -> "Edge": # noqa: N805 """Ensure source is different from target""" @@ -241,33 +273,55 @@ def unique_ids( return model - @model_validator(mode="after") # type: ignore[arg-type] - def edge_refs_valid(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 + @model_validator(mode="after") # type: ignore[arg-type] + def edge_refs_valid( + cls, # noqa: N805 + model: "TopologyGraph", + ) -> "TopologyGraph": """ - Ensure that **every** edge points to valid nodes. - - The validator is executed *after* the entire ``TopologyGraph`` model has - been built, so all servers and the client already exist in ``m.nodes``. + Validate that the graph is self-consistent. - Steps - ----- - 1. Build the set ``valid_ids`` containing: - * all ``Server.id`` values, **plus** - * the single ``Client.id``. - 2. Iterate through each ``Edge`` in ``m.edges`` and raise - :class:`ValueError` if either ``edge.source`` or ``edge.target`` is - **not** present in ``valid_ids``. - - Returning the (unchanged) model signals that the integrity check passed. + * All targets must be nodes declared in ``m.nodes``. + * External IDs are allowed as sources (entry points, generator) but + they must never appear as a target anywhere else. """ - valid_ids = {s.id for s in model.nodes.servers} | {model.nodes.client.id} + # ------------------------------------------------------------------ + # 1. Collect declared node IDs (servers, client, optional LB) + # ------------------------------------------------------------------ + node_ids: set[str] = {srv.id for srv in model.nodes.servers} + node_ids.add(model.nodes.client.id) if model.nodes.load_balancer is not None: - valid_ids.add(model.nodes.load_balancer.id) - - for e in model.edges: - if e.source not in valid_ids or e.target not in valid_ids: - msg = f"Edge {e.source}->{e.target} references unknown node" + node_ids.add(model.nodes.load_balancer.id) + + # ------------------------------------------------------------------ + # 2. Scan every edge once + # ------------------------------------------------------------------ + external_sources: set[str] = set() + + for edge in model.edges: + # ── Rule 1: target must be a declared node + if edge.target not in node_ids: + msg = ( + f"Edge {edge.source}->{edge.target} references " + f"unknown target node '{edge.target}'." + ) raise ValueError(msg) + + # Collect any source that is not a declared node + if edge.source not in node_ids: + external_sources.add(edge.source) + + # ------------------------------------------------------------------ + # 3. Ensure external sources never appear as targets elsewhere + # ------------------------------------------------------------------ + forbidden_targets = external_sources & {e.target for e in model.edges} + if forbidden_targets: + msg = ( + "External IDs cannot be used as targets as well:" + f"{sorted(forbidden_targets)}" + ) + raise ValueError(msg) + return model @model_validator(mode="after") # type: ignore[arg-type] diff --git a/tests/conftest.py b/tests/conftest.py index 09a80d4..39c8b40 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,6 +6,7 @@ from numpy.random import default_rng from app.config.constants import ( + Distribution, EventMetricName, SampledMetricName, SamplePeriods, @@ -17,6 +18,7 @@ from app.schemas.simulation_settings_input import SimulationSettings from app.schemas.system_topology.full_system_topology import ( Client, + Edge, TopologyGraph, TopologyNodes, ) @@ -109,15 +111,23 @@ def rqs_input() -> RqsGeneratorInput: @pytest.fixture def topology_minimal() -> TopologyGraph: """ - A valid topology containing a single client and **no** servers or edges. + A valid *tiny* topology: one generator ➜ one client. - Suitable for low-level tests that do not need to traverse the server - layer or network graph. + The single edge has a negligible latency; its only purpose is to give the + generator a valid ``out_edge`` so that the runtime can start. """ client = Client(id="client-1") - nodes = TopologyNodes(servers=[], client=client) - return TopologyGraph(nodes=nodes, edges=[]) + # Stub edge: generator id comes from rqs_input fixture (“rqs-1”) + edge = Edge( + id="gen-to-client", + source="rqs-1", + target="client-1", + latency=RVConfig(mean=0.001, distribution=Distribution.POISSON), + ) + + nodes = TopologyNodes(servers=[], client=client) + return TopologyGraph(nodes=nodes, edges=[edge]) # --------------------------------------------------------------------------- # # Complete simulation payload # diff --git a/tests/integration/minimal/conftest.py b/tests/integration/minimal/conftest.py index 1c9f81e..434f9f0 100644 --- a/tests/integration/minimal/conftest.py +++ b/tests/integration/minimal/conftest.py @@ -21,12 +21,7 @@ if TYPE_CHECKING: from app.schemas.full_simulation_input import SimulationPayload - from app.schemas.full_simulation_input import ( - SimulationPayload as _Payload, # noqa: F401 - ) - from app.schemas.rqs_generator_input import ( - RqsGeneratorInput as _RqsIn, # noqa: F401 - ) + # ────────────────────────────────────────────────────────────────────────────── # 0-traffic generator (shadows the project-wide fixture) diff --git a/tests/integration/payload/data/invalid/missing_field.yml b/tests/integration/payload/data/invalid/missing_field.yml new file mode 100644 index 0000000..c74102d --- /dev/null +++ b/tests/integration/payload/data/invalid/missing_field.yml @@ -0,0 +1,17 @@ +rqs_input: + id: gen-1 + avg_active_users: { mean: 1 } + avg_request_per_minute_per_user: { mean: 10 } + +topology_graph: + nodes: + client: { id: cli } + servers: + - id: srv-1 + endpoints: + - endpoint_name: ep + steps: + - { kind: cpu_parse, step_operation: { cpu_time: 0.001 } } + + edges: [] +sim_settings: { total_simulation_time: 10 } diff --git a/tests/integration/payload/data/invalid/negative_latency.yml b/tests/integration/payload/data/invalid/negative_latency.yml new file mode 100644 index 0000000..f69fb60 --- /dev/null +++ b/tests/integration/payload/data/invalid/negative_latency.yml @@ -0,0 +1,15 @@ +rqs_input: + id: gen-1 + avg_active_users: { mean: 1 } + avg_request_per_minute_per_user: { mean: 10 } + +topology_graph: + nodes: + client: { id: cli } + servers: [] + edges: + - id: bad-lat + source: gen-1 + target: cli + latency: { mean: -0.001 } +sim_settings: { total_simulation_time: 5 } diff --git a/tests/integration/payload/data/invalid/wrong_enum.yml b/tests/integration/payload/data/invalid/wrong_enum.yml new file mode 100644 index 0000000..58a1c50 --- /dev/null +++ b/tests/integration/payload/data/invalid/wrong_enum.yml @@ -0,0 +1,13 @@ +rqs_input: + id: gen-1 + avg_active_users: { mean: 1 } + avg_request_per_minute_per_user: + mean: 10 + distribution: gamma # not valid enum + +topology_graph: + nodes: + client: { id: cli } + servers: [] + edges: [] +sim_settings: { total_simulation_time: 5 } diff --git a/tests/integration/payload/test_payload_invalid.py b/tests/integration/payload/test_payload_invalid.py new file mode 100644 index 0000000..09249c1 --- /dev/null +++ b/tests/integration/payload/test_payload_invalid.py @@ -0,0 +1,19 @@ +"""test to verify validation on invalid yml""" + +from pathlib import Path + +import pytest +import yaml +from pydantic import ValidationError + +from app.schemas.full_simulation_input import SimulationPayload + +DATA_DIR = Path(__file__).parent / "data" / "invalid" +YMLS = sorted(DATA_DIR.glob("*.yml")) + +@pytest.mark.integration +@pytest.mark.parametrize("yaml_path", YMLS, ids=lambda p: p.stem) +def test_invalid_payloads_raise(yaml_path: Path) -> None : + raw = yaml.safe_load(yaml_path.read_text()) + with pytest.raises(ValidationError): + SimulationPayload.model_validate(raw) diff --git a/tests/integration/single_server/conftest.py b/tests/integration/single_server/conftest.py new file mode 100644 index 0000000..38f575a --- /dev/null +++ b/tests/integration/single_server/conftest.py @@ -0,0 +1,47 @@ +""" +Fixtures for the *single-server* integration scenario: + +generator ──edge──> server ──edge──> client + +The topology is stored as a YAML file (`tests/data/single_server.yml`) so +tests remain declarative and we avoid duplicating Pydantic wiring logic. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import TYPE_CHECKING + +import pytest +import simpy + +if TYPE_CHECKING: # heavy imports only when type-checking + from app.runtime.simulation_runner import SimulationRunner + + +# --------------------------------------------------------------------------- # +# Shared SimPy environment (function-scope so every test starts fresh) # +# --------------------------------------------------------------------------- # +@pytest.fixture +def env() -> simpy.Environment: + """Return an empty ``simpy.Environment`` for each test.""" + return simpy.Environment() + + +# --------------------------------------------------------------------------- # +# Build a SimulationRunner from the YAML scenario # +# --------------------------------------------------------------------------- # +@pytest.fixture +def runner(env: simpy.Environment) -> SimulationRunner: + """ + Load *single_server.yml* through the public constructor + :pymeth:`SimulationRunner.from_yaml`. + """ + # import deferred to avoid ruff TC001 + from app.runtime.simulation_runner import SimulationRunner # noqa: PLC0415 + + yaml_path: Path = ( + Path(__file__).parent / "data" / "single_server.yml" + ) + + return SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) diff --git a/tests/integration/single_server/data/single_server.yml b/tests/integration/single_server/data/single_server.yml new file mode 100644 index 0000000..4072f48 --- /dev/null +++ b/tests/integration/single_server/data/single_server.yml @@ -0,0 +1,54 @@ +# ─────────────────────────────────────────────────────────────── +# FastSim scenario: generator ➜ client ➜ server ➜ client +# ─────────────────────────────────────────────────────────────── + +# 1. Traffic generator (light load) +rqs_input: + id: rqs-1 + avg_active_users: { mean: 5 } + avg_request_per_minute_per_user: { mean: 40 } + user_sampling_window: 60 + +# 2. Topology +topology_graph: + nodes: + client: { id: client-1 } + servers: + - id: srv-1 + server_resources: { cpu_cores: 2, ram_mb: 2048 } + endpoints: + - endpoint_name: ep-1 + probability: 1.0 + steps: + - kind: initial_parsing + step_operation: { cpu_time: 0.001 } + - kind: io_wait + step_operation: { io_waiting_time: 0.002 } + + edges: + - id: gen-to-client + source: rqs-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + + - id: client-to-server + source: client-1 + target: srv-1 + latency: { mean: 0.003, distribution: exponential } + + - id: server-to-client + source: srv-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + +# 3. Simulation settings +sim_settings: + total_simulation_time: 50 + sample_period_s: 0.01 + enabled_sample_metrics: + - ready_queue_len + - event_loop_io_sleep + - ram_in_use + - edge_concurrent_connection + enabled_event_metrics: + - rqs_clock diff --git a/tests/integration/single_server/test_single_server.py b/tests/integration/single_server/test_single_server.py new file mode 100644 index 0000000..498a7a1 --- /dev/null +++ b/tests/integration/single_server/test_single_server.py @@ -0,0 +1,52 @@ +""" +End-to-end verification of a *functional* topology (1 generator, 1 server). + +Assertions cover: + +* non-zero latency stats, +* throughput series length > 0, +* presence of sampled metrics for both edge & server. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest + +from app.config.constants import LatencyKey, SampledMetricName + +if TYPE_CHECKING: # only needed for type-checking + from app.metrics.analyzer import ResultsAnalyzer + from app.runtime.simulation_runner import SimulationRunner + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # +@pytest.mark.integration +def test_single_server_happy_path(runner: SimulationRunner) -> None: + """Run the simulation and ensure that *something* was processed.""" + results: ResultsAnalyzer = runner.run() + + # ── Latency stats must exist ─────────────────────────────────────────── + stats = results.get_latency_stats() + assert stats, "Expected non-empty latency statistics." + assert stats[LatencyKey.TOTAL_REQUESTS] > 0 + assert stats[LatencyKey.MEAN] > 0.0 + + # ── Throughput series must have at least one bucket > 0 ─────────────── + ts, rps = results.get_throughput_series() + assert len(ts) == len(rps) > 0 + assert any(val > 0 for val in rps) + + # ── Sampled metrics must include *one* server and *one* edge ─────────── + sampled = results.get_sampled_metrics() + + # Server RAM & queues + assert SampledMetricName.RAM_IN_USE in sampled + assert sampled[SampledMetricName.RAM_IN_USE], "Server RAM time-series missing." + + # Edge concurrent-connection metric + assert SampledMetricName.EDGE_CONCURRENT_CONNECTION in sampled + assert sampled[SampledMetricName.EDGE_CONCURRENT_CONNECTION], "Edge metric missing." diff --git a/tests/unit/runtime/actors/test_client.py b/tests/unit/runtime/actors/test_client.py index bc17471..7ee194e 100644 --- a/tests/unit/runtime/actors/test_client.py +++ b/tests/unit/runtime/actors/test_client.py @@ -84,6 +84,7 @@ def test_inbound_is_completed() -> None: req = RequestState(id=2, initial_time=0.0) req.record_hop(SystemNodes.GENERATOR, "gen-1", env.now) req.record_hop(SystemEdges.NETWORK_CONNECTION, "edge-X", env.now) + req.record_hop(SystemNodes.CLIENT, "cli-1", env.now) # simulate return inbox.put(req) env.run() diff --git a/tests/unit/runtime/actors/test_edge.py b/tests/unit/runtime/actors/test_edge.py index 37ad296..644b078 100644 --- a/tests/unit/runtime/actors/test_edge.py +++ b/tests/unit/runtime/actors/test_edge.py @@ -82,7 +82,7 @@ def _make_edge( id="edge-1", source="src", target="dst", - latency=RVConfig(mean=0.0, variance=1.0, distribution="normal"), + latency=RVConfig(mean=1.0, variance=1.0, distribution="normal"), dropout_rate=dropout_rate, ) diff --git a/tests/unit/runtime/test_simulation_runner.py b/tests/unit/runtime/test_simulation_runner.py new file mode 100644 index 0000000..d785e3d --- /dev/null +++ b/tests/unit/runtime/test_simulation_runner.py @@ -0,0 +1,159 @@ +"""Unit-tests for :pyclass:`app.runtime.simulation_runner.SimulationRunner`. + +Purpose +------- +Validate each private builder in isolation and run a minimal end-to-end +execution without relying on the full integration scenarios. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING +from unittest.mock import patch + +import pytest +import simpy +import yaml + +from app.metrics.analyzer import ResultsAnalyzer +from app.runtime.simulation_runner import SimulationRunner + +if TYPE_CHECKING: + from pathlib import Path + + from app.runtime.actors.client import ClientRuntime + from app.runtime.actors.rqs_generator import RqsGeneratorRuntime + from app.schemas.full_simulation_input import SimulationPayload + + +# --------------------------------------------------------------------------- # +# Fixtures # +# --------------------------------------------------------------------------- # +@pytest.fixture +def env() -> simpy.Environment: + """Return a fresh SimPy environment for every unit test.""" + return simpy.Environment() + + +@pytest.fixture +def runner( + env: simpy.Environment, + payload_base: SimulationPayload, +) -> SimulationRunner: + """Factory producing an **un-started** SimulationRunner.""" + return SimulationRunner(env=env, simulation_input=payload_base) + + +# --------------------------------------------------------------------------- # +# Builder-level tests # +# --------------------------------------------------------------------------- # +def test_build_rqs_generator_populates_dict(runner: SimulationRunner) -> None: + """_build_rqs_generator() must register one generator runtime.""" + runner._build_rqs_generator() # noqa: SLF001 + assert len(runner._rqs_runtime) == 1 # noqa: SLF001 + gen_rt: RqsGeneratorRuntime = next( + iter(runner._rqs_runtime.values()), # noqa: SLF001 + ) + assert gen_rt.rqs_generator_data.id == runner.rqs_generator.id + + +def test_build_client_populates_dict(runner: SimulationRunner) -> None: + """_build_client() must register exactly one client runtime.""" + runner._build_client() # noqa: SLF001 + assert len(runner._client_runtime) == 1 # noqa: SLF001 + cli_rt: ClientRuntime = next( + iter(runner._client_runtime.values()), # noqa: SLF001 + ) + assert cli_rt.client_config.id == runner.client.id + assert cli_rt.out_edge is None + + +def test_build_servers_keeps_empty_with_minimal_topology( + runner: SimulationRunner, +) -> None: + """Zero servers in the payload → dict stays empty.""" + runner._build_servers() # noqa: SLF001 + assert runner._servers_runtime == {} # noqa: SLF001 + + +def test_build_load_balancer_noop_when_absent( + runner: SimulationRunner, +) -> None: + """No LB in the payload → builder leaves dict empty.""" + runner._build_load_balancer() # noqa: SLF001 + assert runner._lb_runtime == {} # noqa: SLF001 + + +# --------------------------------------------------------------------------- # +# Edges builder # +# --------------------------------------------------------------------------- # +def test_build_edges_with_stub_edge(runner: SimulationRunner) -> None: + """ + `_build_edges()` must register exactly one `EdgeRuntime`, corresponding + to the single stub edge (generator → client) present in the minimal + topology fixture. + """ + runner._build_rqs_generator() # noqa: SLF001 + runner._build_client() # noqa: SLF001 + runner._build_edges() # noqa: SLF001 + assert len(runner._edges_runtime) == 1 # noqa: SLF001 + + +# --------------------------------------------------------------------------- # +# End-to-end “mini” run # +# --------------------------------------------------------------------------- # +def test_run_returns_results_analyzer(runner: SimulationRunner) -> None: + """ + `.run()` must complete even though the client is a sink node. We patch + `_build_client` to assign a no-op edge to avoid assertions. + """ + + class _NoOpEdge: + """Edge stub that silently discards transported states.""" + + def transport(self) -> None: + return + + def patched_build_client(self: SimulationRunner) -> None: + # Call the original builder + SimulationRunner._build_client(self) # noqa: SLF001 + cli_rt = next(iter(self._client_runtime.values())) + cli_rt.out_edge = _NoOpEdge() # type: ignore[assignment] + + with patch.object(runner, "_build_client", patched_build_client.__get__(runner)): + results: ResultsAnalyzer = runner.run() + + assert isinstance(results, ResultsAnalyzer) + assert ( + pytest.approx(runner.env.now) + == runner.simulation_settings.total_simulation_time + ) + + +# --------------------------------------------------------------------------- # +# from_yaml utility # +# --------------------------------------------------------------------------- # +def test_from_yaml_minimal(tmp_path: Path, env: simpy.Environment) -> None: + """from_yaml() parses YAML, validates via Pydantic and returns a runner.""" + yml_payload = { + "rqs_input": { + "id": "gen-yaml", + "avg_active_users": {"mean": 1}, + "avg_request_per_minute_per_user": {"mean": 2}, + "user_sampling_window": 10, + }, + "topology_graph": { + "nodes": {"client": {"id": "cli-yaml"}, "servers": []}, + "edges": [], + }, + "sim_settings": {"total_simulation_time": 5}, + } + + yml_path: Path = tmp_path / "scenario.yml" + yml_path.write_text(yaml.safe_dump(yml_payload)) + + runner = SimulationRunner.from_yaml(env=env, yaml_path=yml_path) + + assert isinstance(runner, SimulationRunner) + assert runner.rqs_generator.id == "gen-yaml" + assert runner.client.id == "cli-yaml" diff --git a/tests/unit/samplers/test_sampler_helper.py b/tests/unit/samplers/test_sampler_helper.py index 7e615d0..ff44c3b 100644 --- a/tests/unit/samplers/test_sampler_helper.py +++ b/tests/unit/samplers/test_sampler_helper.py @@ -176,7 +176,7 @@ def test_general_sampler_normal_path() -> None: def test_general_sampler_poisson_path() -> None: """Poisson branch returns the dummy's preset integer as *float*.""" dummy = cast("np.random.Generator", DummyRNG(poisson_value=4)) - cfg = RVConfig(mean=5.0, variance=5.0, distribution=Distribution.POISSON) + cfg = RVConfig(mean=5.0, distribution=Distribution.POISSON) result = general_sampler(cfg, dummy) assert isinstance(result, float) assert result == 4.0 @@ -192,5 +192,5 @@ def test_general_sampler_lognormal_path() -> None: def test_general_sampler_exponential_path() -> None: """Exponential branch produces a strictly positive float.""" rng = np.random.default_rng(7) - cfg = RVConfig(mean=1.5, variance=1.5, distribution=Distribution.EXPONENTIAL) + cfg = RVConfig(mean=1.5, distribution=Distribution.EXPONENTIAL) assert general_sampler(cfg, rng) > 0.0 From 90df33d4566f6ee21b69667db8e2a048c6e949cb Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Fri, 8 Aug 2025 18:16:27 +0200 Subject: [PATCH 23/52] new readme and guide to build yaml --- README.md | 377 ++++++++++++---- docs/yaml_builder.md | 448 +++++++++++++++++++ {example => examples}/data/single_server.yml | 0 {example => examples}/single_server_yaml.py | 0 {example => examples}/single_server_yml.png | Bin examples/your_example.py | 54 +++ src/app/metrics/analyzer.py | 1 + 7 files changed, 794 insertions(+), 86 deletions(-) create mode 100644 docs/yaml_builder.md rename {example => examples}/data/single_server.yml (100%) rename {example => examples}/single_server_yaml.py (100%) rename {example => examples}/single_server_yml.png (100%) create mode 100644 examples/your_example.py diff --git a/README.md b/README.md index 526a127..bb5d72e 100644 --- a/README.md +++ b/README.md @@ -1,127 +1,332 @@ -# **FastSim Project Overview** +# **FastSim – Event-Loop Aware Simulation for Backend Systems** -## **1. Why FastSim?** +## **1. Overview** -Modern async Python stacks like FastAPI + Uvicorn are incredibly fast, yet sizing them for production often involves guesswork, costly cloud load-tests, or late-stage surprises. **FastSim** fills that gap by acting as a **digital twin** of your service: +Modern asynchronous Python stacks such as **FastAPI + Uvicorn** deliver impressive performance, yet capacity planning for production workloads often relies on guesswork, costly cloud-based load tests, or late-stage troubleshooting. - * It **replicates** the behavior of an async event-loop in SimPy, generating the same kinds of steps (parsing, CPU work, I/O waits) that happen in real code. - * It **models** your infrastructure primitives—CPU cores, connection pools, and rate-limiters—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency. - * It **outputs** the very metrics you would scrape in production (p50/p95/p99 latency, ready-queue lag, concurrency, throughput), but entirely offline, in seconds. +**FastSim** addresses this challenge by providing a **digital twin** of your service that can be run entirely offline. It models event-loop behaviour, resource constraints, and request lifecycles, enabling you to forecast performance under different workloads and architectural choices **before deployment**. -With FastSim, you can ask, *“What happens if traffic doubles on Black Friday?”*, *“How many cores are needed to keep p95 latency below 100 ms?”*, or *“Is our new endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. +FastSim allows you to answer questions such as: -**Outcome:** Data-driven capacity planning, early performance tuning, and far fewer surprises in production. +* *What happens to p95 latency if traffic doubles during a peak event?* +* *How many cores are required to maintain SLAs at scale?* +* *What is the cost-per-request of adding a large language model (LLM) inference step?* -## **2. Installation & Quick Start** +The simulation outputs metrics identical in form to those collected in production—such as p50/p95/p99 latency, concurrency, resource utilisation, and throughput—making results directly actionable. -FastSim is designed to be used as a Python library. +**Outcome:** Data-driven capacity planning, early performance tuning, and reduced operational surprises. + +--- + +## **2. Key Features** + +* **Event-loop accuracy** – Models a single-threaded asynchronous runtime, including CPU-bound work, I/O waits, and parsing. +* **Resource modelling** – Simulates CPU cores, memory, connection pools, and rate limiters as discrete resources. +* **Network simulation** – Models transport latency per edge in the system topology. +* **Workload flexibility** – Supports stochastic arrival processes (e.g., Poisson, Normal) for user traffic generation. +* **Metrics parity with production** – Produces time-series and event-level metrics aligned with observability tools. +* **Offline and repeatable** – No need for costly cloud infrastructure to conduct performance tests. + +--- + +## **3. Installation** + +Until published, clone the repository and install in editable mode: + +### Requirements +- Python 3.11+ (recommended 3.12) +- Poetry ≥ 1.6 + +FastSim uses [Poetry](https://python-poetry.org/) for dependency management. +If you do not have Poetry installed, follow these steps. + +### 3.1 Install Poetry (official method) + +**Linux / macOS** ```bash -# Installation (coming soon to PyPI) -pip install fastsim +curl -sSL https://install.python-poetry.org | python3 - ``` -**Example Usage:** +**Windows (PowerShell)** -1. Define your system topology in a `config.yml` file: +```powershell +(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python - +``` - ```yaml - topology: - servers: - - id: "app-server-1" - # ... server configuration ... - load_balancers: - - id: "main-lb" - backends: ["app-server-1"] - # ... lb configuration ... - settings: - duration_s: 60 - ``` +> **Note:** Ensure that Poetry’s binary directory is in your `PATH`. +> On Linux/macOS this is typically `~/.local/bin`; +> on Windows it is `%APPDATA%\Python\Scripts` or the path printed at the end of installation. -2. Run the simulation from a Python script: +--- - ```python - from fastsim import run_simulation - from fastsim.schemas import SimulationPayload +### 3.2 Clone the repository and set up a local virtual environment - # Load and validate configuration using Pydantic - payload = SimulationPayload.from_yaml("config.yml") +```bash +# Clone the repository +git clone https://github.com/GioeleB00/Fastsim-Backend.git +cd Fastsim-Backend + +# Configure Poetry to always create a local `.venv` inside the project +poetry config virtualenvs.in-project true - # Run the simulation - results = run_simulation(payload) +# Install all dependencies (main + dev) inside the local venv +poetry install --with dev +``` - # Analyze and plot results - results.plot_latency_distribution() - print(results.summary()) - ``` +After this step, you will see a `.venv/` directory inside the project root. +To activate the environment manually: + +```bash +source .venv/bin/activate # Linux / macOS +.venv\Scripts\activate # Windows +``` -## **3. Who Benefits & Why** +Or simply run commands via Poetry without manual activation, for example: -| Audience | Pain-Point Solved | FastSim Value | -| :--- | :--- | :--- | -| **Backend Engineers** | Unsure if a 4-vCPU container can survive a traffic spike. | Run *what-if* scenarios, tweak CPU cores, and get p95 latency metrics before merging code. | -| **DevOps / SRE** | Guesswork in capacity planning; high cost of over-provisioning. | Simulate 1 to N replicas to find the most cost-effective configuration that meets the SLA. | -| **ML / LLM Teams** | LLM inference cost and latency are difficult to predict. | Model the LLM step with a price and latency distribution to estimate cost-per-request. | -| **Educators / Trainers** | Students struggle to visualize event-loop internals. | Visualize GIL ready-queue lag, CPU vs. I/O steps, and the effect of blocking code. | -| **System-Design Interviewees** | Hard to quantify trade-offs in whiteboard interviews. | Prototype real-time metrics to demonstrate how your design scales and where bottlenecks lie. | +```bash +poetry run pytest +poetry run python examples/single_server.py +``` -## **4. Project Structure** +--- + +## **4. Quick Start** + +### 1. Define your simulation payload + +Go to the folder `/examples` open the file `single_server.py` +and run it from the terminal, you will see the output of the system +described in `/examples/data/single_server.yml` and you will see a +`.png` file with different plots. + +If you want to build your own configuration, read the guide in the `/docs` folder on how to craft a `.yml` input correctly. + +```yaml +rqs_input: + id: generator-1 + avg_active_users: + mean: 100 + distribution: poisson + avg_request_per_minute_per_user: + mean: 20 + distribution: poisson + user_sampling_window: 60 + +topology_graph: + nodes: + client: + id: client-1 + type: client + servers: + - id: app-server-1 + type: server + server_resources: + cpu_cores: 2 + ram_mb: 2048 + endpoints: + - endpoint_name: /predict + steps: + - kind: ram + step_operation: { necessary_ram: 100 } + - kind: cpu + step_operation: { cpu_time: 0.005 } + edges: + - id: gen-to-client + source: generator-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + - id: client-to-server + source: client-1 + target: app-server-1 + latency: { mean: 0.003, distribution: exponential } + - id: server-to-client + source: app-server-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + +sim_settings: + total_simulation_time: 300 + sample_period_s: 0.05 + enabled_sample_metrics: + - ready_queue_len + - ram_in_use + enabled_event_metrics: + - rqs_clock +``` -The project is a standard Python library managed with Poetry. +and add it to the `/examples/data` folder + +### 2. Run the simulation + +build a python file in the `/examples` folder and copy the +following script replacing `` with the +real name + + +```python + +from pathlib import Path + +import simpy +import matplotlib.pyplot as plt + +from app.config.constants import LatencyKey +from app.runtime.simulation_runner import SimulationRunner +from app.metrics.analyzer import ResultsAnalyzer + +def print_latency_stats(res: ResultsAnalyzer) -> None: + """Print latency statistics returned by the analyzer.""" + stats = res.get_latency_stats() + print("\n=== LATENCY STATS ===") + if not stats: + print("(empty)") + return + + order: list[LatencyKey] = [ + LatencyKey.TOTAL_REQUESTS, + LatencyKey.MEAN, + LatencyKey.MEDIAN, + LatencyKey.STD_DEV, + LatencyKey.P95, + LatencyKey.P99, + LatencyKey.MIN, + LatencyKey.MAX, + ] + for key in order: + if key in stats: + print(f"{key.name:<20} = {stats[key]:.6f}") + +def save_all_plots(res: ResultsAnalyzer, out_path: Path) -> None: + """Generate the 2x2 plot figure and save it to `out_path`.""" + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + res.plot_latency_distribution(axes[0, 0]) + res.plot_throughput(axes[0, 1]) + res.plot_server_queues(axes[1, 0]) + res.plot_ram_usage(axes[1, 1]) + fig.tight_layout() + fig.savefig(out_path) + print(f"Plots saved to: {out_path}") + +# Paths +yaml_path = Path(__file__).parent / "data" /".yml" +out_path = Path(__file__).parent / "_plots.png" + +# Simulation +env = simpy.Environment() +runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) +results: ResultsAnalyzer = runner.run() + +# Output +print_latency_stats(results) +save_all_plots(results, out_path) ``` -fastsim/ -├── documentation/ -│ └── ... +run the script and you will see the different plots and on your terminal +you will see the latency stats + +--- + +## **5. Target Users and Use Cases** + +| Audience | Challenge | FastSim Value | +| ------------------------ | ------------------------------------------------- | -------------------------------------------------------------------------------- | +| Backend Engineers | Sizing services for variable workloads | Model endpoint workflows and resource bottlenecks before deployment | +| DevOps / SRE | Balancing cost and SLA | Simulate scaling scenarios to choose optimal capacity | +| ML / LLM Teams | Unclear latency/cost impact of inference steps | Integrate stochastic inference times and cost models into the service simulation | +| Educators | Explaining async runtime internals | Demonstrate queueing, blocking effects, and CPU vs. I/O trade-offs | +| System Design Candidates | Quantifying architecture trade-offs in interviews | Prototype a simulated design to visualise scalability and bottlenecks | + +--- + +## **6. Project Structure** + +The project follows a standard Python package layout, managed with Poetry. + +``` +Fastsim-Backend/ +├── examples/ # Examples payloads and datasets +├── scripts/ # Utility scripts (linting, startup) +├── docs/ # Project vision and technical documentation +├── tests/ # Unit and integration tests ├── src/ │ └── app/ -│ ├── config/ -│ ├── metrics/ -│ ├── resources/ -│ ├── runtime/ -│ │ ├── actors/ -│ │ └── rqs_state.py -│ ├── samplers/ -│ └── schemas/ -├── tests/ -│ ├── unit/ -│ └── integration/ -├── .github/ -│ └── workflows/ -│ └── ci-develop.yml +│ ├── config/ # Settings and constants +│ ├── metrics/ # KPI computation and aggregation +│ ├── resources/ # SimPy resource registry +│ ├── runtime/ # Simulation core and actors +│ ├── samplers/ # Random variable generators +│ └── schemas/ # Pydantic models for validation ├── pyproject.toml -├── poetry.lock └── README.md ``` -## **5. Development & Contribution** +--- + +## **7. Development Workflow** + +FastSim uses **Poetry** for dependency management and enforces quality via **Ruff** and **MyPy**. + +| Task | Command | Description | +| ------------- | --------------------------------- | -------------------------------------- | +| Install deps | `poetry install --with dev` | Main and development dependencies | +| Lint & format | `poetry run ruff check src tests` | Style and best-practice checks | +| Type checking | `poetry run mypy src tests` | Static type enforcement | +| Run tests | `poetry run pytest` | Execute all unit and integration tests | + +--- + +## **8. Continuous Integration** + +The GitHub Actions pipeline enforces: + +* **Pull Requests:** Ruff, MyPy, and unit tests for rapid feedback. +* **Develop branch:** Full suite including integration tests and coverage reporting. + +No code is merged without passing all checks, ensuring maintainability and reliability. + +--- + +## **9. Current Limitations (v0.1)** + +1. **Network delay model** – Bandwidth effects and payload size are not yet considered. +2. **Concurrency model** – Single-threaded async event-loop; no native multi-thread or multi-process simulation. +3. **CPU allocation** – One process per server instance; multi-core within a process is not yet modelled. + +In addition to the items already listed (simplified network delay, single-threaded async model, and one process per server), keep in mind: + +* **Stationary, independent workload.** Traffic is sampled from stationary distributions; there is no diurnal seasonality, burst shaping, or feedback coupling (e.g., servers slowing down arrivals). Average users and per-user RPM are sampled independently. +* **Simplified request flow.** Endpoints execute a linear sequence of steps; there is no conditional branching/fan-out within an endpoint (e.g., cache hit/miss paths, error paths) and no per-request control flow. +* **Network realism is limited.** Beyond base latency and optional drops, the model does not account for payload size, bandwidth constraints, TCP behavior (slow start, congestion), retries/timeouts, or jitter. +* **No backpressure or autoscaling.** The generator does not adapt to server state (queues, errors), and there is no policy loop for rate limiting or scaling during the run. +* **Telemetry granularity.** Sampled metrics are collected at a fixed `sample_period_s` and may miss very short-lived spikes unless you lower the period (at a runtime cost). Event resolution itself is not affected by the sampling period. +* **Reproducibility.** Unless you fix a random seed (not yet exposed in all entry points), repeated runs will vary within the chosen distributions. -We welcome contributions\! The development workflow is managed by Poetry and quality is enforced by Ruff and MyPy. +--- -| Task | Command | Notes | -| :--- | :--- | :--- | -| **Install dependencies** | `poetry install --with dev` | Installs main and development packages. | -| **Lint & format** | `poetry run ruff check src tests` | Style and best-practice validations. | -| **Type checking** | `poetry run mypy src tests` | Static type enforcement. | -| **Run all tests** | `poetry run pytest` | Executes the full test suite. | +## Mini Roadmap -### **CI with GitHub Actions** +Short, high-impact items we plan to add next: -We maintain two jobs on the `develop` branch to ensure code quality: +1. **Cache modeling.** First-class cache layers (per-endpoint hit/miss with TTL and warm-up), configurable hit-ratio profiles, and their effect on latency, CPU, and RAM. +2. **LLM inference as a step + cost accounting.** Treat inference as a dedicated endpoint step with its own latency distribution, concurrency limits/batching, and per-request cost model (tokens, provider pricing). +3. **Fault and event injection.** Time-based events (node down/up, degraded edge, error-rate spikes) with deterministic timelines to test resilience and recovery. +4. **Network bandwidth & payload size.** Throughput-aware links, request/response sizes, retries/timeouts, and simple congestion effects. +5. **Branching/control-flow within endpoints.** Conditional steps (e.g., cache hit vs. miss), probabilistic routing, and fan-out/fan-in to external services. +6. **Backpressure and autoscaling loops.** Rate limiting tied to queue depth/latency SLOs and simple scale-up/down policies during a run. - * **Quick (on Pull Requests):** Runs Ruff, MyPy, and unit tests for immediate feedback. - * **Full (on pushes to `develop`):** Runs the full suite, including integration tests and code coverage reports. -This guarantees that every commit in `develop` is style-checked, type-safe, and fully tested. +Future milestones will extend these capabilities. -## **6. Limitations – v0.1 (First Public Release)** +--- -1. **Network Delay Model:** Only pure transport latency is simulated. Bandwidth-related effects (e.g., payload size, link speed) are not yet accounted for. -2. **Concurrency Model:** The simulation models a single-threaded, cooperative event-loop (like `asyncio`). Multi-process or multi-threaded parallelism is not yet supported. -3. **CPU Core Allocation:** Every server instance is pinned to one physical CPU core. Horizontal scaling is achieved by adding more server instances, not by using multiple cores within a single process. +## **10. Documentation** -These constraints will be revisited in future milestones. +Comprehensive documentation is available in the `/docs` directory, covering: -## **7. Documentation** +* Simulation model and architecture +* Schema definitions +* Example scenarios +* Extension guidelines -For a deeper understanding of FastSim, we recommend reading the detailed documentation located in the `/documentation` folder at the root of the project. A guided reading path is suggested within to build a comprehensive understanding of the project's vision, architecture, and technical implementation. \ No newline at end of file +--- \ No newline at end of file diff --git a/docs/yaml_builder.md b/docs/yaml_builder.md new file mode 100644 index 0000000..40f519e --- /dev/null +++ b/docs/yaml_builder.md @@ -0,0 +1,448 @@ +# FastSim – YAML Input Guide + +This guide explains **how to author the simulation YAML** for FastSim, covering every field, valid values, units, constraints, and the validation rules enforced by the Pydantic schemas. + +The YAML you write is parsed into a single model: + +```yaml +rqs_input: # traffic generator (workload) +topology_graph: # system architecture as a directed graph +sim_settings: # global settings and metric collection config +``` + +Everything is **validated up front**. If something is inconsistent (e.g., an edge points to a non-existent node), the simulator raises a clear error before running. + +--- + +## 1) Random Variables (`RVConfig`) + +Many knobs use a **random variable** specification: + +```yaml +mean: # required +distribution: # optional, default: poisson +variance: # optional; required by some distributions +``` + +### Supported distributions + +* `poisson` +* `normal` +* `log_normal` +* `exponential` +* `uniform` + +### Rules & defaults + +* **`mean`** must be numeric (int or float). It is coerced to float. +* If `distribution` is `normal` or `log_normal` **and** `variance` is missing, it is set to `variance = mean`. +* For **edge latency** (see §3.3), additional checks apply: `mean > 0`, and if provided, `variance ≥ 0`. + +**Units** + +* Time values are **seconds**. +* Rates are **requests per minute** (where noted). + +--- + +## 2) Workload: `rqs_input` (Request Generator) + +```yaml +rqs_input: + id: + # type is implicit and fixed to "generator" + avg_active_users: + mean: + distribution: poisson | normal # ONLY these two are allowed + variance: # required if normal and not provided (auto=mean) + avg_request_per_minute_per_user: + mean: + distribution: poisson # MUST be poisson + user_sampling_window: # default 60, bounds [1, 120] +``` + +### Semantics + +* **`avg_active_users`**: expected concurrent users (a random variable). + + * Allowed distributions: **Poisson** or **Normal**. +* **`avg_request_per_minute_per_user`**: per-user request rate (RPM). + + * Must be **Poisson**.\* +* **`user_sampling_window`**: every N seconds the generator re-samples the active user count. + +\* This reflects current sampler support (Poisson–Poisson and Gaussian–Poisson). + +--- + +## 3) System Topology: `topology_graph` + +The system is a **directed graph** of nodes and edges. + +```yaml +topology_graph: + nodes: + client: { id: } + load_balancer: # optional + id: + algorithms: round_robin | least_connection + server_covered: [ , ... ] + servers: + - id: + server_resources: + cpu_cores: + ram_mb: + db_connection_pool: # optional + endpoints: + - endpoint_name: # normalized to lowercase + steps: + - kind: # see §3.2 + step_operation: { : } # exactly ONE key (see §3.2) + edges: + - id: + source: + target: # must be a declared node + latency: { mean: , distribution: , variance: } + probability: <0..1> # default 1.0 + edge_type: network_connection # (enum; current default/only) + dropout_rate: <0..1> # default 0.01 +``` + +### 3.1 Nodes + +#### Client + +```yaml +client: + id: client-1 + # type is fixed to "client" +``` + +#### Server + +```yaml +- id: srv-1 + # type is fixed to "server" + server_resources: + cpu_cores: 1 # default 1, min 1 + ram_mb: 1024 # default 1024, min 256 + db_connection_pool: null # optional; set an integer to enable pool modeling + endpoints: + - endpoint_name: /predict + steps: + # defined in §3.2 +``` + +**Resource semantics** + +* `cpu_cores`: number of worker “core tokens” available for CPU-bound step execution. +* `ram_mb`: total available RAM (MB) tracked as a reservoir; steps reserve then release. +* `db_connection_pool`: optional capacity bound for DB-like steps (future-use; declared here for forward compatibility). + +#### Load Balancer (optional) + +```yaml +load_balancer: + id: lb-1 + algorithms: round_robin | least_connection + server_covered: [ srv-1, srv-2 ] # must be a subset of declared server IDs +``` + +LB **validation**: + +* `server_covered` must be a subset of declared servers. +* You must also define **edges from the LB to each covered server** (see §3.3); otherwise validation fails. + +### 3.2 Endpoints & Steps + +An endpoint is a **linear sequence** of steps. +Each step must declare **exactly one** operation (`step_operation`) whose key matches the step’s kind. + +#### Step kinds (enums) + +**CPU-bound** + +* `initial_parsing` +* `cpu_bound_operation` + +**RAM** + +* `ram` + +**I/O-bound** (all use `io_waiting_time` as the operation key) + +* `io_task_spawn` (spawns a background task, returns immediately) +* `io_llm` (LLM inference call) +* `io_wait` (generic wait, non-blocking) +* `io_db` (DB roundtrip) +* `io_cache` (cache access) + +#### Operation keys (enum `StepOperation`) + +* `cpu_time`: service time (seconds) that **occupies a CPU core/GIL**. +* `necessary_ram`: peak RAM (MB) reserved for the step. +* `io_waiting_time`: passive wait (seconds) **without a CPU core**. + +#### Valid pairings + +* CPU step → `{ cpu_time: }` +* RAM step → `{ necessary_ram: }` +* I/O step → `{ io_waiting_time: }` + +**Example** + +```yaml +endpoints: + - endpoint_name: /hello + steps: + - kind: ram + step_operation: { necessary_ram: 64 } + - kind: initial_parsing + step_operation: { cpu_time: 0.002 } + - kind: io_cache + step_operation: { io_waiting_time: 0.003 } + - kind: io_db + step_operation: { io_waiting_time: 0.012 } + - kind: cpu_bound_operation + step_operation: { cpu_time: 0.001 } +``` + +**Validation enforced** + +* `step_operation` must contain **exactly one** entry. +* The operation key must match the step kind (e.g., RAM cannot use `cpu_time`). +* All numeric values are **strictly positive**. + +### 3.3 Edges + +```yaml +- id: c2s + source: client-1 # may be an external ID only for sources + target: srv-1 # MUST be a declared node + latency: + mean: 0.003 + distribution: exponential + # variance optional; if normal/log_normal and missing → set to mean + probability: 1.0 # optional [0..1] + edge_type: network_connection + dropout_rate: 0.01 # optional [0..1] +``` + +**Semantics** + +* **`source`** can be an external entry point (e.g., `rqs-1`) for inbound edges. +* **`target`** must always reference a declared node: client, server, or LB. +* **`latency`** is a random variable; **`mean > 0`**, **`variance ≥ 0`** (if provided). +* **`probability`** is used when multiple outgoing edges exist from a node. +* **`dropout_rate`** models probabilistic packet/request loss on the link. + +--- + +## 4) Global Settings: `sim_settings` + +```yaml +sim_settings: + total_simulation_time: # default 3600, min 5 + sample_period_s: # default 0.01, bounds [0.001, 0.1] + enabled_sample_metrics: + - ready_queue_len + - event_loop_io_sleep + - ram_in_use + - edge_concurrent_connection + enabled_event_metrics: + - rqs_clock + # - llm_cost # optional, for future accounting +``` + +**Notes** + +* `enabled_sample_metrics` are **time-series** collected every `sample_period_s`. +* `enabled_event_metrics` are **per-event** (e.g., per request) and not tied to the sampling period. +* The defaults already include the four main sampled metrics and `rqs_clock`. + +--- + +## 5) Graph-level Validation Rules (what’s checked before running) + +FastSim validates the entire payload. Key checks include: + +1. **Unique IDs** + + * All server IDs are unique. + * Edge IDs are unique. +2. **Node Types** + + * `type` fields on nodes are fixed to: `client`, `server`, `load_balancer`. +3. **Edge referential integrity** + + * Every **target** is a declared node ID. + * **External IDs** are allowed **only** as **sources**. If an ID appears as an external source, it must **never** appear as a target anywhere. +4. **No self-loops** + + * `source != target` for every edge. +5. **Load balancer sanity** + + * `server_covered` is a subset of declared servers. + * There is an **edge from the LB to every covered server**. + +If any rule is violated, the simulator raises a descriptive error. + +--- + +## 6) End-to-End Examples + +### 6.1 Minimal single-server scenario + +```yaml +rqs_input: + id: rqs-1 + avg_active_users: { mean: 50, distribution: poisson } + avg_request_per_minute_per_user: { mean: 30, distribution: poisson } + user_sampling_window: 60 + +topology_graph: + nodes: + client: { id: client-1 } + servers: + - id: srv-1 + server_resources: { cpu_cores: 1, ram_mb: 1024 } + endpoints: + - endpoint_name: /hello + steps: + - kind: ram + step_operation: { necessary_ram: 32 } + - kind: initial_parsing + step_operation: { cpu_time: 0.002 } + - kind: io_wait + step_operation: { io_waiting_time: 0.010 } + edges: + - id: gen-to-client + source: rqs-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + - id: client-to-server + source: client-1 + target: srv-1 + latency: { mean: 0.003, distribution: exponential } + - id: server-to-client + source: srv-1 + target: client-1 + latency: { mean: 0.003, distribution: exponential } + +sim_settings: + total_simulation_time: 300 + sample_period_s: 0.01 + enabled_sample_metrics: + - ready_queue_len + - event_loop_io_sleep + - ram_in_use + - edge_concurrent_connection + enabled_event_metrics: + - rqs_clock +``` + +### 6.2 With a load balancer and two servers + +```yaml +rqs_input: + id: rqs-1 + avg_active_users: { mean: 120, distribution: poisson } + avg_request_per_minute_per_user: { mean: 20, distribution: poisson } + +topology_graph: + nodes: + client: { id: client-1 } + load_balancer: + id: lb-1 + algorithms: round_robin + server_covered: [ srv-1, srv-2 ] + servers: + - id: srv-1 + server_resources: { cpu_cores: 1, ram_mb: 1024 } + endpoints: + - endpoint_name: /api + steps: + - kind: ram + step_operation: { necessary_ram: 64 } + - kind: cpu_bound_operation + step_operation: { cpu_time: 0.004 } + - id: srv-2 + server_resources: { cpu_cores: 2, ram_mb: 2048 } + endpoints: + - endpoint_name: /api + steps: + - kind: ram + step_operation: { necessary_ram: 64 } + - kind: io_db + step_operation: { io_waiting_time: 0.012 } + + edges: + - { id: gen-client, source: rqs-1, target: client-1, + latency: { mean: 0.003, distribution: exponential } } + + - { id: client-lb, source: client-1, target: lb-1, + latency: { mean: 0.002, distribution: exponential } } + + - { id: lb-srv1, source: lb-1, target: srv-1, + latency: { mean: 0.002, distribution: exponential }, probability: 0.5 } + - { id: lb-srv2, source: lb-1, target: srv-2, + latency: { mean: 0.002, distribution: exponential }, probability: 0.5 } + + - { id: srv1-client, source: srv-1, target: client-1, + latency: { mean: 0.003, distribution: exponential } } + - { id: srv2-client, source: srv-2, target: client-1, + latency: { mean: 0.003, distribution: exponential } } + +sim_settings: + total_simulation_time: 600 + sample_period_s: 0.02 + enabled_sample_metrics: [ ready_queue_len, ram_in_use, edge_concurrent_connection ] + enabled_event_metrics: [ rqs_clock ] +``` + +## 7) Common Pitfalls & How to Avoid Them + +* **Mismatched step operations** + A CPU step must use `cpu_time`; an I/O step must use `io_waiting_time`; a RAM step must use `necessary_ram`. The validator enforces **exactly one** key. + +* **Edge targets must be declared nodes** + `source` can be external (e.g., `rqs-1`), but **no external ID** may ever appear as a **target**. + +* **Load balancer coverage without edges** + If the LB declares `server_covered: [srv-1, srv-2]`, you must also add edges `lb→srv-1` and `lb→srv-2`. + +* **Latency RV rules on edges** + For edge latency, `mean` must be **> 0**; if `variance` is present, it must be **≥ 0**. + +* **Sampling too coarse** + If `sample_period_s` is large, short spikes in queues may be missed. Lower it (e.g., `0.005`) to capture fine-grained bursts—at the cost of larger time-series. + +--- + +## 8) Quick Reference (Enums) + +* **Distributions**: `poisson`, `normal`, `log_normal`, `exponential`, `uniform` +* **Node types**: `generator`, `server`, `client`, `load_balancer` (fixed by model) +* **Edge types**: `network_connection` +* **LB algorithms**: `round_robin`, `least_connection` +* **Step kinds** + CPU: `initial_parsing`, `cpu_bound_operation` + RAM: `ram` + I/O: `io_task_spawn`, `io_llm`, `io_wait`, `io_db`, `io_cache` +* **Step operation keys**: `cpu_time`, `io_waiting_time`, `necessary_ram` +* **Sampled metrics**: `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, `edge_concurrent_connection` +* **Event metrics**: `rqs_clock` (and `llm_cost` reserved for future use) + +--- + +## 9) Units & Conventions + +* **Time**: seconds (`cpu_time`, `io_waiting_time`, latencies, `total_simulation_time`, `sample_period_s`, `user_sampling_window`) +* **RAM**: megabytes (`ram_mb`, `necessary_ram`) +* **Rates**: requests/minute (`avg_request_per_minute_per_user.mean`) +* **Probabilities**: `[0.0, 1.0]` (`probability`, `dropout_rate`) +* **IDs**: strings; must be **unique** per category (servers, edges, LB). + +--- + +If you stick to these rules and examples, your YAML will parse cleanly and the simulation will run with a self-consistent, strongly-validated model. diff --git a/example/data/single_server.yml b/examples/data/single_server.yml similarity index 100% rename from example/data/single_server.yml rename to examples/data/single_server.yml diff --git a/example/single_server_yaml.py b/examples/single_server_yaml.py similarity index 100% rename from example/single_server_yaml.py rename to examples/single_server_yaml.py diff --git a/example/single_server_yml.png b/examples/single_server_yml.png similarity index 100% rename from example/single_server_yml.png rename to examples/single_server_yml.png diff --git a/examples/your_example.py b/examples/your_example.py new file mode 100644 index 0000000..ca31a05 --- /dev/null +++ b/examples/your_example.py @@ -0,0 +1,54 @@ +from pathlib import Path + +import simpy +import matplotlib.pyplot as plt + +from app.config.constants import LatencyKey +from app.runtime.simulation_runner import SimulationRunner +from app.metrics.analyzer import ResultsAnalyzer + +def print_latency_stats(res: ResultsAnalyzer) -> None: + """Print latency statistics returned by the analyzer.""" + stats = res.get_latency_stats() + print("\n=== LATENCY STATS ===") + if not stats: + print("(empty)") + return + + order: list[LatencyKey] = [ + LatencyKey.TOTAL_REQUESTS, + LatencyKey.MEAN, + LatencyKey.MEDIAN, + LatencyKey.STD_DEV, + LatencyKey.P95, + LatencyKey.P99, + LatencyKey.MIN, + LatencyKey.MAX, + ] + for key in order: + if key in stats: + print(f"{key.name:<20} = {stats[key]:.6f}") + +def save_all_plots(res: ResultsAnalyzer, out_path: Path) -> None: + """Generate the 2x2 plot figure and save it to `out_path`.""" + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + res.plot_latency_distribution(axes[0, 0]) + res.plot_throughput(axes[0, 1]) + res.plot_server_queues(axes[1, 0]) + res.plot_ram_usage(axes[1, 1]) + fig.tight_layout() + fig.savefig(out_path) + print(f"Plots saved to: {out_path}") + +# Paths +yaml_path = Path(__file__).parent / "data" / ".yml" +out_path = Path(__file__).parent / "_plots.png" + +# Simulation +env = simpy.Environment() +runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) +results: ResultsAnalyzer = runner.run() + +# Output +print_latency_stats(results) +save_all_plots(results, out_path) diff --git a/src/app/metrics/analyzer.py b/src/app/metrics/analyzer.py index e1aa50e..8308d54 100644 --- a/src/app/metrics/analyzer.py +++ b/src/app/metrics/analyzer.py @@ -55,6 +55,7 @@ def __init__( servers: List of server runtime objects. edges: List of edge runtime objects. settings: Original simulation settings. + """ self._client = client self._servers = servers From 43eb8c50d105372763867000ce836c8c4125bd79 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 8 Aug 2025 18:20:38 +0200 Subject: [PATCH 24/52] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index bb5d72e..9f88a1b 100644 --- a/README.md +++ b/README.md @@ -328,5 +328,6 @@ Comprehensive documentation is available in the `/docs` directory, covering: * Schema definitions * Example scenarios * Extension guidelines +* Guide to build valid .yaml as valid simulation input ---- \ No newline at end of file +--- From 2a306acdf3551498e78aa9d5ca70148d4d078b18 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Wed, 13 Aug 2025 17:22:11 +0200 Subject: [PATCH 25/52] Added pybuilder and unit tests (#12) --- docs/dev_workflow_guide.md | 1 + examples/single_server_pybuilder.png | Bin 0 -> 164870 bytes examples/single_server_pybuilder.py | 293 ++++++++++++++++++ src/app/pybuilder/input_builder.py | 127 ++++++++ .../system_topology/full_system_topology.py | 37 ++- tests/unit/pybuilder/test_input_builder.py | 280 +++++++++++++++++ 6 files changed, 734 insertions(+), 4 deletions(-) create mode 100644 examples/single_server_pybuilder.png create mode 100644 examples/single_server_pybuilder.py create mode 100644 src/app/pybuilder/input_builder.py create mode 100644 tests/unit/pybuilder/test_input_builder.py diff --git a/docs/dev_workflow_guide.md b/docs/dev_workflow_guide.md index d1379db..c0a9705 100644 --- a/docs/dev_workflow_guide.md +++ b/docs/dev_workflow_guide.md @@ -38,6 +38,7 @@ fastsim-backend/ │ │ ├── rqs_state.py # RequestState & Hop │ │ ├── simulation_runner.py # logic to initialize the whole simulation | └── actors/ # SimPy “actors”: Edge, Server, Client, RqsGenerator + ├── pybuilder/ # Pythonic way to build the simulation payload │ ├── samplers/ # stochastic samplers 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YAML): + generator ──edge──> client ──edge──> server ──edge──> client + +Load: + ~100 active users, 20 req/min each. + +Server: + 1 CPU core, 2GB RAM, endpoint with steps: + CPU(1ms) → RAM(100MB) → IO(100ms) + +Network: + 3ms mean (exponential) latency on each edge. + +What this script does: + 1) Build Pydantic models (generator, client, server, edges, settings). + 2) Compose the final SimulationPayload via AsyncFlow (builder pattern). + 3) Run the simulation with SimulationRunner. + 4) Print latency stats, throughput timeline, and a sampled-metrics preview. + 5) (Optional) Visualize the topology with Matplotlib. + +Run: + python run_with_pybuilder.py +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Dict, Iterable, List, Mapping, Tuple + +import numpy as np +import simpy + +# ── FastSim domain imports ─────────────────────────────────────────────────── +from app.pybuilder.input_builder import AsyncFlow +from app.runtime.simulation_runner import SimulationRunner +from app.metrics.analyzer import ResultsAnalyzer +from app.schemas.full_simulation_input import SimulationPayload +from app.schemas.rqs_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings +from app.schemas.system_topology.endpoint import Endpoint +from app.schemas.system_topology.full_system_topology import ( + Client, + Edge, + Server, +) + +from app.config.constants import LatencyKey, SampledMetricName + + +# ───────────────────────────────────────────────────────────── +# Pretty printers (compact, readable output) +# ───────────────────────────────────────────────────────────── +def print_latency_stats(res: ResultsAnalyzer) -> None: + """Print latency statistics calculated by the analyzer.""" + stats: Mapping[LatencyKey, float] = res.get_latency_stats() + print("\n════════ LATENCY STATS ════════") + if not stats: + print("(empty)") + return + + order: List[LatencyKey] = [ + LatencyKey.TOTAL_REQUESTS, + LatencyKey.MEAN, + LatencyKey.MEDIAN, + LatencyKey.STD_DEV, + LatencyKey.P95, + LatencyKey.P99, + LatencyKey.MIN, + LatencyKey.MAX, + ] + for key in order: + if key in stats: + print(f"{key.name:<20} = {stats[key]:.6f}") + + +def print_throughput(res: ResultsAnalyzer) -> None: + """Print the 1-second throughput buckets.""" + timestamps, rps = res.get_throughput_series() + print("\n════════ THROUGHPUT (req/sec) ════════") + if not timestamps: + print("(empty)") + return + + for t, rate in zip(timestamps, rps): + print(f"t={t:4.1f}s → {rate:6.2f} rps") + + +def print_sampled_preview(res: ResultsAnalyzer) -> None: + """ + Print a small preview for each sampled metric series (first 5 values). + This helps verify that sampler pipelines are running. + """ + sampled = res.get_sampled_metrics() + print("\n════════ SAMPLED METRICS (preview) ════════") + if not sampled: + print("(empty)") + return + + for metric, series in sampled.items(): + metric_name = ( + metric.name if isinstance(metric, SampledMetricName) else str(metric) + ) + print(f"\n📈 {metric_name}:") + for entity, vals in series.items(): + head = list(vals[:5]) if vals else [] + print(f" - {entity}: len={len(vals)}, first={head}") + + +# ───────────────────────────────────────────────────────────── +# Tiny helpers for sanity checks (optional) +# ───────────────────────────────────────────────────────────── +def _mean(series: Iterable[float]) -> float: + """Numerically stable mean for a generic float iterable.""" + arr = np.asarray(list(series), dtype=float) + return float(np.mean(arr)) if arr.size else 0.0 + + +def run_sanity_checks( + runner: SimulationRunner, + res: ResultsAnalyzer, +) -> None: + """ + Back-of-the-envelope checks to compare rough expectations vs observations. + These are intentionally simplistic approximations. + """ + print("\n════════ SANITY CHECKS (rough) ════════") + w = runner.simulation_input.rqs_input + lam_rps = ( + float(w.avg_active_users.mean) + * float(w.avg_request_per_minute_per_user.mean) + / 60.0 + ) + + # Observed throughput + _, rps_series = res.get_throughput_series() + rps_observed = _mean(rps_series) + print(f"• Mean throughput (rps) expected≈{lam_rps:.3f} " + f"observed={rps_observed:.3f}") + + # A few sampled signals (RAM, queues) just to show they are populated. + sampled = res.get_sampled_metrics() + ram_series = sampled.get(SampledMetricName.RAM_IN_USE, {}) + ioq_series = sampled.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) + ready_series = sampled.get(SampledMetricName.READY_QUEUE_LEN, {}) + + ram_mean = _mean([_mean(v) for v in ram_series.values()]) if ram_series else 0.0 + ioq_mean = _mean([_mean(v) for v in ioq_series.values()]) if ioq_series else 0.0 + ready_mean = ( + _mean([_mean(v) for v in ready_series.values()]) if ready_series else 0.0 + ) + + print(f"• Mean RAM in use (MB) observed={ram_mean:.3f}") + print(f"• Mean I/O queue length observed={ioq_mean:.3f}") + print(f"• Mean ready queue length observed={ready_mean:.3f}") + + +# ───────────────────────────────────────────────────────────── +# Build the same scenario via AsyncFlow (pybuilder) +# ───────────────────────────────────────────────────────────── +def build_payload_with_pybuilder() -> SimulationPayload: + """ + Construct the SimulationPayload programmatically using the builder. + + This mirrors the YAML: + - Generator (100 users, 20 rpm each) + - Client + - One server with a single endpoint (CPU → RAM → IO) + - Three edges with exponential latency (3ms mean) + - Simulation settings: 500s total, sample period 50ms + """ + # 1) Request generator + generator = RqsGeneratorInput( + id="rqs-1", + avg_active_users={"mean": 100}, + avg_request_per_minute_per_user={"mean": 20}, + user_sampling_window=60, + ) + + # 2) Client + client = Client(id="client-1") + + # 3) Server (1 CPU core, 2GB RAM) with one endpoint and three steps + # We let Pydantic coerce nested dicts for the endpoint steps. + endpoint = Endpoint( + endpoint_name="ep-1", + probability=1.0, + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, + {"kind": "ram", "step_operation": {"necessary_ram": 100}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.1}}, + ], + ) + + server = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], + ) + + # 4) Edges: exponential latency with 3ms mean (same as YAML) + e_gen_client = Edge( + id="gen-to-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e_client_server = Edge( + id="client-to-server", + source="client-1", + target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e_server_client = Edge( + id="server-to-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + + # 5) Simulation settings + settings = SimulationSettings( + total_simulation_time=500, + sample_period_s=0.05, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + # 6) Assemble the payload via the builder (AsyncFlow). + # The builder will validate the final structure on build. + flow = ( + AsyncFlow() + .add_generator(generator) + .add_client(client) + .add_servers(server) + .add_edges(e_gen_client, e_client_server, e_server_client) + .add_simulation_settings(settings) + ) + + return flow.build_payload() + + +# ───────────────────────────────────────────────────────────── +# Main entry-point +# ───────────────────────────────────────────────────────────── +def main() -> None: + """ + Build → wire → run the simulation, then print diagnostics. + Mirrors run_from_yaml.py but uses the pybuilder to construct the input. + Also saves a 2x2 plot figure (latency, throughput, server queues, RAM). + """ + env = simpy.Environment() + payload = build_payload_with_pybuilder() + + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + + # Human-friendly diagnostics + print_latency_stats(results) + print_throughput(results) + print_sampled_preview(results) + + # Optional sanity checks (very rough) + run_sanity_checks(runner, results) + + # Save plots (2x2 figure), same layout as in the YAML-based example + try: + from matplotlib import pyplot as plt # noqa: PLC0415 + + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + results.plot_latency_distribution(axes[0, 0]) + results.plot_throughput(axes[0, 1]) + results.plot_server_queues(axes[1, 0]) + results.plot_ram_usage(axes[1, 1]) + fig.tight_layout() + + out_path = Path(__file__).parent / "single_server_pybuilder.png" + fig.savefig(out_path) + print(f"\n🖼️ Plots saved to: {out_path}") + except Exception as exc: # Matplotlib not installed or plotting failed + print(f"\n[plotting skipped] {exc!r}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/app/pybuilder/input_builder.py b/src/app/pybuilder/input_builder.py new file mode 100644 index 0000000..223cd32 --- /dev/null +++ b/src/app/pybuilder/input_builder.py @@ -0,0 +1,127 @@ +"""Definition of the input of the simulation through python object""" + +from __future__ import annotations + +from typing import Self + +from app.schemas.full_simulation_input import SimulationPayload +from app.schemas.rqs_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings +from app.schemas.system_topology.full_system_topology import ( + Client, + Edge, + LoadBalancer, + Server, + TopologyGraph, + TopologyNodes, +) + + +class AsyncFlow: + """class with method to create the input for the simulation""" + + def __init__(self) -> None: + """Instance attributes necessary to define the simulation payload""" + self._generator: RqsGeneratorInput | None = None + self._client: Client | None = None + self._servers: list[Server] | None = None + self._edges: list[Edge] | None = None + self._sim_settings: SimulationSettings | None = None + self._load_balancer: LoadBalancer | None = None + + def add_generator(self, rqs_generator: RqsGeneratorInput) -> Self: + """Method to instantiate the generator""" + if not isinstance(rqs_generator, RqsGeneratorInput): + msg = "You must add a RqsGeneratorInput instance" + raise TypeError(msg) + self._generator = rqs_generator + return self + + def add_client(self, client: Client) -> Self: + """Method to instantiate the client""" + if not isinstance(client, Client): + msg = "You must add a Client instance" + raise TypeError(msg) + + self._client = client + return self + + def add_servers(self, *servers: Server) -> Self: + """Method to instantiate the server list""" + if self._servers is None: + self._servers = [] + + for server in servers: + if not isinstance(server, Server): + msg = "All the instances must be of the type Server" + raise TypeError(msg) + self._servers.append(server) + return self + + def add_edges(self, *edges: Edge) -> Self: + """Method to instantiate the list of edges""" + if self._edges is None: + self._edges = [] + + for edge in edges: + if not isinstance(edge, Edge): + msg = "All the instances must be of the type Edge" + raise TypeError(msg) + self._edges.append(edge) + return self + + def add_simulation_settings(self, sim_settings: SimulationSettings) -> Self: + """Method to instantiate the settings for the simulation""" + if not isinstance(sim_settings, SimulationSettings): + msg = "The instance must be of the type SimulationSettings" + raise TypeError(msg) + + self._sim_settings = sim_settings + return self + + def add_load_balancer(self, load_balancer: LoadBalancer) -> Self: + """Method to instantiate a load balancer""" + if not isinstance(load_balancer, LoadBalancer): + msg = "The instance must be of the type LoadBalancer" + raise TypeError(msg) + + self._load_balancer = load_balancer + return self + + def build_payload(self) -> SimulationPayload: + """Method to build the payload for the simulation""" + if self._generator is None: + msg = "The generator input must be instantiated before the simulation" + raise ValueError(msg) + if self._client is None: + msg = "The client input must be instantiated before the simulation" + raise ValueError(msg) + if not self._servers: + msg = "You must instantiate at least one server before the simulation" + raise ValueError(msg) + if not self._edges: + msg = "You must instantiate edges before the simulation" + raise ValueError(msg) + if self._sim_settings is None: + msg = "The simulation settings must be instantiated before the simulation" + raise ValueError(msg) + + nodes = TopologyNodes( + servers=self._servers, + client=self._client, + load_balancer=self._load_balancer, + ) + + graph = TopologyGraph( + nodes = nodes, + edges=self._edges, + ) + + return SimulationPayload.model_validate({ + "rqs_input": self._generator, + "topology_graph": graph, + "sim_settings": self._sim_settings, + }) + + + diff --git a/src/app/schemas/system_topology/full_system_topology.py b/src/app/schemas/system_topology/full_system_topology.py index ddd4c5f..f70b16c 100644 --- a/src/app/schemas/system_topology/full_system_topology.py +++ b/src/app/schemas/system_topology/full_system_topology.py @@ -155,6 +155,10 @@ def unique_ids( ) -> "TopologyNodes": """Check that all id are unique""" ids = [server.id for server in model.servers] + [model.client.id] + + if model.load_balancer is not None: + ids.append(model.load_balancer.id) + counter = Counter(ids) duplicate = [node_id for node_id, value in counter.items() if value > 1] if duplicate: @@ -193,7 +197,6 @@ class Edge(BaseModel): source: str target: str latency: RVConfig - probability: float = Field(1.0, ge=0.0, le=1.0) edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION dropout_rate: float = Field( NetworkParameters.DROPOUT_RATE, @@ -230,7 +233,7 @@ def ensure_latency_is_non_negative( if variance is not None and variance < 0: # Variance can be zero msg = ( f"The variance of the latency of the edge {edge_id}" - "must be positive" + "must be non negative" ) raise ValueError(msg) return v @@ -327,7 +330,7 @@ def edge_refs_valid( @model_validator(mode="after") # type: ignore[arg-type] def valid_load_balancer(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 """ - Check de validity of the load balancer: first we check + Check the validity of the load balancer: first we check if is present in the simulation, second we check if the LB list is a proper subset of the server sets of ids, then we check if edge from LB to the servers are well defined @@ -343,7 +346,7 @@ def valid_load_balancer(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: if missing: msg = (f"Load balancer '{lb.id}'" - "references unknown servers: {sorted(missing)}") + f"references unknown servers: {sorted(missing)}") raise ValueError(msg) # edge are well defined @@ -360,3 +363,29 @@ def valid_load_balancer(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: return model + @model_validator(mode="after") # type: ignore[arg-type] + def no_fanout_except_lb(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 + """Ensure only the LB (declared node) can have multiple outgoing edges.""" + lb_id = model.nodes.load_balancer.id if model.nodes.load_balancer else None + + # let us consider only nodes declared in the topology + node_ids: set[str] = {server.id for server in model.nodes.servers} + node_ids.add(model.nodes.client.id) + if lb_id: + node_ids.add(lb_id) + + counts: dict[str, int] = {} + for edge in model.edges: + if edge.source not in node_ids: + continue + counts[edge.source] = counts.get(edge.source, 0) + 1 + + offenders = [src for src, c in counts.items() if c > 1 and src != lb_id] + if offenders: + msg = ( + "Only the load balancer can have multiple outgoing edges. " + f"Offending sources: {offenders}" + ) + raise ValueError(msg) + + return model diff --git a/tests/unit/pybuilder/test_input_builder.py b/tests/unit/pybuilder/test_input_builder.py new file mode 100644 index 0000000..1bdddf8 --- /dev/null +++ b/tests/unit/pybuilder/test_input_builder.py @@ -0,0 +1,280 @@ +""" +Unit tests for the AsyncFlow builder. + +The goal is to verify that: +- The builder enforces types on each `add_*` method. +- Missing components produce clear ValueError exceptions on `build_payload()`. +- A valid, minimal scenario builds a `SimulationPayload` successfully. +- Methods return `self` to support fluent chaining. +- Servers and edges can be added in multiples and preserve order. +""" + +from __future__ import annotations + +import pytest + +from app.pybuilder.input_builder import AsyncFlow +from app.schemas.full_simulation_input import SimulationPayload +from app.schemas.rqs_generator_input import RqsGeneratorInput +from app.schemas.simulation_settings_input import SimulationSettings +from app.schemas.system_topology.endpoint import Endpoint +from app.schemas.system_topology.full_system_topology import Client, Edge, Server + + +# --------------------------------------------------------------------------- # +# Helpers: build minimal, valid components # +# --------------------------------------------------------------------------- # +def make_generator() -> RqsGeneratorInput: + """Return a minimal valid request generator.""" + return RqsGeneratorInput( + id="rqs-1", + avg_active_users={"mean": 10}, + avg_request_per_minute_per_user={"mean": 30}, + user_sampling_window=60, + ) + + +def make_client() -> Client: + """Return a minimal valid client.""" + return Client(id="client-1") + + +def make_endpoint() -> Endpoint: + """Return a minimal endpoint with CPU and IO steps.""" + return Endpoint( + endpoint_name="ep-1", + probability=1.0, + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.001}}, + ], + ) + + +def make_server(server_id: str = "srv-1") -> Server: + """Return a minimal valid server with 1 core, 2GB RAM, and one endpoint.""" + return Server( + id=server_id, + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[make_endpoint()], + ) + + +def make_edges() -> list[Edge]: + """Return a valid edge triplet for the minimal single-server scenario.""" + e1 = Edge( + id="gen-to-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e2 = Edge( + id="client-to-server", + source="client-1", + target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e3 = Edge( + id="server-to-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + return [e1, e2, e3] + + +def make_settings() -> SimulationSettings: + """Return minimal simulation settings within validation bounds.""" + return SimulationSettings( + total_simulation_time=5.0, # lower bound is 5 seconds + sample_period_s=0.1, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + +# --------------------------------------------------------------------------- # +# Positive / “happy path” # +# --------------------------------------------------------------------------- # +def test_builder_happy_path_returns_payload() -> None: + """Building a minimal scenario returns a validated SimulationPayload.""" + flow = AsyncFlow() + generator = make_generator() + client = make_client() + server = make_server() + e1, e2, e3 = make_edges() + settings = make_settings() + + payload = ( + flow.add_generator(generator) + .add_client(client) + .add_servers(server) + .add_edges(e1, e2, e3) + .add_simulation_settings(settings) + .build_payload() + ) + + assert isinstance(payload, SimulationPayload) + assert payload.topology_graph.nodes.client.id == client.id + assert len(payload.topology_graph.nodes.servers) == 1 + assert {e.id for e in payload.topology_graph.edges} == { + "gen-to-client", + "client-to-server", + "server-to-client", + } + + +def test_add_methods_return_self_for_chaining() -> None: + """Every add_* method returns `self` to support fluent chaining.""" + flow = AsyncFlow() + ret = ( + flow.add_generator(make_generator()) + .add_client(make_client()) + .add_servers(make_server()) + .add_edges(*make_edges()) + .add_simulation_settings(make_settings()) + ) + assert ret is flow + + +def test_add_servers_accepts_multiple_and_keeps_order() -> None: + """Adding multiple servers keeps insertion order.""" + flow = AsyncFlow().add_generator(make_generator()).add_client(make_client()) + s1 = make_server("srv-1") + s2 = make_server("srv-2") + s3 = make_server("srv-3") + + flow.add_servers(s1, s2).add_servers(s3) + e1, e2, e3 = make_edges() + settings = make_settings() + payload = ( + flow.add_edges(e1, e2, e3) + .add_simulation_settings(settings) + .build_payload() + ) + + ids = [srv.id for srv in payload.topology_graph.nodes.servers] + assert ids == ["srv-1", "srv-2", "srv-3"] + + +# --------------------------------------------------------------------------- # +# Negative cases: missing components # +# --------------------------------------------------------------------------- # +def test_build_without_generator_raises() -> None: + """Building without a generator fails with a clear error.""" + flow = AsyncFlow() + flow.add_client(make_client()) + flow.add_servers(make_server()) + flow.add_edges(*make_edges()) + flow.add_simulation_settings(make_settings()) + + with pytest.raises( + ValueError, + match="The generator input must be instantiated before the simulation", + ): + flow.build_payload() + + +def test_build_without_client_raises() -> None: + """Building without a client fails with a clear error.""" + flow = AsyncFlow() + flow.add_generator(make_generator()) + flow.add_servers(make_server()) + flow.add_edges(*make_edges()) + flow.add_simulation_settings(make_settings()) + + with pytest.raises( + ValueError, + match="The client input must be instantiated before the simulation", + ): + flow.build_payload() + + +def test_build_without_servers_raises() -> None: + """Building without servers fails with a clear error.""" + flow = AsyncFlow() + flow.add_generator(make_generator()) + flow.add_client(make_client()) + flow.add_edges(*make_edges()) + flow.add_simulation_settings(make_settings()) + + with pytest.raises( + ValueError, + match="You must instantiate at least one server before the simulation", + ): + flow.build_payload() + + +def test_build_without_edges_raises() -> None: + """Building without edges fails with a clear error.""" + flow = AsyncFlow() + flow.add_generator(make_generator()) + flow.add_client(make_client()) + flow.add_servers(make_server()) + flow.add_simulation_settings(make_settings()) + + with pytest.raises( + ValueError, + match="You must instantiate edges before the simulation", + ): + flow.build_payload() + + +def test_build_without_settings_raises() -> None: + """Building without settings fails with a clear error.""" + flow = AsyncFlow() + flow.add_generator(make_generator()) + flow.add_client(make_client()) + flow.add_servers(make_server()) + flow.add_edges(*make_edges()) + + with pytest.raises( + ValueError, + match="The simulation settings must be instantiated before the simulation", + ): + flow.build_payload() + + +# --------------------------------------------------------------------------- # +# Negative cases: type enforcement in add_* methods # +# --------------------------------------------------------------------------- # +def test_add_generator_rejects_wrong_type() -> None: + """`add_generator` rejects non-RqsGeneratorInput instances.""" + flow = AsyncFlow() + with pytest.raises(TypeError): + flow.add_generator("not-a-generator") # type: ignore[arg-type] + + +def test_add_client_rejects_wrong_type() -> None: + """`add_client` rejects non-Client instances.""" + flow = AsyncFlow() + with pytest.raises(TypeError): + flow.add_client(1234) # type: ignore[arg-type] + + +def test_add_servers_rejects_wrong_type() -> None: + """`add_servers` rejects any non-Server in the varargs.""" + flow = AsyncFlow() + good = make_server() + with pytest.raises(TypeError): + flow.add_servers(good, "not-a-server") # type: ignore[arg-type] + + +def test_add_edges_rejects_wrong_type() -> None: + """`add_edges` rejects any non-Edge in the varargs.""" + flow = AsyncFlow() + good = make_edges()[0] + with pytest.raises(TypeError): + flow.add_edges(good, 3.14) # type: ignore[arg-type] + + +def test_add_settings_rejects_wrong_type() -> None: + """`add_simulation_settings` rejects non-SimulationSettings instances.""" + flow = AsyncFlow() + with pytest.raises(TypeError): + flow.add_simulation_settings({"total_simulation_time": 1.0}) # type: ignore[arg-type] From 0098453bc847bd2e02a67744e44ed9590dfd3ecd Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Wed, 13 Aug 2025 18:08:40 +0200 Subject: [PATCH 26/52] Refactor/change project name plus docs improvement (#13) * Changed name from fastsim to asyncflow app folder renamed in asyncflow * improved docs --- README.md | 24 +- docs/dev_workflow_guide.md | 185 ++++-- docs/fastsim-docs/metrics_to_measure.md | 6 +- docs/fastsim-docs/requests_generator.md | 4 +- docs/fastsim-docs/runtime_and_resources.md | 10 +- docs/fastsim-docs/simulation_input.md | 350 +++++------ docs/fastsim-docs/simulation_runner.md | 2 +- docs/fastsim_vision.md | 10 +- docs/pybuilder.md | 458 ++++++++++++++ docs/yaml_builder.md | 6 +- examples/data/single_server.yml | 2 +- examples/single_server_pybuilder.png | Bin 164870 -> 163305 bytes examples/single_server_pybuilder.py | 24 +- examples/single_server_yaml.py | 10 +- examples/single_server_yml.png | Bin 158475 -> 160670 bytes examples/your_example.py | 6 +- poetry.lock | 568 ++++++++++-------- pyproject.toml | 6 +- src/{app => asyncflow}/__init__.py | 0 src/{app => asyncflow}/config/__init__.py | 0 src/{app => asyncflow}/config/constants.py | 2 +- .../config/plot_constants.py | 0 src/{app => asyncflow}/metrics/analyzer.py | 12 +- src/{app => asyncflow}/metrics/client.py | 0 src/{app => asyncflow}/metrics/collector.py | 8 +- src/{app => asyncflow}/metrics/edge.py | 2 +- src/{app => asyncflow}/metrics/server.py | 2 +- .../pybuilder/input_builder.py | 8 +- src/{app => asyncflow}/resources/__init__.py | 0 src/{app => asyncflow}/resources/registry.py | 4 +- .../resources/server_containers.py | 4 +- src/{app => asyncflow}/runtime/__init__.py | 0 .../runtime/actors/client.py | 10 +- src/{app => asyncflow}/runtime/actors/edge.py | 14 +- .../runtime/actors/helpers/lb_algorithms.py | 2 +- .../runtime/actors/load_balancer.py | 10 +- .../runtime/actors/rqs_generator.py | 14 +- .../runtime/actors/server.py | 14 +- src/{app => asyncflow}/runtime/rqs_state.py | 2 +- .../runtime/simulation_runner.py | 22 +- .../samplers/common_helpers.py | 4 +- .../samplers/gaussian_poisson.py | 8 +- .../samplers/poisson_poisson.py | 8 +- .../schemas/full_simulation_input.py | 6 +- .../schemas/random_variables_config.py | 2 +- .../schemas/rqs_generator_input.py | 4 +- .../schemas/simulation_settings_input.py | 2 +- .../schemas/system_topology/endpoint.py | 2 +- .../system_topology/full_system_topology.py | 6 +- tests/conftest.py | 12 +- tests/integration/conftest.py | 2 +- tests/integration/minimal/conftest.py | 10 +- tests/integration/minimal/test_minimal.py | 6 +- .../payload/test_payload_invalid.py | 2 +- tests/integration/single_server/conftest.py | 4 +- .../single_server/data/single_server.yml | 2 +- .../single_server/test_single_server.py | 6 +- tests/unit/metrics/test_analyzer.py | 12 +- tests/unit/pybuilder/test_input_builder.py | 12 +- tests/unit/resources/test_registry.py | 8 +- .../unit/resources/test_server_containers.py | 6 +- tests/unit/runtime/actors/test_client.py | 8 +- tests/unit/runtime/actors/test_edge.py | 12 +- .../unit/runtime/actors/test_load_balancer.py | 8 +- .../unit/runtime/actors/test_rqs_generator.py | 14 +- tests/unit/runtime/actors/test_server.py | 17 +- tests/unit/runtime/test_rqs_state.py | 4 +- tests/unit/runtime/test_simulation_runner.py | 10 +- tests/unit/samplers/test_gaussian_poisson.py | 12 +- tests/unit/samplers/test_poisson_poisson.py | 10 +- tests/unit/samplers/test_sampler_helper.py | 6 +- tests/unit/schemas/test_endpoint_input.py | 4 +- .../unit/schemas/test_full_topology_input.py | 8 +- .../schemas/test_requests_generator_input.py | 8 +- 74 files changed, 1300 insertions(+), 746 deletions(-) create mode 100644 docs/pybuilder.md rename src/{app => asyncflow}/__init__.py (100%) rename src/{app => asyncflow}/config/__init__.py (100%) rename src/{app => asyncflow}/config/constants.py (99%) rename src/{app => asyncflow}/config/plot_constants.py (100%) rename src/{app => asyncflow}/metrics/analyzer.py (95%) rename src/{app => asyncflow}/metrics/client.py (100%) rename src/{app => asyncflow}/metrics/collector.py (91%) rename src/{app => asyncflow}/metrics/edge.py (94%) rename src/{app => asyncflow}/metrics/server.py (94%) rename src/{app => asyncflow}/pybuilder/input_builder.py (93%) rename src/{app => asyncflow}/resources/__init__.py (100%) rename src/{app => asyncflow}/resources/registry.py (86%) rename src/{app => asyncflow}/resources/server_containers.py (94%) rename src/{app => asyncflow}/runtime/__init__.py (100%) rename src/{app => asyncflow}/runtime/actors/client.py (89%) rename src/{app => asyncflow}/runtime/actors/edge.py (88%) rename src/{app => asyncflow}/runtime/actors/helpers/lb_algorithms.py (94%) rename src/{app => asyncflow}/runtime/actors/load_balancer.py (86%) rename src/{app => asyncflow}/runtime/actors/rqs_generator.py (88%) rename src/{app => asyncflow}/runtime/actors/server.py (96%) rename src/{app => asyncflow}/runtime/rqs_state.py (95%) rename src/{app => asyncflow}/runtime/simulation_runner.py (93%) rename src/{app => asyncflow}/samplers/common_helpers.py (96%) rename src/{app => asyncflow}/samplers/gaussian_poisson.py (92%) rename src/{app => asyncflow}/samplers/poisson_poisson.py (90%) rename src/{app => asyncflow}/schemas/full_simulation_input.py (55%) rename src/{app => asyncflow}/schemas/random_variables_config.py (95%) rename src/{app => asyncflow}/schemas/rqs_generator_input.py (92%) rename src/{app => asyncflow}/schemas/simulation_settings_input.py (97%) rename src/{app => asyncflow}/schemas/system_topology/endpoint.py (98%) rename src/{app => asyncflow}/schemas/system_topology/full_system_topology.py (98%) diff --git a/README.md b/README.md index 9f88a1b..2005eea 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ -# **FastSim – Event-Loop Aware Simulation for Backend Systems** +# **AsyncFlow – Event-Loop Aware Simulation for Backend Systems** ## **1. Overview** Modern asynchronous Python stacks such as **FastAPI + Uvicorn** deliver impressive performance, yet capacity planning for production workloads often relies on guesswork, costly cloud-based load tests, or late-stage troubleshooting. -**FastSim** addresses this challenge by providing a **digital twin** of your service that can be run entirely offline. It models event-loop behaviour, resource constraints, and request lifecycles, enabling you to forecast performance under different workloads and architectural choices **before deployment**. +**AsyncFlow** addresses this challenge by providing a **digital twin** of your service that can be run entirely offline. It models event-loop behaviour, resource constraints, and request lifecycles, enabling you to forecast performance under different workloads and architectural choices **before deployment**. -FastSim allows you to answer questions such as: +AsyncFlow allows you to answer questions such as: * *What happens to p95 latency if traffic doubles during a peak event?* * *How many cores are required to maintain SLAs at scale?* @@ -37,7 +37,7 @@ Until published, clone the repository and install in editable mode: - Python 3.11+ (recommended 3.12) - Poetry ≥ 1.6 -FastSim uses [Poetry](https://python-poetry.org/) for dependency management. +AsyncFlow uses [Poetry](https://python-poetry.org/) for dependency management. If you do not have Poetry installed, follow these steps. ### 3.1 Install Poetry (official method) @@ -64,8 +64,8 @@ curl -sSL https://install.python-poetry.org | python3 - ```bash # Clone the repository -git clone https://github.com/GioeleB00/Fastsim-Backend.git -cd Fastsim-Backend +git clone https://github.com/GioeleB00/AsyncFlow-Backend.git +cd AsyncFlow-Backend # Configure Poetry to always create a local `.venv` inside the project poetry config virtualenvs.in-project true @@ -171,9 +171,9 @@ from pathlib import Path import simpy import matplotlib.pyplot as plt -from app.config.constants import LatencyKey -from app.runtime.simulation_runner import SimulationRunner -from app.metrics.analyzer import ResultsAnalyzer +from asyncflow.config.constants import LatencyKey +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer def print_latency_stats(res: ResultsAnalyzer) -> None: """Print latency statistics returned by the analyzer.""" @@ -229,7 +229,7 @@ you will see the latency stats ## **5. Target Users and Use Cases** -| Audience | Challenge | FastSim Value | +| Audience | Challenge | AsyncFlow Value | | ------------------------ | ------------------------------------------------- | -------------------------------------------------------------------------------- | | Backend Engineers | Sizing services for variable workloads | Model endpoint workflows and resource bottlenecks before deployment | | DevOps / SRE | Balancing cost and SLA | Simulate scaling scenarios to choose optimal capacity | @@ -244,7 +244,7 @@ you will see the latency stats The project follows a standard Python package layout, managed with Poetry. ``` -Fastsim-Backend/ +AsyncFlow-Backend/ ├── examples/ # Examples payloads and datasets ├── scripts/ # Utility scripts (linting, startup) ├── docs/ # Project vision and technical documentation @@ -265,7 +265,7 @@ Fastsim-Backend/ ## **7. Development Workflow** -FastSim uses **Poetry** for dependency management and enforces quality via **Ruff** and **MyPy**. +AsyncFlow uses **Poetry** for dependency management and enforces quality via **Ruff** and **MyPy**. | Task | Command | Description | | ------------- | --------------------------------- | -------------------------------------- | diff --git a/docs/dev_workflow_guide.md b/docs/dev_workflow_guide.md index c0a9705..8c1974c 100644 --- a/docs/dev_workflow_guide.md +++ b/docs/dev_workflow_guide.md @@ -1,31 +1,23 @@ # **Development Workflow & Architecture Guide** -This document outlines the standardized development workflow, repository architecture, and branching strategy for the backend of the FastSim project. Adhering to these guidelines ensures consistency, maintainability, and a scalable development process. +This document outlines the standardized development workflow, repository architecture, and branching strategy for the backend of the AsyncFlow project. Adhering to these guidelines ensures consistency, maintainability, and a scalable development process. -## 1. Technology Stack -The project is built upon the following core technologies: +## 1. Repository Layout -- **Backend**: FastAPI -- **Backend Package Manager**: Poetry -- **Frontend**: React + JavaScript -- **Database**: PostgreSQL -- **Caching**: Redis -- **Containerization**: Docker +### 1.1 Backend Service (`AsyncFlow-backend`) -### 2.1 Backend Service (`FastSim-backend`) - -The repository hosts the entire FastAPI backend for FastSim. +The repository hosts the entire FastAPI backend for AsyncFlow. Its job is to expose the REST API, run the discrete-event simulation, talk to the database, and provide metrics. ``` -fastsim-backend/ +AsyncFlow-backend/ ├── example/ # examples of working simulations │ ├── data ├── scripts/ # helper bash scripts (lint, dev-startup, …) │ └── quality-check.sh ├── docs/ # project vision & low-level docs -│ └── fastsim-documentation/ +│ └── AsyncFlow-documentation/ ├── tests/ # unit & integration tests │ ├── unit/ │ └── integration/ @@ -37,15 +29,18 @@ fastsim-backend/ │ ├── runtime/ # simulation core │ │ ├── rqs_state.py # RequestState & Hop │ │ ├── simulation_runner.py # logic to initialize the whole simulation - | └── actors/ # SimPy “actors”: Edge, Server, Client, RqsGenerator - ├── pybuilder/ # Pythonic way to build the simulation payload +│ │ └── actors/ # SimPy “actors”: Edge, Server, Client, RqsGenerator +│ ├── pybuilder/ # Pythonic way to build the simulation payload │ ├── samplers/ # stochastic samplers (Gaussian-Poisson, etc.) │ ├── schemas/ # Pydantic input/output models ├── poetry.lock ├── pyproject.toml └── README.md ``` -### **What each top-level directory in `src/app` does** + +> Note: If your package name under `src/` is `asyncflow/` (instead of `app/`), the structure is identical—only the package folder name changes. + +### What each top-level directory in `src/app` does | Directory | Purpose | | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | @@ -56,31 +51,32 @@ fastsim-backend/ | | - **`rqs_state.py`** — Defines `RequestState` and `Hop` for tracking request lifecycle. | | | - **`simulation_runner.py`** — Entry point for initialising and running simulations. | | | - **`actors/`** — SimPy actor classes representing system components (`RqsGenerator`, `Client`, `Server`, `Edge`) and their behaviour. | +| **`pybuilder/`** | Pythonic builder to programmatically construct validated simulation payloads (alternative to YAML). | | **`samplers/`** | Random-variable samplers for stochastic simulation. Supports Poisson, Normal, and mixed distributions for modelling inter-arrival times and service steps. | | **`schemas/`** | Pydantic models for input/output validation and serialisation. Includes scenario definitions, topology graphs, simulation settings, and results payloads. | --- -### **Other Top-Level Directories** +### Other Top-Level Directories | Directory | Purpose | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | | **`example/`** | Ready-to-run simulation scenarios and example configurations. Includes `data/` with YAML definitions and scripts to demonstrate engine usage. | | **`scripts/`** | Utility shell scripts for development workflow, linting, formatting, and local startup (`quality-check.sh`, etc.). | -| **`docs/`** | Project documentation. Contains both high-level vision documents and low-level technical references (`fastsim-documentation/`). | +| **`docs/`** | Project documentation. Contains both high-level vision documents and low-level technical references (`AsyncFlow-documentation/`). | | **`tests/`** | Automated test suite, split into **unit** and **integration** tests to verify correctness of both individual components and end-to-end scenarios. | --- -## 3. Branching Strategy: Git Flow +## 2. Branching Strategy: Git Flow -To manage code development and releases in a structured manner, we use the **Git Flow** branching model. +To manage code development and releases in a structured manner, we use the **Git Flow** branching model—with an additional **refactor** branch type for non-feature refactoring work. ### Git Flow Workflow Diagram ```mermaid --- -title: Git Flow +title: Git Flow (with refactor/*) --- gitGraph @@ -89,6 +85,7 @@ gitGraph checkout develop commit id: "Setup Project" + %% feature branch branch feature/user-authentication checkout feature/user-authentication commit id: "feat: Add login form" @@ -96,6 +93,15 @@ gitGraph checkout develop merge feature/user-authentication + %% refactor branch (no new features, code cleanup/improvements) + branch refactor/performance-cleanup + checkout refactor/performance-cleanup + commit id: "refactor: simplify hot path" + commit id: "refactor: remove dead code" + checkout develop + merge refactor/performance-cleanup + + %% release branch branch release/v1.0.0 checkout release/v1.0.0 commit id: "fix: Pre-release bug fixes" tag: "v1.0.0" @@ -104,6 +110,7 @@ gitGraph checkout develop merge release/v1.0.0 + %% hotfix branch checkout main branch hotfix/critical-login-bug checkout hotfix/critical-login-bug @@ -122,70 +129,128 @@ This workflow is built upon two long-lived branches and several temporary, suppo #### Main Branches -1. **`main`** - * **Purpose**: This branch contains **production-ready, stable code**. Every commit on `main` represents a new, official release. - * **Rules**: You should **never** commit directly to `main`. It only receives merges from `release/*` and `hotfix/*` branches. Each merge should be tagged with a version number (e.g., `v1.0.0`). +1. **`main`** + **Purpose**: Production-ready, stable code. Every commit on `main` represents an official release. + **Rules**: Never commit directly to `main`. It only receives merges from `release/*` and `hotfix/*`. Each merge should be **tagged** (e.g., `v1.0.0`). -2. **`develop`** - * **Purpose**: This is the main **integration branch** for ongoing development. It contains all completed and tested features that are planned for the next release. - * **Rules**: It's the base for creating new `feature/*` branches. It reflects the most up-to-date state of development. +1. **`develop`** + **Purpose**: The main integration branch for ongoing development. It contains all completed and tested changes planned for the next release. + **Rules**: Base for `feature/*` and `refactor/*` branches. Reflects the most up-to-date development state. #### Supporting Branches -3. **`feature/*`** (e.g., `feature/user-authentication`) - * **Purpose**: To develop a new, specific feature in isolation. - * **Lifecycle**: - 1. Branched off of **`develop`**. - 2. Once development is complete, a **Pull Request (PR)** is opened to merge the changes back into **`develop`**. - 3. After the merge, the `feature/*` branch can be deleted. +3. **`feature/*`** (e.g., `feature/user-authentication`) + **Purpose**: Develop a new, specific feature in isolation. + **Lifecycle**: + + 1. Branched off **`develop`**. + 1. When complete, open a **Pull Request (PR)** back into **`develop`**. + 3. Delete the branch after merge. + +3. **`refactor/*`** (e.g., `refactor/performance-cleanup`) **← new** + **Purpose**: Perform **non-functional code changes** (no new features), such as internal restructurings, performance optimisations, reducing technical debt, renaming, file moves, or dependency hygiene. + **Rules**: -4. **`release/*`** (e.g., `release/v1.2.0`) - * **Purpose**: To prepare for a new production release. This branch is used for final bug fixes, documentation updates, and last-minute testing. It freezes the feature set for the release. - * **Lifecycle**: - 1. Branched off of **`develop`** when it's decided that the next version is feature-complete. - 2. When ready, it is merged into **`main`** (and tagged) and also back into **`develop`** to ensure any fixes made during the release phase are not lost. - 3. After merging, the `release/*` branch can be deleted. + * Must **not** introduce user-visible features or breaking API/DB changes. + * Prefer commit prefix `refactor:`; avoid `feat:`. + * Keep changes scoped and well-described to simplify review. + **Lifecycle**: -5. **`hotfix/*`** (e.g., `hotfix/critical-login-bug`) - * **Purpose**: To quickly patch a critical bug discovered in the production version (`main`). - * **Lifecycle**: - 1. Branched off of **`main`** (from the specific version tag). - 2. Once the fix is ready, it is merged into **`main`** (and a new patch version tag is created, e.g., `v1.0.1`). - 3. It is also merged into **`develop`** to ensure the fix is included in future releases. - 4. After merging, the `hotfix/*` branch can be deleted. + 1. Branched off **`develop`**. + 1. Open a **PR** back into **`develop`** (same review gates as features). + 3. Delete the branch after merge. -## 4. Continuous Integration / Continuous Delivery (CI/CD) Pipeline +5. **`release/*`** (e.g., `release/v1.1.0`) + **Purpose**: Prepare a production release—final bug fixes, docs, and last-minute tests. The feature set is frozen here. + **Lifecycle**: + + 1. Branched off **`develop`** when feature-complete. + 1. Merge into **`main`** (tag version) and back into **`develop`**. + 3. Delete after merges. + +6. **`hotfix/*`** (e.g., `hotfix/critical-login-bug`) + **Purpose**: Quickly patch a critical bug in production. + **Lifecycle**: + + 1. Branched off **`main`** (from a specific tag). + 1. Merge into **`main`** (tag a patch version, e.g., `v1.0.1`) **and** into **`develop`**. + 3. Delete after merges. + +**When to choose which branch?** + +* **New behavior / endpoints / DB migrations** → `feature/*` +* **Internal code improvements only** → `refactor/*` +* **Release prep** → `release/*` +* **Production emergency** → `hotfix/*` + +--- -A robust CI/CD pipeline guarantees that every change pushed to the repository is automatically validated, packaged, and—when appropriate—promoted to the next environment. -Our pipeline is built with **GitHub Actions** and follows a layered approach that mirrors the Git Flow branching model. +## 3. Continuous Integration / Continuous Delivery (CI/CD) Pipeline -We will start to describe the CI part related to push and PR in the develop branch of the backend service. +A robust CI/CD pipeline guarantees that every change is automatically validated, packaged, and—when appropriate—promoted to the next environment. Our pipeline is built with **GitHub Actions** and mirrors the branching model. -### 4.1 CI for project-backend on `develop` +We start with the CI part related to pushes and PRs in the backend service. -#### 4.1.1 Goals +### 3.1 CI for project-backend on `develop` -* **Fast feedback** – linting, type-checking, and unit tests finish in under a minute for every Pull Request. +#### 3.1.1 Goals + +* **Fast feedback** – linting, type-checking, and unit tests finish quickly for every Pull Request. * **Confidence in integration** – migrations, integration tests, and Docker smoke-tests run on every push to `develop`. -* **Deployment safety** – only artifacts produced from a green pipeline can be released or deployed. -* **Parity with production** – the same multi-stage Dockerfile is built and probed in CI, preventing “works-on-my-machine” surprises. +* **Deployment safety** – only artifacts from a green pipeline can be released/deployed. +* **Parity with production** – the same multi-stage Dockerfile is built and probed in CI. -### 4.1.2 Pipeline Layers +#### 3.1.1 Pipeline Layers * **Quick Suite (PR to `develop`)** *Runs in seconds; no external services or containers.* - * Black, isort, Flake8 + * Black, isort, Flake8 (or Ruff if adopted) * mypy static type-checking * Unit tests only (`pytest -m "not integration"`) * **Full Suite (push to `develop`)** *Runs in a few minutes; includes real services and Docker.* - + * Full test suite, including `@pytest.mark.integration` tests - + * Database migrations (PostgreSQL) against a disposable instance + * Redis available for tests if required + * Build multi-stage Docker image and run a quick smoke test + +### 3.1 CI for `feature/*` and `refactor/*` + +* **On PR to `develop`**: run the **Quick Suite** (lint, type-checking, unit tests). +* **Optional (recommended for large changes)**: allow a manual or scheduled **Full Suite** run for the branch to catch integration issues early. +* **On merge to `develop`**: the **Full Suite** runs (as described above). + +> `refactor/*` branches should maintain **zero behavior change**. If a refactor has the potential to alter behavior (e.g., performance-sensitive code), add targeted tests and consider a manual Full Suite run before merge. + +### 3.3 CI for `release/*` + +* Always run the **Full Suite**. +* Build and publish versioned artifacts/images to the registry with the release tag. +* Prepare release notes and changelog generation. +### 3.3 CI for `hotfix/*` +* Run the **Full Suite** against the hotfix branch. +* Tag the patch release on merge to `main` and propagate the merge back to `develop`. +--- + +## 4. Quality Gates & Conventions + +* **Static Analysis**: mypy (no new type errors). +* **Style**: Black/Flake8/isort or Ruff; no lint violations. +* **Tests**: + + * Unit tests for new logic or refactor touch points. + * Integration tests for cross-layer behavior. +* **Commits**: Conventional commits (`feat:`, `fix:`, `refactor:`, `docs:`, `test:`, `chore:` …). +* **Code Review**: PRs must be reviewed and approved; refactors must include rationale in the PR description (what changed, why safe). +* **Documentation**: Update `README`, `docs/`, and API docs when applicable. + +--- +By following this workflow—now with the **refactor** branch type—you keep feature development cleanly separated from codebase improvements, reduce merge friction, and maintain a predictable, high-quality delivery pipeline. diff --git a/docs/fastsim-docs/metrics_to_measure.md b/docs/fastsim-docs/metrics_to_measure.md index 390f6d9..bcffd33 100644 --- a/docs/fastsim-docs/metrics_to_measure.md +++ b/docs/fastsim-docs/metrics_to_measure.md @@ -1,8 +1,8 @@ -### **FastSim — Simulation Metrics** +### **AsyncFlow — Simulation Metrics** -Metrics are the lifeblood of any simulation, transforming a series of abstract events into concrete, actionable insights about system performance, resource utilization, and potential bottlenecks. FastSim provides a flexible and robust metrics collection system designed to give you a multi-faceted view of your system's behavior under load. +Metrics are the lifeblood of any simulation, transforming a series of abstract events into concrete, actionable insights about system performance, resource utilization, and potential bottlenecks. AsyncFlow provides a flexible and robust metrics collection system designed to give you a multi-faceted view of your system's behavior under load. -To achieve this, FastSim categorizes metrics into three distinct types: +To achieve this, AsyncFlow categorizes metrics into three distinct types: 1. **Sampled Metrics (`SampledMetricName`):** These metrics provide a **time-series view** of the system's state. They are captured at fixed, regular intervals (e.g., every 5 milliseconds). This methodology is ideal for understanding trends and measuring the continuous utilization of finite resources. Think of them as periodic snapshots of your system's health. diff --git a/docs/fastsim-docs/requests_generator.md b/docs/fastsim-docs/requests_generator.md index a15e241..eab8080 100644 --- a/docs/fastsim-docs/requests_generator.md +++ b/docs/fastsim-docs/requests_generator.md @@ -6,7 +6,7 @@ This document describes the design of the **requests generator**, which models a ## Model Inputs and Output -Following the FastSim philosophy, we accept a small set of input parameters to drive a “what-if” analysis in a pre-production environment. These inputs let you explore reliability and cost implications under different traffic scenarios. +Following the AsyncFlow philosophy, we accept a small set of input parameters to drive a “what-if” analysis in a pre-production environment. These inputs let you explore reliability and cost implications under different traffic scenarios. ## **Inputs** @@ -386,4 +386,4 @@ The sampling window length governs how often we re-sample $U$. It should reflect **Key takeaway:** By structuring the generator as -$\Lambda = U\,\lambda_r/60$ with a two-stage Poisson→Exponential sampler, FastSim efficiently reproduces compound Poisson traffic dynamics without any complex CDF inversion. +$\Lambda = U\,\lambda_r/60$ with a two-stage Poisson→Exponential sampler, AsyncFlow efficiently reproduces compound Poisson traffic dynamics without any complex CDF inversion. diff --git a/docs/fastsim-docs/runtime_and_resources.md b/docs/fastsim-docs/runtime_and_resources.md index a5dca9c..32f1611 100644 --- a/docs/fastsim-docs/runtime_and_resources.md +++ b/docs/fastsim-docs/runtime_and_resources.md @@ -1,10 +1,10 @@ Of course. This is an excellent request. A deep dive into the "why" and the real-world analogies is what makes documentation truly valuable. -Here is the comprehensive, detailed documentation for the FastSim Runtime Layer, written in English, incorporating all your requests. +Here is the comprehensive, detailed documentation for the AsyncFlow Runtime Layer, written in English, incorporating all your requests. ----- -# **FastSim — The Runtime Layer Documentation** +# **AsyncFlow — The Runtime Layer Documentation** *(Version July 2025 – Aligned with `app/runtime` and `app/resources`)* @@ -80,7 +80,7 @@ Think of `RequestState` as a request context in a modern microservices architect ## **4 The Resource Layer — Modelling Contention ⚙️** In real infrastructures every machine has a hard ceiling: only *N* CPU cores, only *M* MB of RAM. -FastSim mirrors that physical constraint through the **Resource layer**, which exposes pre-filled SimPy containers that actors must draw from. If a token is not available the coroutine simply blocks — giving you back-pressure “for free”. +AsyncFlow mirrors that physical constraint through the **Resource layer**, which exposes pre-filled SimPy containers that actors must draw from. If a token is not available the coroutine simply blocks — giving you back-pressure “for free”. --- @@ -127,7 +127,7 @@ defensive checks beyond the simple dictionary lookup. | RAM container tokens | **cgroup memory limit** or a pod’s allocatable memory; once exhausted new workloads must wait. | Just like a Kubernetes scheduler won’t place a pod if a node lacks free CPU/RAM, -FastSim won’t let an actor proceed until it obtains the necessary tokens. +AsyncFlow won’t let an actor proceed until it obtains the necessary tokens. ## **5. The Actors: Bringing the System to Life** @@ -448,4 +448,4 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]: With these mechanics the `LoadBalancerRuntime` faithfully emulates behaviour of production LBs (NGINX, HAProxy, AWS ALB) while remaining lightweight and -fully deterministic inside the FastSim event loop. +fully deterministic inside the AsyncFlow event loop. diff --git a/docs/fastsim-docs/simulation_input.md b/docs/fastsim-docs/simulation_input.md index ad89247..d5d860c 100644 --- a/docs/fastsim-docs/simulation_input.md +++ b/docs/fastsim-docs/simulation_input.md @@ -1,8 +1,8 @@ -# FastSim — Simulation Input Schema (v2) +# AsyncFlow — Simulation Input Schema (v2) -This document describes the **complete input contract** used by FastSim to run a simulation, the **design rationale** behind it, and the **guarantees** provided by the validation layer. It closes with an **end-to-end example** (YAML) you can drop into the project and run as-is. +This document describes the **complete input contract** used by AsyncFlow to run a simulation, the **design rationale** behind it, and the **validation guarantees** enforced by the Pydantic layer. At the end you’ll find an **end-to-end YAML example** you can run as-is. -The entry point is the Pydantic model: +The entry point is: ```python class SimulationPayload(BaseModel): @@ -14,40 +14,38 @@ class SimulationPayload(BaseModel): Everything the engine needs is captured by these three components: -* **`rqs_input`** — the workload model (how traffic is generated). -* **`topology_graph`** — the system under test, described as a directed graph (nodes & edges). +* **`rqs_input`** — workload model (how traffic is generated). +* **`topology_graph`** — system under test as a directed graph (nodes & edges). * **`sim_settings`** — global simulation controls and which metrics to collect. --- -## Why this shape? (Rationale) +## Rationale -### 1) **Separation of concerns** +### 1) Separation of concerns -* **Workload** (traffic intensity & arrival process) is independent from the **topology** (architecture under test) and from **simulation control** (duration & metrics). -* This lets you reuse the same topology with different workloads, or vice versa, without touching unrelated parts. +* **Workload** (traffic intensity & arrival process) is independent from **topology** (architecture) and **simulation control** (duration & metrics). +* You can reuse the same topology with different workloads (or vice versa) without touching unrelated parts. -### 2) **Validation-first, fail-fast** +### 2) Validation-first, fail-fast -* All inputs are **typed** and **validated** with Pydantic before the engine starts. -* Validation catches type errors, inconsistent references, and illegal combinations (e.g., an I/O step with a CPU metric). -* When a payload parses successfully, the engine can run without defensive checks scattered in runtime code. +* Inputs are **typed** and **validated** before the engine starts. +* Validation catches type errors, dangling references, illegal step definitions, and inconsistent graphs. +* Once a payload parses, the runtime code can remain lean (no defensive checks scattered everywhere). -### 3) **Small-to-large composition** +### 3) Small-to-large composition * The smallest unit is a **`Step`** (one resource-bound operation). -* Steps compose into an **`Endpoint`** (an ordered workflow). +* Steps compose into an **`Endpoint`** (ordered workflow). * Endpoints live on a **`Server`** node with finite resources. * Nodes and **Edges** form a **`TopologyGraph`**. -* A disciplined set of **Enums** (no magic strings) ensure a closed vocabulary. +* A closed set of **Enums** eliminates magic strings. --- -## 1. Workload: `RqsGeneratorInput` +## 1) Workload: `RqsGeneratorInput` -### Purpose - -Defines the traffic generator that produces request arrivals. +**Purpose:** Defines the stochastic traffic generator that produces request arrivals. ```python class RqsGeneratorInput(BaseModel): @@ -55,7 +53,11 @@ class RqsGeneratorInput(BaseModel): type: SystemNodes = SystemNodes.GENERATOR avg_active_users: RVConfig avg_request_per_minute_per_user: RVConfig - user_sampling_window: int = Field( ... ) # seconds + user_sampling_window: int = Field( + default=TimeDefaults.USER_SAMPLING_WINDOW, + ge=TimeDefaults.MIN_USER_SAMPLING_WINDOW, + le=TimeDefaults.MAX_USER_SAMPLING_WINDOW, + ) ``` ### Random variables (`RVConfig`) @@ -67,29 +69,24 @@ class RVConfig(BaseModel): variance: float | None = None ``` -#### Validation & guarantees - -* **`mean` is numeric** - `@field_validator("mean", mode="before")` coerces to `float` and rejects non-numeric values. -* **Auto variance** for Normal/LogNormal - `@model_validator(mode="after")` sets `variance = mean` if missing and the distribution is `NORMAL` or `LOG_NORMAL`. -* **Distribution constraints** on workload: +**Validators & guarantees** - * `avg_request_per_minute_per_user` **must be Poisson** (engine currently optimised for Poisson arrivals). - * `avg_active_users` **must be Poisson or Normal**. - * Enforced via `@field_validator(..., mode="after")` with clear error messages. +* `mean` is **numeric** and coerced to `float`. (Non-numeric → `ValueError`.) +* If `distribution ∈ {NORMAL, LOG_NORMAL}` and `variance is None`, then `variance := mean`. +* Workload-specific constraints: -#### Why these constraints? + * `avg_request_per_minute_per_user.distribution` **must be** `POISSON`. + * `avg_active_users.distribution` **must be** `POISSON` or `NORMAL`. +* `user_sampling_window` is an **integer in seconds**, bounded to `[1, 120]`. -* They reflect the current joint-sampling logic in the generator: **Poisson–Poisson** and **Normal–Poisson** are implemented and tested. Additional combos can be enabled later without changing the public contract. +**Why these constraints?** +They match the currently implemented samplers (Poisson–Poisson and Normal–Poisson). --- -## 2. System Graph: `TopologyGraph` - -### Purpose +## 2) System Graph: `TopologyGraph` -Defines the architecture under test as a **directed graph**: nodes are components (client, server, optional load balancer), edges are network links with latency models. +**Purpose:** Describes the architecture as a **directed graph**. Nodes are macro-components (client, server, optional load balancer); edges are network links with latency models. ```python class TopologyGraph(BaseModel): @@ -97,13 +94,15 @@ class TopologyGraph(BaseModel): edges: list[Edge] ``` -### Nodes +### 2.1 Nodes ```python class TopologyNodes(BaseModel): servers: list[Server] client: Client load_balancer: LoadBalancer | None = None + + # also: model_config = ConfigDict(extra="forbid") ``` #### `Client` @@ -112,23 +111,23 @@ class TopologyNodes(BaseModel): class Client(BaseModel): id: str type: SystemNodes = SystemNodes.CLIENT + # validator: type must equal SystemNodes.CLIENT ``` -* **Validator**: `type` must equal `SystemNodes.CLIENT`. - #### `ServerResources` ```python class ServerResources(BaseModel): - cpu_cores: PositiveInt = Field(...) - db_connection_pool: PositiveInt | None = Field(...) - ram_mb: PositiveInt = Field(...) + cpu_cores: PositiveInt = Field(ServerResourcesDefaults.CPU_CORES, + ge=ServerResourcesDefaults.MINIMUM_CPU_CORES) + db_connection_pool: PositiveInt | None = Field(ServerResourcesDefaults.DB_CONNECTION_POOL) + ram_mb: PositiveInt = Field(ServerResourcesDefaults.RAM_MB, + ge=ServerResourcesDefaults.MINIMUM_RAM_MB) ``` -* Maps directly to SimPy containers (CPU tokens, RAM capacity, etc.). -* Bounds enforced via `Field(ge=..., ...)`. +Each attribute maps directly to a SimPy primitive (core tokens, RAM container, optional DB pool). -#### `Step` (the atomic unit) +#### `Step` (atomic unit) ```python class Step(BaseModel): @@ -136,20 +135,15 @@ class Step(BaseModel): step_operation: dict[StepOperation, PositiveFloat | PositiveInt] ``` -**Key validator (coherence):** - -```python -@model_validator(mode="after") -def ensure_coherence_type_operation(cls, model: "Step") -> "Step": - # exactly one operation key, and it must match the step kind -``` +**Coherence validator** -* If `kind` is CPU → the only key must be `CPU_TIME`. -* If `kind` is RAM → only `NECESSARY_RAM`. -* If `kind` is I/O → only `IO_WAITING_TIME`. -* Values must be positive (`PositiveFloat/PositiveInt`). +* `step_operation` must contain **exactly one** key. +* Valid pairings: -This guarantees every step is **unambiguous** and **physically meaningful**. + * CPU step → `{ cpu_time: PositiveFloat }` + * RAM step → `{ necessary_ram: PositiveInt | PositiveFloat }` + * I/O step → `{ io_waiting_time: PositiveFloat }` +* Any mismatch (e.g., RAM step with `cpu_time`) → `ValueError`. #### `Endpoint` @@ -159,11 +153,10 @@ class Endpoint(BaseModel): steps: list[Step] @field_validator("endpoint_name", mode="before") - def name_to_lower(cls, v: str) -> str: - return v.lower() + def name_to_lower(cls, v): return v.lower() ``` -* Canonical lowercase naming avoids duplicates differing only by case. +Canonical lowercase names avoid accidental duplicates by case. #### `Server` @@ -173,10 +166,9 @@ class Server(BaseModel): type: SystemNodes = SystemNodes.SERVER server_resources: ServerResources endpoints: list[Endpoint] + # validator: type must equal SystemNodes.SERVER ``` -* **Validator**: `type` must equal `SystemNodes.SERVER`. - #### `LoadBalancer` (optional) ```python @@ -185,165 +177,153 @@ class LoadBalancer(BaseModel): type: SystemNodes = SystemNodes.LOAD_BALANCER algorithms: LbAlgorithmsName = LbAlgorithmsName.ROUND_ROBIN server_covered: set[str] = Field(default_factory=set) + # validator: type must equal SystemNodes.LOAD_BALANCER ``` -### Edges +### 2.2 Edges ```python class Edge(BaseModel): id: str - source: str - target: str + source: str # may be an external entrypoint (e.g., generator id) + target: str # MUST be a declared node id latency: RVConfig - probability: float = Field(1.0, ge=0.0, le=1.0) edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION - dropout_rate: float = Field(...) + dropout_rate: float = Field(NetworkParameters.DROPOUT_RATE, + ge=NetworkParameters.MIN_DROPOUT_RATE, + le=NetworkParameters.MAX_DROPOUT_RATE) + # validator: source != target + # validator on latency: mean > 0, variance >= 0 if provided ``` -#### Validation & guarantees +> **Note:** The former `probability` field has been **removed**. Fan-out is controlled at the **load balancer** via `algorithms` (e.g., round-robin, least-connections). Non-LB nodes are not allowed to have multiple outgoing edges (see graph-level validators below). + +### 2.3 Graph-level validators + +The `TopologyGraph` class performs several global checks: + +1. **Unique edge IDs** + + * Duplicate edge ids → `ValueError`. + +2. **Referential integrity** + + * Every **`target`** must be a declared node (`client`, any `server`, optional `load_balancer`). + * **External IDs** (e.g., generator id) are **allowed only as sources** and **must never appear as a target** anywhere. -* **Latency sanity** - `@field_validator("latency", mode="after")` ensures `mean > 0` and `variance >= 0` (if provided). Error messages mention the **edge id** for clarity. -* **No self-loops** - `@model_validator(mode="after")` rejects `source == target`. -* **Unique edge IDs** - `TopologyGraph.unique_ids` enforces uniqueness across `edges`. -* **Referential integrity** - `TopologyGraph.edge_refs_valid` ensures: +3. **Load balancer integrity (if present)** - * Every `target` is a declared node ID. - * **External sources** (e.g., the generator id) are allowed, but **may not** appear as a `target` anywhere. -* **Load balancer integrity** (if present) - `TopologyGraph.valid_load_balancer` enforces: + * `server_covered ⊆ declared server ids`. + * There must be **an outgoing edge from the LB to every covered server**; missing links → `ValueError`. - * `server_covered ⊆ {server ids}`. - * For every covered server there exists an **outgoing edge from the LB** to that server. +4. **Fan-out restriction** -These checks make the graph **closed**, **consistent**, and **wirable** without surprises at runtime. + * Among **declared nodes**, **only the load balancer** may have **multiple outgoing edges**. + * Edges originating from non-declared external sources (e.g., generator) are ignored by this check. + * Violations list the offending source ids. --- -## 3. Simulation Control: `SimulationSettings` +## 3) Simulation Control: `SimulationSettings` ```python class SimulationSettings(BaseModel): - total_simulation_time: int = Field(..., ge=TimeDefaults.MIN_SIMULATION_TIME) + total_simulation_time: int = Field( + default=TimeDefaults.SIMULATION_TIME, + ge=TimeDefaults.MIN_SIMULATION_TIME, + ) enabled_sample_metrics: set[SampledMetricName] = Field(default_factory=...) enabled_event_metrics: set[EventMetricName] = Field(default_factory=...) - sample_period_s: float = Field(..., ge=SamplePeriods.MINIMUM_TIME, le=SamplePeriods.MAXIMUM_TIME) + sample_period_s: float = Field( + default=SamplePeriods.STANDARD_TIME, + ge=SamplePeriods.MINIMUM_TIME, + le=SamplePeriods.MAXIMUM_TIME, + ) ``` -### What it controls +**What it controls** -* **Clock** — `total_simulation_time` (seconds). -* **Sampling cadence** — `sample_period_s` for time-series metrics. -* **What to collect** — two sets of enums: +* **Clock** — `total_simulation_time` in seconds (default 3600, min 5). +* **Sampling cadence** — `sample_period_s` in seconds (default 0.01; bounds `[0.001, 0.1]`). +* **Metric selection** — default sets include: - * `enabled_sample_metrics`: time-series KPIs (e.g., ready queue length, RAM in use, edge concurrency). - * `enabled_event_metrics`: per-event KPIs (e.g., request clocks/latency). + * Sampled (time-series): `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, `edge_concurrent_connection`. + * Event (per-request): `rqs_clock`. -### Why Enums matter here +--- -Letting users pass strings like `"ram_in_use"` is error-prone. By using **`SampledMetricName`** and **`EventMetricName`** enums, the settings are **validated upfront**, so the runtime collector knows exactly which lists to allocate and fill. No hidden `KeyError`s halfway through a run. +## 4) Enums & Units (Quick Reference) ---- +**Distributions:** `poisson`, `normal`, `log_normal`, `exponential`, `uniform` +**Node types:** `generator`, `server`, `client`, `load_balancer` (fixed by models) +**Edge types:** `network_connection` +**LB algorithms:** `round_robin`, `least_connection` +**Step kinds:** -## What these validations buy you +* CPU: `initial_parsing`, `cpu_bound_operation` +* RAM: `ram` +* I/O: `io_task_spawn`, `io_llm`, `io_wait`, `io_db`, `io_cache` + **Step operation keys:** `cpu_time`, `io_waiting_time`, `necessary_ram` + **Sampled metrics:** `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, `edge_concurrent_connection` + **Event metrics:** `rqs_clock` (and `llm_cost` reserved) -* **Type safety** (no accidental strings where enums are expected). -* **Physical realism** (no zero/negative times or RAM). -* **Graph integrity** (no dangling edges or self-loops). -* **Operational clarity** (every step has exactly one effect). -* **Better errors** (validators point to the exact field/entity at fault). +**Units & conventions** -Together, they make the model **predictable** for the simulation engine and **pleasant** to debug. +* **Time:** seconds (`cpu_time`, `io_waiting_time`, latencies, `total_simulation_time`, `sample_period_s`, `user_sampling_window`) +* **RAM:** megabytes (`ram_mb`, `necessary_ram`) +* **Rates:** requests/minute (`avg_request_per_minute_per_user.mean`) +* **Probabilities:** `[0.0, 1.0]` (`dropout_rate`) +* **IDs:** strings; must be **unique** within their category --- -## End-to-End Example (YAML) - -This is a complete, valid payload you can load with `SimulationRunner.from_yaml(...)`. - -```yaml -# ─────────────────────────────────────────────────────────────── -# FastSim scenario: generator → client → server → client -# ─────────────────────────────────────────────────────────────── - -rqs_input: - id: rqs-1 - # avg_active_users can be POISSON or NORMAL; mean is required. - avg_active_users: - mean: 100 - distribution: poisson - # must be POISSON (engine constraint) - avg_request_per_minute_per_user: - mean: 20 - distribution: poisson - user_sampling_window: 60 # seconds - -topology_graph: - nodes: - client: - id: client-1 - type: client - servers: - - id: srv-1 - type: server - server_resources: - cpu_cores: 1 - ram_mb: 2048 - # db_connection_pool: 50 # optional - endpoints: - - endpoint_name: /predict - steps: - - kind: ram - step_operation: { necessary_ram: 100 } - - kind: initial_parsing # CPU step (enum in your code) - step_operation: { cpu_time: 0.001 } - - kind: io_wait # I/O step - step_operation: { io_waiting_time: 0.100 } - - edges: - - id: gen-to-client - source: rqs-1 # external source OK - target: client-1 - latency: { mean: 0.003, distribution: exponential } - - - id: client-to-server - source: client-1 - target: srv-1 - latency: { mean: 0.003, distribution: exponential } - - - id: server-to-client - source: srv-1 - target: client-1 - latency: { mean: 0.003, distribution: exponential } - -sim_settings: - total_simulation_time: 500 - sample_period_s: 0.05 - enabled_sample_metrics: - - ready_queue_len - - event_loop_io_sleep - - ram_in_use - - edge_concurrent_connection - enabled_event_metrics: - - rqs_clock -``` +## 5) Validation Checklist (What is guaranteed if the payload parses) - Notes: -> - * `kind` uses the **EndpointStep** enums you’ve defined (e.g., `ram`, `initial_parsing`, `io_wait`). - * The coherence validator ensures that each `kind` uses the correct `step_operation` key and **exactly one** entry. - * The **edge** constraints guarantee a clean, connected, and sensible graph. +### Workload (`RqsGeneratorInput`, `RVConfig`) ---- +* `mean` is numeric (`int|float`) and coerced to `float`. +* If `distribution ∈ {NORMAL, LOG_NORMAL}` and `variance is None` → `variance := mean`. +* `avg_request_per_minute_per_user.distribution == POISSON`. +* `avg_active_users.distribution ∈ {POISSON, NORMAL}`. +* `user_sampling_window ∈ [1, 120]` seconds. +* `type` fields default to the correct enum (`generator`) and are strongly typed. + +### Steps & Endpoints + +* `endpoint_name` is normalized to lowercase. +* Each `Step` has **exactly one** `step_operation` key. +* `Step.kind` and `step_operation` key **must match**: + + * CPU ↔ `cpu_time` + * RAM ↔ `necessary_ram` + * I/O ↔ `io_waiting_time` +* All step operation values are strictly **positive**. + +### Nodes + +* `Client.type == client`, `Server.type == server`, `LoadBalancer.type == load_balancer` (enforced). +* `ServerResources` obey lower bounds: `cpu_cores ≥ 1`, `ram_mb ≥ 256`. +* `TopologyNodes` contains **unique ids** across `client`, `servers[]`, and (optional) `load_balancer`. Duplicates → `ValueError`. +* `TopologyNodes` forbids unknown fields (`extra="forbid"`). + +### Edges + +* **No self-loops:** `source != target`. +* **Latency sanity:** `latency.mean > 0`; if `variance` is provided, `variance ≥ 0`. Error messages reference the **edge id**. +* `dropout_rate ∈ [0, 1]`. + +### Graph (`TopologyGraph`) + +* **Edge ids are unique.** +* **Targets** are always **declared node ids**. +* **External ids** (e.g., generator) are allowed only as **sources**; they must **never** appear as **targets**. +* **Load balancer integrity:** + + * `server_covered` is a subset of declared servers. + * Every covered server has a **corresponding edge from the LB** (LB → srv). Missing links → `ValueError`. +* **Fan-out restriction:** among **declared nodes**, only the **LB** can have **multiple outgoing edges**. Offenders are listed. + +If your payload passes validation, the engine can wire and run the simulation deterministically with consistent semantics. -## Summary -* The **payload** is small but expressive: workload, topology, and settings. -* The **validators** are doing real work: they make illegal states unrepresentable. -* The **enums** keep the contract tight and maintainable. -* Together, they let you move fast **without** breaking the simulation engine. -If you extend the engine (new distributions, step kinds, metrics), you can **keep the same contract** and enrich the enums & validators to preserve the same guarantees. diff --git a/docs/fastsim-docs/simulation_runner.md b/docs/fastsim-docs/simulation_runner.md index 8153e13..300b226 100644 --- a/docs/fastsim-docs/simulation_runner.md +++ b/docs/fastsim-docs/simulation_runner.md @@ -3,7 +3,7 @@ ## **Overview** -The `SimulationRunner` is the **orchestrator** of the FastSim engine. +The `SimulationRunner` is the **orchestrator** of the AsyncFlow engine. Its main responsibility is to: 1. **Build** simulation actors from a structured input (`SimulationPayload`). diff --git a/docs/fastsim_vision.md b/docs/fastsim_vision.md index 7119807..bf5b88a 100644 --- a/docs/fastsim_vision.md +++ b/docs/fastsim_vision.md @@ -1,12 +1,12 @@ -## 1 Why FastSim? +## 1 Why AsyncFlow? -FastAPI + Uvicorn gives Python teams a lightning-fast async stack, yet sizing it for production still means guess-work, costly cloud load-tests or late surprises. **FastSim** fills that gap by becoming a **digital twin** of your actual service: +FastAPI + Uvicorn gives Python teams a lightning-fast async stack, yet sizing it for production still means guess-work, costly cloud load-tests or late surprises. **AsyncFlow** fills that gap by becoming a **digital twin** of your actual service: * It **replays** your FastAPI + Uvicorn event-loop behavior in SimPy, generating exactly the same kinds of asynchronous steps (parsing, CPU work, I/O, LLM calls) that happen in real code. * It **models** your infrastructure primitives—CPU cores (via a SimPy `Resource`), database pools, rate-limiters, even GPU inference quotas—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency. * It **outputs** the very metrics you’d scrape in production (p50/p95/p99 latency, ready-queue lag, current & max concurrency, throughput, cost per LLM call), but entirely offline, in seconds. -With FastSim you can ask *“What happens if traffic doubles on Black Friday?”*, *“How many cores to keep p95 < 100 ms?”* or *“Is our LLM-driven endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. +With AsyncFlow you can ask *“What happens if traffic doubles on Black Friday?”*, *“How many cores to keep p95 < 100 ms?”* or *“Is our LLM-driven endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. **Outcome:** data-driven capacity planning, early performance tuning, and far fewer “surprises” once you hit production. @@ -26,7 +26,7 @@ With FastSim you can ask *“What happens if traffic doubles on Black Friday?” ## 3 Who benefits & why (detailed) -| Audience | Pain-point solved | FastSim value | +| Audience | Pain-point solved | AsyncFlow value | | ------------------------------ | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Backend engineers** | Unsure if 4 vCPU container survives a marketing spike | Run *what-if* load, tweak CPU cores / pool size, get p95 & max-concurrency before merging. | | **DevOps / SRE** | Guesswork in capacity planning; cost of over-provisioning | Simulate 1 → N replicas, autoscaler thresholds, DB-pool size; pick the cheapest config meeting SLA. | @@ -38,4 +38,4 @@ With FastSim you can ask *“What happens if traffic doubles on Black Friday?” --- -**Bottom-line:** FastSim turns abstract architecture diagrams into concrete numbers—*before* spinning up expensive cloud environments—so you can build, validate and discuss your designs with full confidence. +**Bottom-line:** AsyncFlow turns abstract architecture diagrams into concrete numbers—*before* spinning up expensive cloud environments—so you can build, validate and discuss your designs with full confidence. diff --git a/docs/pybuilder.md b/docs/pybuilder.md new file mode 100644 index 0000000..19b7b24 --- /dev/null +++ b/docs/pybuilder.md @@ -0,0 +1,458 @@ +# AsyncFlow – Programmatic Input Guide (pybuilder) + +This guide shows how to **build the full simulation input in Python** using the +`AsyncFlow` builder (the “pybuilder”), with the same precision and validation +guarantees as the YAML flow. You’ll see **all components, valid values, units, +constraints, and how validation is enforced**. + +Under the hood, the builder assembles a single `SimulationPayload`: + +```python +SimulationPayload( + rqs_input=RqsGeneratorInput(...), # traffic generator (workload) + topology_graph=TopologyGraph(...), # system architecture as a graph + sim_settings=SimulationSettings(...), # global settings and metrics +) +``` + +Everything is **validated up front** by Pydantic. If something is inconsistent +(e.g., an edge points to a non-existent node), a clear error is raised +**before** running the simulation. + +--- + +## Quick Start (Minimal Example) + +```python +from __future__ import annotations + +import simpy + +from asyncflow.pybuilder.input_builder import AsyncFlow +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.endpoint import Endpoint +from asyncflow.schemas.system_topology.full_system_topology import ( + Client, Edge, Server, +) + +# 1) Workload +generator = RqsGeneratorInput( + id="rqs-1", + avg_active_users={"mean": 50, "distribution": "poisson"}, + avg_request_per_minute_per_user={"mean": 30, "distribution": "poisson"}, + user_sampling_window=60, # seconds +) + +# 2) Nodes (client + one server) +client = Client(id="client-1") +endpoint = Endpoint( + endpoint_name="/hello", + probability=1.0, # per-endpoint weight on this server + steps=[ + {"kind": "ram", "step_operation": {"necessary_ram": 32}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ], +) +server = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 1024}, + endpoints=[endpoint], +) + +# 3) Edges (directed, with latency as RV) +edges = [ + Edge( + id="gen-to-client", + source="rqs-1", # external sources allowed + target="client-1", # targets must be declared nodes + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="client-to-server", + source="client-1", + target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="server-to-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), +] + +# 4) Global settings +settings = SimulationSettings( + total_simulation_time=300, # seconds, min 5 + sample_period_s=0.01, # seconds, [0.001 .. 0.1] + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], +) + +# 5) Build (validates everything) +payload: SimulationPayload = ( + AsyncFlow() + .add_generator(generator) + .add_client(client) + .add_servers(server) + .add_edges(*edges) + .add_simulation_settings(settings) + .build_payload() +) + +# 6) Run +env = simpy.Environment() +results = SimulationRunner(env=env, simulation_input=payload).run() +``` + +--- + +## 1) Random Variables (`RVConfig`) + +Where a parameter is stochastic (e.g., edge latency, users, RPM), you pass a +dictionary that Pydantic converts into an `RVConfig`: + +```python +{"mean": , "distribution": , "variance": } +``` + +### Supported distributions + +* `"poisson"` +* `"normal"` +* `"log_normal"` +* `"exponential"` +* `"uniform"` + +### Rules & defaults + +* `mean` is **required** and numeric; coerced to `float`. +* If `distribution` is `"normal"` or `"log_normal"` and `variance` is absent, + it defaults to **`variance = mean`**. +* For **edge latency** (see §3.3): **`mean > 0`** and, if provided, + **`variance ≥ 0`**. + +**Units** + +* Time values are **seconds**. +* Rates are **requests per minute** (where noted). + +--- + +## 2) Workload: `RqsGeneratorInput` + +```python +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput + +generator = RqsGeneratorInput( + id="rqs-1", + avg_active_users={ + "mean": 100, + "distribution": "poisson", # or "normal" + # "variance": , # optional; auto=mean if "normal" + }, + avg_request_per_minute_per_user={ + "mean": 20, + "distribution": "poisson", # must be poisson in current samplers + }, + user_sampling_window=60, # [1 .. 120] seconds +) +``` + +**Semantics** + +* `avg_active_users`: active users as a random variable (Poisson or Normal). +* `avg_request_per_minute_per_user`: per-user RPM (Poisson). +* `user_sampling_window`: re-sample active users every N seconds. + +--- + +## 3) System Topology (`Client`, `Server`, `LoadBalancer`, `Edge`) + +Represent the system as a **directed graph**: nodes (client, servers, optional +LB) and edges (network links). + +### 3.1 Client + +```python +from asyncflow.schemas.system_topology.full_system_topology import Client + +client = Client(id="client-1") # type is fixed to 'client' +``` + +### 3.2 Server & Endpoints + +```python +from asyncflow.schemas.system_topology.endpoint import Endpoint +from asyncflow.schemas.system_topology.full_system_topology import Server + +endpoint = Endpoint( + endpoint_name="/api", # normalized to lowercase internally + probability=1.0, # endpoint selection weight within the server + steps=[ + {"kind": "ram", "step_operation": {"necessary_ram": 64}}, + {"kind": "cpu_bound_operation", "step_operation": {"cpu_time": 0.004}}, + {"kind": "io_db", "step_operation": {"io_waiting_time": 0.012}}, + ], +) + +server = Server( + id="srv-1", # type fixed to 'server' + server_resources={ + "cpu_cores": 2, # int ≥ 1 + "ram_mb": 2048, # int ≥ 256 + "db_connection_pool": None, # optional future-use + }, + endpoints=[endpoint], +) +``` + +**Step kinds** (enums) + +* **CPU**: `"initial_parsing"`, `"cpu_bound_operation"` +* **RAM**: `"ram"` +* **I/O**: `"io_task_spawn"`, `"io_llm"`, `"io_wait"`, `"io_db"`, `"io_cache"` + +**Operation keys** (enum `StepOperation`) + +* `cpu_time` (seconds, positive) +* `necessary_ram` (MB, positive int/float) +* `io_waiting_time` (seconds, positive) + +**Validation enforced** + +* Each step’s `step_operation` has **exactly one** entry. +* The operation **must match** the step kind. +* All numeric values **> 0**. + +**Runtime semantics (high level)** + +* RAM is reserved before CPU, then released at the end. +* CPU tokens acquired lazily for consecutive CPU steps; released on I/O. +* I/O waits **do not** hold a CPU core. + +### 3.3 Load Balancer (optional) + +```python +from asyncflow.schemas.system_topology.full_system_topology import LoadBalancer + +lb = LoadBalancer( + id="lb-1", + algorithms="round_robin", # or "least_connection" + server_covered={"srv-1", "srv-2"}, +) +``` + +**LB validation** + +* `server_covered` must be a subset of declared servers. +* You must define **edges from the LB to each covered server** (see below). + +### 3.4 Edges + +```python +from asyncflow.schemas.system_topology.full_system_topology import Edge + +edge = Edge( + id="client-to-srv1", + source="client-1", # may be external only for sources + target="srv-1", # MUST be a declared node + latency={"mean": 0.003, "distribution": "exponential"}, + probability=1.0, # optional [0..1] + edge_type="network_connection", # current default/only + dropout_rate=0.01, # optional [0..1] +) +``` + +**Semantics** + +* `source`: can be an **external** ID for entry points (e.g., `"rqs-1"`). +* `target`: **must** be a declared node (`client`, `server`, `load_balancer`). +* `latency`: random variable; **`mean > 0`**, `variance ≥ 0` (if provided). +* `probability`: used when a node has multiple outgoing edges (fan-out). + If your code enforces “no fan-out except LB”, do **not** create multiple + outgoing edges from nodes other than the LB. +* `dropout_rate`: per-request/packet drop probability on this link. + +--- + +## 4) Global Settings: `SimulationSettings` + +```python +from asyncflow.schemas.simulation_settings_input import SimulationSettings + +settings = SimulationSettings( + total_simulation_time=600, # seconds, default 3600, min 5 + sample_period_s=0.02, # seconds, [0.001 .. 0.1], default 0.01 + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=[ + "rqs_clock", + # "llm_cost", # optional future accounting + ], +) +``` + +**Notes** + +* Sampled metrics are time-series collected at `sample_period_s`. +* Event metrics are per-event (e.g., per request), not sampled. + +--- + +## 5) Building the Payload with `AsyncFlow` + +```python +from asyncflow.pybuilder.input_builder import AsyncFlow +from asyncflow.schemas.full_simulation_input import SimulationPayload + +flow = ( + AsyncFlow() + .add_generator(generator) + .add_client(client) + .add_servers(server) # varargs; supports multiple + .add_edges(*edges) # varargs; supports multiple + # .add_load_balancer(lb) # optional + .add_simulation_settings(settings) +) + +payload: SimulationPayload = flow.build_payload() +``` + +**What `build_payload()` validates** + +1. **Presence**: generator, client, ≥1 server, ≥1 edge, settings. +2. **Unique IDs**: servers and edges have unique IDs. +3. **Node types**: fixed enums: `client`, `server`, `load_balancer`. +4. **Edge integrity**: every target is a declared node; external IDs allowed only as sources; no self-loops (`source != target`). +5. **Load balancer sanity**: `server_covered ⊆ declared_servers` and there is an edge from the LB to **each** covered server. +6. **(Optional in your codebase)** “No fan-out except LB” validator: multiple + outgoing edges only allowed for the LB. + +If any rule is violated, a **descriptive `ValueError`** pinpoints the problem. + +--- + +## 6) Running the Simulation + +```python +import simpy +from asyncflow.runtime.simulation_runner import SimulationRunner + +env = simpy.Environment() +runner = SimulationRunner(env=env, simulation_input=payload) +results = runner.run() # blocks until total_simulation_time + +# Access results via the ResultsAnalyzer API: +stats = results.get_latency_stats() +ts, rps = results.get_throughput_series() +sampled = results.get_sampled_metrics() +``` + +You can also plot with the analyzer methods: + +```python +from matplotlib import pyplot as plt # optional +fig, axes = plt.subplots(2, 2, figsize=(12, 8)) +results.plot_latency_distribution(axes[0, 0]) +results.plot_throughput(axes[0, 1]) +results.plot_server_queues(axes[1, 0]) +results.plot_ram_usage(axes[1, 1]) +fig.tight_layout() +fig.savefig("single_server_pybuilder.png") +``` + +--- + +## 7) Builder vs YAML: Field Mapping + +| YAML path | Builder (Python) | +| --------------------------------------------- | ---------------------------------------------------------------- | +| `rqs_input.id` | `RqsGeneratorInput(id=...)` | +| `rqs_input.avg_active_users.*` | `RqsGeneratorInput(avg_active_users={...})` | +| `rqs_input.avg_request_per_minute_per_user.*` | `RqsGeneratorInput(avg_request_per_minute_per_user={...})` | +| `rqs_input.user_sampling_window` | `RqsGeneratorInput(user_sampling_window=...)` | +| `topology_graph.nodes.client.id` | `Client(id=...)` | +| `topology_graph.nodes.servers[*]` | `Server(id=..., server_resources={...}, endpoints=[...])` | +| `endpoint.endpoint_name` | `Endpoint(endpoint_name=...)` | +| `endpoint.steps[*]` | `Endpoint(steps=[{"kind": "...","step_operation": {...}}, ...])` | +| `topology_graph.nodes.load_balancer.*` | `LoadBalancer(id=..., algorithms=..., server_covered={...})` | +| `topology_graph.edges[*]` | `Edge(id=..., source=..., target=..., latency={...}, ...)` | +| `sim_settings.*` | `SimulationSettings(...)` | + +--- + +## 8) Common Pitfalls & How to Avoid Them + +* **Mismatched step operations** + A CPU step must use `cpu_time`; an I/O step must use `io_waiting_time`; a RAM + step must use `necessary_ram`. Exactly **one** key per step. + +* **Edge target must be a declared node** + `source` can be external (e.g., `"rqs-1"`), but **no external ID** may ever + appear as a `target`. + +* **Load balancer coverage without edges** + If the LB covers `[srv-1, srv-2]`, you **must** add edges `lb→srv-1` and + `lb→srv-2`. + +* **Latency RV rules on edges** + `mean` must be **> 0**; if `variance` is present, it must be **≥ 0**. + +* **Fan-out rules** + If your codebase enforces “no fan-out except LB”, do not create multiple + outgoing edges from non-LB nodes. If you do allow it, set `probability` + weights so outgoing probabilities per source sum to \~1.0 (or ensure a single + edge per source). + +* **Sampling too coarse** + Large `sample_period_s` may miss short spikes. Lower it to capture bursts + (at the cost of larger time series). + +--- + +## 9) Enums, Units & Conventions (Cheat Sheet) + +* **Distributions**: `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"`, + `"uniform"` +* **Node types**: fixed internally to: `generator`, `server`, `client`, + `load_balancer` +* **Edge type**: `network_connection` +* **LB algorithms**: `"round_robin"`, `"least_connection"` +* **Step kinds** + CPU: `"initial_parsing"`, `"cpu_bound_operation"` + RAM: `"ram"` + I/O: `"io_task_spawn"`, `"io_llm"`, `"io_wait"`, `"io_db"`, `"io_cache"` +* **Step operation keys**: `cpu_time`, `io_waiting_time`, `necessary_ram` +* **Sampled metrics**: `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, + `edge_concurrent_connection` +* **Event metrics**: `rqs_clock` (and `llm_cost` reserved for future use) + +**Units & ranges** + +* **Time**: seconds (`cpu_time`, `io_waiting_time`, edge latency means/variance, + `total_simulation_time`, `sample_period_s`, `user_sampling_window`) +* **RAM**: megabytes (`ram_mb`, `necessary_ram`) +* **Rates**: requests/minute (`avg_request_per_minute_per_user.mean`) +* **Probabilities**: `[0.0, 1.0]` (`probability`, `dropout_rate`) +* **Bounds**: `total_simulation_time ≥ 5`, `sample_period_s ∈ [0.001, 0.1]`, + `cpu_cores ≥ 1`, `ram_mb ≥ 256`, numeric step values > 0 + +--- + +With these patterns, you can build any topology that the YAML supports—**fully +programmatically**, with the same strong validation and clear errors on invalid +configurations. diff --git a/docs/yaml_builder.md b/docs/yaml_builder.md index 40f519e..f3d22e3 100644 --- a/docs/yaml_builder.md +++ b/docs/yaml_builder.md @@ -1,6 +1,6 @@ -# FastSim – YAML Input Guide +# AsyncFlow – YAML Input Guide -This guide explains **how to author the simulation YAML** for FastSim, covering every field, valid values, units, constraints, and the validation rules enforced by the Pydantic schemas. +This guide explains **how to author the simulation YAML** for AsyncFlow, covering every field, valid values, units, constraints, and the validation rules enforced by the Pydantic schemas. The YAML you write is parsed into a single model: @@ -264,7 +264,7 @@ sim_settings: ## 5) Graph-level Validation Rules (what’s checked before running) -FastSim validates the entire payload. Key checks include: +AsyncFlow validates the entire payload. Key checks include: 1. **Unique IDs** diff --git a/examples/data/single_server.yml b/examples/data/single_server.yml index c6e2ce6..844b1ad 100644 --- a/examples/data/single_server.yml +++ b/examples/data/single_server.yml @@ -1,5 +1,5 @@ # ─────────────────────────────────────────────────────────────── -# FastSim scenario: generator ➜ client ➜ server ➜ client +# AsyncFlow scenario: generator ➜ client ➜ server ➜ client # ─────────────────────────────────────────────────────────────── # 1. 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SimulationPayload. Scenario reproduced (same as the previous YAML): @@ -30,26 +30,26 @@ from __future__ import annotations from pathlib import Path -from typing import Dict, Iterable, List, Mapping, Tuple +from typing import Iterable, List, Mapping import numpy as np import simpy -# ── FastSim domain imports ─────────────────────────────────────────────────── -from app.pybuilder.input_builder import AsyncFlow -from app.runtime.simulation_runner import SimulationRunner -from app.metrics.analyzer import ResultsAnalyzer -from app.schemas.full_simulation_input import SimulationPayload -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.endpoint import Endpoint -from app.schemas.system_topology.full_system_topology import ( +# ── AsyncFlow domain imports ─────────────────────────────────────────────────── +from asyncflow.pybuilder.input_builder import AsyncFlow +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.endpoint import Endpoint +from asyncflow.schemas.system_topology.full_system_topology import ( Client, Edge, Server, ) -from app.config.constants import LatencyKey, SampledMetricName +from asyncflow.config.constants import LatencyKey, SampledMetricName # ───────────────────────────────────────────────────────────── diff --git a/examples/single_server_yaml.py b/examples/single_server_yaml.py index 3057a52..fb20d50 100644 --- a/examples/single_server_yaml.py +++ b/examples/single_server_yaml.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -Run a FastSim scenario from a YAML file and print diagnostics. +Run a AsyncFlow scenario from a YAML file and print diagnostics. What it does: - Loads the simulation payload from YAML via `SimulationRunner.from_yaml`. @@ -23,15 +23,15 @@ import matplotlib.pyplot as plt import numpy as np import simpy -from app.config.constants import ( +from asyncflow.config.constants import ( EndpointStepCPU, EndpointStepIO, EndpointStepRAM, LatencyKey, StepOperation, ) -from app.metrics.analyzer import ResultsAnalyzer -from app.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner # ───────────────────────────────────────────────────────────── @@ -233,7 +233,7 @@ def run_sanity_checks(runner: SimulationRunner, res: ResultsAnalyzer) -> None: # ───────────────────────────────────────────────────────────── def main() -> None: """Entry-point: parse args, run simulation, print/plot, sanity-check.""" - parser = ArgumentParser(description="Run FastSim from YAML and print outputs + sanity checks.") + parser = ArgumentParser(description="Run AsyncFlow from YAML and print outputs + sanity checks.") parser.add_argument( "--yaml", type=Path, diff --git a/examples/single_server_yml.png b/examples/single_server_yml.png index 89cca6d5cd245400ed7fe97b9ff0b33123794647..560a4b3e3eabe2e0b8408c3e322656deb770d7af 100644 GIT binary patch literal 160670 zcmce8Wmr{d^zQ)y2`NEJ8bJ^ckPazDP$U%T1_|lz?k*)nLJ(9!Lb|&|N)$wp6i_-u zKQ-1WOC6h;XOb&4Jv z3tr)!eu57F5pj~ya8j`~b#gUyFhMC8I@wv-I$1q1qIWTIaC~5E!+V*B`!YWVy}6T< zouddBm-U~YxNPfS#-({+E(%A%v6ItuM4<=`k-um!C0;y0p`lPWuS?x?OIt~I^&&Mn z?vki`a)Uk?L;9@6nTQ}|jF@a}IeY`s78MNAUVlc`46`Q$7_ zVBCoaVa)2F=O<-gh>KH9=hB-4h@mO;zhA<)g2-~38L*cAOy$axPk8Ts7I*)3(ryWvzQ>eUfoKKl#XqW8FWb+y<4~Nz zs{irH@|a9Ukh+V3pZ^?J<6d@?3~~3sfUL4I(bm@1At$aBh8S`YT-)DXWVRY`W1j>B z1PnHMdm_gca$JaN>e$BJ-{03LF+fvH6Ur*6znDmCHjvQ~fQeV)x~^BXjEk|>cwp1B z_uxbRxuWV>+zF56S9?E~i(Bl|WHYvxi<^w#v{~8Nx8=MuF2tEIA$NsmcEIYDuf)k= 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a/pyproject.toml b/pyproject.toml index 22fcaa2..1348af6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,12 @@ [tool.poetry] -name = "FastSim" +name = "AsyncFlow" version = "0.1.0" -description = "Simulate fastapi event loop to manage resources" +description = "Simulate distributed system for what if analysis for the capacity planning" authors = ["Gioele Botta"] readme = "README.md" packages = [ - { include = "app", from = "src" } + { include = "asyncflow", from = "src" } ] [tool.poetry.dependencies] diff --git a/src/app/__init__.py b/src/asyncflow/__init__.py similarity index 100% rename from src/app/__init__.py rename to src/asyncflow/__init__.py diff --git a/src/app/config/__init__.py b/src/asyncflow/config/__init__.py similarity index 100% rename from src/app/config/__init__.py rename to src/asyncflow/config/__init__.py diff --git a/src/app/config/constants.py b/src/asyncflow/config/constants.py similarity index 99% rename from src/app/config/constants.py rename to src/asyncflow/config/constants.py index 0aa5f9d..de79a33 100644 --- a/src/app/config/constants.py +++ b/src/asyncflow/config/constants.py @@ -1,7 +1,7 @@ """ Application-wide constants and configuration values. -This module groups all the static enumerations used by the FastSim backend +This module groups all the static enumerations used by the AsyncFlow backend so that: JSON / YAML payloads can be strictly validated with Pydantic. diff --git a/src/app/config/plot_constants.py b/src/asyncflow/config/plot_constants.py similarity index 100% rename from src/app/config/plot_constants.py rename to src/asyncflow/config/plot_constants.py diff --git a/src/app/metrics/analyzer.py b/src/asyncflow/metrics/analyzer.py similarity index 95% rename from src/app/metrics/analyzer.py rename to src/asyncflow/metrics/analyzer.py index 8308d54..4db2ee1 100644 --- a/src/app/metrics/analyzer.py +++ b/src/asyncflow/metrics/analyzer.py @@ -7,8 +7,8 @@ import numpy as np -from app.config.constants import LatencyKey, SampledMetricName -from app.config.plot_constants import ( +from asyncflow.config.constants import LatencyKey, SampledMetricName +from asyncflow.config.plot_constants import ( LATENCY_PLOT, RAM_PLOT, SERVER_QUEUES_PLOT, @@ -23,10 +23,10 @@ from matplotlib.axes import Axes from matplotlib.lines import Line2D - from app.runtime.actors.client import ClientRuntime - from app.runtime.actors.edge import EdgeRuntime - from app.runtime.actors.server import ServerRuntime - from app.schemas.simulation_settings_input import SimulationSettings + from asyncflow.runtime.actors.client import ClientRuntime + from asyncflow.runtime.actors.edge import EdgeRuntime + from asyncflow.runtime.actors.server import ServerRuntime + from asyncflow.schemas.simulation_settings_input import SimulationSettings class ResultsAnalyzer: diff --git a/src/app/metrics/client.py b/src/asyncflow/metrics/client.py similarity index 100% rename from src/app/metrics/client.py rename to src/asyncflow/metrics/client.py diff --git a/src/app/metrics/collector.py b/src/asyncflow/metrics/collector.py similarity index 91% rename from src/app/metrics/collector.py rename to src/asyncflow/metrics/collector.py index d2158f5..97421b7 100644 --- a/src/app/metrics/collector.py +++ b/src/asyncflow/metrics/collector.py @@ -4,10 +4,10 @@ import simpy -from app.config.constants import SampledMetricName -from app.runtime.actors.edge import EdgeRuntime -from app.runtime.actors.server import ServerRuntime -from app.schemas.simulation_settings_input import SimulationSettings +from asyncflow.config.constants import SampledMetricName +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.actors.server import ServerRuntime +from asyncflow.schemas.simulation_settings_input import SimulationSettings # The idea for this class is to gather list of runtime objects that # are defined in the central class to build the simulation, in this diff --git a/src/app/metrics/edge.py b/src/asyncflow/metrics/edge.py similarity index 94% rename from src/app/metrics/edge.py rename to src/asyncflow/metrics/edge.py index f714482..f9626dd 100644 --- a/src/app/metrics/edge.py +++ b/src/asyncflow/metrics/edge.py @@ -2,7 +2,7 @@ from collections.abc import Iterable -from app.config.constants import SampledMetricName +from asyncflow.config.constants import SampledMetricName # Initialize one time outside the function all possible metrics # related to the edges, the idea of this structure is to diff --git a/src/app/metrics/server.py b/src/asyncflow/metrics/server.py similarity index 94% rename from src/app/metrics/server.py rename to src/asyncflow/metrics/server.py index 18810ed..6ebb96e 100644 --- a/src/app/metrics/server.py +++ b/src/asyncflow/metrics/server.py @@ -5,7 +5,7 @@ from collections.abc import Iterable -from app.config.constants import SampledMetricName +from asyncflow.config.constants import SampledMetricName # Initialize one time outside the function all possible metrics # related to the servers, the idea of this structure is to diff --git a/src/app/pybuilder/input_builder.py b/src/asyncflow/pybuilder/input_builder.py similarity index 93% rename from src/app/pybuilder/input_builder.py rename to src/asyncflow/pybuilder/input_builder.py index 223cd32..7d3c26f 100644 --- a/src/app/pybuilder/input_builder.py +++ b/src/asyncflow/pybuilder/input_builder.py @@ -4,10 +4,10 @@ from typing import Self -from app.schemas.full_simulation_input import SimulationPayload -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.full_system_topology import ( Client, Edge, LoadBalancer, diff --git a/src/app/resources/__init__.py b/src/asyncflow/resources/__init__.py similarity index 100% rename from src/app/resources/__init__.py rename to src/asyncflow/resources/__init__.py diff --git a/src/app/resources/registry.py b/src/asyncflow/resources/registry.py similarity index 86% rename from src/app/resources/registry.py rename to src/asyncflow/resources/registry.py index 02b7585..6e1e3be 100644 --- a/src/app/resources/registry.py +++ b/src/asyncflow/resources/registry.py @@ -9,8 +9,8 @@ import simpy -from app.resources.server_containers import ServerContainers, build_containers -from app.schemas.system_topology.full_system_topology import TopologyGraph +from asyncflow.resources.server_containers import ServerContainers, build_containers +from asyncflow.schemas.system_topology.full_system_topology import TopologyGraph class ResourcesRuntime: diff --git a/src/app/resources/server_containers.py b/src/asyncflow/resources/server_containers.py similarity index 94% rename from src/app/resources/server_containers.py rename to src/asyncflow/resources/server_containers.py index dcbef1f..ca054c2 100644 --- a/src/app/resources/server_containers.py +++ b/src/asyncflow/resources/server_containers.py @@ -11,8 +11,8 @@ import simpy -from app.config.constants import ServerResourceName -from app.schemas.system_topology.full_system_topology import ( +from asyncflow.config.constants import ServerResourceName +from asyncflow.schemas.system_topology.full_system_topology import ( ServerResources, ) diff --git a/src/app/runtime/__init__.py b/src/asyncflow/runtime/__init__.py similarity index 100% rename from src/app/runtime/__init__.py rename to src/asyncflow/runtime/__init__.py diff --git a/src/app/runtime/actors/client.py b/src/asyncflow/runtime/actors/client.py similarity index 89% rename from src/app/runtime/actors/client.py rename to src/asyncflow/runtime/actors/client.py index 2f2da4b..58ba7e9 100644 --- a/src/app/runtime/actors/client.py +++ b/src/asyncflow/runtime/actors/client.py @@ -5,13 +5,13 @@ import simpy -from app.config.constants import SystemNodes -from app.metrics.client import RqsClock -from app.runtime.actors.edge import EdgeRuntime -from app.schemas.system_topology.full_system_topology import Client +from asyncflow.config.constants import SystemNodes +from asyncflow.metrics.client import RqsClock +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.schemas.system_topology.full_system_topology import Client if TYPE_CHECKING: - from app.runtime.rqs_state import RequestState + from asyncflow.runtime.rqs_state import RequestState diff --git a/src/app/runtime/actors/edge.py b/src/asyncflow/runtime/actors/edge.py similarity index 88% rename from src/app/runtime/actors/edge.py rename to src/asyncflow/runtime/actors/edge.py index 4e2f549..c2c5328 100644 --- a/src/app/runtime/actors/edge.py +++ b/src/asyncflow/runtime/actors/edge.py @@ -12,15 +12,15 @@ import numpy as np import simpy -from app.config.constants import SampledMetricName, SystemEdges -from app.metrics.edge import build_edge_metrics -from app.runtime.rqs_state import RequestState -from app.samplers.common_helpers import general_sampler -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.full_system_topology import Edge +from asyncflow.config.constants import SampledMetricName, SystemEdges +from asyncflow.metrics.edge import build_edge_metrics +from asyncflow.runtime.rqs_state import RequestState +from asyncflow.samplers.common_helpers import general_sampler +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.full_system_topology import Edge if TYPE_CHECKING: - from app.schemas.random_variables_config import RVConfig + from asyncflow.schemas.random_variables_config import RVConfig diff --git a/src/app/runtime/actors/helpers/lb_algorithms.py b/src/asyncflow/runtime/actors/helpers/lb_algorithms.py similarity index 94% rename from src/app/runtime/actors/helpers/lb_algorithms.py rename to src/asyncflow/runtime/actors/helpers/lb_algorithms.py index 90c325e..46078f7 100644 --- a/src/app/runtime/actors/helpers/lb_algorithms.py +++ b/src/asyncflow/runtime/actors/helpers/lb_algorithms.py @@ -2,7 +2,7 @@ -from app.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.actors.edge import EdgeRuntime def least_connections(list_edges: list[EdgeRuntime]) -> EdgeRuntime: diff --git a/src/app/runtime/actors/load_balancer.py b/src/asyncflow/runtime/actors/load_balancer.py similarity index 86% rename from src/app/runtime/actors/load_balancer.py rename to src/asyncflow/runtime/actors/load_balancer.py index b370ff3..fac3f66 100644 --- a/src/app/runtime/actors/load_balancer.py +++ b/src/asyncflow/runtime/actors/load_balancer.py @@ -5,16 +5,16 @@ import simpy -from app.config.constants import LbAlgorithmsName, SystemNodes -from app.runtime.actors.edge import EdgeRuntime -from app.runtime.actors.helpers.lb_algorithms import ( +from asyncflow.config.constants import LbAlgorithmsName, SystemNodes +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.actors.helpers.lb_algorithms import ( least_connections, round_robin, ) -from app.schemas.system_topology.full_system_topology import LoadBalancer +from asyncflow.schemas.system_topology.full_system_topology import LoadBalancer if TYPE_CHECKING: - from app.runtime.rqs_state import RequestState + from asyncflow.runtime.rqs_state import RequestState diff --git a/src/app/runtime/actors/rqs_generator.py b/src/asyncflow/runtime/actors/rqs_generator.py similarity index 88% rename from src/app/runtime/actors/rqs_generator.py rename to src/asyncflow/runtime/actors/rqs_generator.py index d9e7e38..6c983df 100644 --- a/src/app/runtime/actors/rqs_generator.py +++ b/src/asyncflow/runtime/actors/rqs_generator.py @@ -9,10 +9,10 @@ import numpy as np -from app.config.constants import Distribution, SystemNodes -from app.runtime.rqs_state import RequestState -from app.samplers.gaussian_poisson import gaussian_poisson_sampling -from app.samplers.poisson_poisson import poisson_poisson_sampling +from asyncflow.config.constants import Distribution, SystemNodes +from asyncflow.runtime.rqs_state import RequestState +from asyncflow.samplers.gaussian_poisson import gaussian_poisson_sampling +from asyncflow.samplers.poisson_poisson import poisson_poisson_sampling if TYPE_CHECKING: @@ -20,9 +20,9 @@ import simpy - from app.runtime.actors.edge import EdgeRuntime - from app.schemas.rqs_generator_input import RqsGeneratorInput - from app.schemas.simulation_settings_input import SimulationSettings + from asyncflow.runtime.actors.edge import EdgeRuntime + from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput + from asyncflow.schemas.simulation_settings_input import SimulationSettings class RqsGeneratorRuntime: diff --git a/src/app/runtime/actors/server.py b/src/asyncflow/runtime/actors/server.py similarity index 96% rename from src/app/runtime/actors/server.py rename to src/asyncflow/runtime/actors/server.py index d791bbb..7d72de1 100644 --- a/src/app/runtime/actors/server.py +++ b/src/asyncflow/runtime/actors/server.py @@ -9,7 +9,7 @@ import numpy as np import simpy -from app.config.constants import ( +from asyncflow.config.constants import ( EndpointStepCPU, EndpointStepIO, EndpointStepRAM, @@ -18,12 +18,12 @@ StepOperation, SystemNodes, ) -from app.metrics.server import build_server_metrics -from app.resources.server_containers import ServerContainers -from app.runtime.actors.edge import EdgeRuntime -from app.runtime.rqs_state import RequestState -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.full_system_topology import Server +from asyncflow.metrics.server import build_server_metrics +from asyncflow.resources.server_containers import ServerContainers +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.rqs_state import RequestState +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.full_system_topology import Server class ServerRuntime: diff --git a/src/app/runtime/rqs_state.py b/src/asyncflow/runtime/rqs_state.py similarity index 95% rename from src/app/runtime/rqs_state.py rename to src/asyncflow/runtime/rqs_state.py index 1c953fd..71b8389 100644 --- a/src/app/runtime/rqs_state.py +++ b/src/asyncflow/runtime/rqs_state.py @@ -6,7 +6,7 @@ from typing import TYPE_CHECKING, NamedTuple if TYPE_CHECKING: - from app.config.constants import SystemEdges, SystemNodes + from asyncflow.config.constants import SystemEdges, SystemNodes class Hop(NamedTuple): diff --git a/src/app/runtime/simulation_runner.py b/src/asyncflow/runtime/simulation_runner.py similarity index 93% rename from src/app/runtime/simulation_runner.py rename to src/asyncflow/runtime/simulation_runner.py index e2b5c6f..f82fb72 100644 --- a/src/app/runtime/simulation_runner.py +++ b/src/asyncflow/runtime/simulation_runner.py @@ -8,21 +8,21 @@ import simpy import yaml -from app.metrics.analyzer import ResultsAnalyzer -from app.metrics.collector import SampledMetricCollector -from app.resources.registry import ResourcesRuntime -from app.runtime.actors.client import ClientRuntime -from app.runtime.actors.edge import EdgeRuntime -from app.runtime.actors.load_balancer import LoadBalancerRuntime -from app.runtime.actors.rqs_generator import RqsGeneratorRuntime -from app.runtime.actors.server import ServerRuntime -from app.schemas.full_simulation_input import SimulationPayload +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.metrics.collector import SampledMetricCollector +from asyncflow.resources.registry import ResourcesRuntime +from asyncflow.runtime.actors.client import ClientRuntime +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.actors.load_balancer import LoadBalancerRuntime +from asyncflow.runtime.actors.rqs_generator import RqsGeneratorRuntime +from asyncflow.runtime.actors.server import ServerRuntime +from asyncflow.schemas.full_simulation_input import SimulationPayload if TYPE_CHECKING: from collections.abc import Iterable - from app.schemas.rqs_generator_input import RqsGeneratorInput - from app.schemas.system_topology.full_system_topology import ( + from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput + from asyncflow.schemas.system_topology.full_system_topology import ( Client, Edge, LoadBalancer, diff --git a/src/app/samplers/common_helpers.py b/src/asyncflow/samplers/common_helpers.py similarity index 96% rename from src/app/samplers/common_helpers.py rename to src/asyncflow/samplers/common_helpers.py index d594538..123ae4a 100644 --- a/src/app/samplers/common_helpers.py +++ b/src/asyncflow/samplers/common_helpers.py @@ -3,8 +3,8 @@ import numpy as np -from app.config.constants import Distribution -from app.schemas.random_variables_config import RVConfig +from asyncflow.config.constants import Distribution +from asyncflow.schemas.random_variables_config import RVConfig def uniform_variable_generator(rng: np.random.Generator) -> float: diff --git a/src/app/samplers/gaussian_poisson.py b/src/asyncflow/samplers/gaussian_poisson.py similarity index 92% rename from src/app/samplers/gaussian_poisson.py rename to src/asyncflow/samplers/gaussian_poisson.py index bb1f6fd..5caa9ed 100644 --- a/src/app/samplers/gaussian_poisson.py +++ b/src/asyncflow/samplers/gaussian_poisson.py @@ -11,13 +11,13 @@ import numpy as np -from app.config.constants import TimeDefaults -from app.samplers.common_helpers import ( +from asyncflow.config.constants import TimeDefaults +from asyncflow.samplers.common_helpers import ( truncated_gaussian_generator, uniform_variable_generator, ) -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings def gaussian_poisson_sampling( diff --git a/src/app/samplers/poisson_poisson.py b/src/asyncflow/samplers/poisson_poisson.py similarity index 90% rename from src/app/samplers/poisson_poisson.py rename to src/asyncflow/samplers/poisson_poisson.py index 725faa5..5e1b4cc 100644 --- a/src/app/samplers/poisson_poisson.py +++ b/src/asyncflow/samplers/poisson_poisson.py @@ -8,13 +8,13 @@ import numpy as np -from app.config.constants import TimeDefaults -from app.samplers.common_helpers import ( +from asyncflow.config.constants import TimeDefaults +from asyncflow.samplers.common_helpers import ( poisson_variable_generator, uniform_variable_generator, ) -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings def poisson_poisson_sampling( diff --git a/src/app/schemas/full_simulation_input.py b/src/asyncflow/schemas/full_simulation_input.py similarity index 55% rename from src/app/schemas/full_simulation_input.py rename to src/asyncflow/schemas/full_simulation_input.py index 8be873a..504396a 100644 --- a/src/app/schemas/full_simulation_input.py +++ b/src/asyncflow/schemas/full_simulation_input.py @@ -2,9 +2,9 @@ from pydantic import BaseModel -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.full_system_topology import TopologyGraph +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.full_system_topology import TopologyGraph class SimulationPayload(BaseModel): diff --git a/src/app/schemas/random_variables_config.py b/src/asyncflow/schemas/random_variables_config.py similarity index 95% rename from src/app/schemas/random_variables_config.py rename to src/asyncflow/schemas/random_variables_config.py index e15d509..d827b92 100644 --- a/src/app/schemas/random_variables_config.py +++ b/src/asyncflow/schemas/random_variables_config.py @@ -2,7 +2,7 @@ from pydantic import BaseModel, field_validator, model_validator -from app.config.constants import Distribution +from asyncflow.config.constants import Distribution class RVConfig(BaseModel): diff --git a/src/app/schemas/rqs_generator_input.py b/src/asyncflow/schemas/rqs_generator_input.py similarity index 92% rename from src/app/schemas/rqs_generator_input.py rename to src/asyncflow/schemas/rqs_generator_input.py index 4f217e7..f0f63a3 100644 --- a/src/app/schemas/rqs_generator_input.py +++ b/src/asyncflow/schemas/rqs_generator_input.py @@ -3,8 +3,8 @@ from pydantic import BaseModel, Field, field_validator -from app.config.constants import Distribution, SystemNodes, TimeDefaults -from app.schemas.random_variables_config import RVConfig +from asyncflow.config.constants import Distribution, SystemNodes, TimeDefaults +from asyncflow.schemas.random_variables_config import RVConfig class RqsGeneratorInput(BaseModel): diff --git a/src/app/schemas/simulation_settings_input.py b/src/asyncflow/schemas/simulation_settings_input.py similarity index 97% rename from src/app/schemas/simulation_settings_input.py rename to src/asyncflow/schemas/simulation_settings_input.py index a9a90a6..7f0d145 100644 --- a/src/app/schemas/simulation_settings_input.py +++ b/src/asyncflow/schemas/simulation_settings_input.py @@ -2,7 +2,7 @@ from pydantic import BaseModel, Field -from app.config.constants import ( +from asyncflow.config.constants import ( EventMetricName, SampledMetricName, SamplePeriods, diff --git a/src/app/schemas/system_topology/endpoint.py b/src/asyncflow/schemas/system_topology/endpoint.py similarity index 98% rename from src/app/schemas/system_topology/endpoint.py rename to src/asyncflow/schemas/system_topology/endpoint.py index aa40e9e..aa91c7b 100644 --- a/src/app/schemas/system_topology/endpoint.py +++ b/src/asyncflow/schemas/system_topology/endpoint.py @@ -8,7 +8,7 @@ model_validator, ) -from app.config.constants import ( +from asyncflow.config.constants import ( EndpointStepCPU, EndpointStepIO, EndpointStepRAM, diff --git a/src/app/schemas/system_topology/full_system_topology.py b/src/asyncflow/schemas/system_topology/full_system_topology.py similarity index 98% rename from src/app/schemas/system_topology/full_system_topology.py rename to src/asyncflow/schemas/system_topology/full_system_topology.py index f70b16c..7ab91bc 100644 --- a/src/app/schemas/system_topology/full_system_topology.py +++ b/src/asyncflow/schemas/system_topology/full_system_topology.py @@ -18,15 +18,15 @@ ) from pydantic_core.core_schema import ValidationInfo -from app.config.constants import ( +from asyncflow.config.constants import ( LbAlgorithmsName, NetworkParameters, ServerResourcesDefaults, SystemEdges, SystemNodes, ) -from app.schemas.random_variables_config import RVConfig -from app.schemas.system_topology.endpoint import Endpoint +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.system_topology.endpoint import Endpoint #------------------------------------------------------------- # Definition of the nodes structure for the graph representing diff --git a/tests/conftest.py b/tests/conftest.py index 39c8b40..968d835 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,18 +5,18 @@ from numpy.random import Generator as NpGenerator from numpy.random import default_rng -from app.config.constants import ( +from asyncflow.config.constants import ( Distribution, EventMetricName, SampledMetricName, SamplePeriods, TimeDefaults, ) -from app.schemas.full_simulation_input import SimulationPayload -from app.schemas.random_variables_config import RVConfig -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.full_system_topology import ( Client, Edge, TopologyGraph, diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index b46300b..2c94d6f 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -7,7 +7,7 @@ import pytest import simpy -from app.runtime.simulation_runner import SimulationRunner +from asyncflow.runtime.simulation_runner import SimulationRunner if TYPE_CHECKING: from collections.abc import Callable diff --git a/tests/integration/minimal/conftest.py b/tests/integration/minimal/conftest.py index 434f9f0..812ba5e 100644 --- a/tests/integration/minimal/conftest.py +++ b/tests/integration/minimal/conftest.py @@ -14,13 +14,13 @@ import pytest import simpy -from app.config.constants import TimeDefaults -from app.runtime.simulation_runner import SimulationRunner -from app.schemas.random_variables_config import RVConfig -from app.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.config.constants import TimeDefaults +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput if TYPE_CHECKING: - from app.schemas.full_simulation_input import SimulationPayload + from asyncflow.schemas.full_simulation_input import SimulationPayload # ────────────────────────────────────────────────────────────────────────────── diff --git a/tests/integration/minimal/test_minimal.py b/tests/integration/minimal/test_minimal.py index 0ae77c2..2a82601 100644 --- a/tests/integration/minimal/test_minimal.py +++ b/tests/integration/minimal/test_minimal.py @@ -18,11 +18,11 @@ import pytest import simpy -from app.metrics.analyzer import ResultsAnalyzer -from app.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner if TYPE_CHECKING: - from app.schemas.full_simulation_input import SimulationPayload + from asyncflow.schemas.full_simulation_input import SimulationPayload # --------------------------------------------------------------------------- # diff --git a/tests/integration/payload/test_payload_invalid.py b/tests/integration/payload/test_payload_invalid.py index 09249c1..fb700b4 100644 --- a/tests/integration/payload/test_payload_invalid.py +++ b/tests/integration/payload/test_payload_invalid.py @@ -6,7 +6,7 @@ import yaml from pydantic import ValidationError -from app.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.full_simulation_input import SimulationPayload DATA_DIR = Path(__file__).parent / "data" / "invalid" YMLS = sorted(DATA_DIR.glob("*.yml")) diff --git a/tests/integration/single_server/conftest.py b/tests/integration/single_server/conftest.py index 38f575a..f45633a 100644 --- a/tests/integration/single_server/conftest.py +++ b/tests/integration/single_server/conftest.py @@ -16,7 +16,7 @@ import simpy if TYPE_CHECKING: # heavy imports only when type-checking - from app.runtime.simulation_runner import SimulationRunner + from asyncflow.runtime.simulation_runner import SimulationRunner # --------------------------------------------------------------------------- # @@ -38,7 +38,7 @@ def runner(env: simpy.Environment) -> SimulationRunner: :pymeth:`SimulationRunner.from_yaml`. """ # import deferred to avoid ruff TC001 - from app.runtime.simulation_runner import SimulationRunner # noqa: PLC0415 + from asyncflow.runtime.simulation_runner import SimulationRunner # noqa: PLC0415 yaml_path: Path = ( Path(__file__).parent / "data" / "single_server.yml" diff --git a/tests/integration/single_server/data/single_server.yml b/tests/integration/single_server/data/single_server.yml index 4072f48..c6ec078 100644 --- a/tests/integration/single_server/data/single_server.yml +++ b/tests/integration/single_server/data/single_server.yml @@ -1,5 +1,5 @@ # ─────────────────────────────────────────────────────────────── -# FastSim scenario: generator ➜ client ➜ server ➜ client +# AsyncFlow scenario: generator ➜ client ➜ server ➜ client # ─────────────────────────────────────────────────────────────── # 1. Traffic generator (light load) diff --git a/tests/integration/single_server/test_single_server.py b/tests/integration/single_server/test_single_server.py index 498a7a1..4d55310 100644 --- a/tests/integration/single_server/test_single_server.py +++ b/tests/integration/single_server/test_single_server.py @@ -14,11 +14,11 @@ import pytest -from app.config.constants import LatencyKey, SampledMetricName +from asyncflow.config.constants import LatencyKey, SampledMetricName if TYPE_CHECKING: # only needed for type-checking - from app.metrics.analyzer import ResultsAnalyzer - from app.runtime.simulation_runner import SimulationRunner + from asyncflow.metrics.analyzer import ResultsAnalyzer + from asyncflow.runtime.simulation_runner import SimulationRunner # --------------------------------------------------------------------------- # diff --git a/tests/unit/metrics/test_analyzer.py b/tests/unit/metrics/test_analyzer.py index 4aa33b9..eaae45d 100644 --- a/tests/unit/metrics/test_analyzer.py +++ b/tests/unit/metrics/test_analyzer.py @@ -7,14 +7,14 @@ import pytest from matplotlib.figure import Figure -from app.config.constants import LatencyKey -from app.metrics.analyzer import ResultsAnalyzer +from asyncflow.config.constants import LatencyKey +from asyncflow.metrics.analyzer import ResultsAnalyzer if TYPE_CHECKING: - from app.runtime.actors.client import ClientRuntime - from app.runtime.actors.edge import EdgeRuntime - from app.runtime.actors.server import ServerRuntime - from app.schemas.simulation_settings_input import SimulationSettings + from asyncflow.runtime.actors.client import ClientRuntime + from asyncflow.runtime.actors.edge import EdgeRuntime + from asyncflow.runtime.actors.server import ServerRuntime + from asyncflow.schemas.simulation_settings_input import SimulationSettings # ---------------------------------------------------------------------- # diff --git a/tests/unit/pybuilder/test_input_builder.py b/tests/unit/pybuilder/test_input_builder.py index 1bdddf8..3ee710c 100644 --- a/tests/unit/pybuilder/test_input_builder.py +++ b/tests/unit/pybuilder/test_input_builder.py @@ -13,12 +13,12 @@ import pytest -from app.pybuilder.input_builder import AsyncFlow -from app.schemas.full_simulation_input import SimulationPayload -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.endpoint import Endpoint -from app.schemas.system_topology.full_system_topology import Client, Edge, Server +from asyncflow.pybuilder.input_builder import AsyncFlow +from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.endpoint import Endpoint +from asyncflow.schemas.system_topology.full_system_topology import Client, Edge, Server # --------------------------------------------------------------------------- # diff --git a/tests/unit/resources/test_registry.py b/tests/unit/resources/test_registry.py index 6aef76a..34154db 100644 --- a/tests/unit/resources/test_registry.py +++ b/tests/unit/resources/test_registry.py @@ -5,10 +5,10 @@ import pytest import simpy -from app.config.constants import ServerResourceName -from app.resources.registry import ResourcesRuntime -from app.schemas.system_topology.endpoint import Endpoint -from app.schemas.system_topology.full_system_topology import ( +from asyncflow.config.constants import ServerResourceName +from asyncflow.resources.registry import ResourcesRuntime +from asyncflow.schemas.system_topology.endpoint import Endpoint +from asyncflow.schemas.system_topology.full_system_topology import ( Client, Server, ServerResources, diff --git a/tests/unit/resources/test_server_containers.py b/tests/unit/resources/test_server_containers.py index c36f927..3772528 100644 --- a/tests/unit/resources/test_server_containers.py +++ b/tests/unit/resources/test_server_containers.py @@ -2,9 +2,9 @@ import simpy -from app.config.constants import ServerResourceName -from app.resources.server_containers import build_containers -from app.schemas.system_topology.full_system_topology import ServerResources +from asyncflow.config.constants import ServerResourceName +from asyncflow.resources.server_containers import build_containers +from asyncflow.schemas.system_topology.full_system_topology import ServerResources def test_containers_start_full() -> None: diff --git a/tests/unit/runtime/actors/test_client.py b/tests/unit/runtime/actors/test_client.py index 7ee194e..9188d64 100644 --- a/tests/unit/runtime/actors/test_client.py +++ b/tests/unit/runtime/actors/test_client.py @@ -4,10 +4,10 @@ import simpy -from app.config.constants import SystemEdges, SystemNodes -from app.runtime.actors.client import ClientRuntime -from app.runtime.rqs_state import RequestState -from app.schemas.system_topology.full_system_topology import ( +from asyncflow.config.constants import SystemEdges, SystemNodes +from asyncflow.runtime.actors.client import ClientRuntime +from asyncflow.runtime.rqs_state import RequestState +from asyncflow.schemas.system_topology.full_system_topology import ( Client, ) diff --git a/tests/unit/runtime/actors/test_edge.py b/tests/unit/runtime/actors/test_edge.py index 644b078..e180bec 100644 --- a/tests/unit/runtime/actors/test_edge.py +++ b/tests/unit/runtime/actors/test_edge.py @@ -11,16 +11,16 @@ import simpy -from app.config.constants import SampledMetricName, SystemEdges, SystemNodes -from app.runtime.actors.edge import EdgeRuntime -from app.runtime.rqs_state import RequestState -from app.schemas.random_variables_config import RVConfig -from app.schemas.system_topology.full_system_topology import Edge +from asyncflow.config.constants import SampledMetricName, SystemEdges, SystemNodes +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.rqs_state import RequestState +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.system_topology.full_system_topology import Edge if TYPE_CHECKING: import numpy as np - from app.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.simulation_settings_input import SimulationSettings # --------------------------------------------------------------------------- # diff --git a/tests/unit/runtime/actors/test_load_balancer.py b/tests/unit/runtime/actors/test_load_balancer.py index 35e622a..41902e0 100644 --- a/tests/unit/runtime/actors/test_load_balancer.py +++ b/tests/unit/runtime/actors/test_load_balancer.py @@ -7,12 +7,12 @@ import pytest import simpy -from app.config.constants import LbAlgorithmsName, SystemNodes -from app.runtime.actors.load_balancer import LoadBalancerRuntime -from app.schemas.system_topology.full_system_topology import LoadBalancer +from asyncflow.config.constants import LbAlgorithmsName, SystemNodes +from asyncflow.runtime.actors.load_balancer import LoadBalancerRuntime +from asyncflow.schemas.system_topology.full_system_topology import LoadBalancer if TYPE_CHECKING: - from app.runtime.actors.edge import EdgeRuntime + from asyncflow.runtime.actors.edge import EdgeRuntime diff --git a/tests/unit/runtime/actors/test_rqs_generator.py b/tests/unit/runtime/actors/test_rqs_generator.py index bc76f4d..7130306 100644 --- a/tests/unit/runtime/actors/test_rqs_generator.py +++ b/tests/unit/runtime/actors/test_rqs_generator.py @@ -7,18 +7,18 @@ import numpy as np import simpy -from app.config.constants import Distribution -from app.runtime.actors.rqs_generator import RqsGeneratorRuntime +from asyncflow.config.constants import Distribution +from asyncflow.runtime.actors.rqs_generator import RqsGeneratorRuntime if TYPE_CHECKING: import pytest from numpy.random import Generator - from app.runtime.actors.edge import EdgeRuntime - from app.runtime.rqs_state import RequestState - from app.schemas.rqs_generator_input import RqsGeneratorInput - from app.schemas.simulation_settings_input import SimulationSettings + from asyncflow.runtime.actors.edge import EdgeRuntime + from asyncflow.runtime.rqs_state import RequestState + from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput + from asyncflow.schemas.simulation_settings_input import SimulationSettings import importlib @@ -63,7 +63,7 @@ def _make_runtime( # --------------------------------------------------------------------------- # -RGR_MODULE = importlib.import_module("app.runtime.actors.rqs_generator") +RGR_MODULE = importlib.import_module("asyncflow.runtime.actors.rqs_generator") def test_dispatcher_selects_poisson_poisson( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/unit/runtime/actors/test_server.py b/tests/unit/runtime/actors/test_server.py index 9e7edb1..7085e12 100644 --- a/tests/unit/runtime/actors/test_server.py +++ b/tests/unit/runtime/actors/test_server.py @@ -22,19 +22,22 @@ import simpy from numpy.random import default_rng -from app.config.constants import ( +from asyncflow.config.constants import ( EndpointStepCPU, EndpointStepIO, EndpointStepRAM, SampledMetricName, StepOperation, ) -from app.resources.server_containers import build_containers -from app.runtime.actors.server import ServerRuntime -from app.runtime.rqs_state import RequestState -from app.schemas.simulation_settings_input import SimulationSettings -from app.schemas.system_topology.endpoint import Endpoint, Step -from app.schemas.system_topology.full_system_topology import Server, ServerResources +from asyncflow.resources.server_containers import build_containers +from asyncflow.runtime.actors.server import ServerRuntime +from asyncflow.runtime.rqs_state import RequestState +from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.system_topology.endpoint import Endpoint, Step +from asyncflow.schemas.system_topology.full_system_topology import ( + Server, + ServerResources, +) if TYPE_CHECKING: diff --git a/tests/unit/runtime/test_rqs_state.py b/tests/unit/runtime/test_rqs_state.py index d07caa4..eaf752b 100644 --- a/tests/unit/runtime/test_rqs_state.py +++ b/tests/unit/runtime/test_rqs_state.py @@ -1,8 +1,8 @@ """Unit-tests for :class:`RequestState` and :class:`Hop`.""" from __future__ import annotations -from app.config.constants import SystemEdges, SystemNodes -from app.runtime.rqs_state import Hop, RequestState +from asyncflow.config.constants import SystemEdges, SystemNodes +from asyncflow.runtime.rqs_state import Hop, RequestState # --------------------------------------------------------------------------- # # Helpers # diff --git a/tests/unit/runtime/test_simulation_runner.py b/tests/unit/runtime/test_simulation_runner.py index d785e3d..c0f6b2e 100644 --- a/tests/unit/runtime/test_simulation_runner.py +++ b/tests/unit/runtime/test_simulation_runner.py @@ -15,15 +15,15 @@ import simpy import yaml -from app.metrics.analyzer import ResultsAnalyzer -from app.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner if TYPE_CHECKING: from pathlib import Path - from app.runtime.actors.client import ClientRuntime - from app.runtime.actors.rqs_generator import RqsGeneratorRuntime - from app.schemas.full_simulation_input import SimulationPayload + from asyncflow.runtime.actors.client import ClientRuntime + from asyncflow.runtime.actors.rqs_generator import RqsGeneratorRuntime + from asyncflow.schemas.full_simulation_input import SimulationPayload # --------------------------------------------------------------------------- # diff --git a/tests/unit/samplers/test_gaussian_poisson.py b/tests/unit/samplers/test_gaussian_poisson.py index b2b3c2e..4b3ed80 100644 --- a/tests/unit/samplers/test_gaussian_poisson.py +++ b/tests/unit/samplers/test_gaussian_poisson.py @@ -9,16 +9,16 @@ import pytest from numpy.random import Generator, default_rng -from app.config.constants import TimeDefaults -from app.samplers.gaussian_poisson import ( +from asyncflow.config.constants import TimeDefaults +from asyncflow.samplers.gaussian_poisson import ( gaussian_poisson_sampling, ) -from app.schemas.random_variables_config import RVConfig -from app.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput if TYPE_CHECKING: - from app.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.simulation_settings_input import SimulationSettings # --------------------------------------------------------------------------- # FIXTURES @@ -99,7 +99,7 @@ def fake_truncated_gaussian( return 0.0 # force U = 0 monkeypatch.setattr( - "app.samplers.gaussian_poisson.truncated_gaussian_generator", + "asyncflow.samplers.gaussian_poisson.truncated_gaussian_generator", fake_truncated_gaussian, ) diff --git a/tests/unit/samplers/test_poisson_poisson.py b/tests/unit/samplers/test_poisson_poisson.py index 00242fa..fde7d04 100644 --- a/tests/unit/samplers/test_poisson_poisson.py +++ b/tests/unit/samplers/test_poisson_poisson.py @@ -10,14 +10,14 @@ import pytest from numpy.random import Generator, default_rng -from app.config.constants import TimeDefaults -from app.samplers.poisson_poisson import poisson_poisson_sampling -from app.schemas.random_variables_config import RVConfig -from app.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.config.constants import TimeDefaults +from asyncflow.samplers.poisson_poisson import poisson_poisson_sampling +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput if TYPE_CHECKING: - from app.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.simulation_settings_input import SimulationSettings @pytest.fixture diff --git a/tests/unit/samplers/test_sampler_helper.py b/tests/unit/samplers/test_sampler_helper.py index ff44c3b..f6b4241 100644 --- a/tests/unit/samplers/test_sampler_helper.py +++ b/tests/unit/samplers/test_sampler_helper.py @@ -8,8 +8,8 @@ import numpy as np import pytest -from app.config.constants import Distribution -from app.samplers.common_helpers import ( +from asyncflow.config.constants import Distribution +from asyncflow.samplers.common_helpers import ( exponential_variable_generator, general_sampler, lognormal_variable_generator, @@ -17,7 +17,7 @@ truncated_gaussian_generator, uniform_variable_generator, ) -from app.schemas.random_variables_config import RVConfig +from asyncflow.schemas.random_variables_config import RVConfig # --------------------------------------------------------------------------- # # Dummy RNG # diff --git a/tests/unit/schemas/test_endpoint_input.py b/tests/unit/schemas/test_endpoint_input.py index 76d22a2..3813dd6 100644 --- a/tests/unit/schemas/test_endpoint_input.py +++ b/tests/unit/schemas/test_endpoint_input.py @@ -5,13 +5,13 @@ import pytest from pydantic import ValidationError -from app.config.constants import ( +from asyncflow.config.constants import ( EndpointStepCPU, EndpointStepIO, EndpointStepRAM, StepOperation, ) -from app.schemas.system_topology.endpoint import Endpoint, Step +from asyncflow.schemas.system_topology.endpoint import Endpoint, Step # --------------------------------------------------------------------------- # diff --git a/tests/unit/schemas/test_full_topology_input.py b/tests/unit/schemas/test_full_topology_input.py index f6bf1f9..4e27562 100644 --- a/tests/unit/schemas/test_full_topology_input.py +++ b/tests/unit/schemas/test_full_topology_input.py @@ -5,7 +5,7 @@ import pytest from pydantic import ValidationError -from app.config.constants import ( +from asyncflow.config.constants import ( EndpointStepCPU, NetworkParameters, ServerResourcesDefaults, @@ -13,9 +13,9 @@ SystemEdges, SystemNodes, ) -from app.schemas.random_variables_config import RVConfig -from app.schemas.system_topology.endpoint import Endpoint, Step -from app.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.system_topology.endpoint import Endpoint, Step +from asyncflow.schemas.system_topology.full_system_topology import ( Client, Edge, LoadBalancer, diff --git a/tests/unit/schemas/test_requests_generator_input.py b/tests/unit/schemas/test_requests_generator_input.py index f676f4c..66ad037 100644 --- a/tests/unit/schemas/test_requests_generator_input.py +++ b/tests/unit/schemas/test_requests_generator_input.py @@ -4,10 +4,10 @@ import pytest from pydantic import ValidationError -from app.config.constants import Distribution, TimeDefaults -from app.schemas.random_variables_config import RVConfig -from app.schemas.rqs_generator_input import RqsGeneratorInput -from app.schemas.simulation_settings_input import SimulationSettings +from asyncflow.config.constants import Distribution, TimeDefaults +from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.simulation_settings_input import SimulationSettings # --------------------------------------------------------------------------- # # RVCONFIG # From c9007085c55b079c3d2e1b400090b367096528ef Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Wed, 13 Aug 2025 18:13:26 +0200 Subject: [PATCH 27/52] minor changes --- examples/single_server_pybuilder.png | Bin 163305 -> 152423 bytes tests/conftest.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/single_server_pybuilder.png b/examples/single_server_pybuilder.png index 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--------------------------------------------------------------------------- -# RNG +# RANDOM VARIABLE GENERATOR # --------------------------------------------------------------------------- From adcb9ebe4458dd7d3db51a74c54c6b5a99866457 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Thu, 14 Aug 2025 19:15:17 +0200 Subject: [PATCH 28/52] Refactor/pypi preparation (#14) * Reafctor for the folder schemas + defined import for public api * Defined public api, improved docs --- docs/api/components.md | 295 +++++++++++++ docs/api/enums.md | 197 +++++++++ docs/api/high-level.md | 299 ++++++++++++++ docs/api/settings.md | 200 +++++++++ docs/api/workload.md | 197 +++++++++ docs/dev-workflow-guide.md | 273 ++++++++++++ docs/dev_workflow_guide.md | 256 ------------ docs/fastsim_vision.md | 41 -- docs/{pybuilder.md => guides/builder.md} | 186 +++------ .../yaml-builder.md} | 63 ++- docs/index.md | 57 +++ .../metrics/overview.md} | 0 .../metrics/time-series-architecture.md} | 0 .../runtime-and-resources.md} | 0 .../simulation-input.md} | 8 +- .../simulation-runner.md} | 0 .../workload-samplers.md} | 2 +- docs/why-asyncflow.md | 93 +++++ examples/single_server_builder.png | Bin 0 -> 166241 bytes examples/single_server_pybuilder.png | Bin 152423 -> 162085 bytes examples/single_server_pybuilder.py | 30 +- examples/single_server_yml.png | Bin 160670 -> 166693 bytes src/asyncflow/__init__.py | 8 +- .../asyncflow_builder.py} | 20 +- src/asyncflow/components/__init__.py | 15 + src/asyncflow/config/__init__.py | 1 - src/asyncflow/enums/__init__.py | 23 ++ src/asyncflow/metrics/analyzer.py | 2 +- src/asyncflow/metrics/collector.py | 2 +- src/asyncflow/resources/__init__.py | 1 - src/asyncflow/resources/registry.py | 2 +- src/asyncflow/resources/server_containers.py | 4 +- src/asyncflow/runtime/__init__.py | 1 - src/asyncflow/runtime/actors/client.py | 2 +- src/asyncflow/runtime/actors/edge.py | 6 +- src/asyncflow/runtime/actors/load_balancer.py | 4 +- .../{helpers => routing}/lb_algorithms.py | 0 src/asyncflow/runtime/actors/rqs_generator.py | 8 +- src/asyncflow/runtime/actors/server.py | 4 +- src/asyncflow/runtime/simulation_runner.py | 10 +- src/asyncflow/samplers/common_helpers.py | 2 +- src/asyncflow/samplers/gaussian_poisson.py | 6 +- src/asyncflow/samplers/poisson_poisson.py | 6 +- src/asyncflow/schemas/common/__init__.py | 1 + .../random_variables.py} | 0 .../schemas/full_simulation_input.py | 15 - src/asyncflow/schemas/payload.py | 15 + .../simulation.py} | 0 .../system_topology/full_system_topology.py | 391 ------------------ src/asyncflow/schemas/topology/edges.py | 99 +++++ .../{system_topology => topology}/endpoint.py | 0 src/asyncflow/schemas/topology/graph.py | 159 +++++++ src/asyncflow/schemas/topology/nodes.py | 164 ++++++++ .../rqs_generator.py} | 4 +- src/asyncflow/settings/__init__.py | 6 + src/asyncflow/workload/__init__.py | 7 + tests/conftest.py | 20 +- tests/integration/minimal/conftest.py | 10 +- tests/integration/minimal/test_minimal.py | 2 +- .../payload/test_payload_invalid.py | 2 +- tests/unit/metrics/test_analyzer.py | 2 +- tests/unit/pybuilder/test_input_builder.py | 19 +- tests/unit/resources/test_registry.py | 6 +- .../unit/resources/test_server_containers.py | 2 +- tests/unit/runtime/actors/test_client.py | 4 +- tests/unit/runtime/actors/test_edge.py | 6 +- .../unit/runtime/actors/test_load_balancer.py | 2 +- .../unit/runtime/actors/test_rqs_generator.py | 12 +- tests/unit/runtime/actors/test_server.py | 6 +- tests/unit/runtime/test_simulation_runner.py | 2 +- tests/unit/samplers/test_gaussian_poisson.py | 18 +- tests/unit/samplers/test_poisson_poisson.py | 22 +- tests/unit/samplers/test_sampler_helper.py | 2 +- ...est_endpoint_input.py => test_endpoint.py} | 2 +- ...s_generator_input.py => test_generator.py} | 26 +- ...ull_topology_input.py => test_topology.py} | 10 +- 76 files changed, 2336 insertions(+), 1024 deletions(-) create mode 100644 docs/api/components.md create mode 100644 docs/api/enums.md create mode 100644 docs/api/high-level.md create mode 100644 docs/api/settings.md create mode 100644 docs/api/workload.md create mode 100644 docs/dev-workflow-guide.md delete mode 100644 docs/dev_workflow_guide.md delete mode 100644 docs/fastsim_vision.md rename docs/{pybuilder.md => guides/builder.md} (52%) rename docs/{yaml_builder.md => guides/yaml-builder.md} (85%) create mode 100644 docs/index.md rename docs/{fastsim-docs/metrics_to_measure.md => internals/metrics/overview.md} (100%) rename docs/{fastsim-docs/time_series_metric_architecture.md => internals/metrics/time-series-architecture.md} (100%) rename docs/{fastsim-docs/runtime_and_resources.md => internals/runtime-and-resources.md} (100%) rename docs/{fastsim-docs/simulation_input.md => internals/simulation-input.md} (98%) rename docs/{fastsim-docs/simulation_runner.md => internals/simulation-runner.md} (100%) rename docs/{fastsim-docs/requests_generator.md => internals/workload-samplers.md} (99%) create mode 100644 docs/why-asyncflow.md create mode 100644 examples/single_server_builder.png rename src/asyncflow/{pybuilder/input_builder.py => builder/asyncflow_builder.py} (87%) create mode 100644 src/asyncflow/components/__init__.py delete mode 100644 src/asyncflow/config/__init__.py create mode 100644 src/asyncflow/enums/__init__.py delete mode 100644 src/asyncflow/resources/__init__.py delete mode 100644 src/asyncflow/runtime/__init__.py rename src/asyncflow/runtime/actors/{helpers => routing}/lb_algorithms.py (100%) create mode 100644 src/asyncflow/schemas/common/__init__.py rename src/asyncflow/schemas/{random_variables_config.py => common/random_variables.py} (100%) delete mode 100644 src/asyncflow/schemas/full_simulation_input.py create mode 100644 src/asyncflow/schemas/payload.py rename src/asyncflow/schemas/{simulation_settings_input.py => settings/simulation.py} (100%) delete mode 100644 src/asyncflow/schemas/system_topology/full_system_topology.py create mode 100644 src/asyncflow/schemas/topology/edges.py rename src/asyncflow/schemas/{system_topology => topology}/endpoint.py (100%) create mode 100644 src/asyncflow/schemas/topology/graph.py create mode 100644 src/asyncflow/schemas/topology/nodes.py rename src/asyncflow/schemas/{rqs_generator_input.py => workload/rqs_generator.py} (95%) create mode 100644 src/asyncflow/settings/__init__.py create mode 100644 src/asyncflow/workload/__init__.py rename tests/unit/schemas/{test_endpoint_input.py => test_endpoint.py} (98%) rename tests/unit/schemas/{test_requests_generator_input.py => test_generator.py} (92%) rename tests/unit/schemas/{test_full_topology_input.py => test_topology.py} (97%) diff --git a/docs/api/components.md b/docs/api/components.md new file mode 100644 index 0000000..15f97c2 --- /dev/null +++ b/docs/api/components.md @@ -0,0 +1,295 @@ +# AsyncFlow — Public API Reference: `components` + +This page documents the **public topology components** you can import from +`asyncflow.components` to construct a simulation scenario in Python. +These classes are Pydantic models with strict validation and are the +**only pieces you need** to define the *structure* of your system: nodes +(client/servers/LB), endpoints (steps), and network edges. + +> The builder (`AsyncFlow`) will assemble these into the internal graph for you. +> You **do not** need to import internal graph classes. + +--- + +## Imports + +```python +from asyncflow.components import ( + Client, + Server, + ServerResources, + LoadBalancer, + Endpoint, + Edge, +) +# Optional enums (strings are also accepted): +from asyncflow.enums import Distribution +``` + +--- + +## Quick example + +```python +from asyncflow.components import ( + Client, Server, ServerResources, LoadBalancer, Endpoint, Edge +) + +# Nodes +client = Client(id="client-1") + +endpoint = Endpoint( + endpoint_name="/predict", + steps=[ + {"kind": "ram", "step_operation": {"necessary_ram": 64}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ], +) + +server = Server( + id="srv-1", + server_resources=ServerResources(cpu_cores=2, ram_mb=2048), + endpoints=[endpoint], +) + +lb = LoadBalancer(id="lb-1", algorithms="round_robin", server_covered={"srv-1"}) + +# Edges (directed) +edges = [ + Edge( + id="gen-to-client", + source="rqs-1", # external sources allowed (e.g., generator id) + target="client-1", # targets must be declared nodes + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="client-to-lb", + source="client-1", + target="lb-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="lb-to-srv1", + source="lb-1", + target="srv-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="srv1-to-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), +] +``` + +You can then feed these to the `AsyncFlow` builder (not shown here) along with +workload and settings. + +--- + +## Component reference + +### `Client` + +```python +Client(id: str) +``` + +* Represents the client node. +* `type` is fixed internally to `"client"`. +* **Validation:** any non-standard `type` is rejected (guardrail). + +--- + +### `ServerResources` + +```python +ServerResources( + cpu_cores: int = 1, # ≥ 1 NOW MUST BE FIXED TO ONE + ram_mb: int = 1024, # ≥ 256 + db_connection_pool: int | None = None, +) +``` + +* Server capacity knobs used by the runtime (CPU tokens, RAM reservoir, optional DB pool). +* You may pass a **dict** instead of `ServerResources`; Pydantic will coerce it. + +**Bounds & defaults** + +* `cpu_cores ≥ 1` +* `ram_mb ≥ 256` +* `db_connection_pool` optional + +--- + +### `Endpoint` + +```python +Endpoint( + endpoint_name: str, # normalized to lowercase + steps: list[dict], # or Pydantic Step objects (dict is simpler) +) +``` + +Each step is a dict with **exactly one** operation: + +```python +{"kind": , "step_operation": { : }} +``` + +**Valid step kinds and operation keys** + +| Kind (enum string) | Operation dict (exactly 1 key) | Units / constraints | | +| --------------------- | -------------------------------- | ------------------- | ------- | +| `initial_parsing` | `{ "cpu_time": }` | seconds, > 0 | | +| `cpu_bound_operation` | `{ "cpu_time": }` | seconds, > 0 | | +| `ram` | \`{ "necessary\_ram": \ }\` | MB, > 0 | +| `io_task_spawn` | `{ "io_waiting_time": }` | seconds, > 0 | | +| `io_llm` | `{ "io_waiting_time": }` | seconds, > 0 | | +| `io_wait` | `{ "io_waiting_time": }` | seconds, > 0 | | +| `io_db` | `{ "io_waiting_time": }` | seconds, > 0 | | +| `io_cache` | `{ "io_waiting_time": }` | seconds, > 0 | | + +**Validation** + +* `endpoint_name` is lowercased automatically. +* `step_operation` must have **one and only one** entry. +* The operation **must match** the step kind (CPU ↔ `cpu_time`, RAM ↔ `necessary_ram`, IO ↔ `io_waiting_time`). +* All numeric values must be **strictly positive**. + +--- + +### `Server` + +```python +Server( + id: str, + server_resources: ServerResources | dict, + endpoints: list[Endpoint], +) +``` + +* Represents a server node hosting one or more endpoints. +* `type` is fixed internally to `"server"`. +* **Validation:** any non-standard `type` is rejected. + +--- + +### `LoadBalancer` (optional) + +```python +LoadBalancer( + id: str, + algorithms: Literal["round_robin", "least_connection"] = "round_robin", + server_covered: set[str] = set(), +) +``` + +* Declares a logical load balancer and the set of server IDs it can route to. +* **Graph-level rules** (checked when the payload is built): + + * `server_covered` must be a subset of declared server IDs. + * There must be an **edge from the LB to each covered server** (e.g., `lb-1 → srv-1`). + +--- + +### `Edge` + +```python +Edge( + id: str, + source: str, + target: str, + latency: dict | RVConfig, # recommend dict: {"mean": , "distribution": , "variance": } + edge_type: Literal["network_connection"] = "network_connection", + dropout_rate: float = 0.01, # in [0.0, 1.0] +) +``` + +* Directed link between two nodes. +* **Latency** is a random variable; most users pass a dict: + + * `mean: float` (required) + * `distribution: "poisson" | "normal" | "log_normal" | "exponential" | "uniform"` (default: `"poisson"`) + * `variance: float?` (for `normal`/`log_normal`, defaults to `mean` if omitted) + +**Validation** + +* `mean > 0` +* if provided, `variance ≥ 0` +* `dropout_rate ∈ [0.0, 1.0]` +* `source != target` + +**Graph-level rules** (enforced when the full payload is validated) + +* Every **target** must be a **declared node** (`client`, `server`, or `load_balancer`). +* **External IDs** (e.g., `"rqs-1"`) are allowed **only** as **sources**; they cannot appear as targets. +* **Unique edge IDs**. +* **No fan-out except LB**: only the load balancer is allowed to have multiple outgoing edges among declared nodes. + +--- + +## Type coercion & enums + +* You may pass strings for enums (`kind`, `distribution`, etc.); they will be validated against the allowed values. +* For `ServerResources` and `Edge.latency` you can pass dictionaries; Pydantic will coerce them to typed models. +* If you prefer, you can import and use the enums: + + ```python + from asyncflow.enums import Distribution + Edge(..., latency={"mean": 0.003, "distribution": Distribution.EXPONENTIAL}) + ``` + +--- + +## Best practices & pitfalls + +**Do** + +* Keep IDs unique across nodes of the same category and across edges. +* Ensure LB coverage and LB→server edges are in sync. +* Use small, measurable step values first; iterate once you see where queues and delays form. + +**Don’t** + +* Create multiple outgoing edges from non-LB nodes (graph validator will fail). +* Use zero/negative times or RAM (validators will raise). +* Target external IDs (only sources may be external). + +--- + +## Where these components fit + +You will typically combine these **components** with: + +* **workload** (`RqsGenerator`) from `asyncflow.workload` +* **settings** (`SimulationSettings`) from `asyncflow.settings` +* the **builder** (`AsyncFlow`) and **runner** (`SimulationRunner`) from the root package + +Example (wiring, abbreviated): + +```python +from asyncflow import AsyncFlow, SimulationRunner +from asyncflow.workload import RqsGenerator +from asyncflow.settings import SimulationSettings + +flow = ( + AsyncFlow() + .add_generator(RqsGenerator(...)) + .add_client(client) + .add_servers(server) + .add_edges(*edges) + .add_load_balancer(lb) # optional + .add_simulation_settings(SimulationSettings(...)) +) +payload = flow.build_payload() # validates graph-level rules +SimulationRunner(..., simulation_input=payload).run() +``` + +--- + +With these `components`, you can model any topology supported by AsyncFlow— +cleanly, type-checked, and with **clear, early** validation errors when something +is inconsistent. diff --git a/docs/api/enums.md b/docs/api/enums.md new file mode 100644 index 0000000..09aaeb6 --- /dev/null +++ b/docs/api/enums.md @@ -0,0 +1,197 @@ +# AsyncFlow — Public Enums API + +This page documents the **public, user-facing** enums exported from `asyncflow.enums`. These enums exist to remove “magic strings” from scenario code, offer IDE autocomplete, and make input validation more robust. Using them is optional — all Pydantic models still accept the corresponding string values — but recommended for Python users. + +```python +from asyncflow.enums import ( + Distribution, + LbAlgorithmsName, + SampledMetricName, + EventMetricName, + # advanced (optional, if you define steps in Python) + EndpointStepCPU, EndpointStepIO, EndpointStepRAM, StepOperation, +) +``` + +> **Stability:** Values in these enums form part of the **public input contract**. They are semver-stable: new members may be added in minor releases, existing members won’t be renamed or removed except in a major release. + +--- + +## 1) Distribution + +Enumeration of probability distributions accepted by `RVConfig`. + +* `Distribution.POISSON` → `"poisson"` +* `Distribution.NORMAL` → `"normal"` +* `Distribution.LOG_NORMAL` → `"log_normal"` +* `Distribution.EXPONENTIAL` → `"exponential"` +* `Distribution.UNIFORM` → `"uniform"` + +**Used in:** `RVConfig` (e.g., workload users / rpm, edge latency). + +**Notes & validation:** + +* `mean` is required (coerced to float). +* For `NORMAL` and `LOG_NORMAL`, missing `variance` defaults to `mean`. +* For **edge latency** specifically, `mean > 0` and (if present) `variance ≥ 0`. + +**Example** + +```python +from asyncflow.enums import Distribution +from asyncflow.schemas.common.random_variables import RVConfig + +rv = RVConfig(mean=0.003, distribution=Distribution.EXPONENTIAL) +``` + +--- + +## 2) LbAlgorithmsName + +Load-balancing strategies available to the `LoadBalancer` node. + +* `LbAlgorithmsName.ROUND_ROBIN` → `"round_robin"` +* `LbAlgorithmsName.LEAST_CONNECTIONS` → `"least_connection"` + +**Used in:** `LoadBalancer(algorithms=...)`. + +**Example** + +```python +from asyncflow.enums import LbAlgorithmsName +from asyncflow.schemas.topology.nodes import LoadBalancer + +lb = LoadBalancer(id="lb-1", algorithms=LbAlgorithmsName.ROUND_ROBIN, server_covered={"srv-1", "srv-2"}) +``` + +--- + +## 3) SampledMetricName + +Time-series metrics collected at a fixed cadence (`sample_period_s`). + +* `READY_QUEUE_LEN` → `"ready_queue_len"` +* `EVENT_LOOP_IO_SLEEP` → `"event_loop_io_sleep"` +* `RAM_IN_USE` → `"ram_in_use"` +* `EDGE_CONCURRENT_CONNECTION` → `"edge_concurrent_connection"` + +**Used in:** `SimulationSettings(enabled_sample_metrics=...)`. + +**Example** + +```python +from asyncflow.enums import SampledMetricName +from asyncflow.schemas.settings.simulation import SimulationSettings + +settings = SimulationSettings( + total_simulation_time=300, + sample_period_s=0.01, + enabled_sample_metrics={ + SampledMetricName.READY_QUEUE_LEN, + SampledMetricName.RAM_IN_USE, + }, +) +``` + +--- + +## 4) EventMetricName + +Per-event metrics (not sampled). + +* `RQS_CLOCK` → `"rqs_clock"` +* `LLM_COST` → `"llm_cost"` (reserved for future accounting) + +**Used in:** `SimulationSettings(enabled_event_metrics=...)`. + +**Example** + +```python +from asyncflow.enums import EventMetricName +SimulationSettings(enabled_event_metrics={EventMetricName.RQS_CLOCK}) +``` + +--- + +## 5) (Advanced) Endpoint step enums + +You only need these if you create `Endpoint` steps **programmatically** in Python. In YAML you’ll write strings; both modes are supported. + +### 5.1 EndpointStepCPU + +CPU-bound step kinds: + +* `INITIAL_PARSING` → `"initial_parsing"` +* `CPU_BOUND_OPERATION` → `"cpu_bound_operation"` + +### 5.2 EndpointStepRAM + +RAM step kind: + +* `RAM` → `"ram"` + +### 5.3 EndpointStepIO + +I/O-bound step kinds: + +* `TASK_SPAWN` → `"io_task_spawn"` +* `LLM` → `"io_llm"` +* `WAIT` → `"io_wait"` +* `DB` → `"io_db"` +* `CACHE` → `"io_cache"` + +### 5.4 StepOperation + +Operation keys allowed inside `Step.step_operation`: + +* `CPU_TIME` → `"cpu_time"` (seconds, positive) +* `NECESSARY_RAM` → `"necessary_ram"` (MB, positive) +* `IO_WAITING_TIME` → `"io_waiting_time"` (seconds, positive) + +**Validation rules (enforced by the schema):** + +* Every `Step` must have **exactly one** operation key. +* The operation must **match** the step kind: + + * CPU step → `CPU_TIME` + * RAM step → `NECESSARY_RAM` + * I/O step → `IO_WAITING_TIME` + +**Example** + +```python +from asyncflow.enums import EndpointStepCPU, EndpointStepIO, EndpointStepRAM, StepOperation +from asyncflow.schemas.topology.endpoint import Endpoint + +ep = Endpoint( + endpoint_name="/predict", + steps=[ + { "kind": EndpointStepRAM.RAM, "step_operation": { StepOperation.NECESSARY_RAM: 128 } }, + { "kind": EndpointStepCPU.INITIAL_PARSING, "step_operation": { StepOperation.CPU_TIME: 0.002 } }, + { "kind": EndpointStepIO.DB, "step_operation": { StepOperation.IO_WAITING_TIME: 0.012 } }, + ], +) +``` + +--- + +## Usage patterns & tips + +* **Strings vs Enums:** All models accept both. Enums help with IDE hints and prevent typos; strings keep YAML compact. Mix as you like. +* **Keep it public, not internal:** Only the enums above are considered public and stable. Internals like `SystemNodes`, `SystemEdges`, `ServerResourceName`, etc. are intentionally **not exported** (they may change). +* **Forward compatibility:** New enum members may appear in minor releases (e.g., a new `SampledMetricName`). Your existing configs remain valid; just opt in when you need them. + +--- + +## Quick Reference + +| Enum | Where it’s used | Members (strings) | +| ------------------- | ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | +| `Distribution` | `RVConfig` | `poisson`, `normal`, `log_normal`, `exponential`, `uniform` | +| `LbAlgorithmsName` | `LoadBalancer.algorithms` | `round_robin`, `least_connection` | +| `SampledMetricName` | `SimulationSettings.enabled_sample_metrics` | `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, `edge_concurrent_connection` | +| `EventMetricName` | `SimulationSettings.enabled_event_metrics` | `rqs_clock`, `llm_cost` | +| `EndpointStep*` | `Endpoint.steps[*].kind` (Python) | CPU: `initial_parsing`, `cpu_bound_operation`; RAM: `ram`; IO: `io_task_spawn`, `io_llm`, `io_wait`, `io_db`, `io_cache` | +| `StepOperation` | `Endpoint.steps[*].step_operation` | `cpu_time`, `necessary_ram`, `io_waiting_time` | + +--- diff --git a/docs/api/high-level.md b/docs/api/high-level.md new file mode 100644 index 0000000..65c3d24 --- /dev/null +++ b/docs/api/high-level.md @@ -0,0 +1,299 @@ +# AsyncFlow — High-Level API (`AsyncFlow`, `SimulationRunner`) + +This page explains how to programmatically **assemble a validated simulation payload** and **run** it, returning metrics and plots through the analyzer. + +* **Builder**: `AsyncFlow` – compose workload, topology, and settings into a single `SimulationPayload`. +* **Runner**: `SimulationRunner` – wire actors, start processes, collect metrics, and return a `ResultsAnalyzer`. + +--- + +## Imports + +```python +# High-level API +from asyncflow import AsyncFlow, SimulationRunner + +# Public leaf schemas (components & workload) +from asyncflow.components import Client, Server, Endpoint, Edge +from asyncflow.workload import RqsGenerator, RVConfig +from asyncflow.settings import SimulationSettings +``` + +> These are the **only** imports end users need. Internals (actors, registries, etc.) remain private. + +--- + +## Quick start + +A minimal end-to-end example: + +```python +from __future__ import annotations +import simpy + +from asyncflow import AsyncFlow, SimulationRunner +from asyncflow.components import Client, Server, Endpoint, Edge +from asyncflow.workload import RqsGenerator, RVConfig +from asyncflow.settings import SimulationSettings + +# 1) Workload +rqs = RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=50, # Poisson by default + # or Distribution.NORMAL with variance auto=mean + ), + avg_request_per_minute_per_user=RVConfig(mean=30), # MUST be Poisson + user_sampling_window=60, # seconds +) + +# 2) Topology components +client = Client(id="client-1") + +endpoint = Endpoint( + endpoint_name="/hello", + steps=[ + {"kind": "ram", "step_operation": {"necessary_ram": 32}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ], +) + +server = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 1024}, + endpoints=[endpoint], +) + +edges = [ + Edge(id="gen-client", source="rqs-1", target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}), + Edge(id="client-srv1", source="client-1", target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}), + Edge(id="srv1-client", source="srv-1", target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}), +] + +# 3) Settings (baseline sampled metrics are mandatory) +settings = SimulationSettings( + total_simulation_time=300, # seconds (≥ 5) + sample_period_s=0.01, # 0.001 ≤ value ≤ 0.1 + # enabled_sample_metrics and enabled_event_metrics: safe defaults already set +) + +# 4) Build (validates everything with Pydantic) +payload = ( + AsyncFlow() + .add_generator(rqs) + .add_client(client) + .add_servers(server) + .add_edges(*edges) + .add_simulation_settings(settings) + .build_payload() +) + +# 5) Run +env = simpy.Environment() +results = SimulationRunner(env=env, simulation_input=payload).run() + +# 6) Use the analyzer (examples) +print(results.get_latency_stats()) +ts, rps = results.get_throughput_series() +sampled = results.get_sampled_metrics() +``` + +--- + +## `AsyncFlow` — builder (public) + +`AsyncFlow` helps you construct a **self-consistent** `SimulationPayload` with fluent, chainable calls. Every piece you add is type-checked; the final `build_payload()` validates the full graph and settings. + +### API + +```python +class AsyncFlow: + def add_generator(self, rqs_generator: RqsGenerator) -> Self: ... + def add_client(self, client: Client) -> Self: ... + def add_servers(self, *servers: Server) -> Self: ... + def add_edges(self, *edges: Edge) -> Self: ... + def add_simulation_settings(self, sim_settings: SimulationSettings) -> Self: ... + def add_load_balancer(self, load_balancer: LoadBalancer) -> Self: ... + def build_payload(self) -> SimulationPayload: ... +``` + +### Validation performed by `build_payload()` + +On build, the composed payload is validated by the Pydantic schemas: + +1. **Presence** + + * Generator, client, **≥ 1 server**, **≥ 1 edge**, settings are required. + +2. **Unique IDs** + + * Duplicate server IDs or edge IDs are rejected. + +3. **Node types** + + * Fixed enums: `client`, `server`, `load_balancer`; validated on each node. + +4. **Edge integrity** + + * Every edge **target** must be a declared node ID. + * **External IDs** (e.g., the generator id) are allowed **only as sources**. + * **No self-loops** (`source != target`). + +5. **Load balancer sanity** (if present) + + * `server_covered ⊆ declared servers`. + * There must be an **edge from the LB to every covered server**. + +6. **(Engine rule)** “No fan-out except LB” + + * Only the LB may have multiple outgoing edges among declared nodes. + +7. **Latency RV constraints (edges)** + + * `latency.mean > 0`, and if `variance` exists, `variance ≥ 0`. + +If a rule fails, a **descriptive `ValueError`** points at the offending entity/field. + +### Typical errors you might see + +* Missing parts: + `ValueError: The generator input must be instantiated before the simulation` +* Type mis-match: + `TypeError: All the instances must be of the type Server` +* Graph violations: + `ValueError: Edge client-1->srv-X references unknown target node 'srv-X'` +* LB coverage: + `ValueError: Servers ['srv-2'] are covered by LB 'lb-1' but have no outgoing edge from it.` + +--- + +## `SimulationRunner` — orchestrator (public) + +`SimulationRunner` takes a validated `SimulationPayload`, **instantiates all runtimes**, **wires** edges to their target mailboxes, **starts** every actor, **collects** sampled metrics, and advances the SimPy clock. + +### API + +```python +class SimulationRunner: + def __init__(self, *, env: simpy.Environment, simulation_input: SimulationPayload) -> None: ... + def run(self) -> ResultsAnalyzer: ... + @classmethod + def from_yaml(cls, *, env: simpy.Environment, yaml_path: str | Path) -> "SimulationRunner": ... +``` + +* **`env`**: your SimPy environment (you control its lifetime). + +* **`simulation_input`**: the payload returned by `AsyncFlow.build_payload()` (or parsed from YAML). + +* **`run()`**: + + * Builds and wires all runtime actors (`RqsGeneratorRuntime`, `ClientRuntime`, `ServerRuntime`, `LoadBalancerRuntime`, `EdgeRuntime`). + * Starts the **SampledMetricCollector** (baseline sampled metrics are mandatory and collected automatically). + * Runs until `SimulationSettings.total_simulation_time`. + * Returns a **`ResultsAnalyzer`** with helpers like: + + * `get_latency_stats()` + * `get_throughput_series()` + * `get_sampled_metrics()` + * plotting helpers (`plot_latency_distribution`, `plot_throughput`, …). + +* **`from_yaml`**: convenience constructor for loading a full payload from a YAML file and running it immediately. + +### Determinism & RNG + +* The runner uses `numpy.random.default_rng()` internally. + Seeding is not yet exposed as a public parameter; exact reproducibility across runs is **not guaranteed** in this version. + +--- + +## Extended example: with Load Balancer + +```python +from asyncflow.components import Client, Server, Endpoint, Edge +from asyncflow.components import LoadBalancer +from asyncflow import AsyncFlow, SimulationRunner +from asyncflow.workload import RqsGenerator, RVConfig +from asyncflow.settings import SimulationSettings +import simpy + +client = Client(id="client-1") + +srv1 = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 1024}, + endpoints=[Endpoint(endpoint_name="/api", steps=[{"kind":"ram","step_operation":{"necessary_ram":64}}])] +) +srv2 = Server( + id="srv-2", + server_resources={"cpu_cores": 2, "ram_mb": 2048}, + endpoints=[Endpoint(endpoint_name="/api", steps=[{"kind":"io_db","step_operation":{"io_waiting_time":0.012}}])] +) + +lb = LoadBalancer(id="lb-1", algorithms="round_robin", server_covered={"srv-1","srv-2"}) + +edges = [ + Edge(id="gen-client", source="rqs-1", target="client-1", latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="client-lb", source="client-1", target="lb-1", latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="lb-srv1", source="lb-1", target="srv-1", latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="lb-srv2", source="lb-1", target="srv-2", latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="srv1-client", source="srv-1", target="client-1", latency={"mean":0.003,"distribution":"exponential"}), + Edge(id="srv2-client", source="srv-2", target="client-1", latency={"mean":0.003,"distribution":"exponential"}), +] + +payload = ( + AsyncFlow() + .add_generator(RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=120), + avg_request_per_minute_per_user=RVConfig(mean=20), + user_sampling_window=60, + )) + .add_client(client) + .add_servers(srv1, srv2) + .add_load_balancer(lb) + .add_edges(*edges) + .add_simulation_settings(SimulationSettings(total_simulation_time=600, sample_period_s=0.02)) + .build_payload() +) + +env = simpy.Environment() +results = SimulationRunner(env=env, simulation_input=payload).run() +``` + +--- + +## Performance tips + +* **Sampling cost** grows with `total_simulation_time / sample_period_s × (#sampled metrics × entities)`. + For long runs, consider a larger `sample_period_s` (e.g., `0.02–0.05`) to reduce memory while keeping the baseline metrics intact. + +* **Validation first**: prefer failing early by letting `build_payload()` validate everything before the runner starts. + +--- + +## Error handling (what throws) + +* **Type errors** on builder inputs (`TypeError`) when passing the wrong class to `add_*`. +* **Validation errors** (`ValueError`) on `build_payload()` if the graph is inconsistent (unknown targets, duplicates, LB edges missing, self-loops, illegal fan-out, latency rules, etc.). +* **Runtime wiring errors** (`TypeError`) if an unknown runtime target/source type appears while wiring edges (should not occur with a validated payload). + +--- + +## YAML path (alternative) + +You can construct the payload in YAML (see “YAML Input Guide”), then: + +```python +import simpy +from asyncflow import SimulationRunner + +env = simpy.Environment() +runner = SimulationRunner.from_yaml(env=env, yaml_path="scenario.yml") +results = runner.run() +``` + +--- + diff --git a/docs/api/settings.md b/docs/api/settings.md new file mode 100644 index 0000000..2970596 --- /dev/null +++ b/docs/api/settings.md @@ -0,0 +1,200 @@ + +# AsyncFlow — Public API Reference: `settings` + +This page documents the **public settings schema** you import from: + +```python +from asyncflow.settings import SimulationSettings +``` + +These settings control **simulation duration**, **sampling cadence**, and **which metrics are collected**. The model is validated with Pydantic and ships with safe defaults. + +> **Contract note** +> The four **baseline sampled metrics** are **mandatory** in the current release: +> +> * `ready_queue_len` +> * `event_loop_io_sleep` +> * `ram_in_use` +> * `edge_concurrent_connection` +> Future metrics may be opt-in; these four must remain enabled. + +--- + +## Imports + +```python +from asyncflow.settings import SimulationSettings + +# Optional: use enums instead of strings (recommended for IDE/type-checking) +from asyncflow.enums import SampledMetricName, EventMetricName +``` + +--- + +## Quick start + +```python +from asyncflow.settings import SimulationSettings +from asyncflow.enums import SampledMetricName as S, EventMetricName as E + +settings = SimulationSettings( + total_simulation_time=300, # seconds (≥ 5) + sample_period_s=0.01, # seconds, 0.001 ≤ value ≤ 0.1 + # Baseline sampled metrics are mandatory (may include more in future): + enabled_sample_metrics={S.READY_QUEUE_LEN, + S.EVENT_LOOP_IO_SLEEP, + S.RAM_IN_USE, + S.EDGE_CONCURRENT_CONNECTION}, + # Event metrics (RQS_CLOCK is the default/mandatory one today): + enabled_event_metrics={E.RQS_CLOCK}, +) +``` + +Pass the object to the builder: + +```python +from asyncflow import AsyncFlow + +payload = ( + AsyncFlow() + # … add workload, topology, edges … + .add_simulation_settings(settings) + .build_payload() +) +``` + +--- + +## Schema reference + +### `SimulationSettings` + +```python +SimulationSettings( + total_simulation_time: int = 3600, # ≥ 5 + sample_period_s: float = 0.01, # 0.001 ≤ value ≤ 0.1 + enabled_sample_metrics: set[SampledMetricName] = { + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + }, + enabled_event_metrics: set[EventMetricName] = {"rqs_clock"}, +) +``` + +**Fields** + +* **`total_simulation_time`** *(int, default `3600`)* + Simulation horizon in **seconds**. **Validation:** `≥ 5`. + +* **`sample_period_s`** *(float, default `0.01`)* + Sampling cadence for time-series metrics (seconds). + **Validation:** `0.001 ≤ sample_period_s ≤ 0.1`. + **Trade-off:** lower ⇒ higher temporal resolution but more samples/memory. + +* **`enabled_sample_metrics`** *(set of enums/strings; default = baseline 4)* + **Must include at least the baseline set** shown above. You can pass enum + values or the corresponding strings. + +* **`enabled_event_metrics`** *(set of enums/strings; default `{"rqs_clock"}`)* + Per-event KPIs (not tied to `sample_period_s`). `rqs_clock` is required today; + `llm_cost` is reserved for future use. + +--- + +## Supported metric enums + +You may pass **strings** or import the enums (recommended). + +### Sampled (time-series) + +* `ready_queue_len` — event-loop ready-queue length +* `event_loop_io_sleep` — time spent waiting on I/O in the loop +* `ram_in_use` — MB of RAM in use (per server) +* `edge_concurrent_connection` — concurrent connections per edge + +```python +from asyncflow.enums import SampledMetricName as S +baseline = {S.READY_QUEUE_LEN, S.EVENT_LOOP_IO_SLEEP, S.RAM_IN_USE, S.EDGE_CONCURRENT_CONNECTION} +``` + +### Event (per-event) + +* `rqs_clock` — start/end timestamps for each request (basis for latency) +* `llm_cost` — reserved for future cost accounting + +```python +from asyncflow.enums import EventMetricName as E +SimulationSettings(enabled_event_metrics={E.RQS_CLOCK}) +``` + +--- + +## Practical presets + +* **Lean but compliant (fast inner-loop dev)** + Keep baseline metrics; increase the sampling period to reduce cost: + + ```python + SimulationSettings( + total_simulation_time=10, + sample_period_s=0.05, # fewer samples + # enabled_* use defaults with mandatory baseline & rqs_clock + ) + ``` + +* **High-resolution debugging (short runs)** + + ```python + SimulationSettings( + total_simulation_time=60, + sample_period_s=0.002, # finer resolution + ) + ``` + +* **Long scenarios (memory-friendly)** + + ```python + SimulationSettings( + total_simulation_time=1800, + sample_period_s=0.05, # fewer samples over long runs + ) + ``` + +--- + +## YAML ⇄ Python mapping + +| YAML (`sim_settings`) | Python (`SimulationSettings`) | +| -------------------------- | ------------------------------ | +| `total_simulation_time` | `total_simulation_time` | +| `sample_period_s` | `sample_period_s` | +| `enabled_sample_metrics[]` | `enabled_sample_metrics={...}` | +| `enabled_event_metrics[]` | `enabled_event_metrics={...}` | + +Strings in YAML map to the same enum names used by Python. + +--- + +## Validation & guarantees + +* `total_simulation_time ≥ 5` +* `0.001 ≤ sample_period_s ≤ 0.1` +* `enabled_sample_metrics ⊇ {ready_queue_len, event_loop_io_sleep, ram_in_use, edge_concurrent_connection}` +* `enabled_event_metrics` must include `rqs_clock` (current contract) +* Enum names are part of the public contract (stable; new values may be added in minor versions) + +--- + +## Tips & pitfalls + +* **Memory/CPU budgeting**: total samples per metric ≈ + `total_simulation_time / sample_period_s`. Long runs with very small + sampling periods produce large arrays. +* **Use enums for safety**: strings work, but enums enable IDE completion and mypy checks. +* **Forward compatibility**: additional sampled/event metrics may become available; the four baseline sampled metrics remain mandatory for the engine’s collectors. + +--- + +This reflects your current implementation: baseline sampled metrics are **required**; event metrics currently require `rqs_clock`; and sampling bounds match the `SamplePeriods` constants. diff --git a/docs/api/workload.md b/docs/api/workload.md new file mode 100644 index 0000000..c560a65 --- /dev/null +++ b/docs/api/workload.md @@ -0,0 +1,197 @@ +# AsyncFlow — Public Workload API + +This page documents the **workload models** exported from: + +```python +from asyncflow.workload import RqsGenerator, RVConfig +``` + +Use these to describe **how traffic is generated** (active users, per-user RPM, and the re-sampling window). The workload is independent from your topology and settings and plugs into the builder or payload directly. + +> **Stability:** This is part of the public API. Fields and enum values are semver-stable (new options may be added in minor releases; breaking changes only in a major). + +--- + +## Quick start + +```python +from asyncflow.workload import RqsGenerator, RVConfig + +rqs = RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=100, distribution="poisson"), # or "normal" + avg_request_per_minute_per_user=RVConfig(mean=20, distribution="poisson"), + user_sampling_window=60, # seconds, re-sample active users every 60s +) + +# … then compose with the builder +from asyncflow.builder.asyncflow_builder import AsyncFlow +payload = (AsyncFlow() + .add_generator(rqs) + # .add_client(...).add_servers(...).add_edges(...).add_simulation_settings(...) + .build_payload()) +``` + +--- + +## `RqsGenerator` (workload root) + +```python +class RqsGenerator(BaseModel): + id: str + type: SystemNodes = SystemNodes.GENERATOR # fixed + avg_active_users: RVConfig # Poisson or Normal + avg_request_per_minute_per_user: RVConfig # Poisson (required) + user_sampling_window: int = 60 # seconds, bounds [1, 120] +``` + +### Field reference + +| Field | Type | Allowed / Bounds | Description | +| --------------------------------- | --------------- | --------------------------------------- | ------------------------------------------------------------------------------------------------------------ | +| `id` | `str` | — | Identifier used by edges (e.g., `source="rqs-1"`). | +| `type` | enum (fixed) | `generator` | Constant; not user-set. | +| `avg_active_users` | `RVConfig` | **Distribution**: `poisson` or `normal` | Random variable for active concurrent users. If `normal`, variance is auto-filled (see `RVConfig`). | +| `avg_request_per_minute_per_user` | `RVConfig` | **Distribution**: **must be** `poisson` | Per-user rate (RPM). Enforced to Poisson by validator. | +| `user_sampling_window` | `int` (seconds) | `1 ≤ value ≤ 120` | How often to re-sample `avg_active_users`. Larger windows → slower drift; smaller windows → more volatility. | + +> Units: RPM = requests per **minute**; times are in **seconds**. + +--- + +## `RVConfig` (random variables) + +```python +class RVConfig(BaseModel): + mean: float + distribution: Distribution = "poisson" + variance: float | None = None +``` + +### Behavior & validation + +* **`mean`** is required and coerced to `float`. (Generic numeric check; positivity is **contextual**. For example, edge latency enforces `mean > 0`, while workloads accept `mean ≥ 0` and rely on samplers to truncate at 0 when needed.) +* **`distribution`** defaults to `"poisson"`. +* **Variance auto-fill:** if `distribution` is `"normal"` or `"log_normal"` **and** `variance` is omitted, it is set to `variance = mean`. + +### Supported distributions + +* `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"`, `"uniform"` + (For **workload**: `avg_active_users` → Poisson/Normal; `avg_request_per_minute_per_user` → **Poisson only**.) + +--- + +## How the workload is sampled (engine semantics) + +AsyncFlow provides **joint samplers** for the two main cases: + +1. **Poisson–Poisson** (`avg_active_users ~ Poisson`, `rpm ~ Poisson`) + +* Every `user_sampling_window` seconds, draw users: + `U ~ Poisson(mean_users)`. +* Aggregate rate: `Λ = U * (rpm_per_user / 60)` (requests/second). +* Within the window, inter-arrival gaps are exponential: + `Δt ~ Exponential(Λ)` (via inverse CDF). +* If `U == 0`, no arrivals until the next window. + +2. **Gaussian–Poisson** (`avg_active_users ~ Normal`, `rpm ~ Poisson`) + +* Draw users with **truncation at 0** (negative draws become 0): + `U ~ max(N(mean, variance), 0)`. +* Then same steps as above: `Λ = U * (rpm_per_user / 60)`, `Δt ~ Exponential(Λ)`. + +**Implications of `user_sampling_window`:** + +* Smaller windows → more frequent changes in `U` (bursty arrivals). +* Larger windows → steadier rate within each window, fewer rate shifts. + +--- + +## Examples + +### A. Steady mid-load (Poisson–Poisson) + +```python +rqs = RqsGenerator( + id="steady", + avg_active_users=RVConfig(mean=80, distribution="poisson"), + avg_request_per_minute_per_user=RVConfig(mean=15, distribution="poisson"), + user_sampling_window=60, +) +``` + +### B. Bursty users (Gaussian–Poisson) + +```python +rqs = RqsGenerator( + id="bursty", + avg_active_users=RVConfig(mean=50, distribution="normal", variance=200), # bigger var → burstier + avg_request_per_minute_per_user=RVConfig(mean=18, distribution="poisson"), + user_sampling_window=15, # faster re-sampling → faster drift +) +``` + +### C. Tiny smoke test + +```python +rqs = RqsGenerator( + id="smoke", + avg_active_users=RVConfig(mean=1, distribution="poisson"), + avg_request_per_minute_per_user=RVConfig(mean=2, distribution="poisson"), + user_sampling_window=30, +) +``` + +--- + +## YAML / JSON equivalence + +If you configure via YAML/JSON, the equivalent block is: + +```yaml +rqs_input: + id: rqs-1 + avg_active_users: + mean: 100 + distribution: poisson # or normal + # variance: 100 # optional; auto=mean if normal/log_normal + avg_request_per_minute_per_user: + mean: 20 + distribution: poisson # must be poisson + user_sampling_window: 60 # [1..120] seconds +``` + +--- + +## Validation & error messages (what you can expect) + +* `avg_request_per_minute_per_user.distribution != poisson` + → `ValueError("At the moment the variable avg request must be Poisson")` +* `avg_active_users.distribution` not in `{poisson, normal}` + → `ValueError("At the moment the variable active user must be Poisson or Gaussian")` +* Non-numeric `mean` in any `RVConfig` + → `ValueError("mean must be a number (int or float)")` +* `user_sampling_window` outside `[1, 120]` + → Pydantic range validation error with clear bounds in the message. + +> Note: Positivity for means is enforced **contextually**. For workload, negative draws are handled by the samplers (e.g., truncated Normal). For edge latency, positivity is enforced at the edge model level. + +--- + +## Common pitfalls & tips + +* **Using Normal without variance:** If you set `distribution="normal"` and omit `variance`, it auto-fills to `variance=mean`. Set it explicitly if you want heavier or lighter variance than the default. +* **Confusing units:** RPM is **per minute**, not per second. The engine converts internally. +* **Over-reactive windows:** Very small `user_sampling_window` (e.g., `1–5s`) makes the rate jumpy; this is fine for “bursty” scenarios but can be noisy. +* **Zero arrivals:** If a window samples `U=0`, you’ll get no arrivals until the next window; this is expected. + +--- + +## Interplay with Settings & Metrics + +* The workload **does not** depend on the sampling cadence of time-series metrics (`SimulationSettings.sample_period_s`). Sampling controls **observability**, not arrivals. +* **Baseline sampled metrics are mandatory** in the current release (ready-queue length, I/O sleep, RAM, edge concurrency). Future metrics can be opt-in. + +--- + +With `RqsGenerator` + `RVConfig` you can describe steady, bursty, or sparse loads with a few lines—then reuse the same topology and settings to compare how architecture choices behave under different traffic profiles. diff --git a/docs/dev-workflow-guide.md b/docs/dev-workflow-guide.md new file mode 100644 index 0000000..ff0e3ce --- /dev/null +++ b/docs/dev-workflow-guide.md @@ -0,0 +1,273 @@ +# **Development Workflow & Architecture Guide** + +This document describes the development workflow, repository architecture, and branching strategy for **AsyncFlow** +--- + +## 1) Repository Layout + +### 1.1 Project tree (backend) + +``` +AsyncFlow-backend/ +├─ examples/ # runnable examples (YAML & Python) +│ └─ data/ +├─ scripts/ # helper bash scripts (lint, quality, etc.) +│ └─ quality-check.sh +├─ docs/ # product & technical docs +├─ tests/ # unit & integration tests +│ ├─ unit/ +│ └─ integration/ +├─ src/ +│ └─ asyncflow/ # Python package (library) +│ ├─ __init__.py # public "high-level" facade (re-exports) +│ ├─ builder/ +│ │ └─ asyncflow_builder.py # internal builder implementation +│ ├─ components/ # PUBLIC FACADE: leaf Pydantic components +│ │ └─ __init__.py # (barrel: re-exports Client, Server, Endpoint, Edge) +│ ├─ config/ +│ │ ├─ constants.py # enums/constants (source of truth) +│ │ └─ plot_constants.py +│ ├─ enums/ # PUBLIC FACADE: selected enums +│ │ └─ __init__.py # (barrel: re-exports Distribution, SampledMetricName, …) +│ ├─ metrics/ +│ │ ├─ analyzer.py # results post-processing +│ │ ├─ collector.py # sampling collectors +│ │ ├─ client.py +│ │ ├─ edge.py +│ │ └─ server.py +│ ├─ resources/ +│ │ ├─ registry.py +│ │ └─ server_containers.py +│ ├─ runtime/ +│ │ ├─ simulation_runner.py # engine entry-point +│ │ ├─ rqs_state.py +│ │ ├─ actors/ # INTERNAL: Client/Server/Edge/Generator actors +│ │ └─ routing/ +│ │ └─ lb_algorithms.py +│ ├─ samplers/ +│ │ ├─ poisson_poisson.py +│ │ ├─ gaussian_poisson.py +│ │ └─ common_helpers.py +│ ├─ schemas/ # INTERNAL: full Pydantic schema impls +│ │ ├─ payload.py +│ │ ├─ common/ +│ │ │ └─ random_variables.py +│ │ ├─ settings/ +│ │ │ └─ simulation.py +│ │ ├─ topology/ +│ │ │ ├─ edges.py +│ │ │ ├─ endpoint.py +│ │ │ ├─ graph.py +│ │ │ └─ nodes.py +│ │ └─ workload/ +│ │ └─ rqs_generator.py +│ ├─ settings/ # PUBLIC FACADE: SimulationSettings +│ │ └─ __init__.py +│ └─ workload/ # PUBLIC FACADE: RqsGenerator +│ └─ __init__.py +├─ poetry.lock +├─ pyproject.toml +└─ README.md +``` + +**Public API surface (what you guarantee as stable):** + +* High-level: + + ```py + from asyncflow import AsyncFlow, SimulationRunner + ``` +* Components: + + ```py + from asyncflow.components import Client, Server, Endpoint, Edge + ``` +* Workload & Settings: + + ```py + from asyncflow.workload import RqsGenerator + from asyncflow.settings import SimulationSettings + ``` +* Enums: + + ```py + from asyncflow.enums import Distribution, SampledMetricName, EventMetricName, LbAlgorithmsName + ``` + +> Everything under `asyncflow.schemas/`, `asyncflow.runtime/actors/`, `asyncflow.builder/` is **internal** (implementation details). The facades re-export only what users should import. + +### 1.2 What each top-level area does + +| Area | Purpose | +| --------------- | ------------------------------------------------------------------------------------------------------------------------------------ | +| **builder/** | Internal implementation of the pybuilder used by `AsyncFlow`. Users shouldn’t import from here directly. | +| **components/** | **Public facade** for *leaf* Pydantic models used in payloads: `Client`, `Server`, `Endpoint`, `Edge`. | +| **config/** | Constants & enums source-of-truth (kept internal; only *selected* enums are re-exported via `asyncflow.enums`). | +| **enums/** | **Public facade** re-exporting the enums that appear in public payloads (`Distribution`, `SampledMetricName`, `EventMetricName`, …). | +| **metrics/** | Post-processing & visualization support (analyzer & collectors). | +| **resources/** | Runtime SimPy resource wiring (CPU/RAM containers, registries). | +| **runtime/** | The simulation engine entry-point (`SimulationRunner`), request lifecycle, and internal actors. | +| **samplers/** | Random-variable samplers for the generator and steps. | +| **schemas/** | Full Pydantic schema implementation and validation rules (internal). | +| **settings/** | **Public facade** re-exporting `SimulationSettings`. | +| **workload/** | **Public facade** re-exporting `RqsGenerator`. | + +--- + +## 2) Branching Strategy: Git Flow (+ `refactor/*`) + +We use **Git Flow** with an extra branch family for clean refactors. + +### Diagram + +```mermaid +--- +title: Git Flow (with refactor/*) +--- +gitGraph + commit id: "Initial commit" + branch develop + checkout develop + commit id: "Setup Project" + + branch feature/user-authentication + checkout feature/user-authentication + commit id: "feat: Add login form" + commit id: "feat: Add form validation" + checkout develop + merge feature/user-authentication + + branch refactor/performance-cleanup + checkout refactor/performance-cleanup + commit id: "refactor: simplify hot path" + commit id: "refactor: remove dead code" + checkout develop + merge refactor/performance-cleanup + + branch release/v1.0.0 + checkout release/v1.0.0 + commit id: "fix: Pre-release bug fixes" tag: "v1.0.0" + checkout main + merge release/v1.0.0 + checkout develop + merge release/v1.0.0 + + checkout main + branch hotfix/critical-login-bug + checkout hotfix/critical-login-bug + commit id: "fix: Critical production bug" tag: "v1.0.1" + checkout main + merge hotfix/critical-login-bug + checkout develop + merge hotfix/critical-login-bug +``` + +### Branch families + +* **main** – production-ready, tagged releases only (no direct commits). +* **develop** – integration branch; base for `feature/*` and `refactor/*`. +* **feature/**\* – user-visible features (new endpoints/behavior, DB changes). +* **refactor/**\* – **no new features**; internal changes, performance, renames, file moves, debt trimming. Use `refactor:` commit prefix. +* **release/**\* – freeze, harden, docs; merge into `main` (tag) and back into `develop`. +* **hotfix/**\* – urgent production fixes; branch off `main` tag; merge into `main` (tag) and `develop`. + +**When to pick which:** + +* New behavior or external contract → `feature/*` +* Internal cleanup only → `refactor/*` +* Ship a version → `release/*` +* Patch production now → `hotfix/*` + +--- + +## 3) CI/CD Pipeline + +A layered pipeline (GitHub Actions recommended) mirrors the branching model. + +### 3.1 CI on PRs to `develop` (feature/refactor) + +**Quick Suite** (fast, no external services): + +* **Ruff** (or Black/isort/Flake8) → style/lint +* **mypy** → type checking +* **pytest** unit-only: `pytest -m "not integration"` + +### 3.2 CI on push to `develop` + +**Full Suite** (slower; with services): + +* Full tests, including `@pytest.mark.integration` +* Spin up **PostgreSQL**/**Redis** if required by integration tests +* Build multi-stage Docker image & smoke test +* Optionally build docs (mkdocs) to catch docstring regressions + +### 3.3 CI on `release/*` + +* Always run **Full Suite** +* Build and publish versioned images/artifacts +* Generate release notes/changelog + +### 3.4 CI on `hotfix/*` + +* Run **Full Suite** +* Tag patch release on merge to `main` +* Merge back to `develop` + +> Refactors should be **behavior-preserving**. If a refactor touches hot paths, add micro-benchmarks or targeted integration tests and consider running the Full Suite pre-merge. + +--- + +## 4) Quality Gates & Conventions + +* **Style & Lint**: Ruff (or Black + isort + Flake8). No violations. +* **Types**: mypy clean. +* **Tests**: + + * Unit tests for new/refactored code paths + * Integration tests for end-to-end behavior +* **Commits**: Conventional commits (`feat:`, `fix:`, `refactor:`, `docs:`, `test:`, `chore:`). +* **PRs**: Review required; refactors must include rationale and scope. +* **Docs**: Update `docs/` and public API references when you touch public facades. + +--- + +## 5) Public API & Stability Contract + +Only **facade modules** are considered public and stable: + +* High-level: + + ```py + from asyncflow import AsyncFlow, SimulationRunner + ``` +* Components: + + ```py + from asyncflow.components import Client, Server, Endpoint, Edge + ``` +* Workload & Settings: + + ```py + from asyncflow.workload import RqsGenerator + from asyncflow.settings import SimulationSettings + ``` +* Enums: + + ```py + from asyncflow.enums import Distribution, SampledMetricName, EventMetricName, LbAlgorithmsName + ``` + +Everything else—`schemas/`, `runtime/actors/`, `builder/`, `samplers/`, `resources/`—is **internal** and can change without notice. Use **SemVer** for releases; any change to the public facades that breaks compatibility requires a **major** bump. + +--- + +## 6) Developer Commands (Poetry) + +* Install: `poetry install` +* Lint/format: `bash scripts/quality-check.sh` (or your Ruff/Black commands) +* Test (unit only): `pytest -m "not integration"` +* Test (full): `pytest` +* Run example: `python examples/single_server_pybuilder.py` + +--- + diff --git a/docs/dev_workflow_guide.md b/docs/dev_workflow_guide.md deleted file mode 100644 index 8c1974c..0000000 --- a/docs/dev_workflow_guide.md +++ /dev/null @@ -1,256 +0,0 @@ -# **Development Workflow & Architecture Guide** - -This document outlines the standardized development workflow, repository architecture, and branching strategy for the backend of the AsyncFlow project. Adhering to these guidelines ensures consistency, maintainability, and a scalable development process. - - -## 1. Repository Layout - -### 1.1 Backend Service (`AsyncFlow-backend`) - -The repository hosts the entire FastAPI backend for AsyncFlow. -Its job is to expose the REST API, run the discrete-event simulation, talk to the database, and provide metrics. - -``` -AsyncFlow-backend/ -├── example/ # examples of working simulations -│ ├── data -├── scripts/ # helper bash scripts (lint, dev-startup, …) -│ └── quality-check.sh -├── docs/ # project vision & low-level docs -│ └── AsyncFlow-documentation/ -├── tests/ # unit & integration tests -│ ├── unit/ -│ └── integration/ -├── src/ # application code lives here -│ └── app/ -│ ├── config/ # Pydantic Settings + constants -│ ├── metrics/ # logic to compute/aggregate simulation KPIs -│ ├── resources/ # SimPy resource registry (CPU/RAM containers, etc.) -│ ├── runtime/ # simulation core -│ │ ├── rqs_state.py # RequestState & Hop -│ │ ├── simulation_runner.py # logic to initialize the whole simulation -│ │ └── actors/ # SimPy “actors”: Edge, Server, Client, RqsGenerator -│ ├── pybuilder/ # Pythonic way to build the simulation payload -│ ├── samplers/ # stochastic samplers (Gaussian-Poisson, etc.) -│ ├── schemas/ # Pydantic input/output models -├── poetry.lock -├── pyproject.toml -└── README.md -``` - -> Note: If your package name under `src/` is `asyncflow/` (instead of `app/`), the structure is identical—only the package folder name changes. - -### What each top-level directory in `src/app` does - -| Directory | Purpose | -| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **`config/`** | Centralised configuration layer. Contains Pydantic `BaseSettings` classes for reading environment variables and constants/enums used across the simulation engine. | -| **`metrics/`** | Post-processing and analytics. Aggregates raw simulation traces into KPIs such as latency percentiles, throughput, resource utilisation, and other performance metrics. | -| **`resources/`** | Runtime resource registry for simulated hardware components (e.g., SimPy `Container`s for CPU and RAM). Decouples resource management from actor behaviour. | -| **`runtime/`** | Core simulation engine. Orchestrates SimPy execution, maintains request state, and wires together simulation components. Includes: | -| | - **`rqs_state.py`** — Defines `RequestState` and `Hop` for tracking request lifecycle. | -| | - **`simulation_runner.py`** — Entry point for initialising and running simulations. | -| | - **`actors/`** — SimPy actor classes representing system components (`RqsGenerator`, `Client`, `Server`, `Edge`) and their behaviour. | -| **`pybuilder/`** | Pythonic builder to programmatically construct validated simulation payloads (alternative to YAML). | -| **`samplers/`** | Random-variable samplers for stochastic simulation. Supports Poisson, Normal, and mixed distributions for modelling inter-arrival times and service steps. | -| **`schemas/`** | Pydantic models for input/output validation and serialisation. Includes scenario definitions, topology graphs, simulation settings, and results payloads. | - ---- - -### Other Top-Level Directories - -| Directory | Purpose | -| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| **`example/`** | Ready-to-run simulation scenarios and example configurations. Includes `data/` with YAML definitions and scripts to demonstrate engine usage. | -| **`scripts/`** | Utility shell scripts for development workflow, linting, formatting, and local startup (`quality-check.sh`, etc.). | -| **`docs/`** | Project documentation. Contains both high-level vision documents and low-level technical references (`AsyncFlow-documentation/`). | -| **`tests/`** | Automated test suite, split into **unit** and **integration** tests to verify correctness of both individual components and end-to-end scenarios. | - ---- - -## 2. Branching Strategy: Git Flow - -To manage code development and releases in a structured manner, we use the **Git Flow** branching model—with an additional **refactor** branch type for non-feature refactoring work. - -### Git Flow Workflow Diagram - -```mermaid ---- -title: Git Flow (with refactor/*) ---- - -gitGraph - commit id: "Initial commit" - branch develop - checkout develop - commit id: "Setup Project" - - %% feature branch - branch feature/user-authentication - checkout feature/user-authentication - commit id: "feat: Add login form" - commit id: "feat: Add form validation" - checkout develop - merge feature/user-authentication - - %% refactor branch (no new features, code cleanup/improvements) - branch refactor/performance-cleanup - checkout refactor/performance-cleanup - commit id: "refactor: simplify hot path" - commit id: "refactor: remove dead code" - checkout develop - merge refactor/performance-cleanup - - %% release branch - branch release/v1.0.0 - checkout release/v1.0.0 - commit id: "fix: Pre-release bug fixes" tag: "v1.0.0" - checkout main - merge release/v1.0.0 - checkout develop - merge release/v1.0.0 - - %% hotfix branch - checkout main - branch hotfix/critical-login-bug - checkout hotfix/critical-login-bug - commit id: "fix: Critical production bug" tag: "v1.0.1" - checkout main - merge hotfix/critical-login-bug - checkout develop - merge hotfix/critical-login-bug -``` - ---- - -### Git Flow Explained - -This workflow is built upon two long-lived branches and several temporary, supporting branches. - -#### Main Branches - -1. **`main`** - **Purpose**: Production-ready, stable code. Every commit on `main` represents an official release. - **Rules**: Never commit directly to `main`. It only receives merges from `release/*` and `hotfix/*`. Each merge should be **tagged** (e.g., `v1.0.0`). - -1. **`develop`** - **Purpose**: The main integration branch for ongoing development. It contains all completed and tested changes planned for the next release. - **Rules**: Base for `feature/*` and `refactor/*` branches. Reflects the most up-to-date development state. - -#### Supporting Branches - -3. **`feature/*`** (e.g., `feature/user-authentication`) - **Purpose**: Develop a new, specific feature in isolation. - **Lifecycle**: - - 1. Branched off **`develop`**. - 1. When complete, open a **Pull Request (PR)** back into **`develop`**. - 3. Delete the branch after merge. - -3. **`refactor/*`** (e.g., `refactor/performance-cleanup`) **← new** - **Purpose**: Perform **non-functional code changes** (no new features), such as internal restructurings, performance optimisations, reducing technical debt, renaming, file moves, or dependency hygiene. - **Rules**: - - * Must **not** introduce user-visible features or breaking API/DB changes. - * Prefer commit prefix `refactor:`; avoid `feat:`. - * Keep changes scoped and well-described to simplify review. - **Lifecycle**: - - 1. Branched off **`develop`**. - 1. Open a **PR** back into **`develop`** (same review gates as features). - 3. Delete the branch after merge. - -5. **`release/*`** (e.g., `release/v1.1.0`) - **Purpose**: Prepare a production release—final bug fixes, docs, and last-minute tests. The feature set is frozen here. - **Lifecycle**: - - 1. Branched off **`develop`** when feature-complete. - 1. Merge into **`main`** (tag version) and back into **`develop`**. - 3. Delete after merges. - -6. **`hotfix/*`** (e.g., `hotfix/critical-login-bug`) - **Purpose**: Quickly patch a critical bug in production. - **Lifecycle**: - - 1. Branched off **`main`** (from a specific tag). - 1. Merge into **`main`** (tag a patch version, e.g., `v1.0.1`) **and** into **`develop`**. - 3. Delete after merges. - -**When to choose which branch?** - -* **New behavior / endpoints / DB migrations** → `feature/*` -* **Internal code improvements only** → `refactor/*` -* **Release prep** → `release/*` -* **Production emergency** → `hotfix/*` - ---- - -## 3. Continuous Integration / Continuous Delivery (CI/CD) Pipeline - -A robust CI/CD pipeline guarantees that every change is automatically validated, packaged, and—when appropriate—promoted to the next environment. Our pipeline is built with **GitHub Actions** and mirrors the branching model. - -We start with the CI part related to pushes and PRs in the backend service. - -### 3.1 CI for project-backend on `develop` - -#### 3.1.1 Goals - -* **Fast feedback** – linting, type-checking, and unit tests finish quickly for every Pull Request. -* **Confidence in integration** – migrations, integration tests, and Docker smoke-tests run on every push to `develop`. -* **Deployment safety** – only artifacts from a green pipeline can be released/deployed. -* **Parity with production** – the same multi-stage Dockerfile is built and probed in CI. - -#### 3.1.1 Pipeline Layers - -* **Quick Suite (PR to `develop`)** - *Runs in seconds; no external services or containers.* - - * Black, isort, Flake8 (or Ruff if adopted) - * mypy static type-checking - * Unit tests only (`pytest -m "not integration"`) - -* **Full Suite (push to `develop`)** - *Runs in a few minutes; includes real services and Docker.* - - * Full test suite, including `@pytest.mark.integration` tests - * Database migrations (PostgreSQL) against a disposable instance - * Redis available for tests if required - * Build multi-stage Docker image and run a quick smoke test - -### 3.1 CI for `feature/*` and `refactor/*` - -* **On PR to `develop`**: run the **Quick Suite** (lint, type-checking, unit tests). -* **Optional (recommended for large changes)**: allow a manual or scheduled **Full Suite** run for the branch to catch integration issues early. -* **On merge to `develop`**: the **Full Suite** runs (as described above). - -> `refactor/*` branches should maintain **zero behavior change**. If a refactor has the potential to alter behavior (e.g., performance-sensitive code), add targeted tests and consider a manual Full Suite run before merge. - -### 3.3 CI for `release/*` - -* Always run the **Full Suite**. -* Build and publish versioned artifacts/images to the registry with the release tag. -* Prepare release notes and changelog generation. - -### 3.3 CI for `hotfix/*` - -* Run the **Full Suite** against the hotfix branch. -* Tag the patch release on merge to `main` and propagate the merge back to `develop`. - ---- - -## 4. Quality Gates & Conventions - -* **Static Analysis**: mypy (no new type errors). -* **Style**: Black/Flake8/isort or Ruff; no lint violations. -* **Tests**: - - * Unit tests for new logic or refactor touch points. - * Integration tests for cross-layer behavior. -* **Commits**: Conventional commits (`feat:`, `fix:`, `refactor:`, `docs:`, `test:`, `chore:` …). -* **Code Review**: PRs must be reviewed and approved; refactors must include rationale in the PR description (what changed, why safe). -* **Documentation**: Update `README`, `docs/`, and API docs when applicable. - ---- - - -By following this workflow—now with the **refactor** branch type—you keep feature development cleanly separated from codebase improvements, reduce merge friction, and maintain a predictable, high-quality delivery pipeline. diff --git a/docs/fastsim_vision.md b/docs/fastsim_vision.md deleted file mode 100644 index bf5b88a..0000000 --- a/docs/fastsim_vision.md +++ /dev/null @@ -1,41 +0,0 @@ -## 1 Why AsyncFlow? - -FastAPI + Uvicorn gives Python teams a lightning-fast async stack, yet sizing it for production still means guess-work, costly cloud load-tests or late surprises. **AsyncFlow** fills that gap by becoming a **digital twin** of your actual service: - -* It **replays** your FastAPI + Uvicorn event-loop behavior in SimPy, generating exactly the same kinds of asynchronous steps (parsing, CPU work, I/O, LLM calls) that happen in real code. -* It **models** your infrastructure primitives—CPU cores (via a SimPy `Resource`), database pools, rate-limiters, even GPU inference quotas—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency. -* It **outputs** the very metrics you’d scrape in production (p50/p95/p99 latency, ready-queue lag, current & max concurrency, throughput, cost per LLM call), but entirely offline, in seconds. - -With AsyncFlow you can ask *“What happens if traffic doubles on Black Friday?”*, *“How many cores to keep p95 < 100 ms?”* or *“Is our LLM-driven endpoint ready for prime time?”*—and get quantitative answers **before** you deploy. - -**Outcome:** data-driven capacity planning, early performance tuning, and far fewer “surprises” once you hit production. - ---- - -## 2 Project Goals - -| # | Goal | Practical Outcome | -| - | ------------------------- | ------------------------------------------------------------------------ | -| 1 | **Pre-production sizing** | Know core-count, pool-size, replica-count to hit SLA. | -| 2 | **Scenario lab** | Explore traffic models, endpoint mixes, latency distributions, RTT, etc. | -| 3 | **Twin metrics** | Produce the same metrics you’ll scrape in prod (latency, queue, CPU). | -| 4 | **Rapid iteration** | One YAML/JSON config or REST call → full report. | -| 5 | **Educational value** | Visualise how GIL lag, queue length, concurrency react to load. | - ---- - -## 3 Who benefits & why (detailed) - -| Audience | Pain-point solved | AsyncFlow value | -| ------------------------------ | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| **Backend engineers** | Unsure if 4 vCPU container survives a marketing spike | Run *what-if* load, tweak CPU cores / pool size, get p95 & max-concurrency before merging. | -| **DevOps / SRE** | Guesswork in capacity planning; cost of over-provisioning | Simulate 1 → N replicas, autoscaler thresholds, DB-pool size; pick the cheapest config meeting SLA. | -| **ML / LLM product teams** | LLM inference cost & latency hard to predict | Model the LLM step with a price + latency distribution; estimate \$/req and GPU batch gains without real GPU. | -| **Educators / Trainers** | Students struggle to “see” event-loop internals | Visualise GIL ready-queue lag, CPU vs I/O steps, effect of blocking code—perfect for live demos and labs. | -| **Consultants / Architects** | Need a quick PoC of new designs for clients | Drop endpoint definitions in YAML and demo throughput / latency under projected load in minutes. | -| **Open-source community** | Lacks a lightweight Python simulator for ASGI workloads | Extensible codebase; easy to plug in new resources (rate-limit, cache) or traffic models (spike, uniform ramp). | -| **System-design interviewees** | Hard to quantify trade-offs in whiteboard interviews | Prototype real-time metrics—queue lengths, concurrency, latency distributions—to demonstrate in interviews how your design scales and where bottlenecks lie. | - ---- - -**Bottom-line:** AsyncFlow turns abstract architecture diagrams into concrete numbers—*before* spinning up expensive cloud environments—so you can build, validate and discuss your designs with full confidence. diff --git a/docs/pybuilder.md b/docs/guides/builder.md similarity index 52% rename from docs/pybuilder.md rename to docs/guides/builder.md index 19b7b24..e90fe22 100644 --- a/docs/pybuilder.md +++ b/docs/guides/builder.md @@ -1,17 +1,16 @@ -# AsyncFlow – Programmatic Input Guide (pybuilder) +# AsyncFlow – Programmatic Input Guide (builder) This guide shows how to **build the full simulation input in Python** using the -`AsyncFlow` builder (the “pybuilder”), with the same precision and validation -guarantees as the YAML flow. You’ll see **all components, valid values, units, -constraints, and how validation is enforced**. +`AsyncFlow` builder, with the same precision and validation guarantees as the YAML flow. +You’ll see **all components, valid values, units, constraints, and how validation is enforced**. Under the hood, the builder assembles a single `SimulationPayload`: ```python SimulationPayload( - rqs_input=RqsGeneratorInput(...), # traffic generator (workload) - topology_graph=TopologyGraph(...), # system architecture as a graph - sim_settings=SimulationSettings(...), # global settings and metrics + rqs_input=RqsGenerator(...), # traffic generator (workload) + topology_graph=TopologyGraph(...), # system architecture as a graph + sim_settings=SimulationSettings(...), # global settings and metrics ) ``` @@ -28,18 +27,15 @@ from __future__ import annotations import simpy -from asyncflow.pybuilder.input_builder import AsyncFlow -from asyncflow.runtime.simulation_runner import SimulationRunner -from asyncflow.schemas.full_simulation_input import SimulationPayload -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.endpoint import Endpoint -from asyncflow.schemas.system_topology.full_system_topology import ( - Client, Edge, Server, +# Public, user-facing API +from asyncflow import AsyncFlow, SimulationRunner +from asyncflow.components import ( + RqsGenerator, SimulationSettings, Endpoint, Client, Server, Edge ) +from asyncflow.schemas.payload import SimulationPayload # optional, for typing # 1) Workload -generator = RqsGeneratorInput( +generator = RqsGenerator( id="rqs-1", avg_active_users={"mean": 50, "distribution": "poisson"}, avg_request_per_minute_per_user={"mean": 30, "distribution": "poisson"}, @@ -50,11 +46,10 @@ generator = RqsGeneratorInput( client = Client(id="client-1") endpoint = Endpoint( endpoint_name="/hello", - probability=1.0, # per-endpoint weight on this server steps=[ - {"kind": "ram", "step_operation": {"necessary_ram": 32}}, - {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, - {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + {"kind": "ram", "step_operation": {"necessary_ram": 32}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, ], ) server = Server( @@ -103,8 +98,9 @@ payload: SimulationPayload = ( AsyncFlow() .add_generator(generator) .add_client(client) - .add_servers(server) - .add_edges(*edges) + .add_servers(server) # varargs; supports multiple + .add_edges(*edges) # varargs; supports multiple + # .add_load_balancer(lb) # optional .add_simulation_settings(settings) .build_payload() ) @@ -138,8 +134,7 @@ dictionary that Pydantic converts into an `RVConfig`: * `mean` is **required** and numeric; coerced to `float`. * If `distribution` is `"normal"` or `"log_normal"` and `variance` is absent, it defaults to **`variance = mean`**. -* For **edge latency** (see §3.3): **`mean > 0`** and, if provided, - **`variance ≥ 0`**. +* For **edge latency**: **`mean > 0`** and, if provided, **`variance ≥ 0`**. **Units** @@ -148,30 +143,30 @@ dictionary that Pydantic converts into an `RVConfig`: --- -## 2) Workload: `RqsGeneratorInput` +## 2) Workload: `RqsGenerator` ```python -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.components import RqsGenerator -generator = RqsGeneratorInput( +generator = RqsGenerator( id="rqs-1", avg_active_users={ "mean": 100, - "distribution": "poisson", # or "normal" + "distribution": "poisson", # or "normal" # "variance": , # optional; auto=mean if "normal" }, avg_request_per_minute_per_user={ "mean": 20, - "distribution": "poisson", # must be poisson in current samplers + "distribution": "poisson", # must be poisson in current samplers }, - user_sampling_window=60, # [1 .. 120] seconds + user_sampling_window=60, # [1 .. 120] seconds ) ``` **Semantics** -* `avg_active_users`: active users as a random variable (Poisson or Normal). -* `avg_request_per_minute_per_user`: per-user RPM (Poisson). +* `avg_active_users`: active users as a random variable (**Poisson** or **Normal**). +* `avg_request_per_minute_per_user`: per-user RPM (**Poisson** only). * `user_sampling_window`: re-sample active users every N seconds. --- @@ -184,7 +179,7 @@ LB) and edges (network links). ### 3.1 Client ```python -from asyncflow.schemas.system_topology.full_system_topology import Client +from asyncflow.components import Client client = Client(id="client-1") # type is fixed to 'client' ``` @@ -192,25 +187,23 @@ client = Client(id="client-1") # type is fixed to 'client' ### 3.2 Server & Endpoints ```python -from asyncflow.schemas.system_topology.endpoint import Endpoint -from asyncflow.schemas.system_topology.full_system_topology import Server +from asyncflow.components import Endpoint, Server endpoint = Endpoint( - endpoint_name="/api", # normalized to lowercase internally - probability=1.0, # endpoint selection weight within the server + endpoint_name="/api", # normalized to lowercase internally steps=[ - {"kind": "ram", "step_operation": {"necessary_ram": 64}}, + {"kind": "ram", "step_operation": {"necessary_ram": 64}}, {"kind": "cpu_bound_operation", "step_operation": {"cpu_time": 0.004}}, - {"kind": "io_db", "step_operation": {"io_waiting_time": 0.012}}, + {"kind": "io_db", "step_operation": {"io_waiting_time": 0.012}}, ], ) server = Server( - id="srv-1", # type fixed to 'server' + id="srv-1", # type fixed to 'server' server_resources={ "cpu_cores": 2, # int ≥ 1 "ram_mb": 2048, # int ≥ 256 - "db_connection_pool": None, # optional future-use + "db_connection_pool": None, # optional }, endpoints=[endpoint], ) @@ -237,13 +230,14 @@ server = Server( **Runtime semantics (high level)** * RAM is reserved before CPU, then released at the end. -* CPU tokens acquired lazily for consecutive CPU steps; released on I/O. +* CPU tokens are acquired for CPU-bound segments; released when switching to I/O. * I/O waits **do not** hold a CPU core. ### 3.3 Load Balancer (optional) ```python -from asyncflow.schemas.system_topology.full_system_topology import LoadBalancer +from asyncflow.schemas.topology.nodes import LoadBalancer # internal type +# (Use only if you build the graph manually. AsyncFlow builder hides the graph.) lb = LoadBalancer( id="lb-1", @@ -260,16 +254,15 @@ lb = LoadBalancer( ### 3.4 Edges ```python -from asyncflow.schemas.system_topology.full_system_topology import Edge +from asyncflow.components import Edge edge = Edge( id="client-to-srv1", source="client-1", # may be external only for sources target="srv-1", # MUST be a declared node latency={"mean": 0.003, "distribution": "exponential"}, - probability=1.0, # optional [0..1] - edge_type="network_connection", # current default/only - dropout_rate=0.01, # optional [0..1] + # edge_type defaults to "network_connection" + # dropout_rate defaults to 0.01 (0.0 .. 1.0) ) ``` @@ -278,17 +271,14 @@ edge = Edge( * `source`: can be an **external** ID for entry points (e.g., `"rqs-1"`). * `target`: **must** be a declared node (`client`, `server`, `load_balancer`). * `latency`: random variable; **`mean > 0`**, `variance ≥ 0` (if provided). -* `probability`: used when a node has multiple outgoing edges (fan-out). - If your code enforces “no fan-out except LB”, do **not** create multiple - outgoing edges from nodes other than the LB. -* `dropout_rate`: per-request/packet drop probability on this link. +* **Fan-out rule**: the model enforces **“no fan-out except LB”**—i.e., only the load balancer may have multiple outgoing edges. --- ## 4) Global Settings: `SimulationSettings` ```python -from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.components import SimulationSettings settings = SimulationSettings( total_simulation_time=600, # seconds, default 3600, min 5 @@ -316,16 +306,16 @@ settings = SimulationSettings( ## 5) Building the Payload with `AsyncFlow` ```python -from asyncflow.pybuilder.input_builder import AsyncFlow -from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow import AsyncFlow +from asyncflow.schemas.payload import SimulationPayload # optional typing flow = ( AsyncFlow() .add_generator(generator) .add_client(client) - .add_servers(server) # varargs; supports multiple - .add_edges(*edges) # varargs; supports multiple - # .add_load_balancer(lb) # optional + .add_servers(server) # varargs + .add_edges(*edges) # varargs + # .add_load_balancer(lb) .add_simulation_settings(settings) ) @@ -337,10 +327,9 @@ payload: SimulationPayload = flow.build_payload() 1. **Presence**: generator, client, ≥1 server, ≥1 edge, settings. 2. **Unique IDs**: servers and edges have unique IDs. 3. **Node types**: fixed enums: `client`, `server`, `load_balancer`. -4. **Edge integrity**: every target is a declared node; external IDs allowed only as sources; no self-loops (`source != target`). -5. **Load balancer sanity**: `server_covered ⊆ declared_servers` and there is an edge from the LB to **each** covered server. -6. **(Optional in your codebase)** “No fan-out except LB” validator: multiple - outgoing edges only allowed for the LB. +4. **Edge integrity**: every target is a declared node; external IDs allowed only as sources; no self-loops. +5. **Load balancer sanity**: `server_covered ⊆ declared_servers` **and** there is an edge from the LB to **each** covered server. +6. **No fan-out except LB**: only the LB may have multiple outgoing edges. If any rule is violated, a **descriptive `ValueError`** pinpoints the problem. @@ -350,7 +339,7 @@ If any rule is violated, a **descriptive `ValueError`** pinpoints the problem. ```python import simpy -from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow import SimulationRunner env = simpy.Environment() runner = SimulationRunner(env=env, simulation_input=payload) @@ -372,64 +361,15 @@ results.plot_throughput(axes[0, 1]) results.plot_server_queues(axes[1, 0]) results.plot_ram_usage(axes[1, 1]) fig.tight_layout() -fig.savefig("single_server_pybuilder.png") +fig.savefig("single_server_builder.png") ``` --- -## 7) Builder vs YAML: Field Mapping - -| YAML path | Builder (Python) | -| --------------------------------------------- | ---------------------------------------------------------------- | -| `rqs_input.id` | `RqsGeneratorInput(id=...)` | -| `rqs_input.avg_active_users.*` | `RqsGeneratorInput(avg_active_users={...})` | -| `rqs_input.avg_request_per_minute_per_user.*` | `RqsGeneratorInput(avg_request_per_minute_per_user={...})` | -| `rqs_input.user_sampling_window` | `RqsGeneratorInput(user_sampling_window=...)` | -| `topology_graph.nodes.client.id` | `Client(id=...)` | -| `topology_graph.nodes.servers[*]` | `Server(id=..., server_resources={...}, endpoints=[...])` | -| `endpoint.endpoint_name` | `Endpoint(endpoint_name=...)` | -| `endpoint.steps[*]` | `Endpoint(steps=[{"kind": "...","step_operation": {...}}, ...])` | -| `topology_graph.nodes.load_balancer.*` | `LoadBalancer(id=..., algorithms=..., server_covered={...})` | -| `topology_graph.edges[*]` | `Edge(id=..., source=..., target=..., latency={...}, ...)` | -| `sim_settings.*` | `SimulationSettings(...)` | +## 7) Enums, Units & Conventions (Cheat Sheet) ---- - -## 8) Common Pitfalls & How to Avoid Them - -* **Mismatched step operations** - A CPU step must use `cpu_time`; an I/O step must use `io_waiting_time`; a RAM - step must use `necessary_ram`. Exactly **one** key per step. - -* **Edge target must be a declared node** - `source` can be external (e.g., `"rqs-1"`), but **no external ID** may ever - appear as a `target`. - -* **Load balancer coverage without edges** - If the LB covers `[srv-1, srv-2]`, you **must** add edges `lb→srv-1` and - `lb→srv-2`. - -* **Latency RV rules on edges** - `mean` must be **> 0**; if `variance` is present, it must be **≥ 0**. - -* **Fan-out rules** - If your codebase enforces “no fan-out except LB”, do not create multiple - outgoing edges from non-LB nodes. If you do allow it, set `probability` - weights so outgoing probabilities per source sum to \~1.0 (or ensure a single - edge per source). - -* **Sampling too coarse** - Large `sample_period_s` may miss short spikes. Lower it to capture bursts - (at the cost of larger time series). - ---- - -## 9) Enums, Units & Conventions (Cheat Sheet) - -* **Distributions**: `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"`, - `"uniform"` -* **Node types**: fixed internally to: `generator`, `server`, `client`, - `load_balancer` +* **Distributions**: `"poisson"`, `"normal"`, `"log_normal"`, `"exponential"`, `"uniform"` +* **Node types**: fixed internally to `generator`, `server`, `client`, `load_balancer` * **Edge type**: `network_connection` * **LB algorithms**: `"round_robin"`, `"least_connection"` * **Step kinds** @@ -437,22 +377,14 @@ fig.savefig("single_server_pybuilder.png") RAM: `"ram"` I/O: `"io_task_spawn"`, `"io_llm"`, `"io_wait"`, `"io_db"`, `"io_cache"` * **Step operation keys**: `cpu_time`, `io_waiting_time`, `necessary_ram` -* **Sampled metrics**: `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, - `edge_concurrent_connection` +* **Sampled metrics**: `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, `edge_concurrent_connection` * **Event metrics**: `rqs_clock` (and `llm_cost` reserved for future use) **Units & ranges** -* **Time**: seconds (`cpu_time`, `io_waiting_time`, edge latency means/variance, - `total_simulation_time`, `sample_period_s`, `user_sampling_window`) +* **Time**: seconds (`cpu_time`, `io_waiting_time`, latencies, `total_simulation_time`, `sample_period_s`, `user_sampling_window`) * **RAM**: megabytes (`ram_mb`, `necessary_ram`) * **Rates**: requests/minute (`avg_request_per_minute_per_user.mean`) -* **Probabilities**: `[0.0, 1.0]` (`probability`, `dropout_rate`) -* **Bounds**: `total_simulation_time ≥ 5`, `sample_period_s ∈ [0.001, 0.1]`, - `cpu_cores ≥ 1`, `ram_mb ≥ 256`, numeric step values > 0 - ---- +* **Probabilities**: `[0.0, 1.0]` (`dropout_rate`) +* **Bounds**: `total_simulation_time ≥ 5`, `sample_period_s ∈ [0.001, 0.1]`, `cpu_cores ≥ 1`, `ram_mb ≥ 256`, numeric step values > 0 -With these patterns, you can build any topology that the YAML supports—**fully -programmatically**, with the same strong validation and clear errors on invalid -configurations. diff --git a/docs/yaml_builder.md b/docs/guides/yaml-builder.md similarity index 85% rename from docs/yaml_builder.md rename to docs/guides/yaml-builder.md index f3d22e3..113b879 100644 --- a/docs/yaml_builder.md +++ b/docs/guides/yaml-builder.md @@ -64,14 +64,12 @@ rqs_input: ### Semantics * **`avg_active_users`**: expected concurrent users (a random variable). - - * Allowed distributions: **Poisson** or **Normal**. + Allowed distributions: **Poisson** or **Normal**. * **`avg_request_per_minute_per_user`**: per-user request rate (RPM). - - * Must be **Poisson**.\* + Must be **Poisson**.\* * **`user_sampling_window`**: every N seconds the generator re-samples the active user count. -\* This reflects current sampler support (Poisson–Poisson and Gaussian–Poisson). +\* Current joint-sampler support covers Poisson–Poisson and Gaussian–Poisson. --- @@ -103,7 +101,6 @@ topology_graph: source: target: # must be a declared node latency: { mean: , distribution: , variance: } - probability: <0..1> # default 1.0 edge_type: network_connection # (enum; current default/only) dropout_rate: <0..1> # default 0.01 ``` @@ -137,7 +134,7 @@ client: * `cpu_cores`: number of worker “core tokens” available for CPU-bound step execution. * `ram_mb`: total available RAM (MB) tracked as a reservoir; steps reserve then release. -* `db_connection_pool`: optional capacity bound for DB-like steps (future-use; declared here for forward compatibility). +* `db_connection_pool`: optional capacity bound for DB-like steps (future-use). #### Load Balancer (optional) @@ -171,11 +168,11 @@ Each step must declare **exactly one** operation (`step_operation`) whose key ma **I/O-bound** (all use `io_waiting_time` as the operation key) -* `io_task_spawn` (spawns a background task, returns immediately) -* `io_llm` (LLM inference call) -* `io_wait` (generic wait, non-blocking) -* `io_db` (DB roundtrip) -* `io_cache` (cache access) +* `io_task_spawn` (spawns a background task, returns immediately) +* `io_llm` (LLM inference call) +* `io_wait` (generic wait, non-blocking) +* `io_db` (DB roundtrip) +* `io_cache` (cache access) #### Operation keys (enum `StepOperation`) @@ -223,7 +220,6 @@ endpoints: mean: 0.003 distribution: exponential # variance optional; if normal/log_normal and missing → set to mean - probability: 1.0 # optional [0..1] edge_type: network_connection dropout_rate: 0.01 # optional [0..1] ``` @@ -233,8 +229,7 @@ endpoints: * **`source`** can be an external entry point (e.g., `rqs-1`) for inbound edges. * **`target`** must always reference a declared node: client, server, or LB. * **`latency`** is a random variable; **`mean > 0`**, **`variance ≥ 0`** (if provided). -* **`probability`** is used when multiple outgoing edges exist from a node. -* **`dropout_rate`** models probabilistic packet/request loss on the link. +* **Fan-out rule**: only the **load balancer** may have multiple outgoing edges. --- @@ -270,21 +265,29 @@ AsyncFlow validates the entire payload. Key checks include: * All server IDs are unique. * Edge IDs are unique. + 2. **Node Types** * `type` fields on nodes are fixed to: `client`, `server`, `load_balancer`. + 3. **Edge referential integrity** * Every **target** is a declared node ID. * **External IDs** are allowed **only** as **sources**. If an ID appears as an external source, it must **never** appear as a target anywhere. + 4. **No self-loops** * `source != target` for every edge. + 5. **Load balancer sanity** * `server_covered` is a subset of declared servers. * There is an **edge from the LB to every covered server**. +6. **No fan-out except LB** + + * Only the load balancer may have multiple outgoing edges in the declared node set. + If any rule is violated, the simulator raises a descriptive error. --- @@ -384,9 +387,9 @@ topology_graph: latency: { mean: 0.002, distribution: exponential } } - { id: lb-srv1, source: lb-1, target: srv-1, - latency: { mean: 0.002, distribution: exponential }, probability: 0.5 } + latency: { mean: 0.002, distribution: exponential } } - { id: lb-srv2, source: lb-1, target: srv-2, - latency: { mean: 0.002, distribution: exponential }, probability: 0.5 } + latency: { mean: 0.002, distribution: exponential } } - { id: srv1-client, source: srv-1, target: client-1, latency: { mean: 0.003, distribution: exponential } } @@ -400,26 +403,7 @@ sim_settings: enabled_event_metrics: [ rqs_clock ] ``` -## 7) Common Pitfalls & How to Avoid Them - -* **Mismatched step operations** - A CPU step must use `cpu_time`; an I/O step must use `io_waiting_time`; a RAM step must use `necessary_ram`. The validator enforces **exactly one** key. - -* **Edge targets must be declared nodes** - `source` can be external (e.g., `rqs-1`), but **no external ID** may ever appear as a **target**. - -* **Load balancer coverage without edges** - If the LB declares `server_covered: [srv-1, srv-2]`, you must also add edges `lb→srv-1` and `lb→srv-2`. - -* **Latency RV rules on edges** - For edge latency, `mean` must be **> 0**; if `variance` is present, it must be **≥ 0**. - -* **Sampling too coarse** - If `sample_period_s` is large, short spikes in queues may be missed. Lower it (e.g., `0.005`) to capture fine-grained bursts—at the cost of larger time-series. - ---- - -## 8) Quick Reference (Enums) +## 7) Quick Reference (Enums) * **Distributions**: `poisson`, `normal`, `log_normal`, `exponential`, `uniform` * **Node types**: `generator`, `server`, `client`, `load_balancer` (fixed by model) @@ -435,14 +419,13 @@ sim_settings: --- -## 9) Units & Conventions +## 8) Units & Conventions * **Time**: seconds (`cpu_time`, `io_waiting_time`, latencies, `total_simulation_time`, `sample_period_s`, `user_sampling_window`) * **RAM**: megabytes (`ram_mb`, `necessary_ram`) * **Rates**: requests/minute (`avg_request_per_minute_per_user.mean`) -* **Probabilities**: `[0.0, 1.0]` (`probability`, `dropout_rate`) +* **Probabilities**: `[0.0, 1.0]` (`dropout_rate`) * **IDs**: strings; must be **unique** per category (servers, edges, LB). --- -If you stick to these rules and examples, your YAML will parse cleanly and the simulation will run with a self-consistent, strongly-validated model. diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..651a0ee --- /dev/null +++ b/docs/index.md @@ -0,0 +1,57 @@ +Here’s an updated, list-style `index.md` (in English) **without any Tutorials section** and with a clear pointer to the math details in the workload samplers. + +--- + +# AsyncFlow Documentation + +AsyncFlow is a discrete-event simulator for Python async backends (FastAPI/Uvicorn–style). It builds a **digital twin** of your service—traffic, topology, and resources—so you can measure latency, throughput, queueing, RAM, and more **before** you deploy. + +> ⚠️ The package README with `pip install` & a Quickstart will land after the first PyPI release. + +--- + + +## Public API (stable surface) + +* **[High-Level API](api/high-level.md)** — The two entry points you’ll use most: `AsyncFlow` (builder) and `SimulationRunner` (orchestrator). +* **[Components](api/components.md)** — Public Pydantic models for topology: `Client`, `Server`, `Endpoint`, `Edge`, `LoadBalancer`, `ServerResources`. +* **[Workload](api/workload.md)** — Traffic inputs: `RqsGenerator` and `RVConfig` (random variables). +* **[Settings](api/settings.md)** — Global controls: `SimulationSettings` (duration, sampling cadence, metrics). +* **[Enums](api/enums.md)** — Optional importable enums: distributions, step kinds/ops, metric names, node/edge types, LB algorithms. + +--- + +## How-to Guides + +* **[Builder Guide](guides/builder.md)** — Programmatically assemble a `SimulationPayload` in Python with validation and examples. +* **[YAML Input Guide](guides/yaml-builder.md)** — Author scenarios in YAML: exact schema, units, constraints, runnable samples. + +--- + +## Internals (design & rationale) + +> Prefer formal underpinnings? The **Workload Samplers** section includes mathematical details (compound Poisson–Poisson and Normal–Poisson processes, inverse-CDF gaps, truncated Gaussians). + +* **[Simulation Input (contract)](internals/simulation-input.md)** — The complete `SimulationPayload` schema and all validation guarantees (graph integrity, step coherence, etc.). +* **[Simulation Runner](internals/simulation-runner.md)** — Orchestrator design; build → wire → start → run flow; sequence diagrams; extensibility hooks. +* **[Runtime & Resources](internals/runtime-and-resources.md)** — How CPU/RAM/DB are modeled with SimPy containers; decoupling of runtime logic and resources. +* **Metrics** + + * **[Time-Series Architecture](internals/metrics/time-series-architecture.md)** — Registry → runtime state → collector pipeline; why the `if key in …` guard keeps extensibility with zero hot-path cost. +* **[Workload Samplers (math)](internals/workload-samplers.md)** — Formalization of traffic generators: windowed user resampling, rate construction $\Lambda = U \cdot \text{RPM}/60$, exponential inter-arrival via inverse-CDF, latency RV constraints. + +--- + +## Useful mental model + +Every run boils down to this validated input: + +```python +SimulationPayload( + rqs_input=RqsGenerator(...), # workload + topology_graph=TopologyGraph(...), # nodes & edges + sim_settings=SimulationSettings(...), +) +``` + +Build it (via **Builder** or **YAML**) and hand it to `SimulationRunner` to execute and analyze. diff --git a/docs/fastsim-docs/metrics_to_measure.md b/docs/internals/metrics/overview.md similarity index 100% rename from docs/fastsim-docs/metrics_to_measure.md rename to docs/internals/metrics/overview.md diff --git a/docs/fastsim-docs/time_series_metric_architecture.md b/docs/internals/metrics/time-series-architecture.md similarity index 100% rename from docs/fastsim-docs/time_series_metric_architecture.md rename to docs/internals/metrics/time-series-architecture.md diff --git a/docs/fastsim-docs/runtime_and_resources.md b/docs/internals/runtime-and-resources.md similarity index 100% rename from docs/fastsim-docs/runtime_and_resources.md rename to docs/internals/runtime-and-resources.md diff --git a/docs/fastsim-docs/simulation_input.md b/docs/internals/simulation-input.md similarity index 98% rename from docs/fastsim-docs/simulation_input.md rename to docs/internals/simulation-input.md index d5d860c..f6bbbcd 100644 --- a/docs/fastsim-docs/simulation_input.md +++ b/docs/internals/simulation-input.md @@ -7,7 +7,7 @@ The entry point is: ```python class SimulationPayload(BaseModel): """Full input structure to perform a simulation""" - rqs_input: RqsGeneratorInput + rqs_input: RqsGenerator topology_graph: TopologyGraph sim_settings: SimulationSettings ``` @@ -43,12 +43,12 @@ Everything the engine needs is captured by these three components: --- -## 1) Workload: `RqsGeneratorInput` +## 1) Workload: `RqsGenerator` **Purpose:** Defines the stochastic traffic generator that produces request arrivals. ```python -class RqsGeneratorInput(BaseModel): +class RqsGenerator(BaseModel): id: str type: SystemNodes = SystemNodes.GENERATOR avg_active_users: RVConfig @@ -279,7 +279,7 @@ class SimulationSettings(BaseModel): ## 5) Validation Checklist (What is guaranteed if the payload parses) -### Workload (`RqsGeneratorInput`, `RVConfig`) +### Workload (`RqsGenerator`, `RVConfig`) * `mean` is numeric (`int|float`) and coerced to `float`. * If `distribution ∈ {NORMAL, LOG_NORMAL}` and `variance is None` → `variance := mean`. diff --git a/docs/fastsim-docs/simulation_runner.md b/docs/internals/simulation-runner.md similarity index 100% rename from docs/fastsim-docs/simulation_runner.md rename to docs/internals/simulation-runner.md diff --git a/docs/fastsim-docs/requests_generator.md b/docs/internals/workload-samplers.md similarity index 99% rename from docs/fastsim-docs/requests_generator.md rename to docs/internals/workload-samplers.md index eab8080..eb40121 100644 --- a/docs/fastsim-docs/requests_generator.md +++ b/docs/internals/workload-samplers.md @@ -104,7 +104,7 @@ class RVConfig(BaseModel): return model -class RqsGeneratorInput(BaseModel): +class RqsGenerator(BaseModel): """Define the expected variables for the simulation""" id: str diff --git a/docs/why-asyncflow.md b/docs/why-asyncflow.md new file mode 100644 index 0000000..d50d401 --- /dev/null +++ b/docs/why-asyncflow.md @@ -0,0 +1,93 @@ +# Why AsyncFlow + +> **TL;DR**: AsyncFlow is a *digital twin* of your FastAPI/Uvicorn service. It simulates traffic, async steps, and resource limits in seconds—so you can size CPU/pools/replicas and hit your latency SLOs **before** touching the cloud. + +## What it is + +* **Event-loop faithful**: Replays FastAPI-style async behavior in SimPy (parsing, CPU-bound work, I/O waits, LLM calls). +* **Resource-aware**: Models CPU cores (tokens), RAM, DB pools, and routing so you see queueing, contention, and scheduling delays. +* **Prod-style metrics**: Emits p50/p95/p99 latency, throughput, ready-queue lag, concurrency per edge/server—even estimated LLM cost. + +## What you get + +* **Numbers you can plan with**: p95, max concurrency, queue lengths, RAM usage, RPS over time. +* **Rapid “what-if” loops**: Double traffic, change cores/pools, add a replica—see the impact immediately. +* **Cheap, offline iteration**: Results in seconds, no clusters, no load-test bills. + +## 10-second example + +**Minimal scenario (YAML)** + +```yaml +# examples/data/minimal.yml +rqs_input: + id: rqs-1 + avg_active_users: { mean: 50 } # Poisson by default + avg_request_per_minute_per_user: { mean: 20 } # must be Poisson + user_sampling_window: 60 + +topology_graph: + nodes: + client: { id: client-1 } + servers: + - id: srv-1 + server_resources: { cpu_cores: 2, ram_mb: 2048 } + endpoints: + - endpoint_name: /predict + steps: + - kind: initial_parsing + step_operation: { cpu_time: 0.002 } + - kind: io_wait + step_operation: { io_waiting_time: 0.010 } + edges: + - { id: gen-client, source: rqs-1, target: client-1, + latency: { mean: 0.003, distribution: exponential } } + - { id: client-srv, source: client-1, target: srv-1, + latency: { mean: 0.003, distribution: exponential } } + - { id: srv-client, source: srv-1, target: client-1, + latency: { mean: 0.003, distribution: exponential } } + +sim_settings: + total_simulation_time: 300 + sample_period_s: 0.01 + enabled_sample_metrics: [ ready_queue_len, ram_in_use, edge_concurrent_connection ] + enabled_event_metrics: [ rqs_clock ] +``` + +**Run it (Python)** + +```python +from pathlib import Path +import simpy +from asyncflow.runtime.simulation_runner import SimulationRunner + +env = simpy.Environment() +runner = SimulationRunner.from_yaml(env=env, yaml_path=Path("examples/data/minimal.yml")) +results = runner.run() + +print(results.get_latency_stats()) # p50/p95/p99, etc. +print(results.get_throughput_series()) # (timestamps, rps) +``` + +## The mental model + +```mermaid +flowchart LR + RQS[generator] --> C[client] + C --> S[srv-1] + S --> C +``` + +* Each arrow is a **network edge** with its own latency RV. +* Server endpoints are **linear step chains**: CPU → RAM → I/O, etc. +* CPU/DB/RAM are **capacity-limited resources** → queues form under load. + +## Non-goals (by design) + +* Not a replacement for **production** load tests or packet-level network simulators. +* Not a micro-profiler; it models service times and queues, not byte-level protocol details. +* Not an auto-tuner—**you** iterate quickly with data to 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asyncflow.runtime.simulation_runner import SimulationRunner + +__all__ = ["AsyncFlow", "SimulationRunner"] diff --git a/src/asyncflow/pybuilder/input_builder.py b/src/asyncflow/builder/asyncflow_builder.py similarity index 87% rename from src/asyncflow/pybuilder/input_builder.py rename to src/asyncflow/builder/asyncflow_builder.py index 7d3c26f..f6d2cea 100644 --- a/src/asyncflow/pybuilder/input_builder.py +++ b/src/asyncflow/builder/asyncflow_builder.py @@ -4,17 +4,17 @@ from typing import Self -from asyncflow.schemas.full_simulation_input import SimulationPayload -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( Client, - Edge, LoadBalancer, Server, - TopologyGraph, TopologyNodes, ) +from asyncflow.schemas.workload.rqs_generator import RqsGenerator class AsyncFlow: @@ -22,17 +22,17 @@ class AsyncFlow: def __init__(self) -> None: """Instance attributes necessary to define the simulation payload""" - self._generator: RqsGeneratorInput | None = None + self._generator: RqsGenerator | None = None self._client: Client | None = None self._servers: list[Server] | None = None self._edges: list[Edge] | None = None self._sim_settings: SimulationSettings | None = None self._load_balancer: LoadBalancer | None = None - def add_generator(self, rqs_generator: RqsGeneratorInput) -> Self: + def add_generator(self, rqs_generator: RqsGenerator) -> Self: """Method to instantiate the generator""" - if not isinstance(rqs_generator, RqsGeneratorInput): - msg = "You must add a RqsGeneratorInput instance" + if not isinstance(rqs_generator, RqsGenerator): + msg = "You must add a RqsGenerator instance" raise TypeError(msg) self._generator = rqs_generator return self diff --git a/src/asyncflow/components/__init__.py b/src/asyncflow/components/__init__.py new file mode 100644 index 0000000..774a77f --- /dev/null +++ b/src/asyncflow/components/__init__.py @@ -0,0 +1,15 @@ +"""Public components: re-exports Pydantic leaf schemas (topology).""" +from __future__ import annotations + +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.endpoint import Endpoint +from asyncflow.schemas.topology.nodes import ( + Client, + LoadBalancer, + Server, + ServerResources, +) + +__all__ = ["Client", "Edge", "Endpoint", "LoadBalancer", "Server", "ServerResources"] + + diff --git a/src/asyncflow/config/__init__.py b/src/asyncflow/config/__init__.py deleted file mode 100644 index 255bf0d..0000000 --- a/src/asyncflow/config/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Configuration modules and settings.""" diff --git a/src/asyncflow/enums/__init__.py b/src/asyncflow/enums/__init__.py new file mode 100644 index 0000000..a07a18f --- /dev/null +++ b/src/asyncflow/enums/__init__.py @@ -0,0 +1,23 @@ +"""Public enums used in scenario definitions.""" + +from asyncflow.config.constants import ( + Distribution, + EndpointStepCPU, + EndpointStepIO, + EndpointStepRAM, + EventMetricName, + LbAlgorithmsName, + SampledMetricName, + StepOperation, +) + +__all__ = [ + "Distribution", + "EndpointStepCPU", + "EndpointStepIO", + "EndpointStepRAM", + "EventMetricName", + "LbAlgorithmsName", + "SampledMetricName", + "StepOperation", +] diff --git a/src/asyncflow/metrics/analyzer.py b/src/asyncflow/metrics/analyzer.py index 4db2ee1..35b43bb 100644 --- a/src/asyncflow/metrics/analyzer.py +++ b/src/asyncflow/metrics/analyzer.py @@ -26,7 +26,7 @@ from asyncflow.runtime.actors.client import ClientRuntime from asyncflow.runtime.actors.edge import EdgeRuntime from asyncflow.runtime.actors.server import ServerRuntime - from asyncflow.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.settings.simulation import SimulationSettings class ResultsAnalyzer: diff --git a/src/asyncflow/metrics/collector.py b/src/asyncflow/metrics/collector.py index 97421b7..38c2f0d 100644 --- a/src/asyncflow/metrics/collector.py +++ b/src/asyncflow/metrics/collector.py @@ -7,7 +7,7 @@ from asyncflow.config.constants import SampledMetricName from asyncflow.runtime.actors.edge import EdgeRuntime from asyncflow.runtime.actors.server import ServerRuntime -from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.settings.simulation import SimulationSettings # The idea for this class is to gather list of runtime objects that # are defined in the central class to build the simulation, in this diff --git a/src/asyncflow/resources/__init__.py b/src/asyncflow/resources/__init__.py deleted file mode 100644 index 69884c1..0000000 --- a/src/asyncflow/resources/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""python package for resource registry""" diff --git a/src/asyncflow/resources/registry.py b/src/asyncflow/resources/registry.py index 6e1e3be..26576ba 100644 --- a/src/asyncflow/resources/registry.py +++ b/src/asyncflow/resources/registry.py @@ -10,7 +10,7 @@ import simpy from asyncflow.resources.server_containers import ServerContainers, build_containers -from asyncflow.schemas.system_topology.full_system_topology import TopologyGraph +from asyncflow.schemas.topology.graph import TopologyGraph class ResourcesRuntime: diff --git a/src/asyncflow/resources/server_containers.py b/src/asyncflow/resources/server_containers.py index ca054c2..1401247 100644 --- a/src/asyncflow/resources/server_containers.py +++ b/src/asyncflow/resources/server_containers.py @@ -12,9 +12,7 @@ import simpy from asyncflow.config.constants import ServerResourceName -from asyncflow.schemas.system_topology.full_system_topology import ( - ServerResources, -) +from asyncflow.schemas.topology.nodes import ServerResources # ============================================================== # DICT FOR THE REGISTRY TO INITIALIZE RESOURCES FOR EACH SERVER diff --git a/src/asyncflow/runtime/__init__.py b/src/asyncflow/runtime/__init__.py deleted file mode 100644 index fdc562a..0000000 --- a/src/asyncflow/runtime/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""module for the runtime folder""" diff --git a/src/asyncflow/runtime/actors/client.py b/src/asyncflow/runtime/actors/client.py index 58ba7e9..6c752f1 100644 --- a/src/asyncflow/runtime/actors/client.py +++ b/src/asyncflow/runtime/actors/client.py @@ -8,7 +8,7 @@ from asyncflow.config.constants import SystemNodes from asyncflow.metrics.client import RqsClock from asyncflow.runtime.actors.edge import EdgeRuntime -from asyncflow.schemas.system_topology.full_system_topology import Client +from asyncflow.schemas.topology.nodes import Client if TYPE_CHECKING: from asyncflow.runtime.rqs_state import RequestState diff --git a/src/asyncflow/runtime/actors/edge.py b/src/asyncflow/runtime/actors/edge.py index c2c5328..ee2131d 100644 --- a/src/asyncflow/runtime/actors/edge.py +++ b/src/asyncflow/runtime/actors/edge.py @@ -16,11 +16,11 @@ from asyncflow.metrics.edge import build_edge_metrics from asyncflow.runtime.rqs_state import RequestState from asyncflow.samplers.common_helpers import general_sampler -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.full_system_topology import Edge +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge if TYPE_CHECKING: - from asyncflow.schemas.random_variables_config import RVConfig + from asyncflow.schemas.common.random_variables import RVConfig diff --git a/src/asyncflow/runtime/actors/load_balancer.py b/src/asyncflow/runtime/actors/load_balancer.py index fac3f66..498fb18 100644 --- a/src/asyncflow/runtime/actors/load_balancer.py +++ b/src/asyncflow/runtime/actors/load_balancer.py @@ -7,11 +7,11 @@ from asyncflow.config.constants import LbAlgorithmsName, SystemNodes from asyncflow.runtime.actors.edge import EdgeRuntime -from asyncflow.runtime.actors.helpers.lb_algorithms import ( +from asyncflow.runtime.actors.routing.lb_algorithms import ( least_connections, round_robin, ) -from asyncflow.schemas.system_topology.full_system_topology import LoadBalancer +from asyncflow.schemas.topology.nodes import LoadBalancer if TYPE_CHECKING: from asyncflow.runtime.rqs_state import RequestState diff --git a/src/asyncflow/runtime/actors/helpers/lb_algorithms.py b/src/asyncflow/runtime/actors/routing/lb_algorithms.py similarity index 100% rename from src/asyncflow/runtime/actors/helpers/lb_algorithms.py rename to src/asyncflow/runtime/actors/routing/lb_algorithms.py diff --git a/src/asyncflow/runtime/actors/rqs_generator.py b/src/asyncflow/runtime/actors/rqs_generator.py index 6c983df..1b67213 100644 --- a/src/asyncflow/runtime/actors/rqs_generator.py +++ b/src/asyncflow/runtime/actors/rqs_generator.py @@ -21,8 +21,8 @@ import simpy from asyncflow.runtime.actors.edge import EdgeRuntime - from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput - from asyncflow.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.settings.simulation import SimulationSettings + from asyncflow.schemas.workload.rqs_generator import RqsGenerator class RqsGeneratorRuntime: @@ -36,7 +36,7 @@ def __init__( *, env: simpy.Environment, out_edge: EdgeRuntime | None, - rqs_generator_data: RqsGeneratorInput, + rqs_generator_data: RqsGenerator, sim_settings: SimulationSettings, rng: np.random.Generator | None = None, ) -> None: @@ -46,7 +46,7 @@ def __init__( Args: env (simpy.Environment): environment for the simulation out_edge (EdgeRuntime): edge connecting this node with the next one - rqs_generator_data (RqsGeneratorInput): data do define the sampler + rqs_generator_data (RqsGenerator): data do define the sampler sim_settings (SimulationSettings): settings to start the simulation rng (np.random.Generator | None, optional): random variable generator. diff --git a/src/asyncflow/runtime/actors/server.py b/src/asyncflow/runtime/actors/server.py index 7d72de1..0572956 100644 --- a/src/asyncflow/runtime/actors/server.py +++ b/src/asyncflow/runtime/actors/server.py @@ -22,8 +22,8 @@ from asyncflow.resources.server_containers import ServerContainers from asyncflow.runtime.actors.edge import EdgeRuntime from asyncflow.runtime.rqs_state import RequestState -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.full_system_topology import Server +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.nodes import Server class ServerRuntime: diff --git a/src/asyncflow/runtime/simulation_runner.py b/src/asyncflow/runtime/simulation_runner.py index f82fb72..5d112ae 100644 --- a/src/asyncflow/runtime/simulation_runner.py +++ b/src/asyncflow/runtime/simulation_runner.py @@ -16,18 +16,18 @@ from asyncflow.runtime.actors.load_balancer import LoadBalancerRuntime from asyncflow.runtime.actors.rqs_generator import RqsGeneratorRuntime from asyncflow.runtime.actors.server import ServerRuntime -from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.payload import SimulationPayload if TYPE_CHECKING: from collections.abc import Iterable - from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput - from asyncflow.schemas.system_topology.full_system_topology import ( + from asyncflow.schemas.topology.edges import Edge + from asyncflow.schemas.topology.nodes import ( Client, - Edge, LoadBalancer, Server, ) + from asyncflow.schemas.workload.rqs_generator import RqsGenerator # --- PROTOCOL DEFINITION --- # This is the contract that all runtime actors must follow. @@ -63,7 +63,7 @@ def __init__( # instantiation of object needed to build nodes for the runtime phase self.servers: list[Server] = simulation_input.topology_graph.nodes.servers self.client: Client = simulation_input.topology_graph.nodes.client - self.rqs_generator: RqsGeneratorInput = simulation_input.rqs_input + self.rqs_generator: RqsGenerator = simulation_input.rqs_input self.lb: LoadBalancer | None = None self.simulation_settings = simulation_input.sim_settings self.edges: list[Edge] = simulation_input.topology_graph.edges diff --git a/src/asyncflow/samplers/common_helpers.py b/src/asyncflow/samplers/common_helpers.py index 123ae4a..4f2f675 100644 --- a/src/asyncflow/samplers/common_helpers.py +++ b/src/asyncflow/samplers/common_helpers.py @@ -4,7 +4,7 @@ import numpy as np from asyncflow.config.constants import Distribution -from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.common.random_variables import RVConfig def uniform_variable_generator(rng: np.random.Generator) -> float: diff --git a/src/asyncflow/samplers/gaussian_poisson.py b/src/asyncflow/samplers/gaussian_poisson.py index 5caa9ed..b96eca5 100644 --- a/src/asyncflow/samplers/gaussian_poisson.py +++ b/src/asyncflow/samplers/gaussian_poisson.py @@ -16,12 +16,12 @@ truncated_gaussian_generator, uniform_variable_generator, ) -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.workload.rqs_generator import RqsGenerator def gaussian_poisson_sampling( - input_data: RqsGeneratorInput, + input_data: RqsGenerator, sim_settings: SimulationSettings, *, rng: np.random.Generator, diff --git a/src/asyncflow/samplers/poisson_poisson.py b/src/asyncflow/samplers/poisson_poisson.py index 5e1b4cc..ea7a4fb 100644 --- a/src/asyncflow/samplers/poisson_poisson.py +++ b/src/asyncflow/samplers/poisson_poisson.py @@ -13,12 +13,12 @@ poisson_variable_generator, uniform_variable_generator, ) -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.workload.rqs_generator import RqsGenerator def poisson_poisson_sampling( - input_data: RqsGeneratorInput, + input_data: RqsGenerator, sim_settings: SimulationSettings, *, rng: np.random.Generator, diff --git a/src/asyncflow/schemas/common/__init__.py b/src/asyncflow/schemas/common/__init__.py new file mode 100644 index 0000000..206bfb9 --- /dev/null +++ b/src/asyncflow/schemas/common/__init__.py @@ -0,0 +1 @@ +"""Shared, reusable primitives for schema modules (e.g., RVConfig).""" diff --git a/src/asyncflow/schemas/random_variables_config.py b/src/asyncflow/schemas/common/random_variables.py similarity index 100% rename from src/asyncflow/schemas/random_variables_config.py rename to src/asyncflow/schemas/common/random_variables.py diff --git a/src/asyncflow/schemas/full_simulation_input.py b/src/asyncflow/schemas/full_simulation_input.py deleted file mode 100644 index 504396a..0000000 --- a/src/asyncflow/schemas/full_simulation_input.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Definition of the full input for the simulation""" - -from pydantic import BaseModel - -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.full_system_topology import TopologyGraph - - -class SimulationPayload(BaseModel): - """Full input structure to perform a simulation""" - - rqs_input: RqsGeneratorInput - topology_graph: TopologyGraph - sim_settings: SimulationSettings diff --git a/src/asyncflow/schemas/payload.py b/src/asyncflow/schemas/payload.py new file mode 100644 index 0000000..3c889e4 --- /dev/null +++ b/src/asyncflow/schemas/payload.py @@ -0,0 +1,15 @@ +"""Definition of the full input for the simulation""" + +from pydantic import BaseModel + +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.workload.rqs_generator import RqsGenerator + + +class SimulationPayload(BaseModel): + """Full input structure to perform a simulation""" + + rqs_input: RqsGenerator + topology_graph: TopologyGraph + sim_settings: SimulationSettings diff --git a/src/asyncflow/schemas/simulation_settings_input.py b/src/asyncflow/schemas/settings/simulation.py similarity index 100% rename from src/asyncflow/schemas/simulation_settings_input.py rename to src/asyncflow/schemas/settings/simulation.py diff --git a/src/asyncflow/schemas/system_topology/full_system_topology.py b/src/asyncflow/schemas/system_topology/full_system_topology.py deleted file mode 100644 index 7ab91bc..0000000 --- a/src/asyncflow/schemas/system_topology/full_system_topology.py +++ /dev/null @@ -1,391 +0,0 @@ -""" -Define the topology of the system as a directed graph -where nodes represents macro structure (server, client ecc ecc) -and edges how these strcutures are connected and the network -latency necessary for the requests generated to move from -one structure to another -""" - -from collections import Counter - -from pydantic import ( - BaseModel, - ConfigDict, - Field, - PositiveInt, - field_validator, - model_validator, -) -from pydantic_core.core_schema import ValidationInfo - -from asyncflow.config.constants import ( - LbAlgorithmsName, - NetworkParameters, - ServerResourcesDefaults, - SystemEdges, - SystemNodes, -) -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.system_topology.endpoint import Endpoint - -#------------------------------------------------------------- -# Definition of the nodes structure for the graph representing -# the topoogy of the system defined for the simulation -#------------------------------------------------------------- - -# ------------------------------------------------------------- -# CLIENT -# ------------------------------------------------------------- - -class Client(BaseModel): - """Definition of the client class""" - - id: str - type: SystemNodes = SystemNodes.CLIENT - - @field_validator("type", mode="after") - def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 - """Ensure the type of the client is standard""" - if v != SystemNodes.CLIENT: - msg = f"The type should have a standard value: {SystemNodes.CLIENT}" - raise ValueError(msg) - return v - -# ------------------------------------------------------------- -# SERVER RESOURCES -# ------------------------------------------------------------- - -class ServerResources(BaseModel): - """ - Defines the quantifiable resources available on a server node. - Each attribute maps directly to a SimPy resource primitive. - """ - - cpu_cores: PositiveInt = Field( - ServerResourcesDefaults.CPU_CORES, - ge = ServerResourcesDefaults.MINIMUM_CPU_CORES, - description="Number of CPU cores available for processing.", - ) - db_connection_pool: PositiveInt | None = Field( - ServerResourcesDefaults.DB_CONNECTION_POOL, - description="Size of the database connection pool, if applicable.", - ) - - # Risorse modellate come simpy.Container (livello) - ram_mb: PositiveInt = Field( - ServerResourcesDefaults.RAM_MB, - ge = ServerResourcesDefaults.MINIMUM_RAM_MB, - description="Total available RAM in Megabytes.") - - # for the future - # disk_iops_limit: PositiveInt | None = None - # network_throughput_mbps: PositiveInt | None = None - -# ------------------------------------------------------------- -# SERVER -# ------------------------------------------------------------- - -class Server(BaseModel): - """ - definition of the server class: - - id: is the server identifier - - type: is the type of node in the structure - - server resources: is a dictionary to define the resources - of the machine where the server is living - - endpoints: is the list of all endpoints in a server - """ - - id: str - type: SystemNodes = SystemNodes.SERVER - #Later define a valide structure for the keys of server resources - server_resources : ServerResources - endpoints : list[Endpoint] - - @field_validator("type", mode="after") - def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 - """Ensure the type of the server is standard""" - if v != SystemNodes.SERVER: - msg = f"The type should have a standard value: {SystemNodes.SERVER}" - raise ValueError(msg) - return v - -class LoadBalancer(BaseModel): - """ - basemodel for the load balancer - - id: unique name associated to the lb - - type: type of the node in the structure - - server_covered: list of server id connected to the lb - """ - - id: str - type: SystemNodes = SystemNodes.LOAD_BALANCER - algorithms: LbAlgorithmsName = LbAlgorithmsName.ROUND_ROBIN - server_covered: set[str] = Field(default_factory=set) - - - - @field_validator("type", mode="after") - def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 - """Ensure the type of the server is standard""" - if v != SystemNodes.LOAD_BALANCER: - msg = f"The type should have a standard value: {SystemNodes.LOAD_BALANCER}" - raise ValueError(msg) - return v - -# ------------------------------------------------------------- -# NODES CLASS WITH ALL POSSIBLE OBJECTS REPRESENTED BY A NODE -# ------------------------------------------------------------- - -class TopologyNodes(BaseModel): - """ - Definition of the nodes class: - - server: represent all servers implemented in the system - - client: is a simple object with just a name representing - the origin of the graph - """ - - servers: list[Server] - client: Client - load_balancer: LoadBalancer | None = None - - @model_validator(mode="after") # type: ignore[arg-type] - def unique_ids( - cls, # noqa: N805 - model: "TopologyNodes", - ) -> "TopologyNodes": - """Check that all id are unique""" - ids = [server.id for server in model.servers] + [model.client.id] - - if model.load_balancer is not None: - ids.append(model.load_balancer.id) - - counter = Counter(ids) - duplicate = [node_id for node_id, value in counter.items() if value > 1] - if duplicate: - msg = f"The following node ids are duplicate {duplicate}" - raise ValueError(msg) - return model - - model_config = ConfigDict(extra="forbid") - -#------------------------------------------------------------- -# Definition of the edges structure for the graph representing -# the topoogy of the system defined for the simulation -#------------------------------------------------------------- - -class Edge(BaseModel): - """ - A directed connection in the topology graph. - - Attributes - ---------- - source : str - Identifier of the source node (where the request comes from). - target : str - Identifier of the destination node (where the request goes to). - latency : RVConfig - Random-variable configuration for network latency on this link. - probability : float - Probability of taking this edge when there are multiple outgoing links. - Must be in [0.0, 1.0]. Defaults to 1.0 (always taken). - edge_type : SystemEdges - Category of the link (e.g. network, queue, stream). - - """ - - id: str - source: str - target: str - latency: RVConfig - edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION - dropout_rate: float = Field( - NetworkParameters.DROPOUT_RATE, - ge = NetworkParameters.MIN_DROPOUT_RATE, - le = NetworkParameters.MAX_DROPOUT_RATE, - description=( - "for each nodes representing a network we define" - "a probability to drop the request" - ), - ) - - # The idea to put here the control about variance and mean about the edges - # latencies and not in RVConfig is to provide a better error handling - # providing a direct indication of the edge with the error - # The idea to put here the control about variance and mean about the edges - # latencies and not in RVConfig is to provide a better error handling - # providing a direct indication of the edge with the error - @field_validator("latency", mode="after") - def ensure_latency_is_non_negative( - cls, # noqa: N805 - v: RVConfig, - info: ValidationInfo, - ) -> RVConfig: - """Ensures that the latency's mean and variance are positive.""" - mean = v.mean - variance = v.variance - - # We can get the edge ID from the validation context for a better error message - edge_id = info.data.get("id", "unknown") - - if mean <= 0: - msg = f"The mean latency of the edge '{edge_id}' must be positive" - raise ValueError(msg) - if variance is not None and variance < 0: # Variance can be zero - msg = ( - f"The variance of the latency of the edge {edge_id}" - "must be non negative" - ) - raise ValueError(msg) - return v - - - @model_validator(mode="after") # type: ignore[arg-type] - def check_src_trgt_different(cls, model: "Edge") -> "Edge": # noqa: N805 - """Ensure source is different from target""" - if model.source == model.target: - msg = "source and target must be different nodes" - raise ValueError(msg) - return model - - -#------------------------------------------------------------- -# Definition of the Graph structure representing -# the topogy of the system defined for the simulation -#------------------------------------------------------------- - -class TopologyGraph(BaseModel): - """ - data collection for the whole graph representing - the full system - """ - - nodes: TopologyNodes - edges: list[Edge] - - @model_validator(mode="after") # type: ignore[arg-type] - def unique_ids( - cls, # noqa: N805 - model: "TopologyGraph", - ) -> "TopologyGraph": - """Check that all id are unique""" - counter = Counter(edge.id for edge in model.edges) - duplicate = [edge_id for edge_id, value in counter.items() if value > 1] - if duplicate: - msg = f"There are multiple edges with the following ids {duplicate}" - raise ValueError(msg) - return model - - - @model_validator(mode="after") # type: ignore[arg-type] - def edge_refs_valid( - cls, # noqa: N805 - model: "TopologyGraph", - ) -> "TopologyGraph": - """ - Validate that the graph is self-consistent. - - * All targets must be nodes declared in ``m.nodes``. - * External IDs are allowed as sources (entry points, generator) but - they must never appear as a target anywhere else. - """ - # ------------------------------------------------------------------ - # 1. Collect declared node IDs (servers, client, optional LB) - # ------------------------------------------------------------------ - node_ids: set[str] = {srv.id for srv in model.nodes.servers} - node_ids.add(model.nodes.client.id) - if model.nodes.load_balancer is not None: - node_ids.add(model.nodes.load_balancer.id) - - # ------------------------------------------------------------------ - # 2. Scan every edge once - # ------------------------------------------------------------------ - external_sources: set[str] = set() - - for edge in model.edges: - # ── Rule 1: target must be a declared node - if edge.target not in node_ids: - msg = ( - f"Edge {edge.source}->{edge.target} references " - f"unknown target node '{edge.target}'." - ) - raise ValueError(msg) - - # Collect any source that is not a declared node - if edge.source not in node_ids: - external_sources.add(edge.source) - - # ------------------------------------------------------------------ - # 3. Ensure external sources never appear as targets elsewhere - # ------------------------------------------------------------------ - forbidden_targets = external_sources & {e.target for e in model.edges} - if forbidden_targets: - msg = ( - "External IDs cannot be used as targets as well:" - f"{sorted(forbidden_targets)}" - ) - raise ValueError(msg) - - return model - - @model_validator(mode="after") # type: ignore[arg-type] - def valid_load_balancer(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 - """ - Check the validity of the load balancer: first we check - if is present in the simulation, second we check if the LB list - is a proper subset of the server sets of ids, then we check if - edge from LB to the servers are well defined - """ - lb = model.nodes.load_balancer - if lb is None: - return model - - server_ids = {s.id for s in model.nodes.servers} - - # 1) LB list ⊆ server_ids - missing = lb.server_covered - server_ids - if missing: - - msg = (f"Load balancer '{lb.id}'" - f"references unknown servers: {sorted(missing)}") - raise ValueError(msg) - - # edge are well defined - targets_from_lb = {e.target for e in model.edges if e.source == lb.id} - not_linked = lb.server_covered - targets_from_lb - if not_linked: - msg = ( - f"Servers {sorted(not_linked)} are covered by LB '{lb.id}' " - "but have no outgoing edge from it." - ) - - raise ValueError(msg) - - return model - - - @model_validator(mode="after") # type: ignore[arg-type] - def no_fanout_except_lb(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 - """Ensure only the LB (declared node) can have multiple outgoing edges.""" - lb_id = model.nodes.load_balancer.id if model.nodes.load_balancer else None - - # let us consider only nodes declared in the topology - node_ids: set[str] = {server.id for server in model.nodes.servers} - node_ids.add(model.nodes.client.id) - if lb_id: - node_ids.add(lb_id) - - counts: dict[str, int] = {} - for edge in model.edges: - if edge.source not in node_ids: - continue - counts[edge.source] = counts.get(edge.source, 0) + 1 - - offenders = [src for src, c in counts.items() if c > 1 and src != lb_id] - if offenders: - msg = ( - "Only the load balancer can have multiple outgoing edges. " - f"Offending sources: {offenders}" - ) - raise ValueError(msg) - - return model diff --git a/src/asyncflow/schemas/topology/edges.py b/src/asyncflow/schemas/topology/edges.py new file mode 100644 index 0000000..6e3d03b --- /dev/null +++ b/src/asyncflow/schemas/topology/edges.py @@ -0,0 +1,99 @@ +""" +Define the property of the edges of the system representing +links between different nodes +""" + +from pydantic import ( + BaseModel, + Field, + field_validator, + model_validator, +) +from pydantic_core.core_schema import ValidationInfo + +from asyncflow.config.constants import ( + NetworkParameters, + SystemEdges, +) +from asyncflow.schemas.common.random_variables import RVConfig + +#------------------------------------------------------------- +# Definition of the edges structure for the graph representing +# the topoogy of the system defined for the simulation +#------------------------------------------------------------- + +class Edge(BaseModel): + """ + A directed connection in the topology graph. + + Attributes + ---------- + source : str + Identifier of the source node (where the request comes from). + target : str + Identifier of the destination node (where the request goes to). + latency : RVConfig + Random-variable configuration for network latency on this link. + probability : float + Probability of taking this edge when there are multiple outgoing links. + Must be in [0.0, 1.0]. Defaults to 1.0 (always taken). + edge_type : SystemEdges + Category of the link (e.g. network, queue, stream). + + """ + + id: str + source: str + target: str + latency: RVConfig + edge_type: SystemEdges = SystemEdges.NETWORK_CONNECTION + dropout_rate: float = Field( + NetworkParameters.DROPOUT_RATE, + ge = NetworkParameters.MIN_DROPOUT_RATE, + le = NetworkParameters.MAX_DROPOUT_RATE, + description=( + "for each nodes representing a network we define" + "a probability to drop the request" + ), + ) + + # The idea to put here the control about variance and mean about the edges + # latencies and not in RVConfig is to provide a better error handling + # providing a direct indication of the edge with the error + # The idea to put here the control about variance and mean about the edges + # latencies and not in RVConfig is to provide a better error handling + # providing a direct indication of the edge with the error + @field_validator("latency", mode="after") + def ensure_latency_is_non_negative( + cls, # noqa: N805 + v: RVConfig, + info: ValidationInfo, + ) -> RVConfig: + """Ensures that the latency's mean and variance are positive.""" + mean = v.mean + variance = v.variance + + # We can get the edge ID from the validation context for a better error message + edge_id = info.data.get("id", "unknown") + + if mean <= 0: + msg = f"The mean latency of the edge '{edge_id}' must be positive" + raise ValueError(msg) + if variance is not None and variance < 0: # Variance can be zero + msg = ( + f"The variance of the latency of the edge {edge_id}" + "must be non negative" + ) + raise ValueError(msg) + return v + + + @model_validator(mode="after") # type: ignore[arg-type] + def check_src_trgt_different(cls, model: "Edge") -> "Edge": # noqa: N805 + """Ensure source is different from target""" + if model.source == model.target: + msg = "source and target must be different nodes" + raise ValueError(msg) + return model + + diff --git a/src/asyncflow/schemas/system_topology/endpoint.py b/src/asyncflow/schemas/topology/endpoint.py similarity index 100% rename from src/asyncflow/schemas/system_topology/endpoint.py rename to src/asyncflow/schemas/topology/endpoint.py diff --git a/src/asyncflow/schemas/topology/graph.py b/src/asyncflow/schemas/topology/graph.py new file mode 100644 index 0000000..91cf857 --- /dev/null +++ b/src/asyncflow/schemas/topology/graph.py @@ -0,0 +1,159 @@ +""" +Define the topology of the system as a directed graph +where nodes represents macro structure (server, client ecc ecc) +and edges how these strcutures are connected and the network +latency necessary for the requests generated to move from +one structure to another +""" + +from collections import Counter + +from pydantic import ( + BaseModel, + model_validator, +) + +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.nodes import TopologyNodes + +#------------------------------------------------------------- +# Definition of the Graph structure representing +# the topogy of the system defined for the simulation +#------------------------------------------------------------- + +class TopologyGraph(BaseModel): + """ + data collection for the whole graph representing + the full system + """ + + nodes: TopologyNodes + edges: list[Edge] + + @model_validator(mode="after") # type: ignore[arg-type] + def unique_ids( + cls, # noqa: N805 + model: "TopologyGraph", + ) -> "TopologyGraph": + """Check that all id are unique""" + counter = Counter(edge.id for edge in model.edges) + duplicate = [edge_id for edge_id, value in counter.items() if value > 1] + if duplicate: + msg = f"There are multiple edges with the following ids {duplicate}" + raise ValueError(msg) + return model + + + @model_validator(mode="after") # type: ignore[arg-type] + def edge_refs_valid( + cls, # noqa: N805 + model: "TopologyGraph", + ) -> "TopologyGraph": + """ + Validate that the graph is self-consistent. + + * All targets must be nodes declared in ``m.nodes``. + * External IDs are allowed as sources (entry points, generator) but + they must never appear as a target anywhere else. + """ + # ------------------------------------------------------------------ + # 1. Collect declared node IDs (servers, client, optional LB) + # ------------------------------------------------------------------ + node_ids: set[str] = {srv.id for srv in model.nodes.servers} + node_ids.add(model.nodes.client.id) + if model.nodes.load_balancer is not None: + node_ids.add(model.nodes.load_balancer.id) + + # ------------------------------------------------------------------ + # 2. Scan every edge once + # ------------------------------------------------------------------ + external_sources: set[str] = set() + + for edge in model.edges: + # ── Rule 1: target must be a declared node + if edge.target not in node_ids: + msg = ( + f"Edge {edge.source}->{edge.target} references " + f"unknown target node '{edge.target}'." + ) + raise ValueError(msg) + + # Collect any source that is not a declared node + if edge.source not in node_ids: + external_sources.add(edge.source) + + # ------------------------------------------------------------------ + # 3. Ensure external sources never appear as targets elsewhere + # ------------------------------------------------------------------ + forbidden_targets = external_sources & {e.target for e in model.edges} + if forbidden_targets: + msg = ( + "External IDs cannot be used as targets as well:" + f"{sorted(forbidden_targets)}" + ) + raise ValueError(msg) + + return model + + @model_validator(mode="after") # type: ignore[arg-type] + def valid_load_balancer(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 + """ + Check the validity of the load balancer: first we check + if is present in the simulation, second we check if the LB list + is a proper subset of the server sets of ids, then we check if + edge from LB to the servers are well defined + """ + lb = model.nodes.load_balancer + if lb is None: + return model + + server_ids = {s.id for s in model.nodes.servers} + + # 1) LB list ⊆ server_ids + missing = lb.server_covered - server_ids + if missing: + + msg = (f"Load balancer '{lb.id}'" + f"references unknown servers: {sorted(missing)}") + raise ValueError(msg) + + # edge are well defined + targets_from_lb = {e.target for e in model.edges if e.source == lb.id} + not_linked = lb.server_covered - targets_from_lb + if not_linked: + msg = ( + f"Servers {sorted(not_linked)} are covered by LB '{lb.id}' " + "but have no outgoing edge from it." + ) + + raise ValueError(msg) + + return model + + + @model_validator(mode="after") # type: ignore[arg-type] + def no_fanout_except_lb(cls, model: "TopologyGraph") -> "TopologyGraph": # noqa: N805 + """Ensure only the LB (declared node) can have multiple outgoing edges.""" + lb_id = model.nodes.load_balancer.id if model.nodes.load_balancer else None + + # let us consider only nodes declared in the topology + node_ids: set[str] = {server.id for server in model.nodes.servers} + node_ids.add(model.nodes.client.id) + if lb_id: + node_ids.add(lb_id) + + counts: dict[str, int] = {} + for edge in model.edges: + if edge.source not in node_ids: + continue + counts[edge.source] = counts.get(edge.source, 0) + 1 + + offenders = [src for src, c in counts.items() if c > 1 and src != lb_id] + if offenders: + msg = ( + "Only the load balancer can have multiple outgoing edges. " + f"Offending sources: {offenders}" + ) + raise ValueError(msg) + + return model diff --git a/src/asyncflow/schemas/topology/nodes.py b/src/asyncflow/schemas/topology/nodes.py new file mode 100644 index 0000000..d742421 --- /dev/null +++ b/src/asyncflow/schemas/topology/nodes.py @@ -0,0 +1,164 @@ +""" +Define the pydantic schemas of the nodes you are allowed +to define in the topology of the system you would like to +simulate +""" + +from collections import Counter + +from pydantic import ( + BaseModel, + ConfigDict, + Field, + PositiveInt, + field_validator, + model_validator, +) + +from asyncflow.config.constants import ( + LbAlgorithmsName, + ServerResourcesDefaults, + SystemNodes, +) +from asyncflow.schemas.topology.endpoint import Endpoint + +#------------------------------------------------------------- +# Definition of the nodes structure for the graph representing +# the topoogy of the system defined for the simulation +#------------------------------------------------------------- + +# ------------------------------------------------------------- +# CLIENT +# ------------------------------------------------------------- + +class Client(BaseModel): + """Definition of the client class""" + + id: str + type: SystemNodes = SystemNodes.CLIENT + + @field_validator("type", mode="after") + def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 + """Ensure the type of the client is standard""" + if v != SystemNodes.CLIENT: + msg = f"The type should have a standard value: {SystemNodes.CLIENT}" + raise ValueError(msg) + return v + +# ------------------------------------------------------------- +# SERVER RESOURCES +# ------------------------------------------------------------- + +class ServerResources(BaseModel): + """ + Defines the quantifiable resources available on a server node. + Each attribute maps directly to a SimPy resource primitive. + """ + + cpu_cores: PositiveInt = Field( + ServerResourcesDefaults.CPU_CORES, + ge = ServerResourcesDefaults.MINIMUM_CPU_CORES, + description="Number of CPU cores available for processing.", + ) + db_connection_pool: PositiveInt | None = Field( + ServerResourcesDefaults.DB_CONNECTION_POOL, + description="Size of the database connection pool, if applicable.", + ) + + # Risorse modellate come simpy.Container (livello) + ram_mb: PositiveInt = Field( + ServerResourcesDefaults.RAM_MB, + ge = ServerResourcesDefaults.MINIMUM_RAM_MB, + description="Total available RAM in Megabytes.") + + # for the future + # disk_iops_limit: PositiveInt | None = None + # network_throughput_mbps: PositiveInt | None = None + +# ------------------------------------------------------------- +# SERVER +# ------------------------------------------------------------- + +class Server(BaseModel): + """ + definition of the server class: + - id: is the server identifier + - type: is the type of node in the structure + - server resources: is a dictionary to define the resources + of the machine where the server is living + - endpoints: is the list of all endpoints in a server + """ + + id: str + type: SystemNodes = SystemNodes.SERVER + #Later define a valide structure for the keys of server resources + server_resources : ServerResources + endpoints : list[Endpoint] + + @field_validator("type", mode="after") + def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 + """Ensure the type of the server is standard""" + if v != SystemNodes.SERVER: + msg = f"The type should have a standard value: {SystemNodes.SERVER}" + raise ValueError(msg) + return v + +class LoadBalancer(BaseModel): + """ + basemodel for the load balancer + - id: unique name associated to the lb + - type: type of the node in the structure + - server_covered: list of server id connected to the lb + """ + + id: str + type: SystemNodes = SystemNodes.LOAD_BALANCER + algorithms: LbAlgorithmsName = LbAlgorithmsName.ROUND_ROBIN + server_covered: set[str] = Field(default_factory=set) + + + + @field_validator("type", mode="after") + def ensure_type_is_standard(cls, v: SystemNodes) -> SystemNodes: # noqa: N805 + """Ensure the type of the server is standard""" + if v != SystemNodes.LOAD_BALANCER: + msg = f"The type should have a standard value: {SystemNodes.LOAD_BALANCER}" + raise ValueError(msg) + return v + + +# ------------------------------------------------------------- +# NODES CLASS WITH ALL POSSIBLE OBJECTS REPRESENTED BY A NODE +# ------------------------------------------------------------- + +class TopologyNodes(BaseModel): + """ + Definition of the nodes class: + - server: represent all servers implemented in the system + - client: is a simple object with just a name representing + the origin of the graph + """ + + servers: list[Server] + client: Client + load_balancer: LoadBalancer | None = None + + @model_validator(mode="after") # type: ignore[arg-type] + def unique_ids( + cls, # noqa: N805 + model: "TopologyNodes", + ) -> "TopologyNodes": + """Check that all id are unique""" + ids = [server.id for server in model.servers] + [model.client.id] + + if model.load_balancer is not None: + ids.append(model.load_balancer.id) + + counter = Counter(ids) + duplicate = [node_id for node_id, value in counter.items() if value > 1] + if duplicate: + msg = f"The following node ids are duplicate {duplicate}" + raise ValueError(msg) + return model + + model_config = ConfigDict(extra="forbid") diff --git a/src/asyncflow/schemas/rqs_generator_input.py b/src/asyncflow/schemas/workload/rqs_generator.py similarity index 95% rename from src/asyncflow/schemas/rqs_generator_input.py rename to src/asyncflow/schemas/workload/rqs_generator.py index f0f63a3..a6fbf3b 100644 --- a/src/asyncflow/schemas/rqs_generator_input.py +++ b/src/asyncflow/schemas/workload/rqs_generator.py @@ -4,10 +4,10 @@ from pydantic import BaseModel, Field, field_validator from asyncflow.config.constants import Distribution, SystemNodes, TimeDefaults -from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.common.random_variables import RVConfig -class RqsGeneratorInput(BaseModel): +class RqsGenerator(BaseModel): """Define the expected variables for the simulation""" id: str diff --git a/src/asyncflow/settings/__init__.py b/src/asyncflow/settings/__init__.py new file mode 100644 index 0000000..4f4031c --- /dev/null +++ b/src/asyncflow/settings/__init__.py @@ -0,0 +1,6 @@ +"""Public settings API.""" +from __future__ import annotations + +from asyncflow.schemas.settings.simulation import SimulationSettings + +__all__ = ["SimulationSettings"] diff --git a/src/asyncflow/workload/__init__.py b/src/asyncflow/workload/__init__.py new file mode 100644 index 0000000..c4b8735 --- /dev/null +++ b/src/asyncflow/workload/__init__.py @@ -0,0 +1,7 @@ +"""Public workload API.""" +from __future__ import annotations + +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.workload.rqs_generator import RqsGenerator + +__all__ = ["RVConfig", "RqsGenerator"] diff --git a/tests/conftest.py b/tests/conftest.py index 893cb83..80955f0 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -12,16 +12,16 @@ SamplePeriods, TimeDefaults, ) -from asyncflow.schemas.full_simulation_input import SimulationPayload -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( Client, - Edge, - TopologyGraph, TopologyNodes, ) +from asyncflow.schemas.workload.rqs_generator import RqsGenerator # ============================================================================ # STANDARD CONFIGURATION FOR INPUT VARIABLES @@ -90,12 +90,12 @@ def sim_settings( @pytest.fixture -def rqs_input() -> RqsGeneratorInput: +def rqs_input() -> RqsGenerator: """ One active user issuing two requests per minute—sufficient to exercise the entire request-generator pipeline with minimal overhead. """ - return RqsGeneratorInput( + return RqsGenerator( id="rqs-1", avg_active_users=RVConfig(mean=1.0), avg_request_per_minute_per_user=RVConfig(mean=2.0), @@ -136,7 +136,7 @@ def topology_minimal() -> TopologyGraph: @pytest.fixture def payload_base( - rqs_input: RqsGeneratorInput, + rqs_input: RqsGenerator, sim_settings: SimulationSettings, topology_minimal: TopologyGraph, ) -> SimulationPayload: diff --git a/tests/integration/minimal/conftest.py b/tests/integration/minimal/conftest.py index 812ba5e..f29bf49 100644 --- a/tests/integration/minimal/conftest.py +++ b/tests/integration/minimal/conftest.py @@ -16,20 +16,20 @@ from asyncflow.config.constants import TimeDefaults from asyncflow.runtime.simulation_runner import SimulationRunner -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.workload.rqs_generator import RqsGenerator if TYPE_CHECKING: - from asyncflow.schemas.full_simulation_input import SimulationPayload + from asyncflow.schemas.payload import SimulationPayload # ────────────────────────────────────────────────────────────────────────────── # 0-traffic generator (shadows the project-wide fixture) # ────────────────────────────────────────────────────────────────────────────── @pytest.fixture(scope="session") -def rqs_input() -> RqsGeneratorInput: +def rqs_input() -> RqsGenerator: """A generator that never emits any request.""" - return RqsGeneratorInput( + return RqsGenerator( id="rqs-zero", avg_active_users=RVConfig(mean=0.0), avg_request_per_minute_per_user=RVConfig(mean=0.0), diff --git a/tests/integration/minimal/test_minimal.py b/tests/integration/minimal/test_minimal.py index 2a82601..7ae9507 100644 --- a/tests/integration/minimal/test_minimal.py +++ b/tests/integration/minimal/test_minimal.py @@ -22,7 +22,7 @@ from asyncflow.runtime.simulation_runner import SimulationRunner if TYPE_CHECKING: - from asyncflow.schemas.full_simulation_input import SimulationPayload + from asyncflow.schemas.payload import SimulationPayload # --------------------------------------------------------------------------- # diff --git a/tests/integration/payload/test_payload_invalid.py b/tests/integration/payload/test_payload_invalid.py index fb700b4..8cd5226 100644 --- a/tests/integration/payload/test_payload_invalid.py +++ b/tests/integration/payload/test_payload_invalid.py @@ -6,7 +6,7 @@ import yaml from pydantic import ValidationError -from asyncflow.schemas.full_simulation_input import SimulationPayload +from asyncflow.schemas.payload import SimulationPayload DATA_DIR = Path(__file__).parent / "data" / "invalid" YMLS = sorted(DATA_DIR.glob("*.yml")) diff --git a/tests/unit/metrics/test_analyzer.py b/tests/unit/metrics/test_analyzer.py index eaae45d..365cb1c 100644 --- a/tests/unit/metrics/test_analyzer.py +++ b/tests/unit/metrics/test_analyzer.py @@ -14,7 +14,7 @@ from asyncflow.runtime.actors.client import ClientRuntime from asyncflow.runtime.actors.edge import EdgeRuntime from asyncflow.runtime.actors.server import ServerRuntime - from asyncflow.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.settings.simulation import SimulationSettings # ---------------------------------------------------------------------- # diff --git a/tests/unit/pybuilder/test_input_builder.py b/tests/unit/pybuilder/test_input_builder.py index 3ee710c..fa49fda 100644 --- a/tests/unit/pybuilder/test_input_builder.py +++ b/tests/unit/pybuilder/test_input_builder.py @@ -13,20 +13,21 @@ import pytest -from asyncflow.pybuilder.input_builder import AsyncFlow -from asyncflow.schemas.full_simulation_input import SimulationPayload -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.endpoint import Endpoint -from asyncflow.schemas.system_topology.full_system_topology import Client, Edge, Server +from asyncflow.builder.asyncflow_builder import AsyncFlow +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.endpoint import Endpoint +from asyncflow.schemas.topology.nodes import Client, Server +from asyncflow.schemas.workload.rqs_generator import RqsGenerator # --------------------------------------------------------------------------- # # Helpers: build minimal, valid components # # --------------------------------------------------------------------------- # -def make_generator() -> RqsGeneratorInput: +def make_generator() -> RqsGenerator: """Return a minimal valid request generator.""" - return RqsGeneratorInput( + return RqsGenerator( id="rqs-1", avg_active_users={"mean": 10}, avg_request_per_minute_per_user={"mean": 30}, @@ -244,7 +245,7 @@ def test_build_without_settings_raises() -> None: # Negative cases: type enforcement in add_* methods # # --------------------------------------------------------------------------- # def test_add_generator_rejects_wrong_type() -> None: - """`add_generator` rejects non-RqsGeneratorInput instances.""" + """`add_generator` rejects non-RqsGenerator instances.""" flow = AsyncFlow() with pytest.raises(TypeError): flow.add_generator("not-a-generator") # type: ignore[arg-type] diff --git a/tests/unit/resources/test_registry.py b/tests/unit/resources/test_registry.py index 34154db..6581ae0 100644 --- a/tests/unit/resources/test_registry.py +++ b/tests/unit/resources/test_registry.py @@ -7,12 +7,12 @@ from asyncflow.config.constants import ServerResourceName from asyncflow.resources.registry import ResourcesRuntime -from asyncflow.schemas.system_topology.endpoint import Endpoint -from asyncflow.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.topology.endpoint import Endpoint +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( Client, Server, ServerResources, - TopologyGraph, TopologyNodes, ) diff --git a/tests/unit/resources/test_server_containers.py b/tests/unit/resources/test_server_containers.py index 3772528..b7a8243 100644 --- a/tests/unit/resources/test_server_containers.py +++ b/tests/unit/resources/test_server_containers.py @@ -4,7 +4,7 @@ from asyncflow.config.constants import ServerResourceName from asyncflow.resources.server_containers import build_containers -from asyncflow.schemas.system_topology.full_system_topology import ServerResources +from asyncflow.schemas.topology.nodes import ServerResources def test_containers_start_full() -> None: diff --git a/tests/unit/runtime/actors/test_client.py b/tests/unit/runtime/actors/test_client.py index 9188d64..d78c848 100644 --- a/tests/unit/runtime/actors/test_client.py +++ b/tests/unit/runtime/actors/test_client.py @@ -7,9 +7,7 @@ from asyncflow.config.constants import SystemEdges, SystemNodes from asyncflow.runtime.actors.client import ClientRuntime from asyncflow.runtime.rqs_state import RequestState -from asyncflow.schemas.system_topology.full_system_topology import ( - Client, -) +from asyncflow.schemas.topology.nodes import Client # --------------------------------------------------------------------------- # # Dummy edge (no real network) # diff --git a/tests/unit/runtime/actors/test_edge.py b/tests/unit/runtime/actors/test_edge.py index e180bec..1800a12 100644 --- a/tests/unit/runtime/actors/test_edge.py +++ b/tests/unit/runtime/actors/test_edge.py @@ -14,13 +14,13 @@ from asyncflow.config.constants import SampledMetricName, SystemEdges, SystemNodes from asyncflow.runtime.actors.edge import EdgeRuntime from asyncflow.runtime.rqs_state import RequestState -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.system_topology.full_system_topology import Edge +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.topology.edges import Edge if TYPE_CHECKING: import numpy as np - from asyncflow.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.settings.simulation import SimulationSettings # --------------------------------------------------------------------------- # diff --git a/tests/unit/runtime/actors/test_load_balancer.py b/tests/unit/runtime/actors/test_load_balancer.py index 41902e0..1905543 100644 --- a/tests/unit/runtime/actors/test_load_balancer.py +++ b/tests/unit/runtime/actors/test_load_balancer.py @@ -9,7 +9,7 @@ from asyncflow.config.constants import LbAlgorithmsName, SystemNodes from asyncflow.runtime.actors.load_balancer import LoadBalancerRuntime -from asyncflow.schemas.system_topology.full_system_topology import LoadBalancer +from asyncflow.schemas.topology.nodes import LoadBalancer if TYPE_CHECKING: from asyncflow.runtime.actors.edge import EdgeRuntime diff --git a/tests/unit/runtime/actors/test_rqs_generator.py b/tests/unit/runtime/actors/test_rqs_generator.py index 7130306..fef5987 100644 --- a/tests/unit/runtime/actors/test_rqs_generator.py +++ b/tests/unit/runtime/actors/test_rqs_generator.py @@ -17,8 +17,8 @@ from asyncflow.runtime.actors.edge import EdgeRuntime from asyncflow.runtime.rqs_state import RequestState - from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput - from asyncflow.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.settings.simulation import SimulationSettings + from asyncflow.schemas.workload.rqs_generator import RqsGenerator import importlib @@ -42,7 +42,7 @@ def transport(self, state: RequestState) -> None: def _make_runtime( env: simpy.Environment, edge: DummyEdgeRuntime, - rqs_input: RqsGeneratorInput, + rqs_input: RqsGenerator, sim_settings: SimulationSettings, *, seed: int = 0, @@ -67,7 +67,7 @@ def _make_runtime( def test_dispatcher_selects_poisson_poisson( monkeypatch: pytest.MonkeyPatch, - rqs_input: RqsGeneratorInput, + rqs_input: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """Default (Poisson) distribution must invoke *poisson_poisson_sampling*.""" @@ -93,7 +93,7 @@ def _fake_pp(*args: object, **kwargs: object) -> Iterator[float]: def test_dispatcher_selects_gaussian_poisson( monkeypatch: pytest.MonkeyPatch, - rqs_input: RqsGeneratorInput, + rqs_input: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """Normal distribution must invoke *gaussian_poisson_sampling*.""" @@ -124,7 +124,7 @@ def _fake_gp(*args: object, **kwargs: object) -> Iterator[float]: def test_event_arrival_generates_expected_number_of_requests( monkeypatch: pytest.MonkeyPatch, - rqs_input: RqsGeneratorInput, + rqs_input: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """Given a deterministic gap list, exactly that many requests are sent.""" diff --git a/tests/unit/runtime/actors/test_server.py b/tests/unit/runtime/actors/test_server.py index 7085e12..4c915ac 100644 --- a/tests/unit/runtime/actors/test_server.py +++ b/tests/unit/runtime/actors/test_server.py @@ -32,9 +32,9 @@ from asyncflow.resources.server_containers import build_containers from asyncflow.runtime.actors.server import ServerRuntime from asyncflow.runtime.rqs_state import RequestState -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.endpoint import Endpoint, Step -from asyncflow.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.endpoint import Endpoint, Step +from asyncflow.schemas.topology.nodes import ( Server, ServerResources, ) diff --git a/tests/unit/runtime/test_simulation_runner.py b/tests/unit/runtime/test_simulation_runner.py index c0f6b2e..e3b4da7 100644 --- a/tests/unit/runtime/test_simulation_runner.py +++ b/tests/unit/runtime/test_simulation_runner.py @@ -23,7 +23,7 @@ from asyncflow.runtime.actors.client import ClientRuntime from asyncflow.runtime.actors.rqs_generator import RqsGeneratorRuntime - from asyncflow.schemas.full_simulation_input import SimulationPayload + from asyncflow.schemas.payload import SimulationPayload # --------------------------------------------------------------------------- # diff --git a/tests/unit/samplers/test_gaussian_poisson.py b/tests/unit/samplers/test_gaussian_poisson.py index 4b3ed80..657fae9 100644 --- a/tests/unit/samplers/test_gaussian_poisson.py +++ b/tests/unit/samplers/test_gaussian_poisson.py @@ -13,12 +13,12 @@ from asyncflow.samplers.gaussian_poisson import ( gaussian_poisson_sampling, ) -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.workload.rqs_generator import RqsGenerator if TYPE_CHECKING: - from asyncflow.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.settings.simulation import SimulationSettings # --------------------------------------------------------------------------- # FIXTURES @@ -26,9 +26,9 @@ @pytest.fixture -def rqs_cfg() -> RqsGeneratorInput: - """Minimal, valid RqsGeneratorInput for Gaussian-Poisson tests.""" - return RqsGeneratorInput( +def rqs_cfg() -> RqsGenerator: + """Minimal, valid RqsGenerator for Gaussian-Poisson tests.""" + return RqsGenerator( id= "gen-1", avg_active_users=RVConfig( mean=10.0, @@ -47,7 +47,7 @@ def rqs_cfg() -> RqsGeneratorInput: def test_returns_generator_type( - rqs_cfg: RqsGeneratorInput, + rqs_cfg: RqsGenerator, sim_settings: SimulationSettings, rng: Generator, ) -> None: @@ -57,7 +57,7 @@ def test_returns_generator_type( def test_generates_positive_gaps( - rqs_cfg: RqsGeneratorInput, + rqs_cfg: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """ @@ -83,7 +83,7 @@ def test_generates_positive_gaps( def test_zero_users_produces_no_events( monkeypatch: pytest.MonkeyPatch, - rqs_cfg: RqsGeneratorInput, + rqs_cfg: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """ diff --git a/tests/unit/samplers/test_poisson_poisson.py b/tests/unit/samplers/test_poisson_poisson.py index fde7d04..c5d4a18 100644 --- a/tests/unit/samplers/test_poisson_poisson.py +++ b/tests/unit/samplers/test_poisson_poisson.py @@ -12,18 +12,18 @@ from asyncflow.config.constants import TimeDefaults from asyncflow.samplers.poisson_poisson import poisson_poisson_sampling -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.workload.rqs_generator import RqsGenerator if TYPE_CHECKING: - from asyncflow.schemas.simulation_settings_input import SimulationSettings + from asyncflow.schemas.settings.simulation import SimulationSettings @pytest.fixture -def rqs_cfg() -> RqsGeneratorInput: - """Return a minimal, valid RqsGeneratorInput for the sampler tests.""" - return RqsGeneratorInput( +def rqs_cfg() -> RqsGenerator: + """Return a minimal, valid RqsGenerator for the sampler tests.""" + return RqsGenerator( id="gen-1", avg_active_users={"mean": 1.0, "distribution": "poisson"}, avg_request_per_minute_per_user={"mean": 60.0, "distribution": "poisson"}, @@ -36,7 +36,7 @@ def rqs_cfg() -> RqsGeneratorInput: def test_sampler_returns_generator( - rqs_cfg: RqsGeneratorInput, + rqs_cfg: RqsGenerator, sim_settings: SimulationSettings, rng: Generator, ) -> None: @@ -46,7 +46,7 @@ def test_sampler_returns_generator( def test_all_gaps_are_positive( - rqs_cfg: RqsGeneratorInput, + rqs_cfg: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """Every yielded gap must be strictly positive.""" @@ -65,7 +65,7 @@ def test_all_gaps_are_positive( def test_sampler_is_reproducible_with_fixed_seed( - rqs_cfg: RqsGeneratorInput, + rqs_cfg: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """Same RNG seed must produce identical first N gaps.""" @@ -96,7 +96,7 @@ def test_zero_users_produces_no_events( sim_settings: SimulationSettings, ) -> None: """If the mean user count is zero the generator must yield no events.""" - cfg_zero = RqsGeneratorInput( + cfg_zero = RqsGenerator( id="gen-1", avg_active_users=RVConfig(mean=0.0, distribution="poisson"), avg_request_per_minute_per_user=RVConfig(mean=60.0, distribution="poisson"), @@ -115,7 +115,7 @@ def test_zero_users_produces_no_events( def test_cumulative_time_never_exceeds_horizon( - rqs_cfg: RqsGeneratorInput, + rqs_cfg: RqsGenerator, sim_settings: SimulationSettings, ) -> None: """Sum of gaps must stay below the simulation horizon.""" diff --git a/tests/unit/samplers/test_sampler_helper.py b/tests/unit/samplers/test_sampler_helper.py index f6b4241..349a5fd 100644 --- a/tests/unit/samplers/test_sampler_helper.py +++ b/tests/unit/samplers/test_sampler_helper.py @@ -17,7 +17,7 @@ truncated_gaussian_generator, uniform_variable_generator, ) -from asyncflow.schemas.random_variables_config import RVConfig +from asyncflow.schemas.common.random_variables import RVConfig # --------------------------------------------------------------------------- # # Dummy RNG # diff --git a/tests/unit/schemas/test_endpoint_input.py b/tests/unit/schemas/test_endpoint.py similarity index 98% rename from tests/unit/schemas/test_endpoint_input.py rename to tests/unit/schemas/test_endpoint.py index 3813dd6..080f55a 100644 --- a/tests/unit/schemas/test_endpoint_input.py +++ b/tests/unit/schemas/test_endpoint.py @@ -11,7 +11,7 @@ EndpointStepRAM, StepOperation, ) -from asyncflow.schemas.system_topology.endpoint import Endpoint, Step +from asyncflow.schemas.topology.endpoint import Endpoint, Step # --------------------------------------------------------------------------- # diff --git a/tests/unit/schemas/test_requests_generator_input.py b/tests/unit/schemas/test_generator.py similarity index 92% rename from tests/unit/schemas/test_requests_generator_input.py rename to tests/unit/schemas/test_generator.py index 66ad037..608adc4 100644 --- a/tests/unit/schemas/test_requests_generator_input.py +++ b/tests/unit/schemas/test_generator.py @@ -1,13 +1,13 @@ -"""Validation tests for RVConfig, RqsGeneratorInput and SimulationSettings.""" +"""Validation tests for RVConfig, RqsGenerator and SimulationSettings.""" from __future__ import annotations import pytest from pydantic import ValidationError from asyncflow.config.constants import Distribution, TimeDefaults -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.workload.rqs_generator import RqsGenerator # --------------------------------------------------------------------------- # # RVCONFIG # @@ -82,7 +82,7 @@ def test_invalid_distribution_literal_raises() -> None: # --------------------------------------------------------------------------- # -# RQSGENERATORINPUT - USER_SAMPLING_WINDOW & DISTRIBUTION CONSTRAINTS # +# RqsGenerator - USER_SAMPLING_WINDOW & DISTRIBUTION CONSTRAINTS # # --------------------------------------------------------------------------- # @@ -98,7 +98,7 @@ def _valid_normal_cfg(mean: float = 1.0) -> dict[str, float | str]: def test_default_user_sampling_window() -> None: """If user_sampling_window is missing it defaults to the constant.""" - inp = RqsGeneratorInput( + inp = RqsGenerator( id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), @@ -108,7 +108,7 @@ def test_default_user_sampling_window() -> None: def test_explicit_user_sampling_window_kept() -> None: """An explicit user_sampling_window is preserved.""" - inp = RqsGeneratorInput( + inp = RqsGenerator( id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), @@ -120,7 +120,7 @@ def test_explicit_user_sampling_window_kept() -> None: def test_user_sampling_window_not_int_raises() -> None: """A non-integer user_sampling_window raises ValidationError.""" with pytest.raises(ValidationError): - RqsGeneratorInput( + RqsGenerator( id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), @@ -132,7 +132,7 @@ def test_user_sampling_window_above_max_raises() -> None: """user_sampling_window above the max constant raises ValidationError.""" too_large = TimeDefaults.MAX_USER_SAMPLING_WINDOW + 1 with pytest.raises(ValidationError): - RqsGeneratorInput( + RqsGenerator( id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), @@ -143,7 +143,7 @@ def test_user_sampling_window_above_max_raises() -> None: def test_avg_request_must_be_poisson() -> None: """avg_request_per_minute_per_user must be Poisson; Normal raises.""" with pytest.raises(ValidationError): - RqsGeneratorInput( + RqsGenerator( id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_normal_cfg(), @@ -154,7 +154,7 @@ def test_avg_active_users_invalid_distribution_raises() -> None: """avg_active_users cannot be Exponential; only Poisson or Normal allowed.""" bad_cfg = {"mean": 1.0, "distribution": Distribution.EXPONENTIAL} with pytest.raises(ValidationError): - RqsGeneratorInput( + RqsGenerator( id="rqs-1", avg_active_users=bad_cfg, avg_request_per_minute_per_user=_valid_poisson_cfg(), @@ -163,7 +163,7 @@ def test_avg_active_users_invalid_distribution_raises() -> None: def test_valid_poisson_poisson_configuration() -> None: """Poisson-Poisson combo is accepted.""" - cfg = RqsGeneratorInput( + cfg = RqsGenerator( id="rqs-1", avg_active_users=_valid_poisson_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), @@ -177,7 +177,7 @@ def test_valid_poisson_poisson_configuration() -> None: def test_valid_normal_poisson_configuration() -> None: """Normal-Poisson combo is accepted.""" - cfg = RqsGeneratorInput( + cfg = RqsGenerator( id="rqs-1", avg_active_users=_valid_normal_cfg(), avg_request_per_minute_per_user=_valid_poisson_cfg(), diff --git a/tests/unit/schemas/test_full_topology_input.py b/tests/unit/schemas/test_topology.py similarity index 97% rename from tests/unit/schemas/test_full_topology_input.py rename to tests/unit/schemas/test_topology.py index 4e27562..0ef53e0 100644 --- a/tests/unit/schemas/test_full_topology_input.py +++ b/tests/unit/schemas/test_topology.py @@ -13,15 +13,15 @@ SystemEdges, SystemNodes, ) -from asyncflow.schemas.random_variables_config import RVConfig -from asyncflow.schemas.system_topology.endpoint import Endpoint, Step -from asyncflow.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.endpoint import Endpoint, Step +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( Client, - Edge, LoadBalancer, Server, ServerResources, - TopologyGraph, TopologyNodes, ) From 111cf7bef0db70262608970e0e745b88e5c351d7 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 14 Aug 2025 19:19:16 +0200 Subject: [PATCH 29/52] minor changes --- tests/unit/runtime/test_simulation_runner.py | 31 -------------------- 1 file changed, 31 deletions(-) diff --git a/tests/unit/runtime/test_simulation_runner.py b/tests/unit/runtime/test_simulation_runner.py index e3b4da7..e4b8c83 100644 --- a/tests/unit/runtime/test_simulation_runner.py +++ b/tests/unit/runtime/test_simulation_runner.py @@ -99,37 +99,6 @@ def test_build_edges_with_stub_edge(runner: SimulationRunner) -> None: assert len(runner._edges_runtime) == 1 # noqa: SLF001 -# --------------------------------------------------------------------------- # -# End-to-end “mini” run # -# --------------------------------------------------------------------------- # -def test_run_returns_results_analyzer(runner: SimulationRunner) -> None: - """ - `.run()` must complete even though the client is a sink node. We patch - `_build_client` to assign a no-op edge to avoid assertions. - """ - - class _NoOpEdge: - """Edge stub that silently discards transported states.""" - - def transport(self) -> None: - return - - def patched_build_client(self: SimulationRunner) -> None: - # Call the original builder - SimulationRunner._build_client(self) # noqa: SLF001 - cli_rt = next(iter(self._client_runtime.values())) - cli_rt.out_edge = _NoOpEdge() # type: ignore[assignment] - - with patch.object(runner, "_build_client", patched_build_client.__get__(runner)): - results: ResultsAnalyzer = runner.run() - - assert isinstance(results, ResultsAnalyzer) - assert ( - pytest.approx(runner.env.now) - == runner.simulation_settings.total_simulation_time - ) - - # --------------------------------------------------------------------------- # # from_yaml utility # # --------------------------------------------------------------------------- # From 59b54b69c545bab54a1ced4d3db6af2a70d43049 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Thu, 14 Aug 2025 19:21:58 +0200 Subject: [PATCH 30/52] minor changes --- tests/unit/runtime/test_simulation_runner.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/unit/runtime/test_simulation_runner.py b/tests/unit/runtime/test_simulation_runner.py index e4b8c83..3a352f5 100644 --- a/tests/unit/runtime/test_simulation_runner.py +++ b/tests/unit/runtime/test_simulation_runner.py @@ -9,13 +9,11 @@ from __future__ import annotations from typing import TYPE_CHECKING -from unittest.mock import patch import pytest import simpy import yaml -from asyncflow.metrics.analyzer import ResultsAnalyzer from asyncflow.runtime.simulation_runner import SimulationRunner if TYPE_CHECKING: From 02951e680aee52d178d80ea1a89a17f84ddab7c2 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 15 Aug 2025 17:13:38 +0200 Subject: [PATCH 31/52] Features/lb example and docs tutorial (#15) * minor docs changes * improved pytoml * improved docs + LB examples --- docs/api/high-level.md | 299 ----------------- docs/api/high-level/analyzer.md | 211 ++++++++++++ docs/api/high-level/builder.md | 288 ++++++++++++++++ docs/api/high-level/runner.md | 230 +++++++++++++ .../dev-workflow.md} | 2 +- docs/index.md | 1 + .../load_balancer/two_servers.png | Bin 0 -> 142265 bytes .../load_balancer/two_servers.py | 317 ++++++++++++++++++ .../single_server/single_server.png | Bin 0 -> 157658 bytes .../single_server/single_server.py} | 2 +- examples/single_server_builder.png | Bin 166241 -> 0 bytes examples/single_server_pybuilder.png | Bin 162085 -> 0 bytes examples/single_server_yml.png | Bin 166693 -> 0 bytes .../{ => yaml_input}/data/single_server.yml | 0 examples/yaml_input/data/two_servers_lb.yml | 96 ++++++ .../yaml_input/load_balancer/two_servers.png | Bin 0 -> 153719 bytes .../yaml_input/load_balancer/two_servers.py | 283 ++++++++++++++++ .../single_server/single_server.png | Bin 0 -> 155288 bytes .../single_server/single_server.py} | 4 +- examples/{ => yaml_input}/your_example.py | 0 pyproject.toml | 35 +- src/asyncflow/__init__.py | 2 +- src/asyncflow/analysis/__init__.py | 5 + src/asyncflow/metrics/analyzer.py | 6 +- src/asyncflow/py.typed | 0 25 files changed, 1470 insertions(+), 311 deletions(-) delete mode 100644 docs/api/high-level.md create mode 100644 docs/api/high-level/analyzer.md create mode 100644 docs/api/high-level/builder.md create mode 100644 docs/api/high-level/runner.md rename docs/{dev-workflow-guide.md => guides/dev-workflow.md} (99%) create mode 100644 examples/builder_input/load_balancer/two_servers.png create mode 100644 examples/builder_input/load_balancer/two_servers.py create mode 100644 examples/builder_input/single_server/single_server.png rename examples/{single_server_pybuilder.py => builder_input/single_server/single_server.py} (99%) delete mode 100644 examples/single_server_builder.png delete mode 100644 examples/single_server_pybuilder.png delete mode 100644 examples/single_server_yml.png rename examples/{ => yaml_input}/data/single_server.yml (100%) create mode 100644 examples/yaml_input/data/two_servers_lb.yml create mode 100644 examples/yaml_input/load_balancer/two_servers.png create mode 100644 examples/yaml_input/load_balancer/two_servers.py create mode 100644 examples/yaml_input/single_server/single_server.png rename examples/{single_server_yaml.py => yaml_input/single_server/single_server.py} (98%) rename examples/{ => yaml_input}/your_example.py (100%) create mode 100644 src/asyncflow/analysis/__init__.py create mode 100644 src/asyncflow/py.typed diff --git a/docs/api/high-level.md b/docs/api/high-level.md deleted file mode 100644 index 65c3d24..0000000 --- a/docs/api/high-level.md +++ /dev/null @@ -1,299 +0,0 @@ -# AsyncFlow — High-Level API (`AsyncFlow`, `SimulationRunner`) - -This page explains how to programmatically **assemble a validated simulation payload** and **run** it, returning metrics and plots through the analyzer. - -* **Builder**: `AsyncFlow` – compose workload, topology, and settings into a single `SimulationPayload`. -* **Runner**: `SimulationRunner` – wire actors, start processes, collect metrics, and return a `ResultsAnalyzer`. - ---- - -## Imports - -```python -# High-level API -from asyncflow import AsyncFlow, SimulationRunner - -# Public leaf schemas (components & workload) -from asyncflow.components import Client, Server, Endpoint, Edge -from asyncflow.workload import RqsGenerator, RVConfig -from asyncflow.settings import SimulationSettings -``` - -> These are the **only** imports end users need. Internals (actors, registries, etc.) remain private. - ---- - -## Quick start - -A minimal end-to-end example: - -```python -from __future__ import annotations -import simpy - -from asyncflow import AsyncFlow, SimulationRunner -from asyncflow.components import Client, Server, Endpoint, Edge -from asyncflow.workload import RqsGenerator, RVConfig -from asyncflow.settings import SimulationSettings - -# 1) Workload -rqs = RqsGenerator( - id="rqs-1", - avg_active_users=RVConfig(mean=50, # Poisson by default - # or Distribution.NORMAL with variance auto=mean - ), - avg_request_per_minute_per_user=RVConfig(mean=30), # MUST be Poisson - user_sampling_window=60, # seconds -) - -# 2) Topology components -client = Client(id="client-1") - -endpoint = Endpoint( - endpoint_name="/hello", - steps=[ - {"kind": "ram", "step_operation": {"necessary_ram": 32}}, - {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, - {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, - ], -) - -server = Server( - id="srv-1", - server_resources={"cpu_cores": 1, "ram_mb": 1024}, - endpoints=[endpoint], -) - -edges = [ - Edge(id="gen-client", source="rqs-1", target="client-1", - latency={"mean": 0.003, "distribution": "exponential"}), - Edge(id="client-srv1", source="client-1", target="srv-1", - latency={"mean": 0.003, "distribution": "exponential"}), - Edge(id="srv1-client", source="srv-1", target="client-1", - latency={"mean": 0.003, "distribution": "exponential"}), -] - -# 3) Settings (baseline sampled metrics are mandatory) -settings = SimulationSettings( - total_simulation_time=300, # seconds (≥ 5) - sample_period_s=0.01, # 0.001 ≤ value ≤ 0.1 - # enabled_sample_metrics and enabled_event_metrics: safe defaults already set -) - -# 4) Build (validates everything with Pydantic) -payload = ( - AsyncFlow() - .add_generator(rqs) - .add_client(client) - .add_servers(server) - .add_edges(*edges) - .add_simulation_settings(settings) - .build_payload() -) - -# 5) Run -env = simpy.Environment() -results = SimulationRunner(env=env, simulation_input=payload).run() - -# 6) Use the analyzer (examples) -print(results.get_latency_stats()) -ts, rps = results.get_throughput_series() -sampled = results.get_sampled_metrics() -``` - ---- - -## `AsyncFlow` — builder (public) - -`AsyncFlow` helps you construct a **self-consistent** `SimulationPayload` with fluent, chainable calls. Every piece you add is type-checked; the final `build_payload()` validates the full graph and settings. - -### API - -```python -class AsyncFlow: - def add_generator(self, rqs_generator: RqsGenerator) -> Self: ... - def add_client(self, client: Client) -> Self: ... - def add_servers(self, *servers: Server) -> Self: ... - def add_edges(self, *edges: Edge) -> Self: ... - def add_simulation_settings(self, sim_settings: SimulationSettings) -> Self: ... - def add_load_balancer(self, load_balancer: LoadBalancer) -> Self: ... - def build_payload(self) -> SimulationPayload: ... -``` - -### Validation performed by `build_payload()` - -On build, the composed payload is validated by the Pydantic schemas: - -1. **Presence** - - * Generator, client, **≥ 1 server**, **≥ 1 edge**, settings are required. - -2. **Unique IDs** - - * Duplicate server IDs or edge IDs are rejected. - -3. **Node types** - - * Fixed enums: `client`, `server`, `load_balancer`; validated on each node. - -4. **Edge integrity** - - * Every edge **target** must be a declared node ID. - * **External IDs** (e.g., the generator id) are allowed **only as sources**. - * **No self-loops** (`source != target`). - -5. **Load balancer sanity** (if present) - - * `server_covered ⊆ declared servers`. - * There must be an **edge from the LB to every covered server**. - -6. **(Engine rule)** “No fan-out except LB” - - * Only the LB may have multiple outgoing edges among declared nodes. - -7. **Latency RV constraints (edges)** - - * `latency.mean > 0`, and if `variance` exists, `variance ≥ 0`. - -If a rule fails, a **descriptive `ValueError`** points at the offending entity/field. - -### Typical errors you might see - -* Missing parts: - `ValueError: The generator input must be instantiated before the simulation` -* Type mis-match: - `TypeError: All the instances must be of the type Server` -* Graph violations: - `ValueError: Edge client-1->srv-X references unknown target node 'srv-X'` -* LB coverage: - `ValueError: Servers ['srv-2'] are covered by LB 'lb-1' but have no outgoing edge from it.` - ---- - -## `SimulationRunner` — orchestrator (public) - -`SimulationRunner` takes a validated `SimulationPayload`, **instantiates all runtimes**, **wires** edges to their target mailboxes, **starts** every actor, **collects** sampled metrics, and advances the SimPy clock. - -### API - -```python -class SimulationRunner: - def __init__(self, *, env: simpy.Environment, simulation_input: SimulationPayload) -> None: ... - def run(self) -> ResultsAnalyzer: ... - @classmethod - def from_yaml(cls, *, env: simpy.Environment, yaml_path: str | Path) -> "SimulationRunner": ... -``` - -* **`env`**: your SimPy environment (you control its lifetime). - -* **`simulation_input`**: the payload returned by `AsyncFlow.build_payload()` (or parsed from YAML). - -* **`run()`**: - - * Builds and wires all runtime actors (`RqsGeneratorRuntime`, `ClientRuntime`, `ServerRuntime`, `LoadBalancerRuntime`, `EdgeRuntime`). - * Starts the **SampledMetricCollector** (baseline sampled metrics are mandatory and collected automatically). - * Runs until `SimulationSettings.total_simulation_time`. - * Returns a **`ResultsAnalyzer`** with helpers like: - - * `get_latency_stats()` - * `get_throughput_series()` - * `get_sampled_metrics()` - * plotting helpers (`plot_latency_distribution`, `plot_throughput`, …). - -* **`from_yaml`**: convenience constructor for loading a full payload from a YAML file and running it immediately. - -### Determinism & RNG - -* The runner uses `numpy.random.default_rng()` internally. - Seeding is not yet exposed as a public parameter; exact reproducibility across runs is **not guaranteed** in this version. - ---- - -## Extended example: with Load Balancer - -```python -from asyncflow.components import Client, Server, Endpoint, Edge -from asyncflow.components import LoadBalancer -from asyncflow import AsyncFlow, SimulationRunner -from asyncflow.workload import RqsGenerator, RVConfig -from asyncflow.settings import SimulationSettings -import simpy - -client = Client(id="client-1") - -srv1 = Server( - id="srv-1", - server_resources={"cpu_cores": 1, "ram_mb": 1024}, - endpoints=[Endpoint(endpoint_name="/api", steps=[{"kind":"ram","step_operation":{"necessary_ram":64}}])] -) -srv2 = Server( - id="srv-2", - server_resources={"cpu_cores": 2, "ram_mb": 2048}, - endpoints=[Endpoint(endpoint_name="/api", steps=[{"kind":"io_db","step_operation":{"io_waiting_time":0.012}}])] -) - -lb = LoadBalancer(id="lb-1", algorithms="round_robin", server_covered={"srv-1","srv-2"}) - -edges = [ - Edge(id="gen-client", source="rqs-1", target="client-1", latency={"mean":0.002,"distribution":"exponential"}), - Edge(id="client-lb", source="client-1", target="lb-1", latency={"mean":0.002,"distribution":"exponential"}), - Edge(id="lb-srv1", source="lb-1", target="srv-1", latency={"mean":0.002,"distribution":"exponential"}), - Edge(id="lb-srv2", source="lb-1", target="srv-2", latency={"mean":0.002,"distribution":"exponential"}), - Edge(id="srv1-client", source="srv-1", target="client-1", latency={"mean":0.003,"distribution":"exponential"}), - Edge(id="srv2-client", source="srv-2", target="client-1", latency={"mean":0.003,"distribution":"exponential"}), -] - -payload = ( - AsyncFlow() - .add_generator(RqsGenerator( - id="rqs-1", - avg_active_users=RVConfig(mean=120), - avg_request_per_minute_per_user=RVConfig(mean=20), - user_sampling_window=60, - )) - .add_client(client) - .add_servers(srv1, srv2) - .add_load_balancer(lb) - .add_edges(*edges) - .add_simulation_settings(SimulationSettings(total_simulation_time=600, sample_period_s=0.02)) - .build_payload() -) - -env = simpy.Environment() -results = SimulationRunner(env=env, simulation_input=payload).run() -``` - ---- - -## Performance tips - -* **Sampling cost** grows with `total_simulation_time / sample_period_s × (#sampled metrics × entities)`. - For long runs, consider a larger `sample_period_s` (e.g., `0.02–0.05`) to reduce memory while keeping the baseline metrics intact. - -* **Validation first**: prefer failing early by letting `build_payload()` validate everything before the runner starts. - ---- - -## Error handling (what throws) - -* **Type errors** on builder inputs (`TypeError`) when passing the wrong class to `add_*`. -* **Validation errors** (`ValueError`) on `build_payload()` if the graph is inconsistent (unknown targets, duplicates, LB edges missing, self-loops, illegal fan-out, latency rules, etc.). -* **Runtime wiring errors** (`TypeError`) if an unknown runtime target/source type appears while wiring edges (should not occur with a validated payload). - ---- - -## YAML path (alternative) - -You can construct the payload in YAML (see “YAML Input Guide”), then: - -```python -import simpy -from asyncflow import SimulationRunner - -env = simpy.Environment() -runner = SimulationRunner.from_yaml(env=env, yaml_path="scenario.yml") -results = runner.run() -``` - ---- - diff --git a/docs/api/high-level/analyzer.md b/docs/api/high-level/analyzer.md new file mode 100644 index 0000000..8804449 --- /dev/null +++ b/docs/api/high-level/analyzer.md @@ -0,0 +1,211 @@ +# AsyncFlow — Public API Reference: `ResultsAnalyzer` + +`ResultsAnalyzer` is the public object you use **after** a run to compute +latency statistics, derive throughput time-series, and visualize sampled +metrics collected from servers and edges. + +* **Input:** created and returned by `SimulationRunner.run()` +* **Output:** dictionaries and time-series you can print, log, chart, or export + +> **Import (public):** +> +> ```python +> from asyncflow.analysis import ResultsAnalyzer +> ``` + + +--- + +## TL;DR (minimal usage) + +```python +results = SimulationRunner(env=env, simulation_input=payload).run() + +# Aggregates +lat = results.get_latency_stats() # dict of p50, p95, p99, ... +ts, rps = results.get_throughput_series() # per-second timestamps & RPS +series = results.get_sampled_metrics() # nested dict of time-series + +# Plotting (matplotlib) +import matplotlib.pyplot as plt +fig, axes = plt.subplots(2, 2, figsize=(12, 8)) +results.plot_latency_distribution(axes[0, 0]) +results.plot_throughput(axes[0, 1]) +results.plot_server_queues(axes[1, 0]) +results.plot_ram_usage(axes[1, 1]) +fig.tight_layout() +``` + +--- + +## What the analyzer computes + +### Event-level aggregates (from `RQS_CLOCK`) + +* **Latency stats** from per-request `(start_time, finish_time)` tuples: + + * keys: `total_requests, mean, median, std_dev, p95, p99, min, max` +* **Throughput (RPS)** as a time-series: + + * 1-second windows by default (see “Advanced: throughput window”) + +### Sampled time-series (from runtime collectors) + +* Per-entity (server/edge) series for the **baseline mandatory** metrics: + + * `ready_queue_len` (server) + * `event_loop_io_sleep` (server) + * `ram_in_use` (server) + * `edge_concurrent_connection` (edge) + +> These are sampled every `sample_period_s` defined in `SimulationSettings`. + +--- + +## Public API + +### Aggregates + +```python +get_latency_stats() -> dict[LatencyKey, float] +``` + +Returns latency summary statistics. If no requests completed, returns `{}`. + +```python +get_throughput_series() -> tuple[list[float], list[float]] +``` + +Returns `(timestamps_in_seconds, rps_values)`. If no traffic, returns `([], [])`. + +### Sampled metrics + +```python +get_sampled_metrics() -> dict[str, dict[str, list[float]]] +``` + +Returns a nested dictionary: + +```python +{ + "": { "": [v0, v1, ...] } +} +``` + +* Metric names are strings matching the public enums (e.g. `"ready_queue_len"`). +* `entity_id` is a **server id** (for server metrics) or an **edge id** (for edge metrics). + +### Plotting helpers + +All plotting helpers draw on a provided `matplotlib.axes.Axes`: + +```python +plot_latency_distribution(ax: Axes) -> None +plot_throughput(ax: Axes) -> None +plot_server_queues(ax: Axes) -> None +plot_ram_usage(ax: Axes) -> None +``` + +Behavior: + +* If data is missing/empty, the plot shows a “no data” message. +* With a load balancer (multiple servers), per-server lines are labeled by server id automatically. + +--- + +## Return contracts (shapes & keys) + +### `get_latency_stats()` + +Example: + +```python +{ + 'total_requests': 1200.0, + 'mean': 0.0123, + 'median': 0.0108, + 'std_dev': 0.0041, + 'p95': 0.0217, + 'p99': 0.0302, + 'min': 0.0048, + 'max': 0.0625 +} +``` + +### `get_throughput_series()` + +Example: + +```python +timestamps = [1.0, 2.0, 3.0, ...] # seconds from t=0 +rps = [ 36, 41, 38, ...] # requests per second +``` + +### `get_sampled_metrics()` + +Example subset: + +```python +{ + "ready_queue_len": { + "srv-1": [0, 1, 2, 1, ...], + "srv-2": [0, 0, 1, 0, ...], + }, + "event_loop_io_sleep": { + "srv-1": [3, 5, 4, 6, ...], + }, + "ram_in_use": { + "srv-1": [128.0, 160.0, 192.0, ...], + }, + "edge_concurrent_connection": { + "lb-1->srv-1": [0, 1, 1, 2, ...], # your edge ids + } +} +``` + +Time base for these lists is implicit: index `i` corresponds to time `i * sample_period_s`. + +--- + +## Plotting recipes + +### Multi-panel overview + +```python +import matplotlib.pyplot as plt + +fig, axes = plt.subplots(2, 2, figsize=(12, 8)) +results.plot_latency_distribution(axes[0, 0]) +results.plot_throughput(axes[0, 1]) +results.plot_server_queues(axes[1, 0]) +results.plot_ram_usage(axes[1, 1]) + +fig.suptitle("AsyncFlow – Simulation Overview", y=1.02) +fig.tight_layout() +``` + +## Edge cases & guarantees + +* **No traffic:** all getters are safe: + + * `get_latency_stats()` → `{}` + * `get_throughput_series()` → `([], [])` + * Plots show “no data”. +* **Multiple servers / LB:** queue and RAM plots include **one line per server id**. +* **Metric availability:** the analyzer only exposes the **baseline mandatory** sampled metrics; if a metric wasn’t enabled/recorded, it won’t appear in the nested dict. +* **Units:** times are in **seconds**; RAM is in **MB**; RPS is **requests/second**. + +--- + +## Performance characteristics + +* Aggregations (percentiles, std) are **vectorized** via NumPy. +* Memory footprint of sampled series ≈ + `total_simulation_time / sample_period_s × (#metrics × #entities)`. +* Prefer a coarser `sample_period_s` for very long runs. + +--- + + + + diff --git a/docs/api/high-level/builder.md b/docs/api/high-level/builder.md new file mode 100644 index 0000000..4a8ff6c --- /dev/null +++ b/docs/api/high-level/builder.md @@ -0,0 +1,288 @@ +# AsyncFlow — Public API Reference: `AsyncFlow` Builder + +`AsyncFlow` is the **fluent builder** that assembles a complete, validated +`SimulationPayload`. It lets you compose workload, topology, edges, and global +settings with clear types and fail-fast validation. The resulting payload can be +run with `SimulationRunner`. + +* **You write:** small, typed building blocks (workload + components + settings) +* **Builder does:** composition & Pydantic validation (graph integrity, rules) +* **Runner does:** execution & metrics collection + +--- + +## Imports + +```python +# Builder + Runner +from asyncflow import AsyncFlow, SimulationRunner + +# Public leaf schemas +from asyncflow.workload import RqsGenerator, RVConfig +from asyncflow.components import Client, Server, Endpoint, Edge, LoadBalancer +from asyncflow.settings import SimulationSettings +``` + +--- + +## Quick start + +```python +import simpy +from asyncflow import AsyncFlow, SimulationRunner +from asyncflow.workload import RqsGenerator, RVConfig +from asyncflow.components import Client, Server, Endpoint, Edge +from asyncflow.settings import SimulationSettings + +# 1) Workload +rqs = RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=50), # Poisson by default + avg_request_per_minute_per_user=RVConfig(mean=30), # MUST be Poisson +) + +# 2) Components +client = Client(id="client-1") +server = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 1024}, + endpoints=[ + Endpoint( + endpoint_name="/hello", + steps=[ + {"kind": "ram", "step_operation": {"necessary_ram": 32}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ], + ) + ], +) + +edges = [ + Edge(id="gen-client", source="rqs-1", target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}), + Edge(id="client-srv1", source="client-1", target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}), + Edge(id="srv1-client", source="srv-1", target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}), +] + +# 3) Settings (baseline sampled metrics are mandatory by design) +settings = SimulationSettings(total_simulation_time=300, sample_period_s=0.01) + +# 4) Build (validates everything) +payload = ( + AsyncFlow() + .add_generator(rqs) + .add_client(client) + .add_servers(server) + .add_edges(*edges) + .add_simulation_settings(settings) + .build_payload() +) + +# 5) Run +env = simpy.Environment() +results = SimulationRunner(env=env, simulation_input=payload).run() +``` + +--- + +## API + +```python +class AsyncFlow: + def add_generator(self, rqs_generator: RqsGenerator) -> Self: ... + def add_client(self, client: Client) -> Self: ... + def add_servers(self, *servers: Server) -> Self: ... + def add_edges(self, *edges: Edge) -> Self: ... + def add_simulation_settings(self, sim_settings: SimulationSettings) -> Self: ... + def add_load_balancer(self, load_balancer: LoadBalancer) -> Self: ... + def build_payload(self) -> SimulationPayload: ... +``` + +### Method details + +* **`add_generator(rqs_generator)`** + Adds the stochastic workload model. + Errors: `TypeError` if not a `RqsGenerator`. + +* **`add_client(client)`** + Adds the single client node. + Errors: `TypeError` if not a `Client`. + +* **`add_servers(*servers)`** + Adds one or more servers (varargs). + Errors: `TypeError` if any arg is not a `Server`. + +* **`add_edges(*edges)`** + Adds one or more directed edges (varargs). + Errors: `TypeError` if any arg is not an `Edge`. + Notes: *Targets must be declared nodes; sources may be external (e.g. `"rqs-1"`).* + +* **`add_load_balancer(load_balancer)`** *(optional)* + Adds a load balancer node. + Errors: `TypeError` if not a `LoadBalancer`. + +* **`add_simulation_settings(sim_settings)`** + Adds global settings (duration, sampling period, metric selection). + Errors: `TypeError` if not a `SimulationSettings`. + +* **`build_payload()` → `SimulationPayload`** + Finalize composition and run full validation. + Errors: `ValueError` on missing parts or invalid graph. + +--- + +## Validation performed by `build_payload()` + +(Implemented via Pydantic model validation across the payload’s schemas.) + +1. **Presence** + + * Requires: generator, client, **≥ 1 server**, **≥ 1 edge**, settings. + +2. **Unique IDs** + + * Duplicate server IDs or edge IDs are rejected. + +3. **Node types** + + * `client`, `server`, and `load_balancer` are fixed enums; enforced per node. + +4. **Edge integrity** + + * Every **target** must be a declared node ID. + * **External IDs** (e.g. the generator id) are allowed **only** as **sources**. + * **No self-loops** (`source != target`). + +5. **Load balancer sanity** (if present) + + * `server_covered ⊆ declared servers`. + * There is an **outgoing edge from the LB to every covered server**. + +6. **Engine rule: no fan-out except LB** + + * Among declared nodes, only the LB may have multiple outgoing edges. + +7. **Latency RV constraints (edges)** + + * `latency.mean > 0`; if `variance` provided, `variance ≥ 0`. + +If any rule fails, a **descriptive `ValueError`** points to the offending field/entity. + +--- + +## Typical errors & how to fix + +* **Missing parts** + `ValueError: The generator input must be instantiated before the simulation` + → Call the missing `add_*` method before `build_payload()`. + +* **Wrong type passed** + `TypeError: All the instances must be of the type Server` + → Ensure you pass `Server` objects to `add_servers(...)` (not dicts). + +* **Unknown edge target** + `ValueError: Edge client-1->srv-X references unknown target node 'srv-X'` + → Add a `Server(id="srv-X", ...)` or fix the edge target. + +* **LB coverage without edges** + `ValueError: Servers ['srv-2'] are covered by LB 'lb-1' but have no outgoing edge from it.` + → Add `Edge(source="lb-1", target="srv-2", ...)`. + +* **Illegal fan-out** + `ValueError: Only the load balancer can have multiple outgoing edges. Offending sources: ['client-1']` + → Route fan-out through a `LoadBalancer`. + +--- + +## Extended example — with Load Balancer + +```python +from asyncflow import AsyncFlow, SimulationRunner +from asyncflow.workload import RqsGenerator, RVConfig +from asyncflow.components import Client, Server, Endpoint, Edge, LoadBalancer +from asyncflow.settings import SimulationSettings +import simpy + +client = Client(id="client-1") + +srv1 = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 1024}, + endpoints=[Endpoint(endpoint_name="/api", + steps=[{"kind":"ram","step_operation":{"necessary_ram":64}}])], +) +srv2 = Server( + id="srv-2", + server_resources={"cpu_cores": 2, "ram_mb": 2048}, + endpoints=[Endpoint(endpoint_name="/api", + steps=[{"kind":"io_db","step_operation":{"io_waiting_time":0.012}}])], +) + +lb = LoadBalancer(id="lb-1", algorithms="round_robin", server_covered={"srv-1","srv-2"}) + +edges = [ + Edge(id="gen-client", source="rqs-1", target="client-1", + latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="client-lb", source="client-1", target="lb-1", + latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="lb-srv1", source="lb-1", target="srv-1", + latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="lb-srv2", source="lb-1", target="srv-2", + latency={"mean":0.002,"distribution":"exponential"}), + Edge(id="srv1-client", source="srv-1", target="client-1", + latency={"mean":0.003,"distribution":"exponential"}), + Edge(id="srv2-client", source="srv-2", target="client-1", + latency={"mean":0.003,"distribution":"exponential"}), +] + +payload = ( + AsyncFlow() + .add_generator(RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=120), + avg_request_per_minute_per_user=RVConfig(mean=20), + user_sampling_window=60, + )) + .add_client(client) + .add_servers(srv1, srv2) + .add_load_balancer(lb) + .add_edges(*edges) + .add_simulation_settings(SimulationSettings(total_simulation_time=600, sample_period_s=0.02)) + .build_payload() +) + +env = simpy.Environment() +results = SimulationRunner(env=env, simulation_input=payload).run() +``` + +--- + +## Tips & pitfalls + +* **IDs are case-sensitive** and must be unique per category (servers, edges, LB). +* **Edge targets must be declared nodes.** External IDs (like the generator) can only appear as **sources**. +* **LB fan-out only.** If you need branching, introduce a `LoadBalancer`. +* **RqsGenerator constraints:** + `avg_request_per_minute_per_user` **must** be Poisson; + `avg_active_users` must be **Poisson** or **Normal** (variance auto-filled if missing). +* **Step coherence:** + CPU step → `cpu_time`; RAM step → `necessary_ram`; I/O step → `io_waiting_time`. Exactly **one** per step. + +--- + +## Interop: YAML ↔ Python + +You can build the same payload from YAML and then use `SimulationRunner.from_yaml(...)`. Field names mirror the Python model names and the enum values (strings) are identical. + +--- + +## Versioning & stability + +* Exceptions: `TypeError` for wrong types passed to builder; `ValueError` for invalid or incomplete payloads. +* Validation rules and enum names are part of the public contract (semantic versioning applies). +* The builder does not mutate your objects; it assembles and validates them into a `SimulationPayload`. + + diff --git a/docs/api/high-level/runner.md b/docs/api/high-level/runner.md new file mode 100644 index 0000000..27a0c87 --- /dev/null +++ b/docs/api/high-level/runner.md @@ -0,0 +1,230 @@ +# AsyncFlow — Public API Reference: `SimulationRunner` + +`SimulationRunner` is the **orchestrator** of a simulation run. It takes a fully +validated `SimulationPayload`, instantiates the runtime actors, wires their +connections, starts the processes inside a `simpy.Environment`, collects sampled +metrics, advances the virtual clock, and returns a `ResultsAnalyzer` for +post-run querying and plotting. + +Use it together with the `AsyncFlow` builder (Python) or a YAML payload. + +--- + +## Imports + +```python +from asyncflow import SimulationRunner, AsyncFlow # high-level API +from asyncflow.settings import SimulationSettings +from asyncflow.components import Client, Server, Endpoint, Edge, LoadBalancer +from asyncflow.workload import RqsGenerator, RVConfig +import simpy +``` + +--- + +## Quick start + +```python +# 1) Build a validated payload (see the builder docs for details) +payload = ( + AsyncFlow() + .add_generator(RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=50), + avg_request_per_minute_per_user=RVConfig(mean=30), + )) + .add_client(Client(id="client-1")) + .add_servers( + Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 1024}, + endpoints=[Endpoint(endpoint_name="/hello", steps=[ + {"kind": "ram", "step_operation": {"necessary_ram": 32}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ])], + ) + ) + .add_edges( + Edge(id="gen-client", source="rqs-1", target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}), + Edge(id="client-srv1", source="client-1", target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}), + Edge(id="srv1-client", source="srv-1", target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}), + ) + .add_simulation_settings(SimulationSettings(total_simulation_time=300, sample_period_s=0.01)) + .build_payload() +) + +# 2) Run +env = simpy.Environment() +results = SimulationRunner(env=env, simulation_input=payload).run() + +# 3) Analyze +print(results.get_latency_stats()) +ts, rps = results.get_throughput_series() +sampled = results.get_sampled_metrics() +``` + +--- + +## Class reference + +```python +class SimulationRunner: + def __init__(self, *, env: simpy.Environment, simulation_input: SimulationPayload) -> None: ... + def run(self) -> ResultsAnalyzer: ... + @classmethod + def from_yaml(cls, *, env: simpy.Environment, yaml_path: str | Path) -> "SimulationRunner": ... +``` + +### Parameters + +* **`env: simpy.Environment`** + The SimPy environment that controls virtual time. You own its lifetime. + +* **`simulation_input: SimulationPayload`** + A fully validated payload (typically created with `AsyncFlow.build_payload()` or + parsed from YAML). It contains workload, topology graph, and settings. + +### Returns + +* **`run() -> ResultsAnalyzer`** + A results façade exposing: + + * `get_latency_stats() -> dict` (mean, median, p95, p99, …) + * `get_throughput_series() -> (timestamps, rps)` + * `get_sampled_metrics() -> dict[str, dict[str, list[float]]]` + * plotting helpers: `plot_latency_distribution(ax)`, `plot_throughput(ax)`, + `plot_server_queues(ax)`, `plot_ram_usage(ax)` + +### Convenience: YAML entry point + +```python +env = simpy.Environment() +runner = SimulationRunner.from_yaml(env=env, yaml_path="scenario.yml") +results = runner.run() +``` + +`from_yaml` uses `yaml.safe_load` and validates with the same Pydantic schemas, +so it enforces the exact same contract as the builder. + +--- + +## Lifecycle & internal phases + +`run()` performs the following steps: + +1. **Build runtimes** + + * `RqsGeneratorRuntime` (workload) + * `ClientRuntime` + * `ServerRuntime` for each server (CPU/RAM resources bound) + * `LoadBalancerRuntime` (optional) + +2. **Wire edges** + Creates an `EdgeRuntime` for each edge and assigns the appropriate *inbox* + (`simpy.Store`) of the target actor. Sets the `out_edge` (or `out_edges` for + the load balancer) on the source actor. + +3. **Start processes** + Registers every actor’s `.start()` coroutine in the environment and starts the + **SampledMetricCollector** that snapshots: + + * server **ready queue length**, **I/O queue length**, **RAM in use** + * edge **concurrent connections** + at the configured `sample_period_s`. These sampled metrics are **mandatory** + in this version. + +4. **Advance the clock** + `env.run(until=SimulationSettings.total_simulation_time)` + +5. **Return analyzer** + Wraps the collected state into `ResultsAnalyzer` for stats & plots. + +--- + +## Input contract (what the runner expects) + +The runner assumes `simulation_input` has already passed full validation: + +* All edge targets are declared nodes; external IDs appear only as sources. +* Load balancer coverage and edges are coherent. +* No self-loops; only the LB fans out among declared nodes. +* Edge latency RVs have `mean > 0` (and `variance ≥ 0` if provided). +* Server resources meet minimums (≥ 1 core, ≥ 256 MB RAM), etc. + +> Build with `AsyncFlow` or load from YAML — both paths enforce the same rules. + +--- + +## Error handling + +* **Type errors (builder misuse)** should not reach the runner; they’re raised by the builder (`TypeError`) before `build_payload()`. +* **Validation errors** (`ValueError`) are raised during payload construction/validation, not by the runner. +* **Wiring errors** (`TypeError`) are guarded by validation and indicate an unexpected mismatch between payload and runtime types. With a validated payload, you shouldn’t see them. + +--- + +## Determinism & RNG + +The runner uses `numpy.random.default_rng()` internally. Seeding is not yet a +public parameter; exact reproducibility across runs is **not guaranteed** in +this version. If you need strict reproducibility, pin your environment and keep +payloads identical; a dedicated seeding hook may be added in a future release. + +--- + +## Performance characteristics + +* **Runtime cost** scales with the number of requests and the complexity of + endpoint steps (CPU vs I/O waits). +* **Sampling memory** roughly scales as + `(#entities × #enabled sampled metrics) × (total_simulation_time / sample_period_s)`. + For long runs, consider a larger `sample_period_s` (e.g., `0.02–0.05`) to + reduce the size of time series. +* The collector is a single coroutine that performs `O(entities)` appends on + each tick; the hot path inside actors remains `O(1)` per event. + +--- + +## Usage with Load Balancers + +Topologies **with a LB** are first-class: + +* Only the LB may have multiple outgoing edges (fan-out). +* The analyzer operates on **lists** of servers and edges; plots will naturally + show one line per server/edge where appropriate. +* Validation ensures every `server_covered` by the LB has a corresponding LB→server edge. + +--- + +## One-shot runner (recommended) + +A `SimulationRunner` instance is designed to **run once**. For a new scenario +(or new settings), create a **new** `simpy.Environment` and a **new** +`SimulationRunner`. Reusing a runner after `run()` is not supported. + +--- + +## Best practices + +* **Let the builder fail fast.** Always construct payloads via `AsyncFlow` (or YAML + validation) before running. +* **Keep steps coherent.** CPU step → `cpu_time`, RAM step → `necessary_ram`, I/O step → `io_waiting_time`. Exactly one key per step. +* **Model the network realistically.** Put latency RVs on **every** hop that matters (client↔LB, LB↔server, server↔client). +* **Tune sampling.** High-frequency sampling is useful for short diagnostic runs; increase `sample_period_s` for long capacity sweeps. + +--- + +## See also + +* **Builder:** `AsyncFlow` — compose and validate the payload (workload, topology, settings). +* **Analyzer:** `ResultsAnalyzer` — query KPIs and plot latency/throughput/queues/RAM. +* **Workload:** `RqsGenerator`, `RVConfig` — define traffic models (Poisson or Gaussian–Poisson). +* **Components:** `Client`, `Server`, `Endpoint`, `Edge`, `LoadBalancer`. + +This API keeps **assembly** and **execution** separate: you design and validate +your system with `AsyncFlow`, then hand it to `SimulationRunner` to execute and +measure — a clean workflow that scales from minimal examples to complex, +load-balanced topologies. diff --git a/docs/dev-workflow-guide.md b/docs/guides/dev-workflow.md similarity index 99% rename from docs/dev-workflow-guide.md rename to docs/guides/dev-workflow.md index ff0e3ce..c5d06ef 100644 --- a/docs/dev-workflow-guide.md +++ b/docs/guides/dev-workflow.md @@ -1,6 +1,6 @@ # **Development Workflow & Architecture Guide** -This document describes the development workflow, repository architecture, and branching strategy for **AsyncFlow** +This document describes the development workflow, repository architecture, branching strategy and CI/CD for **AsyncFlow** --- ## 1) Repository Layout diff --git a/docs/index.md b/docs/index.md index 651a0ee..a4affc2 100644 --- a/docs/index.md +++ b/docs/index.md @@ -25,6 +25,7 @@ AsyncFlow is a discrete-event simulator for Python async backends (FastAPI/Uvico * **[Builder Guide](guides/builder.md)** — Programmatically assemble a `SimulationPayload` in Python with validation and examples. * **[YAML Input Guide](guides/yaml-builder.md)** — Author scenarios in YAML: exact schema, units, constraints, runnable samples. +* **[Dev workflow Guide](guides/dev-workflow.md)** — Describes the development workflow, repository architecture, branching strategy and CI/CD for **AsyncFlow** --- diff --git a/examples/builder_input/load_balancer/two_servers.png b/examples/builder_input/load_balancer/two_servers.png new file mode 100644 index 0000000000000000000000000000000000000000..11ceaf3b2120d027084b649dd09565b1d0c79ab9 GIT binary patch literal 142265 zcmce;by!v17e9DOX;7s5ML-%vx+DY@5k)|h2I=k&X=#;G2|*F*lI~U-C8R+>kPhjZ zb$s*to0&gnp6B|Ya_>23@4fa~pIG}oy?aNI5RV!Ug+dYDydkfOLSYu7P#BCj*zi9B z)34FthnUlKO{e>|CQhyf4#p^D11CExTPG`XLq->42S;;T8v!1EJ|00XMl&ZTJ4Z2I zUh99}!DH)S%KPxhOdPI)Yj@+JBML=qfc!!GAf0KBLPMc$%FC#^C9Nd8x?LPRY_G3= z&3)-z$VJ9$nCwhW?=CA_Tq}C>$7-1F#hrAA)-YZ6bc#aHxNtI>%VCqL{)UWxcsCZY zacTooWIdQ)cds3ve(QQ{;$mWI;`+ks(~&zpeX)RPkEr91u9YO$slAgW%3y4S)RS4UEUsSM<;S z&us{)3jg~Cocdq_&Huh3P)`n<>)%^2;xrX7{eSit`S*eeX@U)!g9nwg#FB~d$+@Pb 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zsj4at%IJG>rg9P+atJQ}`*SXR0D#6wg#`_?TzNsyYyhN1XK!sB0NK1yEH)dj1IND9e4GN7LC{$cnWu>sST9FFl!=Ma%f@kGW@ct0 zd|ao;zvbtC{+vN5Y@kE}S?SfJeo$nD9voumMyN1XgLYU%hX5&sf<86UjES^C73oPV zbGjXGUO+hMe=i`PFei%)yaLIFGC)E366ofjO1>kRnRIYM8~{(RbL(=-GyYQt*aXf+ zmeV53deB`AcQN(33;1FXhHwJ<0cna}{87e+N`c-6ND?<^NJYAwCcpytR|l*z(r=MP z(_(p!2@?|WFvH!uI8t=2_VmyM%+Vdk;|oB@1+kHKb0Fy@F=VM3gy2S5R9HdAR0M13 za(b|EorT3}$st(W3xGYttxsUeiC8X-N{hW>*@d client ──> LB ──> srv-1 + └─> srv-2 + srv-1 ──> client + srv-2 ──> client + +Load: + ~120 active users, 20 req/min each (Poisson by default). + +Servers: + srv-1: 1 CPU core, 1GB RAM, endpoint with CPU→RAM→IO + srv-2: 2 CPU cores, 2GB RAM, endpoint with RAM→IO(DB-like) + +Network: + 2–3ms mean (exponential) latency on each edge. + +What this script does: + 1) Build Pydantic models (generator, client, LB, servers, edges, settings). + 2) Compose the SimulationPayload via AsyncFlow (builder pattern). + 3) Run the simulation with SimulationRunner. + 4) Print latency stats, throughput timeline, and a sampled-metrics preview. + 5) Save a 2×2 plot figure (latency, throughput, server queues, RAM). +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Iterable, List, Mapping, TYPE_CHECKING + +import numpy as np +import simpy + +# ── AsyncFlow domain imports (match your working paths) ──────────────────────── +from asyncflow.builder.asyncflow_builder import AsyncFlow +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.workload.rqs_generator import RqsGenerator +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.endpoint import Endpoint +from asyncflow.schemas.topology.nodes import Client, Server, LoadBalancer +from asyncflow.schemas.topology.edges import Edge +from asyncflow.config.constants import LatencyKey, SampledMetricName + + + + +# ───────────────────────────────────────────────────────────── +# Pretty printers (compact, readable output) +# ───────────────────────────────────────────────────────────── +def print_latency_stats(res: ResultsAnalyzer) -> None: + stats: Mapping[LatencyKey, float] = res.get_latency_stats() + print("\n════════ LATENCY STATS ════════") + if not stats: + print("(empty)") + return + + order: List[LatencyKey] = [ + LatencyKey.TOTAL_REQUESTS, + LatencyKey.MEAN, + LatencyKey.MEDIAN, + LatencyKey.STD_DEV, + LatencyKey.P95, + LatencyKey.P99, + LatencyKey.MIN, + LatencyKey.MAX, + ] + for key in order: + if key in stats: + print(f"{key.name:<20} = {stats[key]:.6f}") + + +def print_throughput(res: ResultsAnalyzer) -> None: + timestamps, rps = res.get_throughput_series() + print("\n════════ THROUGHPUT (req/sec) ════════") + if not timestamps: + print("(empty)") + return + for t, rate in zip(timestamps, rps): + print(f"t={t:4.1f}s → {rate:6.2f} rps") + + +def print_sampled_preview(res: ResultsAnalyzer) -> None: + sampled = res.get_sampled_metrics() + print("\n════════ SAMPLED METRICS (preview) ════════") + if not sampled: + print("(empty)") + return + + # Keys may be enums or strings depending on your analyzer; handle both. + def _name(m): # pragma: no cover + return m.name if hasattr(m, "name") else str(m) + + for metric, series in sampled.items(): + print(f"\n📈 {_name(metric)}:") + for entity, vals in series.items(): + head = list(vals[:5]) if vals else [] + print(f" - {entity}: len={len(vals)}, first={head}") + + +# ───────────────────────────────────────────────────────────── +# Tiny helpers for sanity checks (optional) +# ───────────────────────────────────────────────────────────── +def _mean(series: Iterable[float]) -> float: + arr = np.asarray(list(series), dtype=float) + return float(np.mean(arr)) if arr.size else 0.0 + + +def run_sanity_checks( + runner: SimulationRunner, + res: ResultsAnalyzer, +) -> None: + print("\n════════ SANITY CHECKS (rough) ════════") + w = runner.simulation_input.rqs_input + lam_rps = ( + float(w.avg_active_users.mean) + * float(w.avg_request_per_minute_per_user.mean) + / 60.0 + ) + + # Observed throughput + _, rps_series = res.get_throughput_series() + rps_observed = _mean(rps_series) + print( + f"• Mean throughput (rps) expected≈{lam_rps:.3f} " + f"observed={rps_observed:.3f}" + ) + + sampled = res.get_sampled_metrics() + ram_series = sampled.get(SampledMetricName.RAM_IN_USE, {}) + ioq_series = sampled.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) + ready_series = sampled.get(SampledMetricName.READY_QUEUE_LEN, {}) + + ram_mean = _mean([_mean(v) for v in ram_series.values()]) if ram_series else 0.0 + ioq_mean = _mean([_mean(v) for v in ioq_series.values()]) if ioq_series else 0.0 + ready_mean = _mean([_mean(v) for v in ready_series.values()]) if ready_series else 0.0 + + print(f"• Mean RAM in use (MB) observed={ram_mean:.3f}") + print(f"• Mean I/O queue length observed={ioq_mean:.3f}") + print(f"• Mean ready queue length observed={ready_mean:.3f}") + + +# ───────────────────────────────────────────────────────────── +# Build the LB + 2 servers scenario via AsyncFlow (builder) +# ───────────────────────────────────────────────────────────── +def build_payload_with_lb() -> SimulationPayload: + """ + Construct the SimulationPayload programmatically using the builder: + - Generator (120 users, 20 rpm each) + - Client + - Load balancer (round_robin) covering two servers + - Two servers with distinct endpoints + - Edges for all hops (gen→client, client→lb, lb→srv1/2, srv1/2→client) + - Simulation settings: 600s total, sample period 20ms + """ + # 1) Request generator + generator = RqsGenerator( + id="rqs-1", + avg_active_users={"mean": 120}, # Poisson default + avg_request_per_minute_per_user={"mean": 20}, # MUST be Poisson + user_sampling_window=60, + ) + + # 2) Client + client = Client(id="client-1") + + # 3) Servers with distinct endpoints + ep_srv1 = Endpoint( + endpoint_name="/api", + # include 'probability' if your Endpoint schema supports it + probability=1.0, # remove if your Endpoint doesn't have this field + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "ram", "step_operation": {"necessary_ram": 64}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.012}}, + ], + ) + srv1 = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 1024}, + endpoints=[ep_srv1], + ) + + ep_srv2 = Endpoint( + endpoint_name="/api", + probability=1.0, # remove if not supported in your schema + steps=[ + {"kind": "ram", "step_operation": {"necessary_ram": 96}}, + {"kind": "io_db", "step_operation": {"io_waiting_time": 0.020}}, + ], + ) + srv2 = Server( + id="srv-2", + server_resources={"cpu_cores": 2, "ram_mb": 2048}, + endpoints=[ep_srv2], + ) + + # 4) Load balancer (round_robin) + lb = LoadBalancer( + id="lb-1", + algorithms="round_robin", + server_covered={"srv-1", "srv-2"}, + ) + + # 5) Edges with exponential latency (2–3 ms) + edges = [ + Edge( + id="gen-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="client-lb", + source="client-1", + target="lb-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="lb-srv1", + source="lb-1", + target="srv-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="lb-srv2", + source="lb-1", + target="srv-2", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="srv1-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="srv2-client", + source="srv-2", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + ] + + # 6) Simulation settings + settings = SimulationSettings( + total_simulation_time=600, + sample_period_s=0.02, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + # 7) Assemble the payload via the builder + flow = ( + AsyncFlow() + .add_generator(generator) + .add_client(client) + .add_servers(srv1, srv2) + .add_load_balancer(lb) + .add_edges(*edges) + .add_simulation_settings(settings) + ) + + return flow.build_payload() + + +# ───────────────────────────────────────────────────────────── +# Main entry-point +# ───────────────────────────────────────────────────────────── +def main() -> None: + """ + Build → wire → run the simulation, then print diagnostics and save plots. + """ + env = simpy.Environment() + payload = build_payload_with_lb() + + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + + # Human-friendly diagnostics + print_latency_stats(results) + print_throughput(results) + print_sampled_preview(results) + + # Optional sanity checks (very rough) + run_sanity_checks(runner, results) + + # Save plots (2×2 figure) + try: + from matplotlib import pyplot as plt # noqa: PLC0415 + + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + results.plot_latency_distribution(axes[0, 0]) + results.plot_throughput(axes[0, 1]) + results.plot_server_queues(axes[1, 0]) + results.plot_ram_usage(axes[1, 1]) + fig.tight_layout() + + out_path = Path(__file__).parent / "two_servers.png" + fig.savefig(out_path) + print(f"\n🖼️ Plots saved to: {out_path}") + except Exception as exc: # Matplotlib not installed or plotting failed + print(f"\n[plotting skipped] {exc!r}") + + +if __name__ == "__main__": + main() diff --git a/examples/builder_input/single_server/single_server.png b/examples/builder_input/single_server/single_server.png new file mode 100644 index 0000000000000000000000000000000000000000..f723f7cc694b965502ac94565c7c58c718a6f0f3 GIT binary patch literal 157658 zcmb@u1yEIc*f+WXr4dkRB*Xw|=@yVsK?FfS>FzFRX^~P=5Rej)?vhSL38gzkkVd+{ zXLEw2S9W=|H1^o#oXQB+r1pz9 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simple LB-aware heuristics. + +Usage: + python src/app/example/run_lb_from_yaml.py \ + --yaml src/app/example/data/two_servers_lb.yml +""" + +from __future__ import annotations + +from argparse import ArgumentParser +from pathlib import Path +from typing import Dict, Iterable, List, Mapping, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import simpy + +from asyncflow.config.constants import ( # only for basic step-kind/ops inspection + EndpointStepCPU, + EndpointStepIO, + EndpointStepRAM, + LatencyKey, + StepOperation, +) +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner + + +# ───────────────────────────────────────────────────────────── +# Pretty printers (same style as your single-server script) +# ───────────────────────────────────────────────────────────── +def print_latency_stats(res: ResultsAnalyzer) -> None: + """Print latency statistics returned by the analyzer.""" + stats: Mapping[LatencyKey, float] = res.get_latency_stats() + print("\n════════ LATENCY STATS ════════") + if not stats: + print("(empty)") + return + + order: List[LatencyKey] = [ + LatencyKey.TOTAL_REQUESTS, + LatencyKey.MEAN, + LatencyKey.MEDIAN, + LatencyKey.STD_DEV, + LatencyKey.P95, + LatencyKey.P99, + LatencyKey.MIN, + LatencyKey.MAX, + ] + for key in order: + if key in stats: + print(f"{key.name:<20} = {stats[key]:.6f}") + + +def print_throughput(res: ResultsAnalyzer) -> None: + """Print 1-second throughput buckets.""" + timestamps, rps = res.get_throughput_series() + print("\n════════ THROUGHPUT (req/sec) ════════") + if not timestamps: + print("(empty)") + return + + for t, rate in zip(timestamps, rps): + print(f"t={t:4.1f}s → {rate:6.2f} rps") + + +def print_sampled_preview(res: ResultsAnalyzer) -> None: + """Print first 5 samples of each sampled metric series.""" + sampled: Dict[str, Dict[str, List[float]]] = res.get_sampled_metrics() + print("\n════════ SAMPLED METRICS ════════") + if not sampled: + print("(empty)") + return + + for metric, series in sampled.items(): + print(f"\n📈 {metric}:") + for entity, vals in series.items(): + head = list(vals[:5]) if vals else [] + print(f" - {entity}: len={len(vals)}, first={head}") + + +# ───────────────────────────────────────────────────────────── +# Plotting +# ───────────────────────────────────────────────────────────── +def save_all_plots(res: ResultsAnalyzer, out_path: Path) -> None: + """Generate the 2x2 plot figure and save it to `out_path`.""" + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + res.plot_latency_distribution(axes[0, 0]) + res.plot_throughput(axes[0, 1]) + res.plot_server_queues(axes[1, 0]) + res.plot_ram_usage(axes[1, 1]) + fig.tight_layout() + fig.savefig(out_path) + print(f"\n🖼️ Plots saved to: {out_path}") + + +# ───────────────────────────────────────────────────────────── +# Sanity checks (LB-aware, still rough) +# ───────────────────────────────────────────────────────────── + +def run_sanity_checks(runner: SimulationRunner, res: ResultsAnalyzer) -> None: + """ + Sanity checks LB-aware (round-robin): observed vs expected + """ + from asyncflow.config.constants import ( + EndpointStepCPU, EndpointStepIO, EndpointStepRAM, StepOperation, LatencyKey + ) + import numpy as np + + def _mean(arr): + a = np.asarray(list(arr), dtype=float) + return float(a.mean()) if a.size else 0.0 + + # 1) λ + w = runner.simulation_input.rqs_input + lam = float(w.avg_active_users.mean) * float(w.avg_request_per_minute_per_user.mean) / 60.0 + + topo = runner.simulation_input.topology_graph + servers = {s.id: s for s in topo.nodes.servers} + client_id = topo.nodes.client.id + lb = topo.nodes.load_balancer + lb_id = lb.id if lb else None + gen_id = runner.simulation_input.rqs_input.id + + # 2) LB (round_robin -> 1/N) + if lb and lb.server_covered: + covered = [sid for sid in lb.server_covered if sid in servers] + N = max(1, len(covered)) + shares = {sid: 1.0 / N for sid in covered} + else: + only = next(iter(servers.keys())) + shares = {only: 1.0} + + # 3) endpoint totals per server + def endpoint_totals(server): + cpu_s = io_s = ram_mb = 0.0 + for ep in getattr(server, "endpoints", []) or []: + prob = getattr(ep, "probability", 1.0) + for step in ep.steps: + k = step.kind + op = step.step_operation + if isinstance(k, EndpointStepCPU): + cpu_s += prob * float(op[StepOperation.CPU_TIME]) + elif isinstance(k, EndpointStepIO): + io_s += prob * float(op[StepOperation.IO_WAITING_TIME]) + elif isinstance(k, EndpointStepRAM): + ram_mb += prob * float(op[StepOperation.NECESSARY_RAM]) + return cpu_s, io_s, ram_mb + + per_srv = {sid: endpoint_totals(srv) for sid, srv in servers.items()} + + # 4) mappa latencies of edges per role (source,target) + mean_gen_client = 0.0; id_gen_client = None + mean_client_lb = 0.0; id_client_lb = None + mean_lb_srv = {} # sid -> mean + mean_srv_client = {} # sid -> mean + id_lb_srv = {} # sid -> edge_id + id_srv_client = {} # sid -> edge_id + + for e in topo.edges: + s, t, mu = e.source, e.target, float(e.latency.mean) + if s == gen_id and t == client_id: + mean_gen_client = mu; id_gen_client = e.id + elif s == client_id and lb_id and t == lb_id: + mean_client_lb = mu; id_client_lb = e.id + elif lb_id and s == lb_id and t in servers: + mean_lb_srv[t] = mu; id_lb_srv[t] = e.id + elif s in servers and t == client_id: + mean_srv_client[s] = mu; id_srv_client[s] = e.id + + # 5) expected: average latencies + cpu_exp = sum(shares[sid] * per_srv[sid][0] for sid in shares) + io_exp = sum(shares[sid] * per_srv[sid][1] for sid in shares) + net_exp = ( + mean_gen_client + mean_client_lb + + sum(shares[sid] * (mean_lb_srv.get(sid, 0.0) + mean_srv_client.get(sid, 0.0)) for sid in shares) + ) + latency_expected = cpu_exp + io_exp + net_exp + + # 6) observed: throughput & latencies + stats = res.get_latency_stats() + latency_observed = float(stats.get(LatencyKey.MEAN, 0.0)) + _, rps_series = res.get_throughput_series() + rps_observed = _mean(rps_series) + + # 7) expected: RAM e I/O queue as a sum over server + ram_expected = sum((shares[sid] * lam) * (per_srv[sid][0] + per_srv[sid][1]) * per_srv[sid][2] for sid in shares) + ioq_expected = sum((shares[sid] * lam) * per_srv[sid][1] for sid in shares) + + # 8) observed: RAM (sum) and I/O queue sum + sampled = res.get_sampled_metrics() + ram_series = sampled.get("ram_in_use", {}) + ioq_series = sampled.get("event_loop_io_sleep", {}) + ram_observed = sum(_mean(vals) for vals in ram_series.values()) if ram_series else 0.0 + ioq_observed = sum(_mean(vals) for vals in ioq_series.values()) if ioq_series else 0.0 + + # 9) print + REL_TOL = 0.30 + def tick(label, exp, obs): + delta = (abs(obs - exp) / abs(exp)) if exp else 0.0 + icon = "✓" if delta <= REL_TOL else "⚠" + print(f"{icon} {label:<28} expected≈{exp:.3f} observed={obs:.3f} Δ={delta*100:.1f}%") + + print("\n════════ SANITY CHECKS (LB-aware) ════════") + tick("Mean throughput (rps)", lam, rps_observed) + tick("Mean latency (s)", latency_expected, latency_observed) + tick("Mean RAM (MB)", ram_expected, ram_observed) + tick("Mean I/O queue", ioq_expected, ioq_observed) + + # 10) Edge concurrency estimation + edge_conc = sampled.get("edge_concurrent_connection", {}) + if edge_conc: + print("\n— Edge concurrency (LB-aware) —") + means_obs = {eid: _mean(vals) for eid, vals in edge_conc.items()} + + if id_gen_client: + tick(f"edge {id_gen_client}", lam * mean_gen_client, means_obs.get(id_gen_client, 0.0)) + if id_client_lb: + tick(f"edge {id_client_lb}", lam * mean_client_lb, means_obs.get(id_client_lb, 0.0)) + + for sid, p in shares.items(): + lam_i = p * lam + eid = id_lb_srv.get(sid) + if eid: + tick(f"edge {eid}", lam_i * mean_lb_srv.get(sid, 0.0), means_obs.get(eid, 0.0)) + eid = id_srv_client.get(sid) + if eid: + tick(f"edge {eid}", lam_i * mean_srv_client.get(sid, 0.0), means_obs.get(eid, 0.0)) + + # Extra + print("\n— Diagnostics —") + print("λ={:.3f} rps | E[cpu]={:.3f}s E[io]={:.3f}s E[net]≈{:.3f}s | E[RAM/req]={:.1f} MB" + .format(lam, cpu_exp, io_exp, net_exp, sum(shares[sid]*per_srv[sid][2] for sid in shares))) + + + +# ───────────────────────────────────────────────────────────── +# Main +# ───────────────────────────────────────────────────────────── +def main() -> None: + """Parse args, run simulation, print/plot, sanity-check (LB topology).""" + parser = ArgumentParser(description="Run AsyncFlow LB scenario from YAML and print outputs + sanity checks.") + parser.add_argument( + "--yaml", + type=Path, + default=Path(__file__).parent.parent / "data" / "two_servers_lb.yml", + help="Path to the simulation YAML file.", + ) + parser.add_argument( + "--out", + type=Path, + default=Path(__file__).parent / "two_servers.png", + help="Path to the output image (plots).", + ) + args = parser.parse_args() + + yaml_path: Path = args.yaml + if not yaml_path.exists(): + raise FileNotFoundError(f"YAML not found: {yaml_path}") + + env = simpy.Environment() + runner = 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default=Path(__file__).parent / "single_server.png", help="Path to the output image (plots).", ) args = parser.parse_args() diff --git a/examples/your_example.py b/examples/yaml_input/your_example.py similarity index 100% rename from examples/your_example.py rename to examples/yaml_input/your_example.py diff --git a/pyproject.toml b/pyproject.toml index 1348af6..6566c84 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,14 +1,41 @@ [tool.poetry] -name = "AsyncFlow" +name = "asyncflow-simulator" version = "0.1.0" -description = "Simulate distributed system for what if analysis for the capacity planning" +description = "Digital-twin simulator for distributed async systems. Build what-if scenarios and quantify capacity, latency and throughput offline—before you deploy." authors = ["Gioele Botta"] readme = "README.md" - +license = "MIT" packages = [ { include = "asyncflow", from = "src" } ] +include = ["LICENSE", "src/asyncflow/py.typed"] +exclude = ["tests", "docs", "scripts", ".github", "alembic"] + +# Better pypi discovery +keywords = [ + "simulation", "simpy", "asyncio", "capacity-planning", "performance", + "fastapi", "uvicorn", "distributed-systems", "queuing-theory" +] + +homepage = "https://github.com//AsyncFlow-Simulator" # TO COMPLETE +repository = "https://github.com//AsyncFlow-Simulator" # TO COMPLETE +documentation = "https://github.com//AsyncFlow-Simulator/tree/main/docs" # TO COMPLETE + +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Simulation", + "Topic :: System :: Distributed Computing", + "Topic :: Software Development :: Testing", + "Topic :: System :: Benchmark", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.12", + "Typing :: Typed", + "Operating System :: OS Independent", +] + [tool.poetry.dependencies] python = "^3.12" pydantic-settings = "^2.10.1" @@ -96,4 +123,4 @@ ignore_missing_imports = true warn_unused_ignores = true show_error_codes = true pretty = true -exclude = ["^alembic/", "^.venv/"] +exclude = ["^.venv/"] diff --git a/src/asyncflow/__init__.py b/src/asyncflow/__init__.py index 5bdbdea..0f38c83 100644 --- a/src/asyncflow/__init__.py +++ b/src/asyncflow/__init__.py @@ -4,4 +4,4 @@ from asyncflow.builder.asyncflow_builder import AsyncFlow from asyncflow.runtime.simulation_runner import SimulationRunner -__all__ = ["AsyncFlow", "SimulationRunner"] +__all__ = ["AsyncFlow", "SimulationRunner"] diff --git a/src/asyncflow/analysis/__init__.py b/src/asyncflow/analysis/__init__.py new file mode 100644 index 0000000..825de6e --- /dev/null +++ b/src/asyncflow/analysis/__init__.py @@ -0,0 +1,5 @@ +"""Public module exposing the results analyzer""" + +from asyncflow.metrics.analyzer import ResultsAnalyzer + +__all__ = ["ResultsAnalyzer"] diff --git a/src/asyncflow/metrics/analyzer.py b/src/asyncflow/metrics/analyzer.py index 35b43bb..16448bc 100644 --- a/src/asyncflow/metrics/analyzer.py +++ b/src/asyncflow/metrics/analyzer.py @@ -193,8 +193,8 @@ def plot_throughput(self, ax: Axes) -> None: def plot_server_queues(self, ax: Axes) -> None: """Plot the server queues""" metrics = self.get_sampled_metrics() - ready = metrics.get(SampledMetricName.READY_QUEUE_LEN, {}) - io_q = metrics.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) + ready = metrics.get(SampledMetricName.READY_QUEUE_LEN.value, {}) + io_q = metrics.get(SampledMetricName.EVENT_LOOP_IO_SLEEP.value, {}) if not (ready or io_q): ax.text(0.5, 0.5, SERVER_QUEUES_PLOT.no_data, ha="center", va="center") @@ -219,7 +219,7 @@ def plot_server_queues(self, ax: Axes) -> None: def plot_ram_usage(self, ax: Axes) -> None: """Plot the ram usage""" metrics = self.get_sampled_metrics() - ram = metrics.get(SampledMetricName.RAM_IN_USE, {}) + ram = metrics.get(SampledMetricName.RAM_IN_USE.value, {}) if not ram: ax.text(0.5, 0.5, RAM_PLOT.no_data, ha="center", va="center") diff --git a/src/asyncflow/py.typed b/src/asyncflow/py.typed new file mode 100644 index 0000000..e69de29 From d77415ef346e381f78f830f3d6ce22f56e357d07 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Fri, 15 Aug 2025 18:36:16 +0200 Subject: [PATCH 32/52] sanity ci check --- docs/guides/builder.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/builder.md b/docs/guides/builder.md index e90fe22..2a99daf 100644 --- a/docs/guides/builder.md +++ b/docs/guides/builder.md @@ -9,7 +9,7 @@ Under the hood, the builder assembles a single `SimulationPayload`: ```python SimulationPayload( rqs_input=RqsGenerator(...), # traffic generator (workload) - topology_graph=TopologyGraph(...), # system architecture as a graph + topology_graph=TopologyGraph(...), # system as a graph sim_settings=SimulationSettings(...), # global settings and metrics ) ``` From de52d0479e7ee877e7deae315b3d162d68faab66 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Sun, 17 Aug 2025 17:43:25 +0200 Subject: [PATCH 33/52] Ci for main (#16) * Readme final, docs improved * Docs improvement, refactor analyzer, example added * Added scripts for setup, added pytest, improved readme * Example LB improvements * CI for main + system tests --- .github/workflows/ci-develop.yml | 91 +--- .github/workflows/ci-main.yml | 0 README.md | 495 +++++++++-------- docs/api/analyzer.md | 208 ++++++++ docs/guides/{builder.md => python-builder.md} | 0 ...load-samplers.md => requests-generator.md} | 0 .../load_balancer/lb_dashboard.png | Bin 0 -> 256871 bytes .../load_balancer/lb_server_srv-1_metrics.png | Bin 0 -> 129889 bytes .../load_balancer/lb_server_srv-2_metrics.png | Bin 0 -> 131498 bytes .../load_balancer/two_servers.png | Bin 142265 -> 0 bytes .../load_balancer/two_servers.py | 310 ++++------- .../single_server/builder_service_plots.png | Bin 0 -> 313774 bytes .../single_server/single_server.png | Bin 157658 -> 0 bytes .../single_server/single_server.py | 307 +++-------- examples/yaml_input/data/two_servers_lb.yml | 127 ++--- examples/yaml_input/lb_dashboard.png | Bin 0 -> 272830 bytes .../yaml_input/lb_server_srv-1_metrics.png | Bin 0 -> 112721 bytes .../yaml_input/lb_server_srv-2_metrics.png | Bin 0 -> 115895 bytes .../yaml_input/load_balancer/two_servers.png | Bin 153719 -> 0 bytes .../yaml_input/load_balancer/two_servers.py | 309 ++--------- .../single_server/single_server.png | Bin 155288 -> 0 bytes .../yaml_input/single_server/single_server.py | 340 +++--------- .../single_server_results_dashboard.png | Bin 0 -> 161480 bytes .../single_server_results_io_queue_srv-1.png | Bin 0 -> 52755 bytes .../single_server_results_ram_srv-1.png | Bin 0 -> 56702 bytes ...ingle_server_results_ready_queue_srv-1.png | Bin 0 -> 61628 bytes examples/yaml_input/your_example.py | 54 -- poetry.lock | 319 +++++------ pyproject.toml | 10 +- pytest.ini | 5 +- readme_img/topology.png | Bin 0 -> 2285328 bytes scripts/dev_setup.ps1 | 176 ++++++ scripts/dev_setup.sh | 135 +++++ scripts/quality_check.ps1 | 14 + .../{quality-check.sh => quality_check.sh} | 1 - scripts/run_sys_tests.ps1 | 55 ++ scripts/run_sys_tests.sh | 42 ++ scripts/run_tests.ps1 | 39 ++ scripts/run_tests.sh | 34 ++ simulation_base.png | Bin 0 -> 123625 bytes src/asyncflow/components/__init__.py | 2 +- src/asyncflow/metrics/analyzer.py | 503 +++++++++++++++--- ...le_server.py => test_int_single_server.py} | 0 tests/system/test_sys_lb_two_servers.py | 180 +++++++ tests/system/test_sys_single_server.py | 136 +++++ tests/unit/metrics/test_analyzer.py | 260 ++++++--- tests/unit/public_api/test_import.py | 86 +++ 47 files changed, 2552 insertions(+), 1686 deletions(-) create mode 100644 .github/workflows/ci-main.yml create mode 100644 docs/api/analyzer.md rename docs/guides/{builder.md => python-builder.md} (100%) rename docs/internals/{workload-samplers.md => requests-generator.md} (100%) create mode 100644 examples/builder_input/load_balancer/lb_dashboard.png create mode 100644 examples/builder_input/load_balancer/lb_server_srv-1_metrics.png create mode 100644 examples/builder_input/load_balancer/lb_server_srv-2_metrics.png delete mode 100644 examples/builder_input/load_balancer/two_servers.png create mode 100644 examples/builder_input/single_server/builder_service_plots.png delete mode 100644 examples/builder_input/single_server/single_server.png create mode 100644 examples/yaml_input/lb_dashboard.png create mode 100644 examples/yaml_input/lb_server_srv-1_metrics.png create mode 100644 examples/yaml_input/lb_server_srv-2_metrics.png delete mode 100644 examples/yaml_input/load_balancer/two_servers.png delete mode 100644 examples/yaml_input/single_server/single_server.png create mode 100644 examples/yaml_input/single_server/single_server_results_dashboard.png create mode 100644 examples/yaml_input/single_server/single_server_results_io_queue_srv-1.png create mode 100644 examples/yaml_input/single_server/single_server_results_ram_srv-1.png create mode 100644 examples/yaml_input/single_server/single_server_results_ready_queue_srv-1.png delete mode 100644 examples/yaml_input/your_example.py create mode 100644 readme_img/topology.png create mode 100644 scripts/dev_setup.ps1 create mode 100644 scripts/dev_setup.sh create mode 100644 scripts/quality_check.ps1 rename scripts/{quality-check.sh => quality_check.sh} (92%) create mode 100644 scripts/run_sys_tests.ps1 create mode 100644 scripts/run_sys_tests.sh create mode 100644 scripts/run_tests.ps1 create mode 100644 scripts/run_tests.sh create mode 100644 simulation_base.png rename tests/integration/single_server/{test_single_server.py => test_int_single_server.py} (100%) create mode 100644 tests/system/test_sys_lb_two_servers.py create mode 100644 tests/system/test_sys_single_server.py create mode 100644 tests/unit/public_api/test_import.py diff --git a/.github/workflows/ci-develop.yml b/.github/workflows/ci-develop.yml index 9727868..0f82cfd 100644 --- a/.github/workflows/ci-develop.yml +++ b/.github/workflows/ci-develop.yml @@ -1,100 +1,51 @@ -name: CI – Develop Branch - - -# Triggers -# -------- -# • pull_request → quick job (lint + type-check + unit tests) -# • push → full job (quick job + DB migrations + integration tests + -# Docker build & smoke test) +name: CI – Main Branch (Single Job, System Required) on: pull_request: - branches: [develop] - push: - branches: [develop] + branches: [main] + workflow_dispatch: +concurrency: + group: ci-main-${{ github.ref }} + cancel-in-progress: true -# Job 1 ─ Quick validation (executed only on pull_request events) -# --------------------------------------------------------------------------- # -# Runs fast checks that give reviewers immediate feedback. No external -# services or Docker are used to keep runtime under one minute. +env: + PYTHON_VERSION: "3.12" + MPLBACKEND: Agg + ASYNCFLOW_RUN_SYSTEM_TESTS: "1" jobs: - quick: - if: github.event_name == 'pull_request' + all-checks: runs-on: ubuntu-latest + timeout-minutes: 25 steps: - # Checkout repository - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - # Install Python 3.12 - - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: - python-version: "3.12" + python-version: ${{ env.PYTHON_VERSION }} + cache: 'pip' - # Restore Poetry cache for faster installs - uses: actions/cache@v3 with: path: ~/.cache/pypoetry key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} - # Install project + development dependencies - - name: Install dependencies + - name: Install Poetry & deps run: | curl -sSL https://install.python-poetry.org | python3 - + export PATH="$HOME/.local/bin:$PATH" poetry config virtualenvs.create false poetry install --with dev --no-interaction - # Code quality gates - - name: Run Ruff (lint & formatting check) + - name: Ruff (lint) run: poetry run ruff check src tests - - name: Run MyPy (type-check) + - name: MyPy (type-check) run: poetry run mypy src tests - - # Unit-tests only (exclude integration markers) - - name: Run unit tests - env: - ENVIRONMENT: test - run: poetry run pytest -m "not integration" --disable-warnings - - - -# Job 2 ─ Full validation (executed only on push events) - full: - if: | - github.event_name == 'push' && - github.ref == 'refs/heads/develop' - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: { python-version: '3.12' } - - uses: actions/cache@v3 - with: - path: ~/.cache/pypoetry - key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} - - name: Install dependencies - run: | - curl -sSL https://install.python-poetry.org | python3 - - poetry config virtualenvs.create false - poetry install --with dev --no-interaction - - - name: Run Ruff - run: poetry run ruff check src tests - - - name: Run mypy - run: poetry run mypy src - - - name: Run all tests + - name: All tests (unit + integration + system) run: | poetry run pytest \ - --cov=src --cov-report=term \ --disable-warnings - - - - \ No newline at end of file diff --git a/.github/workflows/ci-main.yml b/.github/workflows/ci-main.yml new file mode 100644 index 0000000..e69de29 diff --git a/README.md b/README.md index 2005eea..fca1fce 100644 --- a/README.md +++ b/README.md @@ -1,149 +1,98 @@ -# **AsyncFlow – Event-Loop Aware Simulation for Backend Systems** -## **1. Overview** +# AsyncFlow — Event-Loop Aware Simulator for Async Distributed Systems -Modern asynchronous Python stacks such as **FastAPI + Uvicorn** deliver impressive performance, yet capacity planning for production workloads often relies on guesswork, costly cloud-based load tests, or late-stage troubleshooting. +Created and maintained by @GioeleB00. -**AsyncFlow** addresses this challenge by providing a **digital twin** of your service that can be run entirely offline. It models event-loop behaviour, resource constraints, and request lifecycles, enabling you to forecast performance under different workloads and architectural choices **before deployment**. +[![PyPI](https://img.shields.io/pypi/v/asyncflow-sim)](https://pypi.org/project/asyncflow-sim/) +[![Python](https://img.shields.io/pypi/pyversions/asyncflow-sim)](https://pypi.org/project/asyncflow-sim/) +[![License](https://img.shields.io/github/license/AsyncFlow-Sim/AsyncFlow)](LICENSE) +[![Status](https://img.shields.io/badge/status-v0.1.0alpha-orange)](#) +[![Ruff](https://img.shields.io/badge/lint-ruff-informational)](https://github.com/astral-sh/ruff) +[![Typing](https://img.shields.io/badge/typing-mypy-blueviolet)](https://mypy-lang.org/) +[![Tests](https://img.shields.io/badge/tests-pytest-6DA55F)](https://docs.pytest.org/) +[![SimPy](https://img.shields.io/badge/built%20with-SimPy-1f425f)](https://simpy.readthedocs.io/) -AsyncFlow allows you to answer questions such as: +----- -* *What happens to p95 latency if traffic doubles during a peak event?* -* *How many cores are required to maintain SLAs at scale?* -* *What is the cost-per-request of adding a large language model (LLM) inference step?* +AsyncFlow is a discrete-event simulator for modeling and analyzing the performance of asynchronous, distributed backend systems built with SimPy. You describe your system's topology—its servers, network links, and load balancers—and AsyncFlow simulates the entire lifecycle of requests as they move through it. -The simulation outputs metrics identical in form to those collected in production—such as p50/p95/p99 latency, concurrency, resource utilisation, and throughput—making results directly actionable. +It provides a **digital twin** of your service, modeling not just the high-level architecture but also the low-level behavior of each server's **event loop**, including explicit **CPU work**, **RAM residency**, and **I/O waits**. This allows you to run realistic "what-if" scenarios that behave like production systems rather than toy benchmarks. -**Outcome:** Data-driven capacity planning, early performance tuning, and reduced operational surprises. +### What Problem Does It Solve? ---- - -## **2. Key Features** - -* **Event-loop accuracy** – Models a single-threaded asynchronous runtime, including CPU-bound work, I/O waits, and parsing. -* **Resource modelling** – Simulates CPU cores, memory, connection pools, and rate limiters as discrete resources. -* **Network simulation** – Models transport latency per edge in the system topology. -* **Workload flexibility** – Supports stochastic arrival processes (e.g., Poisson, Normal) for user traffic generation. -* **Metrics parity with production** – Produces time-series and event-level metrics aligned with observability tools. -* **Offline and repeatable** – No need for costly cloud infrastructure to conduct performance tests. - ---- - -## **3. Installation** +Modern async stacks like FastAPI are incredibly performant, but predicting their behavior under real-world load is difficult. Capacity planning often relies on guesswork, expensive cloud-based load tests, or discovering bottlenecks only after a production failure. AsyncFlow is designed to replace that uncertainty with **data-driven forecasting**, allowing you to understand how your system will perform before you deploy a single line of code. -Until published, clone the repository and install in editable mode: - -### Requirements -- Python 3.11+ (recommended 3.12) -- Poetry ≥ 1.6 +### How Does It Work? An Example Topology -AsyncFlow uses [Poetry](https://python-poetry.org/) for dependency management. -If you do not have Poetry installed, follow these steps. +AsyncFlow models your system as a directed graph of interconnected components. A typical setup might look like this: -### 3.1 Install Poetry (official method) +![Topology at a glance](readme_img/topology.png) -**Linux / macOS** +### What Questions Can It Answer? -```bash -curl -sSL https://install.python-poetry.org | python3 - -``` - -**Windows (PowerShell)** - -```powershell -(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python - -``` - -> **Note:** Ensure that Poetry’s binary directory is in your `PATH`. -> On Linux/macOS this is typically `~/.local/bin`; -> on Windows it is `%APPDATA%\Python\Scripts` or the path printed at the end of installation. +By running simulations on your defined topology, you can get quantitative answers to critical engineering questions, such as: + * How does **p95 latency** change if active users increase from 100 to 200? + * What is the impact on the system if the **client-to-server network latency** increases by 3ms? + * Will a specific API endpoint—with a pipeline of parsing, RAM allocation, and database I/O—hold its **SLA at a load of 40 requests per second**? --- -### 3.2 Clone the repository and set up a local virtual environment - -```bash -# Clone the repository -git clone https://github.com/GioeleB00/AsyncFlow-Backend.git -cd AsyncFlow-Backend - -# Configure Poetry to always create a local `.venv` inside the project -poetry config virtualenvs.in-project true - -# Install all dependencies (main + dev) inside the local venv -poetry install --with dev -``` - -After this step, you will see a `.venv/` directory inside the project root. -To activate the environment manually: +## Installation -```bash -source .venv/bin/activate # Linux / macOS -.venv\Scripts\activate # Windows -``` +Install from PyPI: `pip install asyncflow-sim` -Or simply run commands via Poetry without manual activation, for example: -```bash -poetry run pytest -poetry run python examples/single_server.py -``` +## Requirements +* **Python 3.12+** (tested on 3.12, 3.13) +* **OS:** Linux, macOS, or Windows +* **Installed automatically (runtime deps):** + **SimPy** (DES engine), **NumPy**, **Matplotlib**, **Pydantic** + **pydantic-settings**, **PyYAML**. --- -## **4. Quick Start** +## Quick Start -### 1. Define your simulation payload +### 1) Define a realistic YAML -Go to the folder `/examples` open the file `single_server.py` -and run it from the terminal, you will see the output of the system -described in `/examples/data/single_server.yml` and you will see a -`.png` file with different plots. +Save as `my_service.yml`. -If you want to build your own configuration, read the guide in the `/docs` folder on how to craft a `.yml` input correctly. +The full YAML schema is explained in `docs/guides/yaml-input-builder.md` and validated by Pydantic models (see `docs/internals/simulation-input.md`). ```yaml rqs_input: id: generator-1 - avg_active_users: - mean: 100 - distribution: poisson - avg_request_per_minute_per_user: - mean: 20 - distribution: poisson + avg_active_users: { mean: 100, distribution: poisson } + avg_request_per_minute_per_user: { mean: 20, distribution: poisson } user_sampling_window: 60 topology_graph: nodes: - client: - id: client-1 - type: client + client: { id: client-1 } + servers: - - id: app-server-1 - type: server - server_resources: - cpu_cores: 2 - ram_mb: 2048 + - id: app-1 + server_resources: { cpu_cores: 1, ram_mb: 2048 } endpoints: - - endpoint_name: /predict + - endpoint_name: /api + # Realistic pipeline on one async server: + # - 2 ms CPU parsing (blocks the event loop) + # - 120 MB RAM working set (held until the request leaves the server) + # - 12 ms DB-like I/O (non-blocking wait) steps: + - kind: initial_parsing + step_operation: { cpu_time: 0.002 } - kind: ram - step_operation: { necessary_ram: 100 } - - kind: cpu - step_operation: { cpu_time: 0.005 } + step_operation: { necessary_ram: 120 } + - kind: io_db + step_operation: { io_waiting_time: 0.012 } + edges: - - id: gen-to-client - source: generator-1 - target: client-1 - latency: { mean: 0.003, distribution: exponential } - - id: client-to-server - source: client-1 - target: app-server-1 - latency: { mean: 0.003, distribution: exponential } - - id: server-to-client - source: app-server-1 - target: client-1 - latency: { mean: 0.003, distribution: exponential } + - { id: gen-client, source: generator-1, target: client-1, + latency: { mean: 0.003, distribution: exponential } } + - { id: client-app, source: client-1, target: app-1, + latency: { mean: 0.003, distribution: exponential } } + - { id: app-client, source: app-1, target: client-1, + latency: { mean: 0.003, distribution: exponential } } sim_settings: total_simulation_time: 300 @@ -151,183 +100,275 @@ sim_settings: enabled_sample_metrics: - ready_queue_len - ram_in_use + - edge_concurrent_connection enabled_event_metrics: - rqs_clock ``` -and add it to the `/examples/data` folder - -### 2. Run the simulation +Prefer building scenarios in Python? There’s a Python builder with the same semantics (create nodes, edges, endpoints programmatically). See **`docs/guides/python-builder.md`**. -build a python file in the `/examples` folder and copy the -following script replacing `` with the -real name +### 2) Run and export charts +Save as `run_my_service.py`. ```python +from __future__ import annotations from pathlib import Path - import simpy import matplotlib.pyplot as plt -from asyncflow.config.constants import LatencyKey from asyncflow.runtime.simulation_runner import SimulationRunner from asyncflow.metrics.analyzer import ResultsAnalyzer -def print_latency_stats(res: ResultsAnalyzer) -> None: - """Print latency statistics returned by the analyzer.""" - stats = res.get_latency_stats() - print("\n=== LATENCY STATS ===") - if not stats: - print("(empty)") - return - - order: list[LatencyKey] = [ - LatencyKey.TOTAL_REQUESTS, - LatencyKey.MEAN, - LatencyKey.MEDIAN, - LatencyKey.STD_DEV, - LatencyKey.P95, - LatencyKey.P99, - LatencyKey.MIN, - LatencyKey.MAX, - ] - for key in order: - if key in stats: - print(f"{key.name:<20} = {stats[key]:.6f}") - -def save_all_plots(res: ResultsAnalyzer, out_path: Path) -> None: - """Generate the 2x2 plot figure and save it to `out_path`.""" - fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + +def main() -> None: + script_dir = Path(__file__).parent + yaml_path = script_dir / "my_service.yml" + out_path = script_dir / "my_service_plots.png" + + env = simpy.Environment() + runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) + res: ResultsAnalyzer = runner.run() + + # Print a concise latency summary + print(res.format_latency_stats()) + + # 2x2: Latency | Throughput | Ready (first server) | RAM (first server) + fig, axes = plt.subplots(2, 2, figsize=(12, 8), dpi=160) + res.plot_latency_distribution(axes[0, 0]) res.plot_throughput(axes[0, 1]) - res.plot_server_queues(axes[1, 0]) - res.plot_ram_usage(axes[1, 1]) + + sids = res.list_server_ids() + if sids: + sid = sids[0] + res.plot_single_server_ready_queue(axes[1, 0], sid) + res.plot_single_server_ram(axes[1, 1], sid) + else: + for ax in (axes[1, 0], axes[1, 1]): + ax.text(0.5, 0.5, "No servers", ha="center", va="center") + ax.axis("off") + fig.tight_layout() fig.savefig(out_path) print(f"Plots saved to: {out_path}") -# Paths -yaml_path = Path(__file__).parent / "data" /".yml" -out_path = Path(__file__).parent / "_plots.png" -# Simulation -env = simpy.Environment() -runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) -results: ResultsAnalyzer = runner.run() - -# Output -print_latency_stats(results) -save_all_plots(results, out_path) +if __name__ == "__main__": + main() ``` -run the script and you will see the different plots and on your terminal -you will see the latency stats ---- +Run the python script + +You’ll get latency stats in the terminal and a PNG with four charts (latency distribution, throughput, server queues, RAM usage). + +**Want more?** -## **5. Target Users and Use Cases** +For ready-to-run scenarios—including examples using the Pythonic builder and multi-server topologies—check out the `examples/` directory in the repository. -| Audience | Challenge | AsyncFlow Value | -| ------------------------ | ------------------------------------------------- | -------------------------------------------------------------------------------- | -| Backend Engineers | Sizing services for variable workloads | Model endpoint workflows and resource bottlenecks before deployment | -| DevOps / SRE | Balancing cost and SLA | Simulate scaling scenarios to choose optimal capacity | -| ML / LLM Teams | Unclear latency/cost impact of inference steps | Integrate stochastic inference times and cost models into the service simulation | -| Educators | Explaining async runtime internals | Demonstrate queueing, blocking effects, and CPU vs. I/O trade-offs | -| System Design Candidates | Quantifying architecture trade-offs in interviews | Prototype a simulated design to visualise scalability and bottlenecks | +## Development + +If you want to contribute or run the full test suite locally, follow these steps. + +### Requirements + +* **Python 3.12+** (tested on 3.12, 3.13) +* **OS:** Linux, macOS, or Windows +* **Runtime deps installed by the package:** SimPy, NumPy, Matplotlib, Pydantic, PyYAML, pydantic-settings + +**Prerequisites:** Git, Python 3.12+ in `PATH`, `curl` (Linux/macOS/WSL), PowerShell 7+ (Windows) --- -## **6. Project Structure** +## Project setup -The project follows a standard Python package layout, managed with Poetry. +```bash +git clone https://github.com/AsyncFlow-Sim/AsyncFlow.git +cd AsyncFlow +``` +From the repo root, run the **one-shot post-clone setup**: + +**Linux / macOS / WSL** + +```bash +bash scripts/dev_setup.sh ``` -AsyncFlow-Backend/ -├── examples/ # Examples payloads and datasets -├── scripts/ # Utility scripts (linting, startup) -├── docs/ # Project vision and technical documentation -├── tests/ # Unit and integration tests -├── src/ -│ └── app/ -│ ├── config/ # Settings and constants -│ ├── metrics/ # KPI computation and aggregation -│ ├── resources/ # SimPy resource registry -│ ├── runtime/ # Simulation core and actors -│ ├── samplers/ # Random variable generators -│ └── schemas/ # Pydantic models for validation -├── pyproject.toml -└── README.md + +**Windows (PowerShell)** + +```powershell +# If scripts are blocked by policy, run this in the same PowerShell session: +# Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass +.\scripts\dev_setup.ps1 ``` ---- +**What this does (concise):** -## **7. Development Workflow** +* Ensures **Poetry** is available (installs if missing). +* Uses a **project-local `.venv`**. +* Removes `poetry.lock` for a **clean dependency resolve** (dev policy). +* Installs the project **with dev extras**. +* Runs **ruff**, **mypy**, and **pytest (with coverage)**. -AsyncFlow uses **Poetry** for dependency management and enforces quality via **Ruff** and **MyPy**. +**Quick sanity check after setup:** -| Task | Command | Description | -| ------------- | --------------------------------- | -------------------------------------- | -| Install deps | `poetry install --with dev` | Main and development dependencies | -| Lint & format | `poetry run ruff check src tests` | Style and best-practice checks | -| Type checking | `poetry run mypy src tests` | Static type enforcement | -| Run tests | `poetry run pytest` | Execute all unit and integration tests | +```bash +poetry --version +poetry run python -V +``` + +> **Note (lock policy):** `dev_setup` intentionally removes `poetry.lock` to avoid cross-platform conflicts during development. + +**Scripts (for quick access):** + +* [`scripts/dev_setup.sh`](scripts/dev_setup.sh) / [`scripts/dev_setup.ps1`](scripts/dev_setup.ps1) +* [`scripts/quality_check.sh`](scripts/quality_check.sh) / [`scripts/quality_check.ps1`](scripts/quality_check.ps1) +* [`scripts/run_tests.sh`](scripts/run_tests.sh) / [`scripts/run_tests.ps1`](scripts/run_tests.ps1) --- -## **8. Continuous Integration** +### Handy scripts (after setup) -The GitHub Actions pipeline enforces: +#### 1) Lint + type check -* **Pull Requests:** Ruff, MyPy, and unit tests for rapid feedback. -* **Develop branch:** Full suite including integration tests and coverage reporting. +**Linux / macOS / WSL** -No code is merged without passing all checks, ensuring maintainability and reliability. +```bash +bash scripts/quality_check.sh +``` ---- +**Windows (PowerShell)** -## **9. Current Limitations (v0.1)** +```powershell +.\scripts\quality_check.ps1 +``` -1. **Network delay model** – Bandwidth effects and payload size are not yet considered. -2. **Concurrency model** – Single-threaded async event-loop; no native multi-thread or multi-process simulation. -3. **CPU allocation** – One process per server instance; multi-core within a process is not yet modelled. +Runs **ruff** (lint/format check) and **mypy** on `src` and `tests`. -In addition to the items already listed (simplified network delay, single-threaded async model, and one process per server), keep in mind: +#### 2) Run tests with coverage (unit + integration) -* **Stationary, independent workload.** Traffic is sampled from stationary distributions; there is no diurnal seasonality, burst shaping, or feedback coupling (e.g., servers slowing down arrivals). Average users and per-user RPM are sampled independently. -* **Simplified request flow.** Endpoints execute a linear sequence of steps; there is no conditional branching/fan-out within an endpoint (e.g., cache hit/miss paths, error paths) and no per-request control flow. -* **Network realism is limited.** Beyond base latency and optional drops, the model does not account for payload size, bandwidth constraints, TCP behavior (slow start, congestion), retries/timeouts, or jitter. -* **No backpressure or autoscaling.** The generator does not adapt to server state (queues, errors), and there is no policy loop for rate limiting or scaling during the run. -* **Telemetry granularity.** Sampled metrics are collected at a fixed `sample_period_s` and may miss very short-lived spikes unless you lower the period (at a runtime cost). Event resolution itself is not affected by the sampling period. -* **Reproducibility.** Unless you fix a random seed (not yet exposed in all entry points), repeated runs will vary within the chosen distributions. +**Linux / macOS / WSL** ---- +```bash +bash scripts/run_tests.sh +``` -## Mini Roadmap +**Windows (PowerShell)** -Short, high-impact items we plan to add next: +```powershell +.\scripts\run_tests.ps1 +``` -1. **Cache modeling.** First-class cache layers (per-endpoint hit/miss with TTL and warm-up), configurable hit-ratio profiles, and their effect on latency, CPU, and RAM. -2. **LLM inference as a step + cost accounting.** Treat inference as a dedicated endpoint step with its own latency distribution, concurrency limits/batching, and per-request cost model (tokens, provider pricing). -3. **Fault and event injection.** Time-based events (node down/up, degraded edge, error-rate spikes) with deterministic timelines to test resilience and recovery. -4. **Network bandwidth & payload size.** Throughput-aware links, request/response sizes, retries/timeouts, and simple congestion effects. -5. **Branching/control-flow within endpoints.** Conditional steps (e.g., cache hit vs. miss), probabilistic routing, and fan-out/fan-in to external services. -6. **Backpressure and autoscaling loops.** Rate limiting tied to queue depth/latency SLOs and simple scale-up/down policies during a run. +#### 3) Run system tests +**Linux / macOS / WSL** -Future milestones will extend these capabilities. +```bash +bash scripts/run_sys_tests.sh +``` ---- +**Windows (PowerShell)** -## **10. Documentation** +```powershell +.\scripts\run_sys_tests.ps1 +``` -Comprehensive documentation is available in the `/docs` directory, covering: +Executes **pytest** with a terminal coverage summary (no XML, no slowest list). -* Simulation model and architecture -* Schema definitions -* Example scenarios -* Extension guidelines -* Guide to build valid .yaml as valid simulation input ---- + +## What AsyncFlow Models (v0.1) + +AsyncFlow provides a detailed simulation of your backend system. Here is a high-level overview of the core components it models. For a deeper technical dive into the implementation and design rationale, follow the links to the internal documentation. + +* **Async Event Loop:** Simulates a single-threaded, non-blocking event loop per server. **CPU steps** block the loop, while **I/O steps** are non-blocking, accurately modeling `asyncio` behavior. + * *(Deep Dive: `docs/internals/runtime-and-resources.md`)* + +* **System Resources:** Models finite server resources, including **CPU cores** and **RAM (MB)**. Requests must acquire these resources, creating natural back-pressure and contention when the system is under load. + * *(Deep Dive: `docs/internals/runtime-and-resources.md`)* + +* **Endpoints & Request Lifecycles:** Models server endpoints as a linear sequence of **steps**. Each step is a distinct operation, such as `cpu_bound_operation`, `io_wait`, or `ram` allocation. + * *(Schema Definition: `docs/internals/simulation-input.md`)* + +* **Network Edges:** Simulates the connections between system components. Each edge has a configurable **latency** (drawn from a probability distribution) and an optional **dropout rate** to model packet loss. + * *(Schema Definition: `docs/internals/simulation-input.md` | Runtime Behavior: `docs/internals/runtime-and-resources.md`)* + +* **Stochastic Workload:** Generates user traffic based on a two-stage sampling model, combining the number of active users and their request rate per minute to produce a realistic, fluctuating load (RPS) on the system. + * *(Modeling Details with mathematical explanation and clear assumptions: `docs/internals/requests-generator.md`)* + +* **Metrics & Outputs:** Collects two types of data: **time-series metrics** (e.g., `ready_queue_len`, `ram_in_use`) and **event-based data** (`RqsClock`). This raw data is used to calculate final KPIs like **p95/p99 latency** and **throughput**. + * *(Metric Reference: `docs/internals/metrics`)* + +## Current Limitations (v0.1) + +* Network realism: base latency + optional drops (no bandwidth/payload/TCP yet). +* Single event loop per server: no multi-process/multi-node servers yet. +* Linear endpoint flows: no branching/fan-out within an endpoint. +* No thread-level concurrency; modeling OS threads and scheduler/context switching is out of scope.” +* Stationary workload: no diurnal patterns or feedback/backpressure. +* Sampling cadence: very short spikes can be missed if `sample_period_s` is large. + + +## Roadmap (Order is not indicative of priority) + +This roadmap outlines the key development areas to transform AsyncFlow into a comprehensive framework for statistical analysis and resilience modeling of distributed systems. + +### 1. Monte Carlo Simulation Engine + +**Why:** To overcome the limitations of a single simulation run and obtain statistically robust results. This transforms the simulator from an "intuition" tool into an engineering tool for data-driven decisions with confidence intervals. + +* **Independent Replications:** Run the same simulation N times with different random seeds to sample the space of possible outcomes. +* **Warm-up Period Management:** Introduce a "warm-up" period to be discarded from the analysis, ensuring that metrics are calculated only on the steady-state portion of the simulation. +* **Ensemble Aggregation:** Calculate means, standard deviations, and confidence intervals for aggregated metrics (latency, throughput) across all replications. +* **Confidence Bands:** Visualize time-series data (e.g., queue lengths) with confidence bands to show variability over time. + +### 2. Realistic Service Times (Stochastic Service Times) + +**Why:** Constant service times underestimate tail latencies (p95/p99), which are almost always driven by "slow" requests. Modeling this variability is crucial for a realistic analysis of bottlenecks. + +* **Distributions for Steps:** Allow parameters like `cpu_time` and `io_waiting_time` in an `EndpointStep` to be sampled from statistical distributions (e.g., Lognormal, Gamma, Weibull) instead of being fixed values. +* **Per-Request Sampling:** Each request will sample its own service times independently, simulating the natural variability of a real-world system. + +### 3. Component Library Expansion + +**Why:** To increase the variety and realism of the architectures that can be modeled. + +* **New System Nodes:** + * `CacheRuntime`: To model caching layers (e.g., Redis) with hit/miss logic, TTL, and warm-up behavior. + * `APIGatewayRuntime`: To simulate API Gateways with features like rate-limiting and authentication caching. + * `DBRuntime`: A more advanced model for databases featuring connection pool contention and row-level locking. +* **New Load Balancer Algorithms:** Add more advanced routing strategies (e.g., Weighted Round Robin, Least Response Time). + +### 4. Fault and Event Injection + +**Why:** To test the resilience and behavior of the system under non-ideal conditions, a fundamental use case for Site Reliability Engineering (SRE). + +* **API for Scheduled Events:** Introduce a system to schedule events at specific simulation times, such as: + * **Node Down/Up:** Turn a server off and on to test the load balancer's failover logic. + * **Degraded Edge:** Drastically increase the latency or drop rate of a network link. + * **Error Bursts:** Simulate a temporary increase in the rate of application errors. + +### 5. Advanced Network Modeling + +**Why:** To more faithfully model network-related bottlenecks that are not solely dependent on latency. + +* **Bandwidth and Payload Size:** Introduce the concepts of link bandwidth and request/response size to simulate delays caused by data transfer. +* **Retries and Timeouts:** Model retry and timeout logic at the client or internal service level. + +### 6. Complex Endpoint Flows + +**Why:** To model more realistic business logic that does not follow a linear path. + +* **Conditional Branching:** Introduce the ability to have conditional steps within an endpoint (e.g., a different path for a cache hit vs. a cache miss). +* **Fan-out / Fan-in:** Model scenarios where a service calls multiple downstream services in parallel and waits for their responses. + +### 7. Backpressure and Autoscaling + +**Why:** To simulate the behavior of modern, adaptive systems that react to load. + +* **Dynamic Rate Limiting:** Introduce backpressure mechanisms where services slow down the acceptance of new requests if their internal queues exceed a certain threshold. +* **Autoscaling Policies:** Model simple Horizontal Pod Autoscaler (HPA) policies where the number of server replicas increases or decreases based on metrics like CPU utilization or queue length. + diff --git a/docs/api/analyzer.md b/docs/api/analyzer.md new file mode 100644 index 0000000..eb86e6b --- /dev/null +++ b/docs/api/analyzer.md @@ -0,0 +1,208 @@ +# ResultsAnalyzer — Public API Documentation + +Analyze and visualize the outcome of an AsyncFlow simulation. +`ResultsAnalyzer` consumes raw runtime objects (client, servers, edges, settings), +computes latency and throughput aggregates, exposes sampled series, and offers +compact plotting helpers built on Matplotlib. + +--- + +## Quick start + +```python +import simpy +from matplotlib import pyplot as plt +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer, SampledMetricName + +# 1) Run a simulation and get an analyzer +env = simpy.Environment() +runner = SimulationRunner.from_yaml(env=env, yaml_path="data/single_server.yml") +res: ResultsAnalyzer = runner.run() + +# 2) Text summary +print(res.format_latency_stats()) + +# 3) Plot the dashboard (latency histogram + throughput) +fig, (ax_lat, ax_rps) = plt.subplots(1, 2, figsize=(12, 4), dpi=160) +res.plot_base_dashboard(ax_lat, ax_rps) +fig.tight_layout() +fig.savefig("dashboard.png") + +# 4) Single-server plots +server_id = res.list_server_ids()[0] +fig_rdy, ax_rdy = plt.subplots(figsize=(8, 4), dpi=160) +res.plot_single_server_ready_queue(ax_rdy, server_id) +fig_rdy.tight_layout() +fig_rdy.savefig(f"ready_{server_id}.png") +``` + +--- + +## Data model & units + +* **Latency**: seconds (s). +* **Throughput**: requests per second (RPS). +* **Sampled metrics** (per server/edge): series captured at a fixed sampling + period `settings.sample_period_s` (e.g., queue length, RAM usage). + Units depend on the metric (RAM is typically MB). + +--- + +## Computed metrics + +* **Latency statistics** (global): + `TOTAL_REQUESTS, MEAN, MEDIAN, STD_DEV, P95, P99, MIN, MAX`. +* **Throughput time series**: per-window RPS (default cached at 1 s buckets). +* **Sampled metrics**: raw, per-entity series keyed by + `SampledMetricName` (or its string value). + +--- + +## Class reference + +### Constructor + +```python +ResultsAnalyzer( + *, + client: ClientRuntime, + servers: list[ServerRuntime], + edges: list[EdgeRuntime], + settings: SimulationSettings, +) +``` + +The analyzer is **lazy**: metrics are computed on first access. + +### Core methods + +* `process_all_metrics() -> None` + Forces computation of latency stats, throughput cache (1 s), and sampled metrics. + +* `get_latency_stats() -> dict[LatencyKey, float]` + Returns the global latency stats. Computes them if needed. + +* `format_latency_stats() -> str` + Returns a ready-to-print block with latency statistics. + +* `get_throughput_series(window_s: float | None = None) -> tuple[list[float], list[float]]` + Returns `(timestamps, rps)`. If `window_s` is `None` or `1.0`, the cached + 1-second series is returned; otherwise a fresh series is computed. + +* `get_sampled_metrics() -> dict[str, dict[str, list[float]]]` + Returns sampled metrics as `{metric_key: {entity_id: [values...]}}`. + +* `get_metric_map(key: SampledMetricName | str) -> dict[str, list[float]]` + Gets the per-entity series map for a metric. Accepts either the enum value or + the raw string key. + +* `get_series(key: SampledMetricName | str, entity_id: str) -> tuple[list[float], list[float]]` + Returns time/value series for a given metric and entity. + Time coordinates are `i * settings.sample_period_s`. + +* `list_server_ids() -> list[str]` + Returns server IDs in a stable, topology order. + +--- + +## Plotting helpers + +All plotting methods draw on a **Matplotlib `Axes`** provided by the caller and +do **not** manage figure lifecycles. + +> When there is no data for the requested plot, the axis is annotated with the +> corresponding `no_data` message from `plot_constants`. + +### Dashboard + +* `plot_base_dashboard(ax_latency: Axes, ax_throughput: Axes) -> None` + Convenience: calls the two methods below. + +* `plot_latency_distribution(ax: Axes) -> None` + Latency histogram with **vertical overlays** (mean, P50, P95, P99) and a + **single legend box** (top-right) that shows each statistic with its matching + colored handle. + +* `plot_throughput(ax: Axes, *, window_s: float | None = None) -> None` + Throughput line with **horizontal overlays** (mean, P95, max) and a + **single legend box** (top-right) that shows values and colors for each line. + +### Single-server plots + +Each single-server plot: + +* draws the main series, + +* overlays **mean / min / max** as horizontal lines (distinct styles/colors), + +* shows a **single legend box** with values for mean/min/max, + +* **does not** include a legend entry for the main series (title suffices). + +* `plot_single_server_ready_queue(ax: Axes, server_id: str) -> None` + Ready queue length over time (per server). + +* `plot_single_server_io_queue(ax: Axes, server_id: str) -> None` + I/O queue/sleep metric over time (per server). + +* `plot_single_server_ram(ax: Axes, server_id: str) -> None` + RAM usage over time (per server). + +## Behavior & design notes + +* **Laziness & caching** + + * Latency stats and the 1 s throughput series are cached on first use. + * Calling `get_throughput_series(window_s=...)` with a custom window computes + a fresh series (not cached). + +* **Stability** + + * `list_server_ids()` follows the topology order for readability across runs. + +* **Error handling** + + * Multi-server plotting methods validate the number of axes and raise + `ValueError` with a descriptive message. + +* **Matplotlib integration** + + * The analyzer **does not** close figures or call `plt.show()`. + * Titles, axes labels, and “no data” messages are taken from + `asyncflow.config.plot_constants`. + +* **Thread-safety** + + * The analyzer is not designed for concurrent mutation. Use from a single + thread after the simulation completes. + +--- + +## Examples + +### Custom throughput window + +```python +fig, ax = plt.subplots(figsize=(8, 3), dpi=160) +res.plot_throughput(ax, window_s=2.0) # 2-second buckets +fig.tight_layout() +fig.savefig("throughput_2s.png") +``` + +### Access a sampled metric series + +```python +from asyncflow.metrics.analyzer import SampledMetricName + +server_id = res.list_server_ids()[0] +t, qlen = res.get_series(SampledMetricName.READY_QUEUE_LEN, server_id) +# t: [0.0, 0.1, 0.2, ...] (scaled by sample_period_s) +# qlen: [.. values ..] +``` + +--- + +If you need additional KPIs (e.g., tail latency over time, backlog, or +utilization), the current structure makes it straightforward to add new helpers +alongside the existing plotting methods. diff --git a/docs/guides/builder.md b/docs/guides/python-builder.md similarity index 100% rename from docs/guides/builder.md rename to docs/guides/python-builder.md diff --git 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zcwJ*)zyNgc+uJWMA>TnbMj$ptJ_m)g@QOkWOw;wi0QN36_yCY15k&g$DFt0d)_CBy zA|xU42JyD#a=2pwn=uO)nHbnOppoAIBm5&FHY&;+>`naIDY8u!^qd3y=Ne(F_G$q# zBL55@2S-Fk<{~&Q=Hp~EGz2KPOU_%mTK59a-8dgoC&hpq5e5ow;WFpoA3s!~nI}Hr zDR4JA0SR;(Wa$xvI>>JtoeXYyLfC;aDoun3mVAUvy*d)09tMf07dhrZyKK3Jkr)^m z)B`0YQY2Fh7W~IC)jjKsC$YP@ur^PgKJ|jJE|-JmvNkNrKD5Xq(G1K<3P{gF>a~RW zsj4at%IJG>rg9P+atJQ}`*SXR0D#6wg#`_?TzNsyYyhN1XK!sB0NK1yEH)dj1IND9e4GN7LC{$cnWu>sST9FFl!=Ma%f@kGW@ct0 zd|ao;zvbtC{+vN5Y@kE}S?SfJeo$nD9voumMyN1XgLYU%hX5&sf<86UjES^C73oPV zbGjXGUO+hMe=i`PFei%)yaLIFGC)E366ofjO1>kRnRIYM8~{(RbL(=-GyYQt*aXf+ zmeV53deB`AcQN(33;1FXhHwJ<0cna}{87e+N`c-6ND?<^NJYAwCcpytR|l*z(r=MP z(_(p!2@?|WFvH!uI8t=2_VmyM%+Vdk;|oB@1+kHKb0Fy@F=VM3gy2S5R9HdAR0M13 za(b|EorT3}$st(W3xGYttxsUeiC8X-N{hW>*@d client ──> LB ──> srv-1 - └─> srv-2 - srv-1 ──> client - srv-2 ──> client - -Load: - ~120 active users, 20 req/min each (Poisson by default). - -Servers: - srv-1: 1 CPU core, 1GB RAM, endpoint with CPU→RAM→IO - srv-2: 2 CPU cores, 2GB RAM, endpoint with RAM→IO(DB-like) - -Network: - 2–3ms mean (exponential) latency on each edge. - -What this script does: - 1) Build Pydantic models (generator, client, LB, servers, edges, settings). - 2) Compose the SimulationPayload via AsyncFlow (builder pattern). - 3) Run the simulation with SimulationRunner. - 4) Print latency stats, throughput timeline, and a sampled-metrics preview. - 5) Save a 2×2 plot figure (latency, throughput, server queues, RAM). +Didactic example: AsyncFlow with a Load Balancer and two **identical** servers. + +Goal +---- +Show a realistic, symmetric backend behind a load balancer, and export plots +that match the public `ResultsAnalyzer` API (no YAML needed). + +Topology +-------- + generator ──edge──> client ──edge──> LB ──edge──> srv-1 + └──edge──> srv-2 + srv-1 ──edge──> client + srv-2 ──edge──> client + +Load model +---------- +~120 active users, 20 requests/min each (Poisson-like aggregate by default). + +Server model (both srv-1 and srv-2) +----------------------------------- +• 1 CPU cores, 2 GB RAM +• Endpoint pipeline: CPU(2 ms) → RAM(128 MB) → I/O wait (15 ms) + - CPU step blocks the event loop + - RAM step holds a working set until the request completes + - I/O step is non-blocking (event-loop friendly) + +Network model +------------- +Every edge uses an exponential latency with mean 3 ms. + +Outputs +------- +• Prints latency statistics to stdout +• Saves, in the same folder as this script: + - `lb_dashboard.png` (Latency histogram + Throughput) + - `lb_server__metrics.png` for each server (Ready / I/O / RAM) """ from __future__ import annotations from pathlib import Path -from typing import Iterable, List, Mapping, TYPE_CHECKING -import numpy as np import simpy +import matplotlib.pyplot as plt + +# Public AsyncFlow API (builder-style) +from asyncflow import AsyncFlow +from asyncflow.components import Client, Server, Edge, Endpoint, LoadBalancer +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator -# ── AsyncFlow domain imports (match your working paths) ──────────────────────── -from asyncflow.builder.asyncflow_builder import AsyncFlow +# Runner + Analyzer from asyncflow.runtime.simulation_runner import SimulationRunner from asyncflow.metrics.analyzer import ResultsAnalyzer -from asyncflow.schemas.payload import SimulationPayload -from asyncflow.schemas.workload.rqs_generator import RqsGenerator -from asyncflow.schemas.settings.simulation import SimulationSettings -from asyncflow.schemas.topology.endpoint import Endpoint -from asyncflow.schemas.topology.nodes import Client, Server, LoadBalancer -from asyncflow.schemas.topology.edges import Edge -from asyncflow.config.constants import LatencyKey, SampledMetricName - - - - -# ───────────────────────────────────────────────────────────── -# Pretty printers (compact, readable output) -# ───────────────────────────────────────────────────────────── -def print_latency_stats(res: ResultsAnalyzer) -> None: - stats: Mapping[LatencyKey, float] = res.get_latency_stats() - print("\n════════ LATENCY STATS ════════") - if not stats: - print("(empty)") - return - - order: List[LatencyKey] = [ - LatencyKey.TOTAL_REQUESTS, - LatencyKey.MEAN, - LatencyKey.MEDIAN, - LatencyKey.STD_DEV, - LatencyKey.P95, - LatencyKey.P99, - LatencyKey.MIN, - LatencyKey.MAX, - ] - for key in order: - if key in stats: - print(f"{key.name:<20} = {stats[key]:.6f}") - - -def print_throughput(res: ResultsAnalyzer) -> None: - timestamps, rps = res.get_throughput_series() - print("\n════════ THROUGHPUT (req/sec) ════════") - if not timestamps: - print("(empty)") - return - for t, rate in zip(timestamps, rps): - print(f"t={t:4.1f}s → {rate:6.2f} rps") - - -def print_sampled_preview(res: ResultsAnalyzer) -> None: - sampled = res.get_sampled_metrics() - print("\n════════ SAMPLED METRICS (preview) ════════") - if not sampled: - print("(empty)") - return - - # Keys may be enums or strings depending on your analyzer; handle both. - def _name(m): # pragma: no cover - return m.name if hasattr(m, "name") else str(m) - - for metric, series in sampled.items(): - print(f"\n📈 {_name(metric)}:") - for entity, vals in series.items(): - head = list(vals[:5]) if vals else [] - print(f" - {entity}: len={len(vals)}, first={head}") - - -# ───────────────────────────────────────────────────────────── -# Tiny helpers for sanity checks (optional) -# ───────────────────────────────────────────────────────────── -def _mean(series: Iterable[float]) -> float: - arr = np.asarray(list(series), dtype=float) - return float(np.mean(arr)) if arr.size else 0.0 - - -def run_sanity_checks( - runner: SimulationRunner, - res: ResultsAnalyzer, -) -> None: - print("\n════════ SANITY CHECKS (rough) ════════") - w = runner.simulation_input.rqs_input - lam_rps = ( - float(w.avg_active_users.mean) - * float(w.avg_request_per_minute_per_user.mean) - / 60.0 - ) - # Observed throughput - _, rps_series = res.get_throughput_series() - rps_observed = _mean(rps_series) - print( - f"• Mean throughput (rps) expected≈{lam_rps:.3f} " - f"observed={rps_observed:.3f}" - ) - - sampled = res.get_sampled_metrics() - ram_series = sampled.get(SampledMetricName.RAM_IN_USE, {}) - ioq_series = sampled.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) - ready_series = sampled.get(SampledMetricName.READY_QUEUE_LEN, {}) - - ram_mean = _mean([_mean(v) for v in ram_series.values()]) if ram_series else 0.0 - ioq_mean = _mean([_mean(v) for v in ioq_series.values()]) if ioq_series else 0.0 - ready_mean = _mean([_mean(v) for v in ready_series.values()]) if ready_series else 0.0 - print(f"• Mean RAM in use (MB) observed={ram_mean:.3f}") - print(f"• Mean I/O queue length observed={ioq_mean:.3f}") - print(f"• Mean ready queue length observed={ready_mean:.3f}") - - -# ───────────────────────────────────────────────────────────── -# Build the LB + 2 servers scenario via AsyncFlow (builder) -# ───────────────────────────────────────────────────────────── -def build_payload_with_lb() -> SimulationPayload: - """ - Construct the SimulationPayload programmatically using the builder: - - Generator (120 users, 20 rpm each) - - Client - - Load balancer (round_robin) covering two servers - - Two servers with distinct endpoints - - Edges for all hops (gen→client, client→lb, lb→srv1/2, srv1/2→client) - - Simulation settings: 600s total, sample period 20ms - """ - # 1) Request generator +def main() -> None: + # ── 1) Build the scenario programmatically (no YAML) ──────────────────── + # Workload (traffic generator) generator = RqsGenerator( id="rqs-1", - avg_active_users={"mean": 120}, # Poisson default - avg_request_per_minute_per_user={"mean": 20}, # MUST be Poisson + avg_active_users={"mean": 120}, + avg_request_per_minute_per_user={"mean": 20}, user_sampling_window=60, ) - # 2) Client + # Client client = Client(id="client-1") - # 3) Servers with distinct endpoints - ep_srv1 = Endpoint( + # Two identical servers: CPU(2ms) → RAM(128MB) → IO(15ms) + endpoint = Endpoint( endpoint_name="/api", - # include 'probability' if your Endpoint schema supports it - probability=1.0, # remove if your Endpoint doesn't have this field + probability=1.0, steps=[ {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, - {"kind": "ram", "step_operation": {"necessary_ram": 64}}, - {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.012}}, + {"kind": "ram", "step_operation": {"necessary_ram": 128}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.015}}, ], ) + srv1 = Server( id="srv-1", - server_resources={"cpu_cores": 1, "ram_mb": 1024}, - endpoints=[ep_srv1], - ) - - ep_srv2 = Endpoint( - endpoint_name="/api", - probability=1.0, # remove if not supported in your schema - steps=[ - {"kind": "ram", "step_operation": {"necessary_ram": 96}}, - {"kind": "io_db", "step_operation": {"io_waiting_time": 0.020}}, - ], + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], ) srv2 = Server( id="srv-2", - server_resources={"cpu_cores": 2, "ram_mb": 2048}, - endpoints=[ep_srv2], + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], ) - # 4) Load balancer (round_robin) + # Load balancer (round-robin) lb = LoadBalancer( id="lb-1", algorithms="round_robin", server_covered={"srv-1", "srv-2"}, ) - # 5) Edges with exponential latency (2–3 ms) + # Network edges (3 ms mean, exponential) edges = [ Edge( id="gen-client", @@ -219,19 +110,19 @@ def build_payload_with_lb() -> SimulationPayload: id="client-lb", source="client-1", target="lb-1", - latency={"mean": 0.002, "distribution": "exponential"}, + latency={"mean": 0.003, "distribution": "exponential"}, ), Edge( id="lb-srv1", source="lb-1", target="srv-1", - latency={"mean": 0.002, "distribution": "exponential"}, + latency={"mean": 0.003, "distribution": "exponential"}, ), Edge( id="lb-srv2", source="lb-1", target="srv-2", - latency={"mean": 0.002, "distribution": "exponential"}, + latency={"mean": 0.003, "distribution": "exponential"}, ), Edge( id="srv1-client", @@ -247,10 +138,10 @@ def build_payload_with_lb() -> SimulationPayload: ), ] - # 6) Simulation settings + # Simulation settings settings = SimulationSettings( total_simulation_time=600, - sample_period_s=0.02, + sample_period_s=0.05, enabled_sample_metrics=[ "ready_queue_len", "event_loop_io_sleep", @@ -260,8 +151,8 @@ def build_payload_with_lb() -> SimulationPayload: enabled_event_metrics=["rqs_clock"], ) - # 7) Assemble the payload via the builder - flow = ( + # Assemble the payload with the builder + payload = ( AsyncFlow() .add_generator(generator) .add_client(client) @@ -269,48 +160,41 @@ def build_payload_with_lb() -> SimulationPayload: .add_load_balancer(lb) .add_edges(*edges) .add_simulation_settings(settings) - ) - - return flow.build_payload() - + ).build_payload() -# ───────────────────────────────────────────────────────────── -# Main entry-point -# ───────────────────────────────────────────────────────────── -def main() -> None: - """ - Build → wire → run the simulation, then print diagnostics and save plots. - """ + # ── 2) Run the simulation ─────────────────────────────────────────────── env = simpy.Environment() - payload = build_payload_with_lb() - runner = SimulationRunner(env=env, simulation_input=payload) results: ResultsAnalyzer = runner.run() - # Human-friendly diagnostics - print_latency_stats(results) - print_throughput(results) - print_sampled_preview(results) - - # Optional sanity checks (very rough) - run_sanity_checks(runner, results) + # ── 3) Print a concise latency summary ────────────────────────────────── + print(results.format_latency_stats()) - # Save plots (2×2 figure) - try: - from matplotlib import pyplot as plt # noqa: PLC0415 + # ── 4) Save plots (same directory as this script) ─────────────────────── + out_dir = Path(__file__).parent - fig, axes = plt.subplots(2, 2, figsize=(12, 8)) - results.plot_latency_distribution(axes[0, 0]) - results.plot_throughput(axes[0, 1]) - results.plot_server_queues(axes[1, 0]) - results.plot_ram_usage(axes[1, 1]) - fig.tight_layout() - - out_path = Path(__file__).parent / "two_servers.png" - fig.savefig(out_path) - print(f"\n🖼️ Plots saved to: {out_path}") - except Exception as exc: # Matplotlib not installed or plotting failed - print(f"\n[plotting skipped] {exc!r}") + # 4a) Dashboard: latency + throughput (single figure) + fig_dash, axes = plt.subplots( + 1, 2, figsize=(14, 5), dpi=160, constrained_layout=True + ) + results.plot_latency_distribution(axes[0]) + results.plot_throughput(axes[1]) + dash_path = out_dir / "lb_dashboard.png" + fig_dash.savefig(dash_path, bbox_inches="tight") + print(f"🖼️ Dashboard saved to: {dash_path}") + + # 4b) Per-server figures: Ready | I/O | RAM (one row per server) + for sid in results.list_server_ids(): + fig_srv, axs = plt.subplots( + 1, 3, figsize=(18, 4.2), dpi=160, constrained_layout=True + ) + results.plot_single_server_ready_queue(axs[0], sid) + results.plot_single_server_io_queue(axs[1], sid) + results.plot_single_server_ram(axs[2], sid) + fig_srv.suptitle(f"Server metrics — {sid}", fontsize=16) + srv_path = out_dir / f"lb_server_{sid}_metrics.png" + fig_srv.savefig(srv_path, bbox_inches="tight") + print(f"🖼️ Per-server plots saved to: {srv_path}") if __name__ == "__main__": diff --git a/examples/builder_input/single_server/builder_service_plots.png b/examples/builder_input/single_server/builder_service_plots.png new file mode 100644 index 0000000000000000000000000000000000000000..22fc27d645d5ad39dd0ecaa7db3372bd72a515f0 GIT binary patch literal 313774 zcmeFZWms2R7d^T`6hsqaOpphLPIlI|PSzGi3@#=Pjuy7hZgTV9;O6IKFn4mY za}?p>vH71r!ENhc#-nv?E()J=(N12=5kV*t(0?%QIlqWTFc9RPjI^3t;_nGpH8KtA z3!7$flhUEOrGnPMy2e*LBg)#yb$)a0weH;wYhWp3HQJ!^3^_meL#fOl40jKQ?S2r~ z_G#Hq{4R1ALwk&rDPbBz)Ab}*7t@i3?Vbh=794+s>HqlZ+-+ST@C*Jwz7$_vH26P$ zC|%6||NqYaT#^6pS0mk;oPq);Qa*Ewnf%qv?5zL&7T}Z&nB%aE1m4 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res.get_latency_stats() - print("\n════════ LATENCY STATS ════════") - if not stats: - print("(empty)") - return - - # Keep deterministic ordering for readability. - order: List[LatencyKey] = [ - LatencyKey.TOTAL_REQUESTS, - LatencyKey.MEAN, - LatencyKey.MEDIAN, - LatencyKey.STD_DEV, - LatencyKey.P95, - LatencyKey.P99, - LatencyKey.MIN, - LatencyKey.MAX, - ] - for key in order: - if key in stats: - print(f"{key.name:<20} = {stats[key]:.6f}") - - -def print_throughput(res: ResultsAnalyzer) -> None: - """Print 1-second throughput buckets.""" - timestamps, rps = res.get_throughput_series() - print("\n════════ THROUGHPUT (req/sec) ════════") - if not timestamps: - print("(empty)") - return - - for t, rate in zip(timestamps, rps): - print(f"t={t:4.1f}s → {rate:6.2f} rps") - - -def print_sampled_preview(res: ResultsAnalyzer) -> None: - """Print first 5 samples of each sampled metric series.""" - sampled: Dict[str, Dict[str, List[float]]] = res.get_sampled_metrics() - print("\n════════ SAMPLED METRICS ════════") - if not sampled: - print("(empty)") - return - - for metric, series in sampled.items(): - print(f"\n📈 {metric}:") - for entity, vals in series.items(): - head = list(vals[:5]) if vals else [] - print(f" - {entity}: len={len(vals)}, first={head}") - - -# ───────────────────────────────────────────────────────────── -# Plotting -# ───────────────────────────────────────────────────────────── -def save_all_plots(res: ResultsAnalyzer, out_path: Path) -> None: - """Generate the 2x2 plot figure and save it to `out_path`.""" - fig, axes = plt.subplots(2, 2, figsize=(12, 8)) - res.plot_latency_distribution(axes[0, 0]) - res.plot_throughput(axes[0, 1]) - res.plot_server_queues(axes[1, 0]) - res.plot_ram_usage(axes[1, 1]) - fig.tight_layout() - fig.savefig(out_path) - print(f"\n🖼️ Plots saved to: {out_path}") - - -# ───────────────────────────────────────────────────────────── -# Sanity checks (expected vs observed) -# ───────────────────────────────────────────────────────────── -REL_TOL = 0.30 # 30% tolerance for rough sanity checks - - -def _tick(label: str, expected: float, observed: float) -> None: - """Print a ✓ or ⚠ depending on relative error vs `REL_TOL`.""" - if expected == 0.0: - delta_pct = 0.0 - icon = "•" - else: - delta = abs(observed - expected) / abs(expected) - delta_pct = delta * 100.0 - icon = "✓" if delta <= REL_TOL else "⚠" - print(f"{icon} {label:<28} expected≈{expected:.3f} observed={observed:.3f} Δ={delta_pct:.1f}%") - -def _endpoint_totals(runner: SimulationRunner) -> Tuple[float, float, float]: - """ - Return (CPU_seconds, IO_seconds, RAM_MB) of the first endpoint on the first server. +# SimPy environment is required by SimulationRunner.from_yaml +import simpy - This keeps the check simple. If you use multiple endpoints weighted by probability, - extend this function to compute a probability-weighted average. - """ - servers = runner.simulation_input.topology_graph.nodes.servers - if not servers or not servers[0].endpoints: - return (0.0, 0.0, 0.0) +# matplotlib is needed to create figures for plotting +import matplotlib.pyplot as plt - ep = servers[0].endpoints[0] - cpu_s = 0.0 - io_s = 0.0 - ram_mb = 0.0 +# The only imports a user needs to run a simulation +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner - for step in ep.steps: - if isinstance(step.kind, EndpointStepCPU): - cpu_s += float(step.step_operation[StepOperation.CPU_TIME]) - elif isinstance(step.kind, EndpointStepIO): - io_s += float(step.step_operation[StepOperation.IO_WAITING_TIME]) - elif isinstance(step.kind, EndpointStepRAM): - ram_mb += float(step.step_operation[StepOperation.NECESSARY_RAM]) +# --- Basic Logging Setup --- +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") - return (cpu_s, io_s, ram_mb) - -def _edges_mean_latency(runner: SimulationRunner) -> float: - """Sum of edge mean latencies across the graph (simple additive approximation).""" - return float(sum(e.latency.mean for e in runner.simulation_input.topology_graph.edges)) - - -def _mean(series: Iterable[float]) -> float: - """Numerically stable mean for a generic float iterable.""" - arr = np.asarray(list(series), dtype=float) - return float(np.mean(arr)) if arr.size else 0.0 - - -def run_sanity_checks(runner: SimulationRunner, res: ResultsAnalyzer) -> None: - """ - Compare expected vs observed metrics using back-of-the-envelope approximations. - - Approximations used: - - Throughput ≈ λ = users * RPM / 60 - - Mean latency ≈ CPU_s + IO_s + NET_s (ignores queueing inside the server) - - Mean RAM ≈ λ * T_srv * RAM_per_request (Little’s law approximation) - - Mean I/O queue length ≈ λ * IO_s - - Edge concurrency ≈ λ * edge_mean_latency - """ - print("\n════════ SANITY CHECKS (expected vs observed) ════════") - - # Arrival rate λ (requests per second) - w = runner.simulation_input.rqs_input - lam_rps = float(w.avg_active_users.mean) * float(w.avg_request_per_minute_per_user.mean) / 60.0 - - # Endpoint sums - cpu_s, io_s, ram_mb = _endpoint_totals(runner) - net_s = _edges_mean_latency(runner) - t_srv = cpu_s + io_s - latency_expected = cpu_s + io_s + net_s - - # Observed latency, throughput - stats = res.get_latency_stats() - latency_observed = float(stats.get(LatencyKey.MEAN, 0.0)) - _, rps_series = res.get_throughput_series() - rps_observed = _mean(rps_series) - - # Observed RAM and queues - sampled = res.get_sampled_metrics() - ram_series = sampled.get("ram_in_use", {}) - ram_means = [_mean(vals) for vals in ram_series.values()] - ram_observed = float(sum(ram_means)) if ram_means else 0.0 - - ready_series = sampled.get("ready_queue_len", {}) - ioq_series = sampled.get("event_loop_io_sleep", {}) - ready_observed = _mean([_mean(v) for v in ready_series.values()]) if ready_series else 0.0 - ioq_observed = _mean([_mean(v) for v in ioq_series.values()]) if ioq_series else 0.0 - - # Expected quantities (very rough) - rps_expected = lam_rps - ram_expected = lam_rps * t_srv * ram_mb - ioq_expected = lam_rps * io_s - - _tick("Mean throughput (rps)", rps_expected, rps_observed) - _tick("Mean latency (s)", latency_expected, latency_observed) - _tick("Mean RAM (MB)", ram_expected, ram_observed) - _tick("Mean I/O queue", ioq_expected, ioq_observed) - - # Edge concurrency - edge_conc = sampled.get("edge_concurrent_connection", {}) - if edge_conc: - print("\n— Edge concurrency —") - edge_means: Dict[str, float] = {eid: _mean(vals) for eid, vals in edge_conc.items()} - for e in runner.simulation_input.topology_graph.edges: - exp = lam_rps * float(e.latency.mean) - obs = edge_means.get(e.id, 0.0) - _tick(f"edge {e.id}", exp, obs) - - # Extra diagnostics - print("\n— Diagnostics —") - print( - "λ={:.3f} rps | CPU_s={:.3f} IO_s={:.3f} NET_s≈{:.3f} | RAM/req={:.1f} MB" - .format(lam_rps, cpu_s, io_s, net_s, ram_mb) - ) - print("T_srv={:.3f}s → RAM_expected≈λ*T_srv*RAM = {:.1f} MB".format(t_srv, ram_expected)) - - -# ───────────────────────────────────────────────────────────── -# Main -# ───────────────────────────────────────────────────────────── def main() -> None: - """Entry-point: parse args, run simulation, print/plot, sanity-check.""" - parser = ArgumentParser(description="Run AsyncFlow from YAML and print outputs + sanity checks.") - parser.add_argument( - "--yaml", - type=Path, - default=Path(__file__).parent.parent / "data" /"single_server.yml", - help="Path to the simulation YAML file.", - ) - parser.add_argument( - "--out", - type=Path, - default=Path(__file__).parent / "single_server.png", - help="Path to the output image (plots).", - ) - args = parser.parse_args() + """Defines paths, runs the simulation, and generates all outputs.""" + # --- 1. Define File Paths --- + script_dir = Path(__file__).parent # <-- same folder as this file + out_dir = script_dir # <-- save outputs here + yaml_path = script_dir.parent / "data" / "single_server.yml" + output_base_name = "single_server_results" # prefix for all output files - yaml_path: Path = args.yaml if not yaml_path.exists(): - raise FileNotFoundError(f"YAML not found: {yaml_path}") + raise FileNotFoundError(f"YAML configuration file not found: {yaml_path}") - # Build runner from YAML and execute - env = simpy.Environment() - runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) + # --- 2. Run the Simulation --- + print(f"🚀 Loading and running simulation from: {yaml_path}") + env = simpy.Environment() # Create the SimPy environment + runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) # pass env results: ResultsAnalyzer = runner.run() - - # Prints - print_latency_stats(results) - print_throughput(results) - print_sampled_preview(results) - - # Sanity checks - run_sanity_checks(runner, results) - - # Plots - save_all_plots(results, args.out) + print("✅ Simulation finished!") + + # Plot 1: The main dashboard (Latency Distribution + Throughput) + fig_base, axes_base = plt.subplots(1, 2, figsize=(14, 5)) + results.plot_base_dashboard(axes_base[0], axes_base[1]) + fig_base.tight_layout() + base_plot_path = out_dir / f"{output_base_name}_dashboard.png" + fig_base.savefig(base_plot_path) + print(f"🖼️ Base dashboard saved to: {base_plot_path}") + + # Plot 2: Individual plots for each server's metrics + server_ids = results.list_server_ids() + for sid in server_ids: + # Ready queue (separate) + fig_rdy, ax_rdy = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ready_queue(ax_rdy, sid) + fig_rdy.tight_layout() + rdy_path = out_dir / f"{output_base_name}_ready_queue_{sid}.png" + fig_rdy.savefig(rdy_path) + print(f"🖼️ Ready queue for '{sid}' saved to: {rdy_path}") + + # I/O queue (separate) + fig_io, ax_io = plt.subplots(figsize=(10, 5)) + results.plot_single_server_io_queue(ax_io, sid) + fig_io.tight_layout() + io_path = out_dir / f"{output_base_name}_io_queue_{sid}.png" + fig_io.savefig(io_path) + print(f"🖼️ I/O queue for '{sid}' saved to: {io_path}") + + # RAM (separate) + fig_r, ax_r = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ram(ax_r, sid) + fig_r.tight_layout() + r_path = out_dir / f"{output_base_name}_ram_{sid}.png" + fig_r.savefig(r_path) + print(f"🖼️ RAM plot for '{sid}' saved to: {r_path}") if __name__ == "__main__": diff --git a/examples/yaml_input/single_server/single_server_results_dashboard.png 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+1,6 @@ [tool.poetry] -name = "asyncflow-simulator" -version = "0.1.0" +name = "AsyncFlow-Sim" +version = "0.1.0a1" description = "Digital-twin simulator for distributed async systems. Build what-if scenarios and quantify capacity, latency and throughput offline—before you deploy." authors = ["Gioele Botta"] readme = "README.md" @@ -18,9 +18,9 @@ keywords = [ "fastapi", "uvicorn", "distributed-systems", "queuing-theory" ] -homepage = "https://github.com//AsyncFlow-Simulator" # TO COMPLETE -repository = "https://github.com//AsyncFlow-Simulator" # TO COMPLETE -documentation = "https://github.com//AsyncFlow-Simulator/tree/main/docs" # TO COMPLETE +homepage = "https://github.com/AsyncFlow-Sim" +repository = "https://github.com/AsyncFlow-Sim/AsyncFlow" +documentation = "https://github.com/AsyncFlow-Sim/AsyncFlow-Sim/tree/v0.1.0/docs" classifiers = [ "Development Status :: 3 - Alpha", diff --git a/pytest.ini b/pytest.ini index 6d4afe2..c61714b 100644 --- a/pytest.ini +++ b/pytest.ini @@ -2,5 +2,6 @@ addopts = -ra -q testpaths = tests markers = - integration: tests that require external services (database, HTTP calls, etc.) -asyncio_mode = auto \ No newline at end of file + 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re-exports Pydantic leaf schemas (topology).""" +"""Public components: re-exports Pydantic schemas (topology).""" from __future__ import annotations from asyncflow.schemas.topology.edges import Edge diff --git a/src/asyncflow/metrics/analyzer.py b/src/asyncflow/metrics/analyzer.py index 16448bc..b9a6ea2 100644 --- a/src/asyncflow/metrics/analyzer.py +++ b/src/asyncflow/metrics/analyzer.py @@ -17,7 +17,7 @@ ) if TYPE_CHECKING: - + # Standard library typing imports in type-checking block (TC003). from collections.abc import Iterable from matplotlib.axes import Axes @@ -29,6 +29,10 @@ from asyncflow.schemas.settings.simulation import SimulationSettings +# Short alias to keep signatures within 88 chars (E501). +Series = tuple[list[float], list[float]] + + class ResultsAnalyzer: """Analyze and visualize the results of a completed simulation. @@ -36,9 +40,12 @@ class ResultsAnalyzer: - latency statistics - throughput time series (RPS) - sampled metrics from servers and edges + + It also exposes compact plotting/rendering helpers so that CLI scripts + can be short and consistent. """ - # Class attribute to define the period to calculate the throughput in s + # Default bucket size (seconds) used for cached throughput. _WINDOW_SIZE_S: float = 1.0 def __init__( @@ -49,14 +56,7 @@ def __init__( edges: list[EdgeRuntime], settings: SimulationSettings, ) -> None: - """ - Args: - client: Client runtime object, containing RqsClock entries. - servers: List of server runtime objects. - edges: List of edge runtime objects. - settings: Original simulation settings. - - """ + """Initialize with the runtime objects and original settings.""" self._client = client self._servers = servers self._edges = edges @@ -65,25 +65,13 @@ def __init__( # Lazily computed caches self.latencies: list[float] | None = None self.latency_stats: dict[LatencyKey, float] | None = None - self.throughput_series: tuple[list[float], list[float]] | None = None + self.throughput_series: Series | None = None + # Sampled metrics are stored with string metric keys for simplicity. self.sampled_metrics: dict[str, dict[str, list[float]]] | None = None - @staticmethod - def _apply_plot_cfg( - ax: Axes, - cfg: PlotCfg, - *, - legend_handles: Iterable[Line2D] | None = None, - ) -> None: - """Apply title / axis labels / grid and (optionally) legend to ax.""" - ax.set_title(cfg.title) - ax.set_xlabel(cfg.x_label) - ax.set_ylabel(cfg.y_label) - ax.grid(visible=True) - - if legend_handles: - ax.legend(handles=legend_handles) - + # ───────────────────────────────────────────── + # Core computation + # ───────────────────────────────────────────── def process_all_metrics(self) -> None: """Compute all aggregated and sampled metrics if not already done.""" if self.latency_stats is None and self._client.rqs_clock: @@ -93,15 +81,16 @@ def process_all_metrics(self) -> None: self._extract_sampled_metrics() def _process_event_metrics(self) -> None: - """Calculate latency stats and throughput time series (RPS).""" + """Calculate latency stats and throughput time series (1s RPS).""" # 1) Latencies self.latencies = [ - clock.finish - clock.start for clock in self._client.rqs_clock + clock.finish - clock.start + for clock in self._client.rqs_clock ] # 2) Summary stats if self.latencies: - arr = np.array(self.latencies) + arr = np.array(self.latencies, dtype=float) self.latency_stats = { LatencyKey.TOTAL_REQUESTS: float(arr.size), LatencyKey.MEAN: float(np.mean(arr)), @@ -115,24 +104,23 @@ def _process_event_metrics(self) -> None: else: self.latency_stats = {} - # 3) Throughput per 1s window + # 3) Throughput per 1s window (cached) completion_times = sorted(clock.finish for clock in self._client.rqs_clock) end_time = self._settings.total_simulation_time timestamps: list[float] = [] rps_values: list[float] = [] - count = 0 idx = 0 current_end = ResultsAnalyzer._WINDOW_SIZE_S while current_end <= end_time: + count = 0 while idx < len(completion_times) and completion_times[idx] <= current_end: count += 1 idx += 1 timestamps.append(current_end) rps_values.append(count / ResultsAnalyzer._WINDOW_SIZE_S) current_end += ResultsAnalyzer._WINDOW_SIZE_S - count = 0 self.throughput_series = (timestamps, rps_values) @@ -143,6 +131,7 @@ def _extract_sampled_metrics(self) -> None: for server in self._servers: sid = server.server_config.id for name, values in server.enabled_metrics.items(): + # Store with string key for a consistent external API. metrics[name.value][sid] = values for edge in self._edges: @@ -152,18 +141,79 @@ def _extract_sampled_metrics(self) -> None: self.sampled_metrics = metrics + # ───────────────────────────────────────────── + # Public accessors & formatting + # ───────────────────────────────────────────── + def list_server_ids(self) -> list[str]: + """Return server IDs in a stable order as given in the topology.""" + return [s.server_config.id for s in self._servers] + def get_latency_stats(self) -> dict[LatencyKey, float]: """Return latency statistics, computing them if necessary.""" self.process_all_metrics() return self.latency_stats or {} - def get_throughput_series(self) -> tuple[list[float], list[float]]: - """Return (timestamps, RPS). Empty lists when no traffic.""" + def format_latency_stats(self) -> str: + """Return a human-readable block with latency stats.""" + stats = self.get_latency_stats() + if not stats: + return "Latency stats: (empty)" + + by_name: dict[str, float] = { + getattr(k, "name", str(k)): v + for k, v in stats.items() + } + order = [ + "TOTAL_REQUESTS", + "MEAN", + "MEDIAN", + "STD_DEV", + "P95", + "P99", + "MIN", + "MAX", + ] + + lines = ["════════ LATENCY STATS ════════"] + # PERF401: build then extend instead of append in a loop. + formatted = [ + f"{k:<20} = {by_name[k]:.6f}" + for k in order + if k in by_name + ] + lines.extend(formatted) + return "\n".join(lines) + + def get_throughput_series( + self, + window_s: float | None = None, + ) -> Series: + """Return (timestamps, RPS). If `window_s` is provided, recompute on the fly.""" self.process_all_metrics() - if self.throughput_series is None: - return [], [] - return self.throughput_series + # Use cached (1s) series when suitable. + if window_s is None or window_s == ResultsAnalyzer._WINDOW_SIZE_S: + return self.throughput_series or ([], []) + + # Recompute with a custom window size. + completion_times = sorted(clock.finish for clock in self._client.rqs_clock) + end_time = self._settings.total_simulation_time + + timestamps: list[float] = [] + rps_values: list[float] = [] + idx = 0 + current_end = float(window_s) + + while current_end <= end_time: + count = 0 + while idx < len(completion_times) and completion_times[idx] <= current_end: + count += 1 + idx += 1 + timestamps.append(current_end) + rps_values.append(count / float(window_s)) + current_end += float(window_s) + + return (timestamps, rps_values) def get_sampled_metrics(self) -> dict[str, dict[str, list[float]]]: """Return sampled metrics from servers and edges.""" @@ -171,64 +221,369 @@ def get_sampled_metrics(self) -> dict[str, dict[str, list[float]]]: assert self.sampled_metrics is not None return self.sampled_metrics + def get_metric_map(self, key: SampledMetricName | str) -> dict[str, list[float]]: + """Return a series map for a metric, tolerant to enum/string keys.""" + self.process_all_metrics() + assert self.sampled_metrics is not None + + if isinstance(key, SampledMetricName): + # Prefer the canonical .value key; fall back to .name. + found = ( + self.sampled_metrics.get(key.value) + or self.sampled_metrics.get(key.name, {}) + ) + return found or {} + # If caller used a raw string: + return self.sampled_metrics.get(key, {}) + + def get_series(self, key: SampledMetricName | str, entity_id: str) -> Series: + """Return (times, values) for a given sampled metric and entity id.""" + series_map = self.get_metric_map(key) + vals = series_map.get(entity_id, []) + times = (np.arange(len(vals)) * self._settings.sample_period_s).tolist() + return times, vals + + # ───────────────────────────────────────────── + # Plotting helpers + # ───────────────────────────────────────────── + @staticmethod + def _apply_plot_cfg( + ax: Axes, + cfg: PlotCfg, + *, + legend_handles: Iterable[Line2D] | None = None, + ) -> None: + """Apply title / axis labels / grid and (optionally) legend to ax.""" + ax.set_title(cfg.title) + ax.set_xlabel(cfg.x_label) + ax.set_ylabel(cfg.y_label) + ax.grid(visible=True) + if legend_handles: + ax.legend(handles=legend_handles) + + def plot_base_dashboard(self, ax_latency: Axes, ax_throughput: Axes) -> None: + """Plot a 2x1 header: latency histogram + throughput line.""" + self.plot_latency_distribution(ax_latency) + self.plot_throughput(ax_throughput) + def plot_latency_distribution(self, ax: Axes) -> None: - """Plot the distribution of the latency""" + """Plot latency histogram with mean/P50/P95/P99 lines and a single + legend box with values. + """ + self.process_all_metrics() if not self.latencies: ax.text(0.5, 0.5, LATENCY_PLOT.no_data, ha="center", va="center") return - ax.hist(self.latencies, bins=50) + # Colors that pop on blue/white + col_mean = "#d62728" # red + col_p50 = "#ff7f0e" # orange + col_p95 = "#2ca02c" # green + col_p99 = "#9467bd" # purple + hist_color = "#1f77b4" # soft blue + + arr = np.asarray(self.latencies, dtype=float) + v_mean = float(np.mean(arr)) + v_p50 = float(np.percentile(arr, 50)) + v_p95 = float(np.percentile(arr, 95)) + v_p99 = float(np.percentile(arr, 99)) + + # Histogram (subtle to let overlays stand out) + ax.hist( + arr, bins=50, color=hist_color, alpha=0.40, + edgecolor="none", zorder=1, + ) + + # Vertical overlays + ax.axvline( + v_mean, color=col_mean, linestyle=":", linewidth=1.8, + alpha=0.95, zorder=3, + ) + ax.axvline( + v_p50, color=col_p50, linestyle="-.", linewidth=1.6, + alpha=0.90, zorder=3, + ) + ax.axvline( + v_p95, color=col_p95, linestyle="--", linewidth=1.6, + alpha=0.90, zorder=3, + ) + ax.axvline( + v_p99, color=col_p99, linestyle="--", linewidth=1.6, + alpha=0.90, zorder=3, + ) + + # Build legend handles (dummy lines, no data) + h_mean = ax.plot( + [], [], color=col_mean, linestyle=":", linewidth=2.4, + label=f"mean = {v_mean:.3f}", + )[0] + h_p50 = ax.plot( + [], [], color=col_p50, linestyle="-.", linewidth=2.4, + label=f"P50 = {v_p50:.3f}", + )[0] + h_p95 = ax.plot( + [], [], color=col_p95, linestyle="--", linewidth=2.4, + label=f"P95 = {v_p95:.3f}", + )[0] + h_p99 = ax.plot( + [], [], color=col_p99, linestyle="--", linewidth=2.4, + label=f"P99 = {v_p99:.3f}", + )[0] + + # Titles / labels / grid self._apply_plot_cfg(ax, LATENCY_PLOT) - def plot_throughput(self, ax: Axes) -> None: - """Plot the distribution of the throughput""" - timestamps, values = self.get_throughput_series() + # Legend (top-right) with readable background + leg = ax.legend( + handles=[h_mean, h_p50, h_p95, h_p99], + loc="upper right", + bbox_to_anchor=(0.98, 0.98), + borderaxespad=0.0, + framealpha=0.90, + fancybox=True, + handlelength=2.6, + fontsize=9.5, + ) + leg.get_frame().set_facecolor("white") + + + def plot_throughput(self, ax: Axes, *, window_s: float | None = None) -> None: + """Plot throughput with mean/P95/max lines, EWMA curve, and a single + legend box with values. + """ + timestamps, values = self.get_throughput_series(window_s=window_s) if not timestamps: ax.text(0.5, 0.5, THROUGHPUT_PLOT.no_data, ha="center", va="center") return - ax.plot(timestamps, values, marker="o", linestyle="-") + # Colors (high contrast on blue/white) + col_series = "#1f77b4" # blue main series + col_mean = "#d62728" # red + col_p95 = "#2ca02c" # green + col_max = "#9467bd" # purple + + + vals = np.asarray(values, dtype=float) + v_mean = float(np.mean(vals)) + v_p95 = float(np.percentile(vals, 95)) + v_max = float(np.max(vals)) + + # Main series + ax.plot( + timestamps, vals, marker="o", linewidth=1.6, alpha=0.95, + color=col_series, zorder=2, + ) + + # Horizontal overlays (match legend colors) + ax.axhline( + v_mean, color=col_mean, linestyle=":", linewidth=1.8, + alpha=0.95, zorder=4, + ) + ax.axhline( + v_p95, color=col_p95, linestyle="--", linewidth=1.6, + alpha=0.90, zorder=4, + ) + ax.axhline( + v_max, color=col_max, linestyle="--", linewidth=1.6, + alpha=0.90, zorder=4, + ) + + # Legend handles (dummy, no data) + h_mean = ax.plot( + [], [], color=col_mean, linestyle=":", linewidth=2.4, + label=f"mean = {v_mean:.3f}", + )[0] + h_p95 = ax.plot( + [], [], color=col_p95, linestyle="--", linewidth=2.4, + label=f"P95 = {v_p95:.3f}", + )[0] + h_max = ax.plot( + [], [], color=col_max, linestyle="--", linewidth=2.4, + label=f"max = {v_max:.3f}", + )[0] + + # Apply base cfg (titles/labels/grid) self._apply_plot_cfg(ax, THROUGHPUT_PLOT) - def plot_server_queues(self, ax: Axes) -> None: - """Plot the server queues""" - metrics = self.get_sampled_metrics() - ready = metrics.get(SampledMetricName.READY_QUEUE_LEN.value, {}) - io_q = metrics.get(SampledMetricName.EVENT_LOOP_IO_SLEEP.value, {}) + # Legend: upper-right; single box with values + leg = ax.legend( + handles=[h_mean, h_p95, h_max], + loc="upper right", + bbox_to_anchor=(0.98, 0.98), + borderaxespad=0.0, + framealpha=0.90, + fancybox=True, + handlelength=2.6, + fontsize=9.5, + ) + leg.get_frame().set_facecolor("white") + + - if not (ready or io_q): + def plot_single_server_ready_queue(self, ax: Axes, server_id: str) -> None: + """Plot Ready queue with mean/min/max lines and a single legend box with + values. No trend/ewma, no legend entry for the main series. + """ + times, vals = self.get_series(SampledMetricName.READY_QUEUE_LEN, server_id) + if not vals: ax.text(0.5, 0.5, SERVER_QUEUES_PLOT.no_data, ha="center", va="center") return - samples = len(next(iter(ready.values()), [])) - times = np.arange(samples) * self._settings.sample_period_s - - for sid, vals in ready.items(): - ax.plot(times, vals, label=f"{sid} {SERVER_QUEUES_PLOT.ready_label}") - for sid, vals in io_q.items(): - ax.plot( - times, - vals, - label=f"{sid} {SERVER_QUEUES_PLOT.io_label}", - linestyle="--", - ) + # Colors consistent with other charts + col_mean = "#d62728" # red + col_min = "#2ca02c" # green + col_max = "#9467bd" # purple + + y = np.asarray(vals, dtype=float) + v_mean = float(np.mean(y)) + v_min = float(np.min(y)) + v_max = float(np.max(y)) + + # Main series (no label/legend as requested) + ax.plot(times, y, linewidth=1.6, alpha=0.95) + + # Overlays + ax.axhline(v_mean, color=col_mean, linestyle=":", linewidth=1.8, alpha=0.95) + ax.axhline(v_min, color=col_min, linestyle="--", linewidth=1.6, alpha=0.90) + ax.axhline(v_max, color=col_max, linestyle="--", linewidth=1.6, alpha=0.90) + + # Legend handles (dummy lines with values) + h_mean = ax.plot( + [], [], color=col_mean, linestyle=":", linewidth=2.4, + label=f"mean = {v_mean:.3f}", + )[0] + h_min = ax.plot( + [], [], color=col_min, linestyle="--", linewidth=2.4, + label=f"min = {v_min:.3f}", + )[0] + h_max = ax.plot( + [], [], color=col_max, linestyle="--", linewidth=2.4, + label=f"max = {v_max:.3f}", + )[0] + + ax.set_title(f"Ready Queue — {server_id}") + ax.set_xlabel(SERVER_QUEUES_PLOT.x_label) + ax.set_ylabel(SERVER_QUEUES_PLOT.y_label) + ax.grid(visible=True) + + leg = ax.legend( + handles=[h_mean, h_min, h_max], + loc="upper right", + bbox_to_anchor=(0.98, 0.98), + borderaxespad=0.0, + framealpha=0.90, + fancybox=True, + handlelength=2.6, + fontsize=9.5, + ) + leg.get_frame().set_facecolor("white") + + def plot_single_server_io_queue(self, ax: Axes, server_id: str) -> None: + """Plot I/O queue with mean/min/max lines and a single legend box with + values. No trend/ewma, no legend entry for the main series. + """ + times, vals = self.get_series(SampledMetricName.EVENT_LOOP_IO_SLEEP, server_id) + if not vals: + ax.text(0.5, 0.5, SERVER_QUEUES_PLOT.no_data, ha="center", va="center") + return - self._apply_plot_cfg(ax, SERVER_QUEUES_PLOT, legend_handles=ax.lines) + col_mean = "#d62728" # red + col_min = "#2ca02c" # green + col_max = "#9467bd" # purple + + y = np.asarray(vals, dtype=float) + v_mean = float(np.mean(y)) + v_min = float(np.min(y)) + v_max = float(np.max(y)) + + ax.plot(times, y, linewidth=1.6, alpha=0.95) + + ax.axhline(v_mean, color=col_mean, linestyle=":", linewidth=1.8, alpha=0.95) + ax.axhline(v_min, color=col_min, linestyle="--", linewidth=1.6, alpha=0.90) + ax.axhline(v_max, color=col_max, linestyle="--", linewidth=1.6, alpha=0.90) + + h_mean = ax.plot( + [], [], color=col_mean, linestyle=":", linewidth=2.4, + label=f"mean = {v_mean:.3f}", + )[0] + h_min = ax.plot( + [], [], color=col_min, linestyle="--", linewidth=2.4, + label=f"min = {v_min:.3f}", + )[0] + h_max = ax.plot( + [], [], color=col_max, linestyle="--", linewidth=2.4, + label=f"max = {v_max:.3f}", + )[0] + + ax.set_title(f"I/O Queue — {server_id}") + ax.set_xlabel(SERVER_QUEUES_PLOT.x_label) + ax.set_ylabel(SERVER_QUEUES_PLOT.y_label) + ax.grid(visible=True) + + leg = ax.legend( + handles=[h_mean, h_min, h_max], + loc="upper right", + bbox_to_anchor=(0.98, 0.98), + borderaxespad=0.0, + framealpha=0.90, + fancybox=True, + handlelength=2.6, + fontsize=9.5, + ) + leg.get_frame().set_facecolor("white") - def plot_ram_usage(self, ax: Axes) -> None: - """Plot the ram usage""" - metrics = self.get_sampled_metrics() - ram = metrics.get(SampledMetricName.RAM_IN_USE.value, {}) - if not ram: + def plot_single_server_ram(self, ax: Axes, server_id: str) -> None: + """Plot RAM usage with mean/min/max lines and a single legend box with + values. No trend/ewma, no legend entry for the main series. + """ + times, vals = self.get_series(SampledMetricName.RAM_IN_USE, server_id) + if not vals: ax.text(0.5, 0.5, RAM_PLOT.no_data, ha="center", va="center") return - samples = len(next(iter(ram.values()))) - times = np.arange(samples) * self._settings.sample_period_s - - for sid, vals in ram.items(): - ax.plot(times, vals, label=f"{sid} {RAM_PLOT.legend_label}") + col_mean = "#d62728" # red + col_min = "#2ca02c" # green + col_max = "#9467bd" # purple + + y = np.asarray(vals, dtype=float) + v_mean = float(np.mean(y)) + v_min = float(np.min(y)) + v_max = float(np.max(y)) + + ax.plot(times, y, linewidth=1.6, alpha=0.95) + + ax.axhline(v_mean, color=col_mean, linestyle=":", linewidth=1.8, alpha=0.95) + ax.axhline(v_min, color=col_min, linestyle="--", linewidth=1.6, alpha=0.90) + ax.axhline(v_max, color=col_max, linestyle="--", linewidth=1.6, alpha=0.90) + + h_mean = ax.plot( + [], [], color=col_mean, linestyle=":", linewidth=2.4, + label=f"mean = {v_mean:.3f}", + )[0] + h_min = ax.plot( + [], [], color=col_min, linestyle="--", linewidth=2.4, + label=f"min = {v_min:.3f}", + )[0] + h_max = ax.plot( + [], [], color=col_max, linestyle="--", linewidth=2.4, + label=f"max = {v_max:.3f}", + )[0] + + ax.set_title(f"{RAM_PLOT.title} — {server_id}") + ax.set_xlabel(RAM_PLOT.x_label) + ax.set_ylabel(RAM_PLOT.y_label) + ax.grid(visible=True) - self._apply_plot_cfg(ax, RAM_PLOT, legend_handles=ax.lines) + leg = ax.legend( + handles=[h_mean, h_min, h_max], + loc="upper right", + bbox_to_anchor=(0.98, 0.98), + borderaxespad=0.0, + framealpha=0.90, + fancybox=True, + handlelength=2.6, + fontsize=9.5, + ) + leg.get_frame().set_facecolor("white") diff --git a/tests/integration/single_server/test_single_server.py b/tests/integration/single_server/test_int_single_server.py similarity index 100% rename from tests/integration/single_server/test_single_server.py rename to tests/integration/single_server/test_int_single_server.py diff --git a/tests/system/test_sys_lb_two_servers.py b/tests/system/test_sys_lb_two_servers.py new file mode 100644 index 0000000..0f7f6eb --- /dev/null +++ b/tests/system/test_sys_lb_two_servers.py @@ -0,0 +1,180 @@ +"""System test: load balancer + two identical servers (seeded, reproducible). + +Topology: + + generator → client → LB(round_robin) → srv-1 + └→ srv-2 + srv-1 → client + srv-2 → client + +Each server endpoint: CPU(2 ms) → RAM(128 MB) → IO(12 ms) +Edges: exponential latency ~2–3 ms. +We check: +- latency stats / throughput sanity vs nominal λ (~40 rps); +- balanced traffic across srv-1 / srv-2 via edge concurrency and RAM means. +""" + +from __future__ import annotations + +import os +import random +from typing import Dict, List + +import numpy as np +import pytest +import simpy + +from asyncflow import AsyncFlow +from asyncflow.components import Client, Edge, Endpoint, LoadBalancer, Server +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator +from asyncflow.config.constants import LatencyKey + +pytestmark = [ + pytest.mark.system, + pytest.mark.skipif( + os.getenv("ASYNCFLOW_RUN_SYSTEM_TESTS") != "1", + reason="System tests disabled (set ASYNCFLOW_RUN_SYSTEM_TESTS=1 to run).", + ), +] + +SEED = 4242 +REL_TOL = 0.30 # 30% for λ/latency +BAL_TOL = 0.25 # 25% imbalance tolerated between the two backends + + +def _seed_all(seed: int = SEED) -> None: + random.seed(seed) + np.random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + + +def _build_payload(): + gen = RqsGenerator( + id="rqs-1", + avg_active_users={"mean": 120}, + avg_request_per_minute_per_user={"mean": 20}, + user_sampling_window=60, + ) + client = Client(id="client-1") + + endpoint = Endpoint( + endpoint_name="/api", + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "ram", "step_operation": {"necessary_ram": 128}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.012}}, + ], + ) + srv1 = Server(id="srv-1", server_resources={"cpu_cores": 1, "ram_mb": 2048}, endpoints=[endpoint]) + srv2 = Server(id="srv-2", server_resources={"cpu_cores": 1, "ram_mb": 2048}, endpoints=[endpoint]) + + lb = LoadBalancer(id="lb-1", algorithms="round_robin", server_covered={"srv-1", "srv-2"}) + + edges = [ + Edge( + id="gen-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="client-lb", + source="client-1", + target="lb-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="lb-srv1", + source="lb-1", + target="srv-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="lb-srv2", + source="lb-1", + target="srv-2", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="srv1-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="srv2-client", + source="srv-2", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + ] + + settings = SimulationSettings( + total_simulation_time=600, + sample_period_s=0.05, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + flow = ( + AsyncFlow() + .add_generator(gen) + .add_client(client) + .add_load_balancer(lb) + .add_servers(srv1, srv2) + .add_edges(*edges) + .add_simulation_settings(settings) + ) + return flow.build_payload() + + +def _rel_diff(a: float, b: float) -> float: + denom = max(1e-9, (abs(a) + abs(b)) / 2.0) + return abs(a - b) / denom + + +def test_system_lb_two_servers_balanced_and_sane() -> None: + """End-to-end LB scenario: sanity + balance checks with seeded RNGs.""" + _seed_all() + + env = simpy.Environment() + runner = SimulationRunner(env=env, simulation_input=_build_payload()) + res: ResultsAnalyzer = runner.run() + + # Latency sanity + stats = res.get_latency_stats() + assert stats and LatencyKey.TOTAL_REQUESTS in stats + mean_lat = float(stats.get(LatencyKey.MEAN, 0.0)) + assert 0.020 <= mean_lat <= 0.060 + + # Throughput sanity vs nominal λ ≈ 40 rps + _, rps = res.get_throughput_series() + assert rps, "No throughput series produced." + rps_mean = float(np.mean(rps)) + lam = 120 * 20 / 60.0 + assert abs(rps_mean - lam) / lam <= REL_TOL + + # Load balance check: edge concurrency lb→srv1 vs lb→srv2 close + sampled = res.get_sampled_metrics() + edge_cc: Dict[str, List[float]] = sampled.get("edge_concurrent_connection", {}) + assert "lb-srv1" in edge_cc and "lb-srv2" in edge_cc + m1, m2 = float(np.mean(edge_cc["lb-srv1"])), float(np.mean(edge_cc["lb-srv2"])) + assert _rel_diff(m1, m2) <= BAL_TOL + + # Server metrics present and broadly similar (RAM means close-ish) + ram_map: Dict[str, List[float]] = sampled.get("ram_in_use", {}) + assert "srv-1" in ram_map and "srv-2" in ram_map + ram1, ram2 = float(np.mean(ram_map["srv-1"])), float(np.mean(ram_map["srv-2"])) + assert _rel_diff(ram1, ram2) <= BAL_TOL + + # IDs reported by analyzer + sids = res.list_server_ids() + assert set(sids) == {"srv-1", "srv-2"} diff --git a/tests/system/test_sys_single_server.py b/tests/system/test_sys_single_server.py new file mode 100644 index 0000000..160899c --- /dev/null +++ b/tests/system/test_sys_single_server.py @@ -0,0 +1,136 @@ +"""System test: single-server scenario (deterministic-seeded, reproducible). + +Runs a compact but realistic topology: + + generator → client → srv-1 → client + +Endpoint on srv-1: CPU(1.5 ms) → RAM(96 MB) → IO(10 ms) +Edges: exponential latency ~3 ms each way. + +Assertions (with sensible tolerances): +- non-empty latency stats; mean latency in a plausible band; +- mean throughput close to the nominal λ (±30%); +- sampled metrics exist for srv-1 and are non-empty. +""" + +from __future__ import annotations + +import os +import random +from typing import Dict, List + +import numpy as np +import pytest +import simpy + +from asyncflow import AsyncFlow +from asyncflow.components import Client, Edge, Endpoint, Server +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator +from asyncflow.config.constants import LatencyKey + +# Mark as system and skip unless explicitly enabled in CI (or locally) +pytestmark = [ + pytest.mark.system, + pytest.mark.skipif( + os.getenv("ASYNCFLOW_RUN_SYSTEM_TESTS") != "1", + reason="System tests disabled (set ASYNCFLOW_RUN_SYSTEM_TESTS=1 to run).", + ), +] + +SEED = 1337 +REL_TOL = 0.30 # 30% tolerance for stochastic expectations + + +def _seed_all(seed: int = SEED) -> None: + random.seed(seed) + np.random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + + +def _build_payload(): + # Workload: ~26.7 rps (80 users * 20 rpm / 60) + gen = RqsGenerator( + id="rqs-1", + avg_active_users={"mean": 80}, + avg_request_per_minute_per_user={"mean": 20}, + user_sampling_window=60, + ) + client = Client(id="client-1") + + ep = Endpoint( + endpoint_name="/api", + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.0015}}, + {"kind": "ram", "step_operation": {"necessary_ram": 96}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ], + ) + srv = Server(id="srv-1", server_resources={"cpu_cores": 1, "ram_mb": 2048}, endpoints=[ep]) + + edges = [ + Edge( + id="gen-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="client-srv", + source="client-1", + target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="srv-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + ] + + settings = SimulationSettings( + total_simulation_time=180, # virtual time; keeps wall time fast + sample_period_s=0.05, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + flow = AsyncFlow().add_generator(gen).add_client(client).add_servers(srv).add_edges(*edges) + flow = flow.add_simulation_settings(settings) + return flow.build_payload() + + +def test_system_single_server_end_to_end() -> None: + """End-to-end single-server check with tolerances and seeded RNGs.""" + _seed_all() + + env = simpy.Environment() + runner = SimulationRunner(env=env, simulation_input=_build_payload()) + res: ResultsAnalyzer = runner.run() + + # Latency stats present and plausible + stats = res.get_latency_stats() + assert stats and LatencyKey.TOTAL_REQUESTS in stats + mean_lat = float(stats.get(LatencyKey.MEAN, 0.0)) + assert 0.015 <= mean_lat <= 0.060 + + # Throughput close to nominal lambda + timestamps, rps = res.get_throughput_series() + assert timestamps, "No throughput series produced." + rps_mean = float(np.mean(rps)) + lam = 80 * 20 / 60.0 + assert abs(rps_mean - lam) / lam <= REL_TOL + + # Sampled metrics exist for srv-1 + sampled: Dict[str, Dict[str, List[float]]] = res.get_sampled_metrics() + for key in ("ready_queue_len", "event_loop_io_sleep", "ram_in_use"): + assert key in sampled and "srv-1" in sampled[key] + assert len(sampled[key]["srv-1"]) > 0 diff --git a/tests/unit/metrics/test_analyzer.py b/tests/unit/metrics/test_analyzer.py index 365cb1c..901b646 100644 --- a/tests/unit/metrics/test_analyzer.py +++ b/tests/unit/metrics/test_analyzer.py @@ -1,4 +1,15 @@ -"""Unit-tests for ``ResultsAnalyzer`` (latency, throughput, plots).""" +"""Extra unit tests for ``ResultsAnalyzer`` helpers and plots. + +This suite complements the basic analyzer tests by exercising: +- formatting helpers (latency stats pretty-printer), +- server-id ordering, +- throughput recomputation with a custom window, +- metric accessors tolerant to enum/string keys, +- per-metric series time bases, +- the compact "base dashboard" plotting helper, +- single-server plots (ready queue, I/O queue, RAM), +- multi-server helpers (axes allocation and error handling). +""" from __future__ import annotations @@ -7,8 +18,8 @@ import pytest from matplotlib.figure import Figure -from asyncflow.config.constants import LatencyKey -from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.analysis import ResultsAnalyzer +from asyncflow.enums import SampledMetricName if TYPE_CHECKING: from asyncflow.runtime.actors.client import ClientRuntime @@ -18,13 +29,13 @@ # ---------------------------------------------------------------------- # -# Dummy objects (test doubles) # +# Test doubles (minimal) # # ---------------------------------------------------------------------- # class DummyClock: - """Clock with *start* / *finish* timestamps to emulate a request.""" + """Clock with *start* and *finish* timestamps to emulate one request.""" def __init__(self, start: float, finish: float) -> None: - """Save *start* and *finish* times.""" + """Initialize a synthetic request completion interval.""" self.start = start self.finish = finish @@ -41,23 +52,23 @@ class DummyName: """Mimic an Enum member that carries a ``.value`` attribute.""" def __init__(self, value: str) -> None: - """Store the dummy string *value*.""" + """Store the underlying string *value* used as a metric key.""" self.value = value class DummyServerConfig: - """Lightweight replacement for the real ``ServerConfig``.""" + """Minimal server config with only the ``id`` attribute.""" def __init__(self, identifier: str) -> None: - """Expose only the *id* field required by the analyzer.""" + """Set the server identifier used by the analyzer.""" self.id = identifier class DummyServer: - """Stub for ``ServerRuntime`` exposing ``enabled_metrics``.""" + """Stub for ``ServerRuntime`` exposing ``enabled_metrics`` and config.""" def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: - """Create a fake server with the given *metrics*.""" + """Create a fake server with the given per-metric time series.""" self.server_config = DummyServerConfig(identifier) self.enabled_metrics = { DummyName(name): values for name, values in metrics.items() @@ -65,18 +76,18 @@ def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: class DummyEdgeConfig: - """Minified replacement for the real ``EdgeConfig``.""" + """Minimal edge config with only the ``id`` attribute.""" def __init__(self, identifier: str) -> None: - """Expose only the *id* property.""" + """Set the edge identifier used by the analyzer.""" self.id = identifier class DummyEdge: - """Stub for ``EdgeRuntime`` exposing ``enabled_metrics``.""" + """Stub for ``EdgeRuntime`` exposing ``enabled_metrics`` and config.""" def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: - """Create a fake edge with the given *metrics*.""" + """Create a fake edge with the given per-metric time series.""" self.edge_config = DummyEdgeConfig(identifier) self.enabled_metrics = { DummyName(name): values for name, values in metrics.items() @@ -84,107 +95,196 @@ def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: # ---------------------------------------------------------------------- # -# Fixtures # +# Fixtures # # ---------------------------------------------------------------------- # @pytest.fixture -def simple_analyzer(sim_settings: SimulationSettings) -> ResultsAnalyzer: - """ - Analyzer with two synthetic requests (durations 1 s and 2 s) on a - 3-second horizon and **no** sampled metrics. +def analyzer_with_metrics(sim_settings: SimulationSettings) -> ResultsAnalyzer: + """Provide an analyzer with one server and ready/io/ram signals. + + The fixture sets: + - total_simulation_time = 3 s, + - sample_period_s = 1 s, + - two completed requests at t=1s and t=2s. """ sim_settings.total_simulation_time = 3 - clocks = [DummyClock(0.0, 1.0), DummyClock(0.0, 2.0)] - client = DummyClient(clocks) + sim_settings.sample_period_s = 1.0 + client = DummyClient([DummyClock(0.0, 1.0), DummyClock(0.0, 2.0)]) + server = DummyServer( + "srvX", + { + "ready_queue_len": [0, 1, 2], + "event_loop_io_sleep": [0, 0, 1], + "ram_in_use": [10.0, 20.0, 30.0], + }, + ) + edge = DummyEdge("edgeX", {}) return ResultsAnalyzer( client=cast("ClientRuntime", client), - servers=[], - edges=[], + servers=[cast("ServerRuntime", server)], + edges=[cast("EdgeRuntime", edge)], settings=sim_settings, ) # ---------------------------------------------------------------------- # -# Tests for computed metrics # +# Accessors / formatting # # ---------------------------------------------------------------------- # -def test_latency_stats(simple_analyzer: ResultsAnalyzer) -> None: - stats = simple_analyzer.get_latency_stats() - assert stats[LatencyKey.TOTAL_REQUESTS] == 2.0 - assert stats[LatencyKey.MEAN] == pytest.approx(1.5) - assert stats[LatencyKey.MEDIAN] == pytest.approx(1.5) - assert stats[LatencyKey.MIN] == pytest.approx(1.0) - assert stats[LatencyKey.MAX] == pytest.approx(2.0) - assert stats[LatencyKey.P95] == pytest.approx(1.95, rel=1e-3) +def test_format_latency_stats_contains_header_and_lines( + analyzer_with_metrics: ResultsAnalyzer, +) -> None: + """Ensure the formatted stats contain a header and canonical keys.""" + text = analyzer_with_metrics.format_latency_stats() + assert "LATENCY STATS" in text + assert "MEAN" in text + assert "MEDIAN" in text + + +def test_list_server_ids_preserves_topology_order( + sim_settings: SimulationSettings, +) -> None: + """Verify that server IDs are returned in topology order.""" + sim_settings.total_simulation_time = 1 + client = DummyClient([]) + s1 = DummyServer("s1", {}) + s2 = DummyServer("s2", {}) + s3 = DummyServer("s3", {}) + an = ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[ + cast("ServerRuntime", s1), + cast("ServerRuntime", s2), + cast("ServerRuntime", s3), + ], + edges=[], + settings=sim_settings, + ) + assert an.list_server_ids() == ["s1", "s2", "s3"] -def test_throughput_series(simple_analyzer: ResultsAnalyzer) -> None: - timestamps, rps = simple_analyzer.get_throughput_series() - assert timestamps == [1.0, 2.0, 3.0] - assert rps == [1.0, 1.0, 0.0] +# ---------------------------------------------------------------------- # +# Throughput with custom window # +# ---------------------------------------------------------------------- # +def test_get_throughput_series_custom_window_half_second( + analyzer_with_metrics: ResultsAnalyzer, +) -> None: + """Check recomputation of throughput with a 0.5 s window.""" + # Completions at 1s and 2s; with 0.5s buckets counts are [0,1,0,1,0,0]. + # Rates are counts / 0.5 => [0, 2, 0, 2, 0, 0]. + ts, rps = analyzer_with_metrics.get_throughput_series(window_s=0.5) + assert ts[:4] == [0.5, 1.0, 1.5, 2.0] + assert rps[:4] == [0.0, 2.0, 0.0, 2.0] -def test_sampled_metrics_empty(simple_analyzer: ResultsAnalyzer) -> None: - assert simple_analyzer.get_sampled_metrics() == {} +# ---------------------------------------------------------------------- # +# Metric map / series helpers # +# ---------------------------------------------------------------------- # +def test_get_metric_map_accepts_enum_and_string( + analyzer_with_metrics: ResultsAnalyzer, +) -> None: + """Ensure metric retrieval works for enum and raw-string keys.""" + m_enum = analyzer_with_metrics.get_metric_map( + SampledMetricName.READY_QUEUE_LEN, + ) + m_str = analyzer_with_metrics.get_metric_map("ready_queue_len") + + # PT018: split assertions into multiple parts. + assert "srvX" in m_enum + assert "srvX" in m_str + assert m_enum["srvX"] == [0, 1, 2] + assert m_str["srvX"] == [0, 1, 2] + +def test_get_series_respects_sample_period( + sim_settings: SimulationSettings, +) -> None: + """Confirm that series time base honors ``sample_period_s``.""" + sim_settings.total_simulation_time = 5 + sim_settings.sample_period_s = 1.5 + client = DummyClient([]) + server = DummyServer("srv1", {"ready_queue_len": [3, 4, 5]}) + an = ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[cast("ServerRuntime", server)], + edges=[], + settings=sim_settings, + ) + times, vals = an.get_series(SampledMetricName.READY_QUEUE_LEN, "srv1") + assert vals == [3, 4, 5] + assert times == [0.0, 1.5, 3.0] # ---------------------------------------------------------------------- # -# Tests for plotting methods # +# Plotting: base dashboard # # ---------------------------------------------------------------------- # -def test_plot_latency_distribution(simple_analyzer: ResultsAnalyzer) -> None: +def test_plot_base_dashboard_sets_titles( + analyzer_with_metrics: ResultsAnalyzer, +) -> None: + """Validate that the base dashboard sets expected axis titles.""" fig = Figure() - ax = fig.subplots() - simple_analyzer.process_all_metrics() - simple_analyzer.plot_latency_distribution(ax) - assert ax.get_title() == "Request Latency Distribution" + ax_lat, ax_thr = fig.subplots(1, 2) + analyzer_with_metrics.plot_base_dashboard(ax_lat, ax_thr) + assert ax_lat.get_title() == "Request Latency Distribution" + assert ax_thr.get_title() == "Throughput (RPS)" -def test_plot_throughput(simple_analyzer: ResultsAnalyzer) -> None: +# ---------------------------------------------------------------------- # +# Plotting: single-server dedicated plots # +# ---------------------------------------------------------------------- # +def test_plot_single_server_ready_queue( + analyzer_with_metrics: ResultsAnalyzer, +) -> None: + """Ready-queue plot should have a title and a legend with mean/min/max.""" fig = Figure() ax = fig.subplots() - simple_analyzer.process_all_metrics() - simple_analyzer.plot_throughput(ax) - assert ax.get_title() == "Throughput (RPS)" + analyzer_with_metrics.plot_single_server_ready_queue(ax, "srvX") + assert "Ready Queue" in ax.get_title() + + legend = ax.get_legend() + assert legend is not None + + labels = [t.get_text() for t in legend.get_texts()] + assert any(lbl.lower().startswith("mean") for lbl in labels) + assert any(lbl.lower().startswith("min") for lbl in labels) + assert any(lbl.lower().startswith("max") for lbl in labels) + assert len(labels) == 3 -def test_plot_server_queues_with_data(sim_settings: SimulationSettings) -> None: - sim_settings.total_simulation_time = 3 - client = DummyClient([]) - server = DummyServer("srv1", {"ready_queue_len": [1, 2, 3]}) - edge = DummyEdge("edge1", {}) - analyzer = ResultsAnalyzer( - client=cast("ClientRuntime", client), - servers=[cast("ServerRuntime", server)], - edges=[cast("EdgeRuntime", edge)], - settings=sim_settings, - ) +def test_plot_single_server_io_queue( + analyzer_with_metrics: ResultsAnalyzer, +) -> None: + """I/O-queue plot should have a title and a legend with mean/min/max.""" fig = Figure() ax = fig.subplots() - analyzer.process_all_metrics() - analyzer.plot_server_queues(ax) - assert ax.get_title() == "Server Queues" + analyzer_with_metrics.plot_single_server_io_queue(ax, "srvX") + + assert "I/O Queue" in ax.get_title() + legend = ax.get_legend() assert legend is not None - texts = [t.get_text() for t in legend.get_texts()] - assert "srv1 Ready queue" in texts + labels = [t.get_text() for t in legend.get_texts()] + assert any(lbl.lower().startswith("mean") for lbl in labels) + assert any(lbl.lower().startswith("min") for lbl in labels) + assert any(lbl.lower().startswith("max") for lbl in labels) + assert len(labels) == 3 -def test_plot_ram_usage_with_data(sim_settings: SimulationSettings) -> None: - sim_settings.total_simulation_time = 3 - client = DummyClient([]) - edge = DummyEdge("edgeA", {"ram_in_use": [10.0, 20.0]}) - analyzer = ResultsAnalyzer( - client=cast("ClientRuntime", client), - servers=[], - edges=[cast("EdgeRuntime", edge)], - settings=sim_settings, - ) +def test_plot_single_server_ram( + analyzer_with_metrics: ResultsAnalyzer, +) -> None: + """RAM plot should have a title and a legend with mean/min/max.""" fig = Figure() ax = fig.subplots() - analyzer.process_all_metrics() - analyzer.plot_ram_usage(ax) - assert ax.get_title() == "RAM Usage" + analyzer_with_metrics.plot_single_server_ram(ax, "srvX") + + assert "RAM" in ax.get_title() + legend = ax.get_legend() assert legend is not None - texts = [t.get_text() for t in legend.get_texts()] - assert "edgeA RAM" in texts + + labels = [t.get_text() for t in legend.get_texts()] + assert any(lbl.lower().startswith("mean") for lbl in labels) + assert any(lbl.lower().startswith("min") for lbl in labels) + assert any(lbl.lower().startswith("max") for lbl in labels) + assert len(labels) == 3 + diff --git a/tests/unit/public_api/test_import.py b/tests/unit/public_api/test_import.py new file mode 100644 index 0000000..cd708bc --- /dev/null +++ b/tests/unit/public_api/test_import.py @@ -0,0 +1,86 @@ +"""Unit tests for the public components import surface. + +Verifies that: +- `asyncflow.components` exposes the expected `__all__`. +- All symbols in `__all__` are importable and are classes. +""" + +from __future__ import annotations + +import importlib +from typing import TYPE_CHECKING + +from asyncflow.components import ( + Client, + Edge, + Endpoint, + LoadBalancer, + Server, + ServerResources, +) +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator, RVConfig + +if TYPE_CHECKING: + from collections.abc import Iterable + + + +def _assert_all_equals(module_name: str, expected: Iterable[str]) -> None: + """Assert that a module's __all__ exactly matches `expected`.""" + mod = importlib.import_module(module_name) + assert hasattr(mod, "__all__"), f"{module_name} is missing __all__" + assert set(mod.__all__) == set(expected), ( + f"{module_name}.__all__ mismatch:\n" + f" expected: {set(expected)}\n" + f" actual: {set(mod.__all__)}" + ) + + +def test_components_public_symbols() -> None: + """`asyncflow.components` exposes the expected names.""" + expected = [ + "Client", + "Edge", + "Endpoint", + "LoadBalancer", + "Server", + "ServerResources", + ] + _assert_all_equals("asyncflow.components", expected) + + +def test_components_symbols_are_importable_classes() -> None: + """All public symbols are importable and are classes.""" + # Basic type sanity (avoid heavy imports/instantiation) + for cls, name in [ + (Client, "Client"), + (Edge, "Edge"), + (Endpoint, "Endpoint"), + (LoadBalancer, "LoadBalancer"), + (Server, "Server"), + (ServerResources, "ServerResources"), + ]: + assert isinstance(cls, type), f"{name} should be a class type" + assert cls.__name__ == name + +def test_workload_public_symbols() -> None: + """`asyncflow.workload` exposes RVConfig and RqsGenerator.""" + _assert_all_equals("asyncflow.workload", ["RVConfig", "RqsGenerator"]) + + +def test_workload_symbols_are_importable_classes() -> None: + """Public symbols are importable and are classes.""" + for cls, name in [(RVConfig, "RVConfig"), (RqsGenerator, "RqsGenerator")]: + assert isinstance(cls, type), f"{name} should be a class" + assert cls.__name__ == name + +def test_settings_public_symbols() -> None: + """`asyncflow.settings` exposes SimulationSettings.""" + _assert_all_equals("asyncflow.settings", ["SimulationSettings"]) + + +def test_settings_symbol_is_importable_class() -> None: + """Public symbol is importable and is a class.""" + assert isinstance(SimulationSettings, type), "SimulationSettings should be a class" + assert SimulationSettings.__name__ == "SimulationSettings" From fb0802711f819d04295d6546278623ca1e769d8a Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Sun, 17 Aug 2025 17:44:47 +0200 Subject: [PATCH 34/52] fixing a bug --- .github/workflows/ci-develop.yml | 91 ++++++++++++++++++++++++-------- .github/workflows/ci-main.yml | 51 ++++++++++++++++++ 2 files changed, 121 insertions(+), 21 deletions(-) diff --git a/.github/workflows/ci-develop.yml b/.github/workflows/ci-develop.yml index 0f82cfd..9727868 100644 --- a/.github/workflows/ci-develop.yml +++ b/.github/workflows/ci-develop.yml @@ -1,51 +1,100 @@ -name: CI – Main Branch (Single Job, System Required) +name: CI – Develop Branch + + +# Triggers +# -------- +# • pull_request → quick job (lint + type-check + unit tests) +# • push → full job (quick job + DB migrations + integration tests + +# Docker build & smoke test) on: pull_request: - branches: [main] - workflow_dispatch: + branches: [develop] + push: + branches: [develop] -concurrency: - group: ci-main-${{ github.ref }} - cancel-in-progress: true -env: - PYTHON_VERSION: "3.12" - MPLBACKEND: Agg - ASYNCFLOW_RUN_SYSTEM_TESTS: "1" +# Job 1 ─ Quick validation (executed only on pull_request events) +# --------------------------------------------------------------------------- # +# Runs fast checks that give reviewers immediate feedback. No external +# services or Docker are used to keep runtime under one minute. jobs: - all-checks: + quick: + if: github.event_name == 'pull_request' runs-on: ubuntu-latest - timeout-minutes: 25 steps: - - uses: actions/checkout@v4 + # Checkout repository + - uses: actions/checkout@v3 - - uses: actions/setup-python@v5 + # Install Python 3.12 + - uses: actions/setup-python@v4 with: - python-version: ${{ env.PYTHON_VERSION }} - cache: 'pip' + python-version: "3.12" + # Restore Poetry cache for faster installs - uses: actions/cache@v3 with: path: ~/.cache/pypoetry key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} - - name: Install Poetry & deps + # Install project + development dependencies + - name: Install dependencies run: | curl -sSL https://install.python-poetry.org | python3 - - export PATH="$HOME/.local/bin:$PATH" poetry config virtualenvs.create false poetry install --with dev --no-interaction - - name: Ruff (lint) + # Code quality gates + - name: Run Ruff (lint & formatting check) run: poetry run ruff check src tests - - name: MyPy (type-check) + - name: Run MyPy (type-check) run: poetry run mypy src tests + + # Unit-tests only (exclude integration markers) + - name: Run unit tests + env: + ENVIRONMENT: test + run: poetry run pytest -m "not integration" --disable-warnings + + + +# Job 2 ─ Full validation (executed only on push events) + full: + if: | + github.event_name == 'push' && + github.ref == 'refs/heads/develop' + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: { python-version: '3.12' } + - uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} - - name: All tests (unit + integration + system) + - name: Install dependencies + run: | + curl -sSL https://install.python-poetry.org | python3 - + poetry config virtualenvs.create false + poetry install --with dev --no-interaction + + - name: Run Ruff + run: poetry run ruff check src tests + + - name: Run mypy + run: poetry run mypy src + + - name: Run all tests run: | poetry run pytest \ + --cov=src --cov-report=term \ --disable-warnings + + + + \ No newline at end of file diff --git a/.github/workflows/ci-main.yml b/.github/workflows/ci-main.yml index e69de29..dcac0fb 100644 --- a/.github/workflows/ci-main.yml +++ b/.github/workflows/ci-main.yml @@ -0,0 +1,51 @@ +name: CI – Main Branch + +on: + pull_request: + branches: [main] + workflow_dispatch: + +concurrency: + group: ci-main-${{ github.ref }} + cancel-in-progress: true + +env: + PYTHON_VERSION: "3.12" + MPLBACKEND: Agg + ASYNCFLOW_RUN_SYSTEM_TESTS: "1" + +jobs: + all-checks: + runs-on: ubuntu-latest + timeout-minutes: 25 + + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: ${{ env.PYTHON_VERSION }} + cache: 'pip' + + - uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} + + - name: Install Poetry & deps + run: | + curl -sSL https://install.python-poetry.org | python3 - + export PATH="$HOME/.local/bin:$PATH" + poetry config virtualenvs.create false + poetry install --with dev --no-interaction + + - name: Ruff (lint) + run: poetry run ruff check src tests + + - name: MyPy (type-check) + run: poetry run mypy src tests + + - name: All tests (unit + integration + system) + run: | + poetry run pytest \ + --disable-warnings From 09d99dcba923f16fb628e41091fa8102bf00ad34 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Sun, 17 Aug 2025 17:57:48 +0200 Subject: [PATCH 35/52] bug fixed --- .gitattributes | 12 ++++ simulation_base.png | Bin 123625 -> 0 bytes tests/system/test_sys_lb_two_servers.py | 60 ++++++++++++------ tests/system/test_sys_single_server.py | 77 ++++++++++++++---------- 4 files changed, 100 insertions(+), 49 deletions(-) create mode 100644 .gitattributes delete mode 100644 simulation_base.png diff --git a/.gitattributes b/.gitattributes new file mode 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CPU(2 ms) → RAM(128 MB) → IO(12 ms) -Edges: exponential latency ~2–3 ms. +Edges: exponential latency ~2-3 ms. We check: - latency stats / throughput sanity vs nominal λ (~40 rps); - balanced traffic across srv-1 / srv-2 via edge concurrency and RAM means. @@ -18,7 +18,7 @@ import os import random -from typing import Dict, List +from typing import TYPE_CHECKING import numpy as np import pytest @@ -26,11 +26,15 @@ from asyncflow import AsyncFlow from asyncflow.components import Client, Edge, Endpoint, LoadBalancer, Server -from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.config.constants import LatencyKey from asyncflow.runtime.simulation_runner import SimulationRunner from asyncflow.settings import SimulationSettings from asyncflow.workload import RqsGenerator -from asyncflow.config.constants import LatencyKey + +if TYPE_CHECKING: + # Imported only for type checking (ruff: TC001) + from asyncflow.metrics.analyzer import ResultsAnalyzer + from asyncflow.schemas.payload import SimulationPayload pytestmark = [ pytest.mark.system, @@ -41,17 +45,17 @@ ] SEED = 4242 -REL_TOL = 0.30 # 30% for λ/latency -BAL_TOL = 0.25 # 25% imbalance tolerated between the two backends +REL_TOL = 0.30 # 30% for λ/latency +BAL_TOL = 0.25 # 25% imbalance tolerated between the two backends def _seed_all(seed: int = SEED) -> None: random.seed(seed) - np.random.seed(seed) + np.random.seed(seed) # noqa: NPY002 os.environ["PYTHONHASHSEED"] = str(seed) -def _build_payload(): +def _build_payload() -> SimulationPayload: gen = RqsGenerator( id="rqs-1", avg_active_users={"mean": 120}, @@ -68,10 +72,22 @@ def _build_payload(): {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.012}}, ], ) - srv1 = Server(id="srv-1", server_resources={"cpu_cores": 1, "ram_mb": 2048}, endpoints=[endpoint]) - srv2 = Server(id="srv-2", server_resources={"cpu_cores": 1, "ram_mb": 2048}, endpoints=[endpoint]) + srv1 = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], + ) + srv2 = Server( + id="srv-2", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], + ) - lb = LoadBalancer(id="lb-1", algorithms="round_robin", server_covered={"srv-1", "srv-2"}) + lb = LoadBalancer( + id="lb-1", + algorithms="round_robin", + server_covered={"srv-1", "srv-2"}, + ) edges = [ Edge( @@ -151,7 +167,8 @@ def test_system_lb_two_servers_balanced_and_sane() -> None: # Latency sanity stats = res.get_latency_stats() - assert stats and LatencyKey.TOTAL_REQUESTS in stats + assert stats, "Expected non-empty stats." + assert LatencyKey.TOTAL_REQUESTS in stats mean_lat = float(stats.get(LatencyKey.MEAN, 0.0)) assert 0.020 <= mean_lat <= 0.060 @@ -164,15 +181,22 @@ def test_system_lb_two_servers_balanced_and_sane() -> None: # Load balance check: edge concurrency lb→srv1 vs lb→srv2 close sampled = res.get_sampled_metrics() - edge_cc: Dict[str, List[float]] = sampled.get("edge_concurrent_connection", {}) - assert "lb-srv1" in edge_cc and "lb-srv2" in edge_cc - m1, m2 = float(np.mean(edge_cc["lb-srv1"])), float(np.mean(edge_cc["lb-srv2"])) + edge_cc: dict[str, list[float]] = sampled.get( + "edge_concurrent_connection", + {}, + ) + assert "lb-srv1" in edge_cc + assert "lb-srv2" in edge_cc + m1 = float(np.mean(edge_cc["lb-srv1"])) + m2 = float(np.mean(edge_cc["lb-srv2"])) assert _rel_diff(m1, m2) <= BAL_TOL # Server metrics present and broadly similar (RAM means close-ish) - ram_map: Dict[str, List[float]] = sampled.get("ram_in_use", {}) - assert "srv-1" in ram_map and "srv-2" in ram_map - ram1, ram2 = float(np.mean(ram_map["srv-1"])), float(np.mean(ram_map["srv-2"])) + ram_map: dict[str, list[float]] = sampled.get("ram_in_use", {}) + assert "srv-1" in ram_map + assert "srv-2" in ram_map + ram1 = float(np.mean(ram_map["srv-1"])) + ram2 = float(np.mean(ram_map["srv-2"])) assert _rel_diff(ram1, ram2) <= BAL_TOL # IDs reported by analyzer diff --git a/tests/system/test_sys_single_server.py b/tests/system/test_sys_single_server.py index 160899c..ff2cd32 100644 --- a/tests/system/test_sys_single_server.py +++ b/tests/system/test_sys_single_server.py @@ -1,23 +1,23 @@ -"""System test: single-server scenario (deterministic-seeded, reproducible). - -Runs a compact but realistic topology: +"""System test: single server (seeded, reproducible). +Topology: generator → client → srv-1 → client -Endpoint on srv-1: CPU(1.5 ms) → RAM(96 MB) → IO(10 ms) -Edges: exponential latency ~3 ms each way. +Endpoint: + CPU(1 ms) → RAM(64 MB) → IO(10 ms) +Edges: exponential latency ~2-3 ms. -Assertions (with sensible tolerances): -- non-empty latency stats; mean latency in a plausible band; -- mean throughput close to the nominal λ (±30%); -- sampled metrics exist for srv-1 and are non-empty. +Checks: +- latency stats present and plausible (broad bounds); +- throughput roughly consistent with nominal λ; +- basic sampled metrics present for srv-1. """ from __future__ import annotations import os import random -from typing import Dict, List +from typing import TYPE_CHECKING import numpy as np import pytest @@ -25,13 +25,16 @@ from asyncflow import AsyncFlow from asyncflow.components import Client, Edge, Endpoint, Server -from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.config.constants import LatencyKey from asyncflow.runtime.simulation_runner import SimulationRunner from asyncflow.settings import SimulationSettings from asyncflow.workload import RqsGenerator -from asyncflow.config.constants import LatencyKey -# Mark as system and skip unless explicitly enabled in CI (or locally) +if TYPE_CHECKING: + # Imported only for type checking (ruff: TC001) + from asyncflow.metrics.analyzer import ResultsAnalyzer + from asyncflow.schemas.payload import SimulationPayload + pytestmark = [ pytest.mark.system, pytest.mark.skipif( @@ -41,16 +44,16 @@ ] SEED = 1337 -REL_TOL = 0.30 # 30% tolerance for stochastic expectations +REL_TOL = 0.35 # generous bound for simple sanity def _seed_all(seed: int = SEED) -> None: random.seed(seed) - np.random.seed(seed) + np.random.seed(seed) # noqa: NPY002 os.environ["PYTHONHASHSEED"] = str(seed) -def _build_payload(): +def _build_payload() -> SimulationPayload: # Workload: ~26.7 rps (80 users * 20 rpm / 60) gen = RqsGenerator( id="rqs-1", @@ -63,12 +66,16 @@ def _build_payload(): ep = Endpoint( endpoint_name="/api", steps=[ - {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.0015}}, - {"kind": "ram", "step_operation": {"necessary_ram": 96}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, + {"kind": "ram", "step_operation": {"necessary_ram": 64}}, {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, ], ) - srv = Server(id="srv-1", server_resources={"cpu_cores": 1, "ram_mb": 2048}, endpoints=[ep]) + srv = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[ep], + ) edges = [ Edge( @@ -81,7 +88,7 @@ def _build_payload(): id="client-srv", source="client-1", target="srv-1", - latency={"mean": 0.003, "distribution": "exponential"}, + latency={"mean": 0.002, "distribution": "exponential"}, ), Edge( id="srv-client", @@ -92,7 +99,7 @@ def _build_payload(): ] settings = SimulationSettings( - total_simulation_time=180, # virtual time; keeps wall time fast + total_simulation_time=400, sample_period_s=0.05, enabled_sample_metrics=[ "ready_queue_len", @@ -103,13 +110,19 @@ def _build_payload(): enabled_event_metrics=["rqs_clock"], ) - flow = AsyncFlow().add_generator(gen).add_client(client).add_servers(srv).add_edges(*edges) + flow = ( + AsyncFlow() + .add_generator(gen) + .add_client(client) + .add_servers(srv) + .add_edges(*edges) + ) flow = flow.add_simulation_settings(settings) return flow.build_payload() -def test_system_single_server_end_to_end() -> None: - """End-to-end single-server check with tolerances and seeded RNGs.""" +def test_system_single_server_sane() -> None: + """End-to-end single-server scenario: sanity checks with seeded RNGs.""" _seed_all() env = simpy.Environment() @@ -118,19 +131,21 @@ def test_system_single_server_end_to_end() -> None: # Latency stats present and plausible stats = res.get_latency_stats() - assert stats and LatencyKey.TOTAL_REQUESTS in stats + assert stats, "Expected non-empty stats." + assert LatencyKey.TOTAL_REQUESTS in stats mean_lat = float(stats.get(LatencyKey.MEAN, 0.0)) assert 0.015 <= mean_lat <= 0.060 - # Throughput close to nominal lambda - timestamps, rps = res.get_throughput_series() - assert timestamps, "No throughput series produced." + # Throughput sanity vs nominal λ + _, rps = res.get_throughput_series() + assert rps, "No throughput series produced." rps_mean = float(np.mean(rps)) lam = 80 * 20 / 60.0 assert abs(rps_mean - lam) / lam <= REL_TOL - # Sampled metrics exist for srv-1 - sampled: Dict[str, Dict[str, List[float]]] = res.get_sampled_metrics() + # Sampled metrics present for srv-1 + sampled: dict[str, dict[str, list[float]]] = res.get_sampled_metrics() for key in ("ready_queue_len", "event_loop_io_sleep", "ram_in_use"): - assert key in sampled and "srv-1" in sampled[key] + assert key in sampled + assert "srv-1" in sampled[key] assert len(sampled[key]["srv-1"]) > 0 From 1c47441f76951c2769d027ab37105adcbd8b7efc Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Sun, 17 Aug 2025 18:23:24 +0200 Subject: [PATCH 36/52] version bump --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 09c14e1..6aec40a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "AsyncFlow-Sim" -version = "0.1.0a1" +version = "0.1.0" description = "Digital-twin simulator for distributed async systems. Build what-if scenarios and quantify capacity, latency and throughput offline—before you deploy." authors = ["Gioele Botta"] readme = "README.md" From f57059bc75e3449b60bbdfb8c2a217f01aad0f67 Mon Sep 17 00:00:00 2001 From: GioeleB00 Date: Sun, 17 Aug 2025 22:21:11 +0200 Subject: [PATCH 37/52] version bump --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3af003d..aea9845 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "asyncflow-sim" -version = "0.1.0a2" +version = "0.1.0" description = "Digital-twin simulator for distributed async systems. Build what-if scenarios and quantify capacity, latency and throughput offline, before you deploy." authors = ["Gioele Botta"] readme = "README.md" From e9afe18dee533a4d86b531536182a41eaeb58c7e Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Tue, 19 Aug 2025 17:30:02 +0200 Subject: [PATCH 38/52] Feature/event injection input (#19) * Structure defined for the pydantic model representing Event to be injected * Pydantic validation in the payload for events part 1 * Definition of the input for the events injection part 2 * minor fixes ruff and mypy compliant * full tests and docs added for input event injection * fixed minor bugs --- docs/api/event-injection.md | 246 +++++++++++++++ src/asyncflow/components/__init__.py | 11 +- src/asyncflow/config/constants.py | 13 + src/asyncflow/schemas/event/injection.py | 119 ++++++++ src/asyncflow/schemas/payload.py | 189 +++++++++++- tests/conftest.py | 1 + tests/unit/public_api/test_import.py | 3 + tests/unit/schemas/test_event_injection.py | 196 ++++++++++++ tests/unit/schemas/test_payload.py | 333 +++++++++++++++++++++ 9 files changed, 1109 insertions(+), 2 deletions(-) create mode 100644 docs/api/event-injection.md create mode 100644 src/asyncflow/schemas/event/injection.py create mode 100644 tests/unit/schemas/test_event_injection.py create mode 100644 tests/unit/schemas/test_payload.py diff --git a/docs/api/event-injection.md b/docs/api/event-injection.md new file mode 100644 index 0000000..9156364 --- /dev/null +++ b/docs/api/event-injection.md @@ -0,0 +1,246 @@ +# EventInjection — Public API Documentation + +## Overview + +`EventInjection` declares a **time-bounded event** that affects a component in the simulation. Each event targets either a **server** or a **network edge**, and is delimited by a `start` marker and an `end` marker. + +Supported families (per code): + +* **Server availability**: `SERVER_DOWN` → `SERVER_UP` +* **Network latency spike (deterministic offset in seconds)**: `NETWORK_SPIKE_START` → `NETWORK_SPIKE_END` + For network spikes, the `Start` marker carries the amplitude in seconds via `spike_s`. + +Strictness: + +* Models use `ConfigDict(extra="forbid", frozen=True)` + → unknown fields are rejected; instances are immutable at runtime. + +--- + +## Data Model + +### `Start` + +* `kind: Literal[SERVER_DOWN, NETWORK_SPIKE_START]` + Event family selector. +* `t_start: NonNegativeFloat` + Start time in **seconds** from simulation start; **≥ 0.0**. +* `spike_s: PositiveFloat | None` + **Required** and **> 0** **only** when `kind == NETWORK_SPIKE_START`. + **Forbidden** (must be omitted/`None`) for any other kind. + +### `End` + +* `kind: Literal[SERVER_UP, NETWORK_SPIKE_END]` + Must match the start family (see invariants). +* `t_end: PositiveFloat` + End time in **seconds**; **> 0.0**. + +### `EventInjection` + +* `event_id: str` + Unique identifier within the simulation payload. +* `target_id: str` + Identifier of the affected component (server or edge) as defined in the topology. +* `start: Start` + Start marker. +* `end: End` + End marker. + +--- + +## Validation & Invariants (as implemented) + +### Within `EventInjection` + +1. **Family coherence** + + * `SERVER_DOWN` → `SERVER_UP` + * `NETWORK_SPIKE_START` → `NETWORK_SPIKE_END` + Any other pairing raises: + + ``` + The event {event_id} must have as value of kind in end {expected} + ``` +2. **Temporal ordering** + + * `t_start < t_end` (with `t_start ≥ 0.0`, `t_end > 0.0`) + Error: + + ``` + The starting time for the event {event_id} must be smaller than the ending time + ``` +3. **Network spike parameter** + + * If `start.kind == NETWORK_SPIKE_START` ⇒ `start.spike_s` **must** be provided and be a positive float (seconds). + Error: + + ``` + The field spike_s for the event {event_id} must be defined as a positive float (seconds) + ``` + * Otherwise (`SERVER_DOWN`) ⇒ `start.spike_s` **must be omitted** / `None`. + Error: + + ``` + Event {event_id}: spike_s must be omitted for non-network events + ``` + +### Enforced at `SimulationPayload` level + +4. **Unique event IDs** + Error: + + ``` + The id's representing different events must be unique + ``` +5. **Target existence & compatibility** + + * For server events (`SERVER_DOWN`), `target_id` must refer to a **server**. + * For network spikes (`NETWORK_SPIKE_START`), `target_id` must refer to an **edge**. + Errors: + + ``` + The target id {target_id} related to the event {event_id} does not exist + ``` + + ``` + The event {event_id} regarding a server does not have a compatible target id + ``` + + ``` + The event {event_id} regarding an edge does not have a compatible target id + ``` +6. **Times within simulation horizon** (with `T = sim_settings.total_simulation_time`) + + * `t_start >= 0.0` and `t_start <= T` + * `t_end <= T` + Errors: + + ``` + Event '{event_id}': start time t_start={t:.6f} must be >= 0.0 + Event '{event_id}': start time t_start={t:.6f} exceeds simulation horizon T={T:.6f} + Event '{event_id}': end time t_end={t:.6f} exceeds simulation horizon T={T:.6f} + ``` +7. **Global liveness rule (servers)** + The payload is rejected if **all servers are down at the same moment**. + Implementation detail: the timeline is ordered so that, at identical timestamps, **`END` is processed before `START`** to avoid transient all-down states. + Error: + + ``` + At time {time:.6f} all servers are down; keep at least one up + ``` + +--- + +## Runtime Semantics (summary) + +* **Server events**: the targeted server is unavailable between the start and end markers; the system enforces that at least one server remains up at all times. +* **Network spike events**: the targeted edge’s latency sampler is deterministically **shifted by `spike_s` seconds** during the event window (additive congestion model). The underlying distribution is not reshaped—samples are translated by a constant offset. + +*(This reflects the agreed model: deterministic additive offset on edges.)* + +--- + +## Units & Precision + +* All times and offsets are in **seconds** (floating-point). +* Provide values with the precision your simulator supports; microsecond-level precision is acceptable if needed. + +--- + +## Authoring Guidelines + +* **Do not include `spike_s`** for non-network events. +* Use **stable, meaningful `event_id`** values for auditability. +* Keep events within the **simulation horizon**. +* When multiple markers share the same timestamp, rely on the engine’s **END-before-START** ordering for determinism. + +--- + +## Examples + +### 1) Valid — Server maintenance window + +```yaml +event_id: ev-maint-001 +target_id: srv-1 +start: { kind: SERVER_DOWN, t_start: 120.0 } +end: { kind: SERVER_UP, t_end: 240.0 } +``` + +### 2) Valid — Network spike on an edge (+8 ms) + +```yaml +event_id: ev-spike-008ms +target_id: edge-12 +start: { kind: NETWORK_SPIKE_START, t_start: 10.0, spike_s: 0.008 } +end: { kind: NETWORK_SPIKE_END, t_end: 25.0 } +``` + +### 3) Invalid — Missing `spike_s` for a network spike + +```yaml +event_id: ev-missing-spike +target_id: edge-5 +start: { kind: NETWORK_SPIKE_START, t_start: 5.0 } +end: { kind: NETWORK_SPIKE_END, t_end: 15.0 } +``` + +Error: + +``` +The field spike_s for the event ev-missing-spike must be defined as a positive float (seconds) +``` + +### 4) Invalid — `spike_s` present for a server event + +```yaml +event_id: ev-bad-spike +target_id: srv-2 +start: { kind: SERVER_DOWN, t_start: 50.0, spike_s: 0.005 } +end: { kind: SERVER_UP, t_end: 60.0 } +``` + +Error: + +``` +Event ev-bad-spike: spike_s must be omitted for non-network events +``` + +### 5) Invalid — Mismatched families + +```yaml +event_id: ev-bad-kinds +target_id: edge-1 +start: { kind: NETWORK_SPIKE_START, t_start: 5.0, spike_s: 0.010 } +end: { kind: SERVER_UP, t_end: 15.0 } +``` + +Error: + +``` +The event ev-bad-kinds must have as value of kind in end NETWORK_SPIKE_END +``` + +### 6) Invalid — Start not before End + +```yaml +event_id: ev-bad-time +target_id: srv-2 +start: { kind: SERVER_DOWN, t_start: 300.0 } +end: { kind: SERVER_UP, t_end: 300.0 } +``` + +Error: + +``` +The starting time for the event ev-bad-time must be smaller than the ending time +``` + +--- + +## Notes for Consumers + +* The schema is **strict**: misspelled fields (e.g., `t_strat`) are rejected. +* The engine may combine multiple active network spikes on the same edge by **summing** their `spike_s` values while they overlap (handled by runtime bookkeeping). +* This document describes exactly what is present in the provided code and validators; no additional fields or OpenAPI metadata are assumed. diff --git a/src/asyncflow/components/__init__.py b/src/asyncflow/components/__init__.py index 52d66c7..2f40f69 100644 --- a/src/asyncflow/components/__init__.py +++ b/src/asyncflow/components/__init__.py @@ -1,6 +1,7 @@ """Public components: re-exports Pydantic schemas (topology).""" from __future__ import annotations +from asyncflow.schemas.event.injection import EventInjection from asyncflow.schemas.topology.edges import Edge from asyncflow.schemas.topology.endpoint import Endpoint from asyncflow.schemas.topology.nodes import ( @@ -10,6 +11,14 @@ ServerResources, ) -__all__ = ["Client", "Edge", "Endpoint", "LoadBalancer", "Server", "ServerResources"] +__all__ = [ + "Client", + "Edge", + "Endpoint", + "EventInjection", + "LoadBalancer", + "Server", + "ServerResources", + ] diff --git a/src/asyncflow/config/constants.py b/src/asyncflow/config/constants.py index de79a33..29b2229 100644 --- a/src/asyncflow/config/constants.py +++ b/src/asyncflow/config/constants.py @@ -175,6 +175,19 @@ class SystemEdges(StrEnum): NETWORK_CONNECTION = "network_connection" +# ====================================================================== +# CONSTANTS FOR THE EVENT TO INJECT IN THE SIMULATION +# ====================================================================== + +class EventDescription(StrEnum): + """Description for the events you may inject during the simulation""" + + SERVER_UP = "server_up" + SERVER_DOWN = "server_down" + NETWORK_SPIKE_START = "network_spike_start" + NETWORK_SPIKE_END = "network_spike_end" + + # ====================================================================== # CONSTANTS FOR SAMPLED METRICS # ====================================================================== diff --git a/src/asyncflow/schemas/event/injection.py b/src/asyncflow/schemas/event/injection.py new file mode 100644 index 0000000..266f920 --- /dev/null +++ b/src/asyncflow/schemas/event/injection.py @@ -0,0 +1,119 @@ +"""Pydantic model to inject event during the simulation""" + +from typing import Literal + +from pydantic import ( + BaseModel, + ConfigDict, + NonNegativeFloat, + PositiveFloat, + model_validator, +) + +from asyncflow.config.constants import EventDescription + +# Event input schema: +# - Each event has its own identifier (event_id) and references the affected +# component via target_id. +# - The event window is represented by two markers, Start and End. +# - We constrain kind with Literal[...] over EventDescription (a StrEnum), +# so Pydantic enforces allowed values automatically for both Start and End. +# - Both marker models use ConfigDict(extra="forbid", frozen=True): +# extra="forbid" rejects unknown fields (e.g., catches t_strat vs t_start); +# frozen=True makes instances immutable at runtime for stability. + +class Start(BaseModel): + """Start marker for an event window.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + # Only "start" kinds allowed here + kind: Literal[ + EventDescription.SERVER_DOWN, + EventDescription.NETWORK_SPIKE_START, + ] + t_start: NonNegativeFloat # seconds from simulation start + spike_s: None | PositiveFloat = None + + + +class End(BaseModel): + """End marker for an event window.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + # Only "end" kinds allowed here + kind: Literal[ + EventDescription.SERVER_UP, + EventDescription.NETWORK_SPIKE_END, + ] + t_end: PositiveFloat # strictly > 0 + +class EventInjection(BaseModel): + """Definition of the input structure to define an event in the simulation""" + + event_id: str + target_id: str + start: Start + end: End + + # Yaml example: + # event_id: ev-1 + # target_id: srv-1 + # start: { kind: SERVER_DOWN, t_start: 120.0 } + # end: { kind: SERVER_UP, t_end: 240.0 } + + @model_validator(mode="after") # type: ignore[arg-type] + def ensure_start_end_compatibility( + cls, # noqa: N805 + model: "EventInjection", + ) -> "EventInjection": + """ + Check the compatibility between Start and End both at level + of time interval and kind + """ + # Ensure kind for Start and End are compatible + start_to_end = { + EventDescription.SERVER_DOWN: EventDescription.SERVER_UP, + EventDescription.NETWORK_SPIKE_START: EventDescription.NETWORK_SPIKE_END, + } + + expected = start_to_end[model.start.kind] + if model.end.kind != expected: + msg = (f"The event {model.event_id} must have " + f"as value of kind in end {expected}") + raise ValueError(msg) + + # Ensure the time sequence is well defined + if model.start.t_start >= model.end.t_end: + msg=(f"The starting time for the event {model.event_id} " + "must be smaller than the ending time") + raise ValueError(msg) + + return model + + + @model_validator(mode="after") # type: ignore[arg-type] + def ensure_spike_exist_on_network_event( + cls, # noqa: N805 + model: "EventInjection", + ) -> "EventInjection": + """ + When a network event is selected the spike must be + indicated + """ + if (model.start.kind == EventDescription.NETWORK_SPIKE_START + and model.start.spike_s is None): + msg = ( + f"The field spike_s for the event {model.event_id} " + "must be defined as a positive float" + ) + raise ValueError(msg) + + if (model.start.kind != EventDescription.NETWORK_SPIKE_START + and model.start.spike_s is not None): + msg = f"Event {model.event_id}: spike_s must be omitted" + raise ValueError(msg) + + + return model diff --git a/src/asyncflow/schemas/payload.py b/src/asyncflow/schemas/payload.py index 3c889e4..4b593b0 100644 --- a/src/asyncflow/schemas/payload.py +++ b/src/asyncflow/schemas/payload.py @@ -1,7 +1,9 @@ """Definition of the full input for the simulation""" -from pydantic import BaseModel +from pydantic import BaseModel, field_validator, model_validator +from asyncflow.config.constants import EventDescription +from asyncflow.schemas.event.injection import EventInjection from asyncflow.schemas.settings.simulation import SimulationSettings from asyncflow.schemas.topology.graph import TopologyGraph from asyncflow.schemas.workload.rqs_generator import RqsGenerator @@ -13,3 +15,188 @@ class SimulationPayload(BaseModel): rqs_input: RqsGenerator topology_graph: TopologyGraph sim_settings: SimulationSettings + events: list[EventInjection] | None = None + + @field_validator("events", mode="after") + def ensure_event_id_is_unique( + cls, # noqa: N805 + v: list[EventInjection] | None, + ) -> list[EventInjection] | None: + """Ensure the id uniqueness of the events id""" + if v is None: + return v + + event_id = [event.event_id for event in v] + set_event_id = set(event_id) + + if len(event_id) != len(set_event_id): + msg = "The id's representing different events must be unique" + raise ValueError(msg) + return v + + @model_validator(mode="after") # type: ignore[arg-type] + def ensure_components_ids_is_compatible( + cls, # noqa: N805 + model: "SimulationPayload", + ) -> "SimulationPayload": + """ + Ensure the id related to the target component of the event + exist + """ + if model.events is None: + return model + + servers_list = model.topology_graph.nodes.servers + edges_list = model.topology_graph.edges + valid_ids = ( + {server.id for server in servers_list} + | {edge.id for edge in edges_list} + ) + + for event in model.events: + if event.target_id not in valid_ids: + msg = (f"The target id {event.target_id} related to " + f"the event {event.event_id} does not exist") + raise ValueError(msg) + + return model + + @model_validator(mode="after") # type: ignore[arg-type] + def ensure_event_time_inside_simulation_horizon( + cls, # noqa: N805 + model: "SimulationPayload", + ) -> "SimulationPayload": + """ + The time interval associated to each event must be in + the simulation horizon + """ + if model.events is None: + return model + + horizon = float(model.sim_settings.total_simulation_time) + + for ev in model.events: + t_start = ev.start.t_start + t_end = ev.end.t_end + + if t_start < 0.0: + msg = ( + f"Event '{ev.event_id}': start time t_start={t_start:.6f} " + "must be >= 0.0" + ) + raise ValueError(msg) + + if t_start > horizon: + msg = ( + f"Event '{ev.event_id}': start time t_start={t_start:.6f} " + f"exceeds simulation horizon T={horizon:.6f}" + ) + raise ValueError(msg) + + # t_end is PositiveFloat by schema, but still guard the horizon. + if t_end > horizon: + msg = ( + f"Event '{ev.event_id}': end time t_end={t_end:.6f} " + f"exceeds simulation horizon T={horizon:.6f}" + ) + raise ValueError(msg) + + return model + + @model_validator(mode="after") # type: ignore[arg-type] + def ensure_compatibility_event_kind_target_id( + cls, # noqa: N805 + model: "SimulationPayload", + ) -> "SimulationPayload": + """ + The kind of the event must be compatible with the target id + type, for example we cannot have an event regarding a server + with a target id associated to an edge + """ + if model.events is None: + return model + + servers_list = model.topology_graph.nodes.servers + edges_list = model.topology_graph.edges + + # We need just the Start or End kind because + # we have a validation for the coherence between + # the starting event kind and the finishing event kind + server_kind = {EventDescription.SERVER_DOWN} + edge_kind = {EventDescription.NETWORK_SPIKE_START} + + servers_ids = {server.id for server in servers_list} + edges_ids = {edge.id for edge in edges_list} + + for event in model.events: + if event.start.kind in server_kind and event.target_id not in servers_ids: + msg = (f"The event {event.event_id} regarding a server does not have " + "a compatible target id") + raise ValueError(msg) + if event.start.kind in edge_kind and event.target_id not in edges_ids: + msg = (f"The event {event.event_id} regarding an edge does not have " + "a compatible target id") + raise ValueError(msg) + + + return model + + + @model_validator(mode="after") # type: ignore[arg-type] + def ensure_not_all_servers_are_down_simultaneously( + cls, # noqa: N805 + model: "SimulationPayload", + ) -> "SimulationPayload": + """ + We will not accept the condition to have all server down + at the same moment, always at least one server must be up + and running + """ + if model.events is None: + return model + + # First let us build a list of events related to the servers + servers_list = model.topology_graph.nodes.servers + servers_ids = {server.id for server in servers_list} + server_events = [ + event for event in model.events + if event.target_id in servers_ids + ] + + # Helpers needed in the algorithm to define a specific ordering + # procedure + start = "start" + end = "end" + + # Let us define a list of tuple as a timeline, this approach ensure + # the possibility to have different servers going up or down at the + # same time, a more elegant approach through an hashmap has been + # considered however it would require an extra assumption that all + # the times had to be different, we thought that this would be too + # strict + timeline: list[tuple[float, str, str]] = [] + for event in server_events: + timeline.append((event.start.t_start, start, event.target_id)) + timeline.append((event.end.t_end, end, event.target_id)) + + # Let us order the timeline by time if there are multiple events at the + # same time process first the end type events + timeline.sort(key=lambda x: (x[0], x[1] == start)) + + # Definition of a set to verify the condition that at least one server must + # be up + server_down = set() + for time, kind, server_id in timeline: + if kind == end: + server_down.discard(server_id) + else: # "start" + server_down.add(server_id) + if len(server_down) == len(servers_ids): + msg = ( + f"At time {time:.6f} all servers are down; keep at least one up" + ) + raise ValueError(msg) + + return model + + diff --git a/tests/conftest.py b/tests/conftest.py index 80955f0..b0fd5de 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -150,4 +150,5 @@ def payload_base( rqs_input=rqs_input, topology_graph=topology_minimal, sim_settings=sim_settings, + ) diff --git a/tests/unit/public_api/test_import.py b/tests/unit/public_api/test_import.py index cd708bc..2bea333 100644 --- a/tests/unit/public_api/test_import.py +++ b/tests/unit/public_api/test_import.py @@ -14,6 +14,7 @@ Client, Edge, Endpoint, + EventInjection, LoadBalancer, Server, ServerResources, @@ -43,6 +44,7 @@ def test_components_public_symbols() -> None: "Client", "Edge", "Endpoint", + "EventInjection", "LoadBalancer", "Server", "ServerResources", @@ -57,6 +59,7 @@ def test_components_symbols_are_importable_classes() -> None: (Client, "Client"), (Edge, "Edge"), (Endpoint, "Endpoint"), + (EventInjection, "EventInjection"), (LoadBalancer, "LoadBalancer"), (Server, "Server"), (ServerResources, "ServerResources"), diff --git a/tests/unit/schemas/test_event_injection.py b/tests/unit/schemas/test_event_injection.py new file mode 100644 index 0000000..36edbaa --- /dev/null +++ b/tests/unit/schemas/test_event_injection.py @@ -0,0 +1,196 @@ +"""Unit tests for the EventInjection Pydantic models. + +This suite verifies: +- Family coherence: SERVER_DOWN→SERVER_UP and + NETWORK_SPIKE_START→NETWORK_SPIKE_END. +- Temporal ordering: t_start < t_end, with field constraints. +- Spike semantics: spike_s is required only for NETWORK_SPIKE_START + and forbidden otherwise. +- Strictness: unknown fields are rejected; models are frozen. +""" + +from __future__ import annotations + +import re +from typing import Any + +import pytest +from pydantic import ValidationError + +from asyncflow.config.constants import EventDescription +from asyncflow.schemas.event.injection import End, EventInjection, Start + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _mk_server_down(start_t: float, end_t: float) -> EventInjection: + """Build a minimal server down/up event with the given times.""" + start = Start(kind=EventDescription.SERVER_DOWN, t_start=start_t) + end = End(kind=EventDescription.SERVER_UP, t_end=end_t) + return EventInjection( + event_id="ev-server-1", + target_id="srv-1", + start=start, + end=end, + ) + + +def _mk_network_spike( + start_t: float, + end_t: float, + spike_s: float | None, +) -> EventInjection: + """Build a minimal network spike event with the given times and spike.""" + start = Start( + kind=EventDescription.NETWORK_SPIKE_START, + t_start=start_t, + spike_s=spike_s, + ) + end = End(kind=EventDescription.NETWORK_SPIKE_END, t_end=end_t) + return EventInjection( + event_id="ev-spike-1", + target_id="edge-1", + start=start, + end=end, + ) + + +# --------------------------------------------------------------------------- +# Start/End family coherence +# --------------------------------------------------------------------------- + +def test_family_coherence_server_ok() -> None: + """SERVER_DOWN followed by SERVER_UP should validate.""" + model = _mk_server_down(start_t=10.0, end_t=20.0) + assert model.start.kind is EventDescription.SERVER_DOWN + assert model.end.kind is EventDescription.SERVER_UP + + +def test_family_coherence_network_ok() -> None: + """NETWORK_SPIKE_START followed by NETWORK_SPIKE_END should validate.""" + model = _mk_network_spike(start_t=1.0, end_t=2.0, spike_s=0.005) + assert model.start.kind is EventDescription.NETWORK_SPIKE_START + assert model.end.kind is EventDescription.NETWORK_SPIKE_END + + +def test_family_mismatch_raises() -> None: + """Mismatched start/end families must raise a ValueError.""" + start = Start(kind=EventDescription.SERVER_DOWN, t_start=1.0) + end = End(kind=EventDescription.NETWORK_SPIKE_END, t_end=2.0) + with pytest.raises(ValueError, match=r"must have .* kind in end"): + EventInjection( + event_id="ev-bad", + target_id="srv-1", + start=start, + end=end, + ) + + +# --------------------------------------------------------------------------- +# Temporal ordering & per-field constraints +# --------------------------------------------------------------------------- + +def test_time_ordering_start_before_end() -> None: + """t_start must be strictly less than t_end.""" + with pytest.raises(ValueError, match=r"smaller than the ending time"): + _mk_server_down(start_t=10.0, end_t=10.0) + + +def test_start_non_negative_enforced() -> None: + """Start.t_start is NonNegativeFloat; negatives raise ValidationError.""" + with pytest.raises(ValidationError): + Start(kind=EventDescription.SERVER_DOWN, t_start=-1.0) + + +def test_end_positive_enforced() -> None: + """End.t_end is PositiveFloat; non-positive values raise ValidationError.""" + with pytest.raises(ValidationError): + End(kind=EventDescription.SERVER_UP, t_end=0.0) + + +# --------------------------------------------------------------------------- +# Spike semantics +# --------------------------------------------------------------------------- + +def test_network_spike_requires_spike_s() -> None: + """NETWORK_SPIKE_START requires spike_s (seconds) to be present.""" + # Define the event id for the matching condition. + event_id = "ev-spike-1" + + # Define the full message to be matched + expected_message = ( + f"The field spike_s for the event {event_id} " + "must be defined as a positive float" + ) + + with pytest.raises(ValidationError, match=re.escape(expected_message)): + _mk_network_spike(start_t=0.5, end_t=1.5, spike_s=None) + + +def test_network_spike_positive_spike_s_enforced() -> None: + """spike_s uses PositiveFloat; negative values raise ValidationError.""" + with pytest.raises(ValidationError): + _mk_network_spike(start_t=0.0, end_t=1.0, spike_s=-0.001) + + +def test_spike_s_forbidden_for_server_events() -> None: + """For non-network events, spike_s must be omitted.""" + event_id = "ev-bad-spike" + expected_message = f"Event {event_id}: spike_s must be omitted" + start = Start( + kind=EventDescription.SERVER_DOWN, + t_start=0.0, + spike_s=0.001, + ) + end = End(kind=EventDescription.SERVER_UP, t_end=1.0) + with pytest.raises(ValueError, match=re.escape(expected_message)): + EventInjection( + event_id="ev-bad-spike", + target_id="srv-1", + start=start, + end=end, + ) + + +# --------------------------------------------------------------------------- +# Strictness (extra fields) and immutability (frozen models) +# --------------------------------------------------------------------------- + +def test_extra_fields_forbidden_in_start() -> None: + """Unknown fields in Start must be rejected due to extra='forbid'.""" + payload: dict[str, Any] = { + "kind": EventDescription.SERVER_DOWN, + "t_start": 0.0, + "unknown_field": 123, + } + with pytest.raises(ValidationError): + Start.model_validate(payload) + + +def test_extra_fields_forbidden_in_end() -> None: + """Unknown fields in End must be rejected due to extra='forbid'.""" + payload: dict[str, Any] = { + "kind": EventDescription.SERVER_UP, + "t_end": 1.0, + "unknown_field": True, + } + with pytest.raises(ValidationError): + End.model_validate(payload) + + +def test_start_is_frozen_and_immutable() -> None: + """Start is frozen; attempting to mutate fields must raise an error.""" + start = Start(kind=EventDescription.SERVER_DOWN, t_start=0.0) + # Cast to Any to avoid mypy's read-only property check; runtime must fail. + start_any: Any = start + with pytest.raises(ValidationError, match="Instance is frozen"): + start_any.t_start = 1.0 + + +def test_end_is_frozen_and_immutable() -> None: + """End is frozen; attempting to mutate fields must raise an error.""" + end = End(kind=EventDescription.SERVER_UP, t_end=1.0) + end_any: Any = end + with pytest.raises(ValidationError, match="Instance is frozen"): + end_any.t_end = 2.0 diff --git a/tests/unit/schemas/test_payload.py b/tests/unit/schemas/test_payload.py new file mode 100644 index 0000000..5ae8d55 --- /dev/null +++ b/tests/unit/schemas/test_payload.py @@ -0,0 +1,333 @@ +"""Unit tests for the SimulationPayload Pydantic model. + +This suite verifies: +- Unique event IDs constraint. +- Target existence against the topology graph. +- Event times inside the simulation horizon. +- Kind/target compatibility (server vs. edge). +- Global liveness: not all servers down simultaneously. + +All tests are ruff- and mypy-friendly (short lines, precise raises, and +single statements inside raises blocks). They reuse fixtures from +conftest.py where convenient and build custom topologies when needed. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest + +from asyncflow.config.constants import Distribution, EventDescription +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.event.injection import End, EventInjection, Start +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( + Client, + Server, + TopologyNodes, +) + +if TYPE_CHECKING: + from asyncflow.schemas.settings.simulation import SimulationSettings + from asyncflow.schemas.workload.rqs_generator import RqsGenerator + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _mk_network_spike( + event_id: str, + target_id: str, + start_t: float, + end_t: float, + spike_s: float, +) -> EventInjection: + """Build a NETWORK_SPIKE event for the given target edge.""" + start = Start( + kind=EventDescription.NETWORK_SPIKE_START, + t_start=start_t, + spike_s=spike_s, + ) + end = End(kind=EventDescription.NETWORK_SPIKE_END, t_end=end_t) + return EventInjection( + event_id=event_id, + target_id=target_id, + start=start, + end=end, + ) + + +def _mk_server_window( + event_id: str, + target_id: str, + start_t: float, + end_t: float, +) -> EventInjection: + """Build a SERVER_DOWN → SERVER_UP event for the given server.""" + start = Start(kind=EventDescription.SERVER_DOWN, t_start=start_t) + end = End(kind=EventDescription.SERVER_UP, t_end=end_t) + return EventInjection( + event_id=event_id, + target_id=target_id, + start=start, + end=end, + ) + + +def _topology_with_min_edge() -> TopologyGraph: + """Create a tiny topology with one client and one minimal edge.""" + client = Client(id="client-1") + edge = Edge( + id="gen-to-client", + source="rqs-1", + target="client-1", + latency=RVConfig(mean=0.001, distribution=Distribution.POISSON), + ) + nodes = TopologyNodes(servers=[], client=client) + return TopologyGraph(nodes=nodes, edges=[edge]) + + +def _topology_with_two_servers_and_edge() -> TopologyGraph: + """Create a topology with two servers and a minimal edge.""" + client = Client(id="client-1") + servers = [ + Server(id="srv-1", server_resources={"cpu_cores": 1}, endpoints=[]), + Server(id="srv-2", server_resources={"cpu_cores": 1}, endpoints=[]), +] + edge = Edge( + id="gen-to-client", + source="rqs-1", + target="client-1", + latency=RVConfig(mean=0.001, distribution=Distribution.POISSON), + ) + nodes = TopologyNodes(servers=servers, client=client) + return TopologyGraph(nodes=nodes, edges=[edge]) + + +# --------------------------------------------------------------------------- +# Unique event IDs +# --------------------------------------------------------------------------- + + +def test_unique_event_ids_ok( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """Different event_id values should validate.""" + topo = _topology_with_min_edge() + ev1 = _mk_network_spike( + "ev-a", "gen-to-client", start_t=0.0, end_t=1.0, spike_s=0.001, + ) + ev2 = _mk_network_spike( + "ev-b", "gen-to-client", start_t=2.0, end_t=3.0, spike_s=0.002, + ) + payload = SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev1, ev2], + ) + assert payload.events is not None + assert len(payload.events) == 2 + + +def test_duplicate_event_ids_rejected( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """Duplicate event_id values must be rejected.""" + topo = _topology_with_min_edge() + ev1 = _mk_network_spike( + "ev-dup", "gen-to-client", start_t=0.0, end_t=1.0, spike_s=0.001, + ) + ev2 = _mk_network_spike( + "ev-dup", "gen-to-client", start_t=2.0, end_t=3.0, spike_s=0.002, + ) + with pytest.raises(ValueError, match=r"must be unique"): + SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev1, ev2], + ) + + +# --------------------------------------------------------------------------- +# Target existence +# --------------------------------------------------------------------------- + + +def test_target_id_must_exist( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """Target IDs not present in the topology must be rejected.""" + topo = _topology_with_min_edge() + ev = _mk_network_spike( + "ev-x", "missing-edge", start_t=0.0, end_t=1.0, spike_s=0.001, + ) + with pytest.raises(ValueError, match=r"does not exist"): + SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev], + ) + + +# --------------------------------------------------------------------------- +# Event times within horizon +# --------------------------------------------------------------------------- + + +def test_start_time_exceeds_horizon_rejected( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """Start time greater than the horizon must be rejected.""" + topo = _topology_with_min_edge() + horizon = float(sim_settings.total_simulation_time) + ev = _mk_network_spike( + "ev-hz-start", + "gen-to-client", + start_t=horizon + 0.1, + end_t=horizon + 0.2, + spike_s=0.001, + ) + with pytest.raises(ValueError, match=r"exceeds simulation horizon"): + SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev], + ) + + +def test_end_time_exceeds_horizon_rejected( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """End time greater than the horizon must be rejected.""" + topo = _topology_with_min_edge() + horizon = float(sim_settings.total_simulation_time) + ev = _mk_network_spike( + "ev-hz-end", + "gen-to-client", + start_t=horizon - 0.1, + end_t=horizon + 0.1, + spike_s=0.001, + ) + with pytest.raises(ValueError, match=r"exceeds simulation horizon"): + SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev], + ) + + +# --------------------------------------------------------------------------- +# Kind/target compatibility +# --------------------------------------------------------------------------- + + +def test_server_event_cannot_target_edge( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """SERVER_DOWN should not target an edge ID.""" + topo = _topology_with_min_edge() + ev = _mk_server_window( + "ev-srv-bad", + target_id="gen-to-client", + start_t=0.0, + end_t=1.0, + ) + with pytest.raises(ValueError, match=r"regarding a server .* compatible"): + SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev], + ) + + +def test_edge_event_ok_on_edge( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """NETWORK_SPIKE event is valid when it targets an edge ID.""" + topo = _topology_with_min_edge() + ev = _mk_network_spike( + "ev-edge-ok", "gen-to-client", start_t=0.0, end_t=1.0, spike_s=0.001, + ) + payload = SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev], + ) + assert payload.events is not None + assert payload.events[0].target_id == "gen-to-client" + + +# --------------------------------------------------------------------------- +# Global liveness: not all servers down simultaneously +# --------------------------------------------------------------------------- + + +def test_reject_when_all_servers_down_at_same_time( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """ + It should raise a ValidationError if there is any time interval during which + all servers are scheduled to be down simultaneously. + """ + topo = _topology_with_two_servers_and_edge() + + # --- SETUP: Use a longer simulation horizon for this specific test --- + # The default `sim_settings` fixture has a short horizon (e.g., 5s) to + # keep most tests fast. For this test, we need a longer horizon to + # ensure the event times themselves are valid. + sim_settings.total_simulation_time = 30 # e.g., 30 seconds + + # The event times are now valid within the new horizon. + # srv-1 is down [10, 20), srv-2 is down [15, 25). + # This creates an overlap in [15, 20) where both are down. + ev_a = _mk_server_window("ev-a", "srv-1", start_t=10.0, end_t=20.0) + ev_b = _mk_server_window("ev-b", "srv-2", start_t=15.0, end_t=25.0) + + # Now the test will bypass the time horizon validation and trigger + # the correct validator that checks for server downtime overlap. + with pytest.raises(ValueError, match=r"all servers are down"): + SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev_a, ev_b], + ) + + +def test_accept_when_never_all_down( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """Payload is valid when at least one server stays up at all times.""" + topo = _topology_with_two_servers_and_edge() + + # --- SETUP: Use a longer simulation horizon for this specific test --- + # As before, we need to ensure the event times are valid within the + # simulation's total duration. + sim_settings.total_simulation_time = 30 # e.g., 30 seconds + + # Staggered windows: srv-1 down [10, 15), srv-2 down [15, 20). + # There is no point in time where both are down. + ev_a = _mk_server_window("ev-a", "srv-1", start_t=10.0, end_t=15.0) + ev_b = _mk_server_window("ev-b", "srv-2", start_t=15.0, end_t=20.0) + + # This should now pass validation without raising an error. + payload = SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev_a, ev_b], + ) + assert payload.events is not None + assert len(payload.events) == 2 From e869e67ab9c1548e3efc1d55c26179f8bff1cc19 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Thu, 28 Aug 2025 18:32:11 +0200 Subject: [PATCH 39/52] Feature/event injection runtime (#20) * Events injection for edges part 1 * Logic + docs for the event injection to simulate a server down * Added pydantic validation + unit + int tests for eventinjection * added exaple for event inj yaml + builder added int tests * improved server model + system tests for eventinjection * ruff small fix --- CHANGELOG.MD | 106 ++++++ docs/internals/edges-events-injection.md | 277 ++++++++++++++++ docs/internals/server-events-injection.md | 203 ++++++++++++ .../event_injection/lb_two_servers.py | 240 ++++++++++++++ .../lb_two_servers_events_dashboard.png | Bin 0 -> 152946 bytes .../lb_two_servers_events_io_queue_srv-1.png | Bin 0 -> 37796 bytes .../lb_two_servers_events_io_queue_srv-2.png | Bin 0 -> 37525 bytes .../lb_two_servers_events_ram_srv-1.png | Bin 0 -> 37754 bytes .../lb_two_servers_events_ram_srv-2.png | Bin 0 -> 39688 bytes ...b_two_servers_events_ready_queue_srv-1.png | Bin 0 -> 56754 bytes ...b_two_servers_events_ready_queue_srv-2.png | Bin 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bytes ...ingle_server_results_ready_queue_srv-1.png | Bin 61628 -> 0 bytes pyproject.toml | 2 +- src/asyncflow/builder/asyncflow_builder.py | 53 +++ src/asyncflow/components/__init__.py | 2 +- src/asyncflow/runtime/actors/edge.py | 42 ++- src/asyncflow/runtime/actors/load_balancer.py | 51 +-- .../runtime/actors/routing/lb_algorithms.py | 49 ++- src/asyncflow/runtime/actors/server.py | 155 +++++---- src/asyncflow/runtime/events/injection.py | 254 +++++++++++++++ src/asyncflow/runtime/simulation_runner.py | 120 ++++++- .../schemas/{event => events}/injection.py | 0 src/asyncflow/schemas/payload.py | 54 +++- src/asyncflow/schemas/topology/nodes.py | 2 + tests/conftest.py | 10 +- .../event_injection/lb_two_servers.py | 113 +++++++ .../event_injection/single_server.py | 108 +++++++ .../load_balancer/test_lb_basic.py | 130 ++++++++ .../single_server/test_int_single_server.py | 19 +- .../system/test_sys_ev_inj_lb_two_servers.py | 237 ++++++++++++++ tests/system/test_sys_ev_inj_single_server.py | 199 ++++++++++++ .../unit/runtime/actors/test_load_balancer.py | 48 +-- tests/unit/runtime/actors/test_server.py | 304 ++++++++++++++---- .../runtime/events/test_injection_edges.py | 296 +++++++++++++++++ .../runtime/events/test_injection_servers.py | 133 ++++++++ .../events/test_injection_servers_edges.py | 237 ++++++++++++++ tests/unit/runtime/test_simulation_runner.py | 172 +++++++++- tests/unit/schemas/test_event_injection.py | 2 +- tests/unit/schemas/test_payload.py | 43 ++- 86 files changed, 4128 insertions(+), 242 deletions(-) create mode 100644 CHANGELOG.MD create mode 100644 docs/internals/edges-events-injection.md create mode 100644 docs/internals/server-events-injection.md create mode 100644 examples/builder_input/event_injection/lb_two_servers.py create mode 100644 examples/builder_input/event_injection/lb_two_servers_events_plots/lb_two_servers_events_dashboard.png create mode 100644 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mode 100644 src/asyncflow/runtime/events/injection.py rename src/asyncflow/schemas/{event => events}/injection.py (100%) create mode 100644 tests/integration/event_injection/lb_two_servers.py create mode 100644 tests/integration/event_injection/single_server.py create mode 100644 tests/integration/load_balancer/test_lb_basic.py create mode 100644 tests/system/test_sys_ev_inj_lb_two_servers.py create mode 100644 tests/system/test_sys_ev_inj_single_server.py create mode 100644 tests/unit/runtime/events/test_injection_edges.py create mode 100644 tests/unit/runtime/events/test_injection_servers.py create mode 100644 tests/unit/runtime/events/test_injection_servers_edges.py diff --git a/CHANGELOG.MD b/CHANGELOG.MD new file mode 100644 index 0000000..c7ba4cc --- /dev/null +++ b/CHANGELOG.MD @@ -0,0 +1,106 @@ + +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). + +## \[Unreleased] + +### Added + +* **Event Injection input schema** (merged on `develop`): + + * Declarative events with `start` / `end` markers (server **down/up**, network **spike start/end**). + * Strong validation (Pydantic): + + * `event_id` uniqueness. + * `target_id` must exist (server or edge). + * Start/end times within the simulation horizon and well-ordered. + * Kind ↔ target compatibility (server events must target servers, network events must target edges). + * Safety guard: **never all servers down simultaneously**. +* Documentation comments and examples for authoring event windows in YAML. + +### In Progress + +* **Runtime Event Scheduler**: SimPy process to apply events at scheduled times. +* **Edge spike handling (phase 1)**: deterministic latency **offsets** during spike windows (no changes to base distributions yet). + +--- + +## \[0.1.0a2] – 2025-08-17 + +### Fixed + +* **Quickstart YAML in README**: corrected field to ensure a smooth first run for new users. + +### Notes + +* Minor docs polish only; no runtime changes. + +--- + +## \[0.1.0a1] – 2025-08-17 + +### Changed + +* Repository aligned with the **PyPI 0.1.0a1** build. +* Packaging metadata tidy-up in `pyproject.toml`. + +### CI + +* Main workflow now also triggers on **push** to `main`. + +### Notes + +* No functional/runtime changes. + +--- + +## \[v0.1.0-alpha] – 2025-08-17 + +**First public alpha** of AsyncFlow — a SimPy-based, **event-loop-aware** simulator for async distributed systems. + +### Highlights + +* **Event-loop model** per server: explicit **CPU** (blocking), **I/O waits** (non-blocking), **RAM** residency. +* **Topology graph**: generator → client → (LB, optional) → servers; multi-server via **round-robin**; **stochastic network latency** and optional dropouts. +* **Workload**: stochastic traffic via simple RV configs (Poisson defaults). + +### Metrics & Analyzer + +* **Event metrics**: `RqsClock` (end-to-end latency). +* **Sampled metrics**: `ready_queue_len`, `event_loop_io_sleep`, `ram_in_use`, `edge_concurrent_connection`. +* **Analyzer API** (`ResultsAnalyzer`): + + * `get_latency_stats()`, `get_throughput_series()` + * Plots: `plot_latency_distribution()`, `plot_throughput()` + * Per-server: `plot_single_server_ready_queue()`, `plot_single_server_io_queue()`, `plot_single_server_ram()` + * Compact dashboards. + +### Examples + +* YAML quickstart (single server). +* Pythonic builder: + + * Single server. + * **Load balancer + two servers** example with saved figures. + +### Tooling & CI + +* One-shot setup scripts (`dev_setup`, `quality_check`, `run_tests`, `run_sys_tests`) for Linux/macOS/Windows. +* GitHub Actions: Ruff + MyPy + Pytest; **system tests gate merges** into `main`. + +### Compatibility + +* **Python 3.12+** (Linux/macOS/Windows). +* Install from PyPI: `pip install asyncflow-sim`. + +### Known Limitations (alpha) + +* Network: latency + optional drops (no bandwidth/payload/TCP yet). +* Single event loop per server (no multi-process/node). +* Linear endpoint pipelines (no branching/fan-out inside endpoints). +* Stationary workload; very short spikes may be missed if `sample_period_s` is large. + + diff --git a/docs/internals/edges-events-injection.md b/docs/internals/edges-events-injection.md new file mode 100644 index 0000000..210d190 --- /dev/null +++ b/docs/internals/edges-events-injection.md @@ -0,0 +1,277 @@ +# Edge Event Injection: Architecture & Operations + +This document explains how **edge-level events** (e.g., deterministic latency spikes) are modeled, centralized, and injected into the simulation. It covers: + +* Data model (start/end markers & validation) +* The **central event runtime** (timeline, cumulative offsets, live adapters) +* How **SimulationRunner** wires everything +* How **EdgeRuntime** consumes the adapters during delivery +* Ordering, correctness guarantees, and trade-offs +* Extension points and maintenance tips + +--- + +## 1) Conceptual Model + +### What’s an “edge event”? + +An edge event is a **time-bounded effect** applied to a specific network edge (link). Today we support **latency spikes**: while the event is active, the edge’s transit time is increased by a fixed offset (`spike_s`) in seconds. + +### Event markers + +Events are defined with two **markers**: + +* `Start` (`kind` in `{NETWORK_SPIKE_START, SERVER_DOWN}`) +* `End` (`kind` in `{NETWORK_SPIKE_END, SERVER_UP}`) + +Validation guarantees: + +* **Kind pairing** is coherent (e.g., `NETWORK_SPIKE_START` ↔ `NETWORK_SPIKE_END`). +* **Time ordering**: `t_start < t_end`. +* For network spike events, **`spike_s` is required** and positive. + +> These guarantees are enforced by the Pydantic models and their `model_validator`s in the schema layer, *before* runtime. + +--- + +## 2) Centralized Event Registry: `EventInjectionRuntime` + +`EventInjectionRuntime` centralizes all event logic and exposes **live read-only views** (adapters) to edge actors. + +### Responsibilities & Data + +* **Input**: + + * `events: list[EventInjection] | None` + * `edges: list[Edge]`, `servers: list[Server]`, `env: simpy.Environment` +* **Internal state**: + + * `self._edges_events: dict[event_id, dict[edge_id, float]]` + Mapping from event → edge → spike amplitude (`spike_s`). + This allows multiple events per edge and distinguishes overlapping events. + * `self._edges_spike: dict[edge_id, float]` + **Cumulative** spike currently active per edge (updated at runtime). + * `self._edges_affected: set[edge_id]` + All edges that are ever impacted by at least one event. + * `self._edges_timeline: list[tuple[time, event_id, edge_id, mark]]` + Absolute timestamps (`time`) with `mark ∈ {start, end}` for **edges**. + * (We also construct a server timeline, reserved for future server-side effects.) + +> If `events` is `None` or empty, the runtime initializes to empty sets/maps and **does nothing** when started. + +### Build step (performed in `__init__`) + +1. Early return if there are no events (keeps empty adapters). +2. Partition events by **target type** (edge vs server). +3. For each **edge** event: + + * Record `spike_s` in `self._edges_events[event_id][edge_id]`. + * Append `(t_start, event_id, edge_id, start)` and `(t_end, event_id, edge_id, end)` to the **edge timeline**. + * Add `edge_id` to `self._edges_affected`. +4. **Sort** timelines by `(time, mark == start, event_id, edge_id)` so that at equal time, **end** is processed **before start**. + (Because `False < True`, `end` precedes `start`.) + +### Runtime step (SimPy process) + +The coroutine `self._assign_edges_spike()`: + +* Iterates the ordered timeline of **absolute** timestamps. +* Converts absolute `t_event` to relative waits via `dt = t_event - last_t`. +* After waiting `dt`, applies the state change: + + * On **start**: `edges_spike[edge_id] += delta` + * On **end**: `edges_spike[edge_id] -= delta` + +This gives a continuously updated, **cumulative** spike per edge, enabling **overlapping events** to stack linearly. + +### Public adapters (read-only views) + +* `edges_spike: dict[str, float]` — current cumulative spike per edge. +* `edges_affected: set[str]` — edges that may ever be affected. + +These are **shared** with `EdgeRuntime` instances, so updates made by the central process are immediately visible to the edges **without any signaling or copying**. + +--- + +## 3) Wiring & Lifecycle: `SimulationRunner` + +`SimulationRunner` orchestrates creation, wiring, and startup order. + +### Build phase + +1. Build node runtimes (request generator, client, servers, optional load-balancer). +2. Build **edge runtimes** (`EdgeRuntime`) with their target boxes (stores). +3. **Build events**: + + * If `simulation_input.events` is empty/None → **skip** (no process, no adapters). + * Else: + + * Construct **one** `EventInjectionRuntime`. + * Extract adapters: `edges_affected`, `edges_spike`. + * Attach these **same objects** to **every** `EdgeRuntime`. + (EdgeRuntime performs a membership check; harmless for unaffected edges.) + +> We deliberately attach adapters to all edges for simplicity. This is O(1) memory for references, and O(1) runtime per delivery (one membership + dict lookup). If desired, the runner could pass adapters **only** to affected edges—this would save a branch per delivery at the cost of more conditional wiring logic. + +### Start phase (order matters) + +* `EventInjectionRuntime.start()` — **first** + Ensures that the spike timeline is active before edges start delivering; the first edge transport will see the correct offset when due. +* Start all other actors. +* Start the metric collector (RAM / queues / connections snapshots). +* `env.run(until=total_simulation_time)` to advance the clock. + +### Why this order? + +* Prevents race conditions where the first edge message observes stale (`0.0`) spike at time ≈ `t_start`. +* Keeps the architecture deterministic and easy to reason about. + +--- + +## 4) Edge Consumption: `EdgeRuntime` + +Each edge has: + +* `edges_affected: Container[str] | None` +* `edges_spike: Mapping[str, float] | None` + +During `_deliver(state)`: + +1. Sample base latency from the configured RV. +2. If adapters are present **and** `edge_id ∈ edges_affected`: + + * Read `spike = edges_spike.get(edge_id, 0.0)` + * `effective = base_latency + spike` +3. `yield env.timeout(effective)` + +No further coordination required: the **central** process updates `edges_spike` as time advances, so each delivery observes the **current** spike. + +--- + +## 5) Correctness & Guarantees + +* **Temporal correctness**: Absolute → relative time conversion (`dt = t_event - last_t`) ensures the process applies changes at the exact timestamps. Sorting ensures **END** is processed before **START** when times coincide, so zero-length events won’t “leak” positive offset. +* **Coherence**: Pydantic validators enforce event pairing and time ordering. +* **Immutability**: Marker models are frozen; unknown fields are forbidden. +* **Overlap**: Multiple events on the same edge stack linearly (`+=`/`-=`). + +--- + +## 6) Performance & Trade-offs + +### Centralized vs Distributed + +* **Chosen**: one central `EventInjectionRuntime` with live adapters. + + * **Pros**: simple mental model; single source of truth; O(1) read for edges; no per-edge coroutines; minimal memory traffic. + * **Cons**: single process to maintain (but it’s lightweight); edges branch on membership. + +* **Alternative A**: deliver the **full** event runtime object to each edge. + + * **Cons**: wider API surface; tighter coupling; harder to evolve; edges would get capabilities they don’t need (SRP violation). + +* **Alternative B**: per-edge local event processes. + + * **Cons**: one coroutine per edge (N processes), more scheduler overhead, duplicated logic & sorting. + +### Passing adapters to *all* edges vs only affected edges + +* **Chosen**: pass to all edges. + + * **Pros**: wiring stays uniform; negligible memory; O(1) branch in `_deliver`. + * **Cons**: trivial per-delivery branch even for unaffected edges. +* **Alternative**: only affected edges receive adapters. + + * **Pros**: removes one branch at delivery. + * **Cons**: more conditional wiring, more moving parts for little gain. + +--- + +## 7) Sequence Overview + +``` +SimulationRunner.run() + ├─ _build_rqs_generator() + ├─ _build_client() + ├─ _build_servers() + ├─ _build_load_balancer() + ├─ _build_edges() + ├─ _build_events() + │ └─ EventInjectionRuntime(...): + │ - build _edges_events, _edges_affected + │ - build & sort _edges_timeline + │ + ├─ _start_events() + │ └─ start _assign_edges_spike() (central timeline process) + │ + ├─ _start_all_processes() (edges, client, servers, etc.) + ├─ _start_metric_collector() + └─ env.run(until = T) +``` + +During `EdgeRuntime._deliver()`: + +``` +base = sample(latency_rv) +if adapters_present and edge_id in edges_affected: + spike = edges_spike.get(edge_id, 0.0) + effective = base + spike +else: + effective = base +yield env.timeout(effective) +``` + +--- + +## 8) Extensibility + +* **Other edge effects**: add new event kinds and store per-edge state (e.g., drop-rate bumps) in `_edges_events` and update logic in `_assign_edges_spike()`. +* **Server outages**: server timeline is already scaffolded; add a server process to open/close resources (e.g., capacity=0 during downtime). +* **Non-deterministic spikes**: swap `float` `spike_s` for a small sampler (callable) and apply the sampled value at each **start**, or at each **delivery** (define semantics). +* **Per-edge filtering in runner** (micro-optimization): only wire adapters to affected edges. + +--- + +## 9) Operational Notes & Best Practices + +* **Start order** matters: always start `EventInjectionRuntime` *before* edges. +* **Adapters must be shared** (not copied) to preserve live updates. +* **Keep `edges_spike` additive** (no negative values unless you introduce “negative spikes” intentionally). +* **Time units**: seconds everywhere; keep it consistent with sampling. +* **Validation first**: reject malformed events early (schema layer), *not* in runtime. + +--- + +## 10) Glossary + +* **Adapter**: a minimal, read-only view (e.g., `Mapping[str, float]`, `Container[str]`) handed to edges to observe central state without owning it. +* **Timeline**: sorted list of `(time, event_id, edge_id, mark)` where `mark ∈ {start, end}`. +* **Spike**: deterministic latency offset to be added to the sampled base latency. + +--- + +## 11) Example (end-to-end) + +**YAML (conceptual)** + +```yaml +events: + - event_id: ev-spike-1 + target_id: edge-42 + start: { kind: NETWORK_SPIKE_START, t_start: 12.0, spike_s: 0.050 } + end: { kind: NETWORK_SPIKE_END, t_end: 18.0 } +``` + +**Runtime effect** + +* From `t ∈ [12, 18)`, `edge-42` adds **+50 ms** to its sampled latency. +* Overlapping events stack: `edges_spike["edge-42"]` is the **sum** of active spikes. + +--- + +## 12) Summary + +* We centralize event logic in **`EventInjectionRuntime`** and expose **live adapters** to edges. +* Edges read **current cumulative spikes** at delivery time—**no coupling** and **no extra processes per edge**. +* The runner keeps the flow simple and deterministic: **build → wire → start events → start actors → run**. +* The architecture is **extensible**, **testable**, and **performant** for realistic workloads. diff --git a/docs/internals/server-events-injection.md b/docs/internals/server-events-injection.md new file mode 100644 index 0000000..8bc7165 --- /dev/null +++ b/docs/internals/server-events-injection.md @@ -0,0 +1,203 @@ +# Server Event Injection — End-to-End Design & Rationale + +This document explains how **server-level events** (planned outages) are modeled and executed across all layers of the simulation stack. It complements the Edge Event Injection design. + +--- + +## 1) Goals + +* Hide outage semantics from the load balancer algorithms: **they see only the current set of edges**. +* Keep **runtime cost O(1)** per transition (down/up). +* Preserve determinism and fairness when servers rejoin. +* Centralize event logic; avoid per-server coroutines and ad-hoc flags. + +--- + +## 2) Participants (layers) + +* **Schema / Validation (Pydantic)**: validates `EventInjection` objects (pairing, order, target existence). +* **SimulationRunner**: builds runtimes; owns the **single shared** `OrderedDict[str, EdgeRuntime]` used by the LB (`_lb_out_edges`). +* **EventInjectionRuntime**: central event engine; builds the **server timeline** and a **reverse index** `server_id → (edge_id, EdgeRuntime)`; mutates `_lb_out_edges` at runtime. +* **LoadBalancerRuntime**: reads `_lb_out_edges` to select the next edge (RR / least-connections). **No outage logic inside.** +* **EdgeRuntime (LB→Server edges)**: unaffected by server outages; disappears from the LB’s choice set while the server is down. +* **ServerRuntime**: unaffected structurally; no extra checks for “am I down?”. +* **SimPy Environment**: schedules the central outage coroutine. +* **Metric Collector**: optional; observes effects but is not part of the mechanism. + +--- + +## 3) Data & Structures + +* **`_lb_out_edges: OrderedDict[str, EdgeRuntime]`** + Single shared map of **currently routable** LB→server edges. + + * Removal/Insertion/Move are **O(1)**. + * Aliased into both `LoadBalancerRuntime` and `EventInjectionRuntime`. + +* **`_servers_timeline: list[tuple[time, event_id, server_id, mark]]`** + Absolute timestamps, sorted by `(time, mark == start, event_id, server_id)` so **END precedes START** when equal. + +* **`_edge_by_server: dict[str, tuple[str, EdgeRuntime]]`** + Reverse index built from `_lb_out_edges` at initialization. + +--- + +## 4) Build-time Responsibilities + +* **SimulationRunner** + + 1. Build LB and pass it `_lb_out_edges` (empty at first). + 2. Build edges; when wiring LB→Server, insert that edge into `_lb_out_edges`. + 3. Build `EventInjectionRuntime`, passing: + + * validated `events` + * `servers` and `edges` (IDs for sanity checks) + * aliased `_lb_out_edges` + +* **EventInjectionRuntime.**init**** + + * Partition events; construct ` _servers_timeline`. + * Sort timeline (END before START at equal `time`). + * Build ` _edge_by_server` by scanning `_lb_out_edges` (edge target → server\_id). + +--- + +## 5) Run-time Responsibilities + +* **EventInjectionRuntime.\_assign\_server\_state()** + + * Iterate the server timeline with absolute→relative waits: `dt = t_event − last_t`, then `yield env.timeout(dt)`. + * On `SERVER_DOWN` (START): + `lb_out_edges.pop(edge_id, None)` + * On `SERVER_UP` (END): + + ``` + lb_out_edges[edge_id] = edge_runtime + lb_out_edges.move_to_end(edge_id) # fairness on rejoin + ``` + +* **LoadBalancerRuntime** + + * For each request, read `_lb_out_edges` and apply the chosen algorithm. If a server is down, its edge simply **isn’t there**. + +* **EdgeRuntime & ServerRuntime** + + * No additional work: outage is reflected entirely by presence/absence of the LB→server edge. + +--- + +## 6) Sequence Overview (all layers) + +``` +User YAML ──► Schema/Validation + │ (pairing, ordering, target checks) + ▼ + SimulationRunner + │ _lb_out_edges: OrderedDict[...] (shared object) + │ build LB, edges (LB→S inserted into _lb_out_edges) + │ build EventInjectionRuntime(..., lb_out_edges=alias) + │ + ├─ _start_events() + │ └─ EventInjectionRuntime.start() + │ └─ start _assign_server_state() (SimPy proc) + │ + ├─ _start_all_processes() + │ ├─ LoadBalancerRuntime.start() + │ ├─ EdgeRuntime.start() (if any process) + │ └─ ServerRuntime.start() + │ + └─ env.run(until=T) + +Runtime progression (example): +t=5s EventInjectionRuntime: SERVER_DOWN(S1) + └─ _edge_by_server[S1] -> (edge-S1, edge_rt) + └─ _lb_out_edges.pop("edge-S1") # O(1) + +t=7s LoadBalancerRuntime picks next edge + └─ "edge-S1" not present → never selected + +t=10s EventInjectionRuntime: SERVER_UP(S1) + └─ _lb_out_edges["edge-S1"] = edge_rt # O(1) + └─ _lb_out_edges.move_to_end("edge-S1") # fairness + +t>10s LoadBalancerRuntime now sees edge-S1 again + └─ RR/LC proceeds as usual +``` + +--- + +## 7) Correctness & Determinism + +* **Exact timing**: absolute→relative conversion ensures transitions happen at precise timestamps. +* **END before START** at identical times prevents spuriously “stuck down” outcomes for back-to-back events. +* **Fair rejoin**: `move_to_end` reintroduces the server in a predictable RR position (least recently used). + (Least-connections remains deterministic because the edge reappears with its current connection count.) +* **Availability constraint**: schema can enforce “at least one server up,” avoiding degenerate LB states. + +--- + +## 8) Design Choices & Rationale + +* **Mutate the edge set, not the algorithm** + Removing/adding the LB→server edge keeps LB code **pure** and reusable; no conditional branches for “down servers”. +* **Single shared `OrderedDict`** + + * O(1) for remove/insert/rotate. + * Aliasing between LB and injector removes the need for signaling or copies. +* **Centralized coroutine** + One SimPy process for server outages scales better than per-server processes; simpler mental model. +* **Reverse index `server_id → edge`** + Constant-time resolution; avoids coupling servers to LB or vice-versa. + +--- + +## 9) Performance + +* **Build**: + + * Timeline construction: O(#server-events) + * Sort: O(#server-events · log #server-events) +* **Run**: + + * Each transition: O(1) (pop/set/move) + * LB pick: unchanged (RR O(1), LC O(n)) +* **Space**: + + * Reverse index: O(#servers with LB edges) + * Timeline: O(#server-events) + +--- + +## 10) Failure Modes & Guards + +* Unknown server in an event → rejected by schema (or ignored with a log if you prefer leniency). +* Concurrent DOWN/UP at same timestamp → resolved by timeline ordering (END first). +* All servers down → disallowed by schema (or handled by LB guard if you opt in later). +* Missing reverse mapping (no LB) → injector safely no-ops. + +--- + +## 11) Extensibility + +* **Multiple LB instances**: make the reverse index `(lb_id, server_id) → edge_id`, or pass per-LB `lb_out_edges`. +* **Partial capacity**: instead of removing edges, attach capacity/weight and have the LB respect it (requires extending LB policy). +* **Dynamic scale-out**: adding new servers at runtime is the same operation as “UP” with a previously unseen edge. + +--- + +## 12) Operational Notes + +* Start the **event coroutine** before LB to avoid off-by-one delivery at `t_start`. +* Keep `_lb_out_edges` the **only source of truth** for routable edges. +* If you also use edge-level spikes, both coroutines can run concurrently; they are independent. + +--- + +## 13) Summary + +We model server outages by **mutating the LB’s live edge set** via a centralized event runtime: + +* **O(1)** down/up transitions by `pop`/`set` on a shared `OrderedDict`. +* LB algorithms remain untouched and deterministic. +* A single SimPy coroutine drives the timeline; a reverse index resolves targets in constant time. +* The design is minimal, performant, and easy to extend to richer failure models. diff --git a/examples/builder_input/event_injection/lb_two_servers.py b/examples/builder_input/event_injection/lb_two_servers.py new file mode 100644 index 0000000..8af411f --- /dev/null +++ b/examples/builder_input/event_injection/lb_two_servers.py @@ -0,0 +1,240 @@ +""" +AsyncFlow builder example — LB + 2 servers (medium load) with events. + +Topology + generator → client → LB → srv-1 + └→ srv-2 + srv-1 → client + srv-2 → client + +Workload + ~40 rps (120 users × 20 req/min ÷ 60). + +Events + - Edge spike on client→LB (+15 ms) @ [100s, 160s] + - srv-1 outage @ [180s, 240s] + - Edge spike on LB→srv-2 (+20 ms) @ [300s, 360s] + - srv-2 outage @ [360s, 420s] + - Edge spike on gen→client (+10 ms) @ [480s, 540s] + +Outputs + PNGs saved under `lb_two_servers_events_plots/` next to this script: + - dashboard (latency + throughput) + - per-server plots: ready queue, I/O queue, RAM +""" + +from __future__ import annotations + +from pathlib import Path + +import matplotlib.pyplot as plt +import simpy + +# Public builder API +from asyncflow import AsyncFlow +from asyncflow.components import Client, Server, Edge, Endpoint, LoadBalancer +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator + +# Runner + Analyzer +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner + + +def build_and_run() -> ResultsAnalyzer: + """Build the scenario via the builder and run the simulation.""" + # ── Workload (generator) ─────────────────────────────────────────────── + generator = RqsGenerator( + id="rqs-1", + avg_active_users={"mean": 120}, + avg_request_per_minute_per_user={"mean": 20}, + user_sampling_window=60, + ) + + # ── Client ──────────────────────────────────────────────────────────── + client = Client(id="client-1") + + # ── Servers (identical endpoint: CPU 2ms → RAM 128MB → IO 12ms) ─────── + endpoint = Endpoint( + endpoint_name="/api", + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.002}}, + {"kind": "ram", "step_operation": {"necessary_ram": 128}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.012}}, + ], + ) + srv1 = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], + ) + srv2 = Server( + id="srv-2", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], + ) + + # ── Load Balancer ───────────────────────────────────────────────────── + lb = LoadBalancer( + id="lb-1", + algorithms="round_robin", + server_covered=["srv-1", "srv-2"], + ) + + # ── Edges (exponential latency) ─────────────────────────────────────── + e_gen_client = Edge( + id="gen-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e_client_lb = Edge( + id="client-lb", + source="client-1", + target="lb-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ) + e_lb_srv1 = Edge( + id="lb-srv1", + source="lb-1", + target="srv-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ) + e_lb_srv2 = Edge( + id="lb-srv2", + source="lb-1", + target="srv-2", + latency={"mean": 0.002, "distribution": "exponential"}, + ) + e_srv1_client = Edge( + id="srv1-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e_srv2_client = Edge( + id="srv2-client", + source="srv-2", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + + # ── Simulation settings ─────────────────────────────────────────────── + settings = SimulationSettings( + total_simulation_time=600, + sample_period_s=0.05, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + # ── Assemble payload + events via builder ───────────────────────────── + payload = ( + AsyncFlow() + .add_generator(generator) + .add_client(client) + .add_servers(srv1, srv2) + .add_load_balancer(lb) + .add_edges( + e_gen_client, + e_client_lb, + e_lb_srv1, + e_lb_srv2, + e_srv1_client, + e_srv2_client, + ) + .add_simulation_settings(settings) + # Events + .add_network_spike( + event_id="ev-spike-1", + edge_id="client-lb", + t_start=100.0, + t_end=160.0, + spike_s=0.015, # +15 ms + ) + .add_server_outage( + event_id="ev-srv1-down", + server_id="srv-1", + t_start=180.0, + t_end=240.0, + ) + .add_network_spike( + event_id="ev-spike-2", + edge_id="lb-srv2", + t_start=300.0, + t_end=360.0, + spike_s=0.020, # +20 ms + ) + .add_server_outage( + event_id="ev-srv2-down", + server_id="srv-2", + t_start=360.0, + t_end=420.0, + ) + .add_network_spike( + event_id="ev-spike-3", + edge_id="gen-client", + t_start=480.0, + t_end=540.0, + spike_s=0.010, # +10 ms + ) + .build_payload() + ) + + # ── Run ─────────────────────────────────────────────────────────────── + env = simpy.Environment() + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + return results + + +def main() -> None: + res = build_and_run() + print(res.format_latency_stats()) + + # Output directory next to this script + script_dir = Path(__file__).parent + out_dir = script_dir / "lb_two_servers_events_plots" + out_dir.mkdir(parents=True, exist_ok=True) + + # Dashboard (latency + throughput) + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + res.plot_base_dashboard(axes[0], axes[1]) + fig.tight_layout() + dash_path = out_dir / "lb_two_servers_events_dashboard.png" + fig.savefig(dash_path) + print(f"Saved: {dash_path}") + + # Per-server plots + for sid in res.list_server_ids(): + # Ready queue + f1, a1 = plt.subplots(figsize=(10, 5)) + res.plot_single_server_ready_queue(a1, sid) + f1.tight_layout() + p1 = out_dir / f"lb_two_servers_events_ready_queue_{sid}.png" + f1.savefig(p1) + print(f"Saved: {p1}") + + # I/O queue + f2, a2 = plt.subplots(figsize=(10, 5)) + res.plot_single_server_io_queue(a2, sid) + f2.tight_layout() + p2 = out_dir / f"lb_two_servers_events_io_queue_{sid}.png" + f2.savefig(p2) + print(f"Saved: {p2}") + + # RAM usage + f3, a3 = plt.subplots(figsize=(10, 5)) + res.plot_single_server_ram(a3, sid) + f3.tight_layout() + p3 = out_dir / f"lb_two_servers_events_ram_{sid}.png" + f3.savefig(p3) + print(f"Saved: {p3}") + + +if __name__ == "__main__": + main() diff --git a/examples/builder_input/event_injection/lb_two_servers_events_plots/lb_two_servers_events_dashboard.png b/examples/builder_input/event_injection/lb_two_servers_events_plots/lb_two_servers_events_dashboard.png new file mode 100644 index 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completion + - I/O step is non-blocking (event-loop friendly) + +Network model + Each edge has exponential latency with mean 3 ms. + +Events + - ev-spike-1: deterministic latency spike (+20 ms) on client→server edge, + active from t=120s to t=240s + - ev-outage-1: server outage for srv-1 from t=300s to t=360s + +Outputs + - Prints latency statistics to stdout + - Saves PNGs in `single_server_plot/` next to this script: + * dashboard (latency + throughput) + * per-server plots (ready queue, I/O queue, RAM) +""" + +from __future__ import annotations + +from pathlib import Path +import simpy +import matplotlib.pyplot as plt + +# Public AsyncFlow API (builder) +from asyncflow import AsyncFlow +from asyncflow.components import Client, Server, Edge, Endpoint +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator + +# Runner + Analyzer +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer + + +def build_and_run() -> ResultsAnalyzer: + """Build the scenario via the Pythonic builder and run the simulation.""" + # Workload (generator) + generator = RqsGenerator( + id="rqs-1", + avg_active_users={"mean": 100}, + avg_request_per_minute_per_user={"mean": 20}, + user_sampling_window=60, + ) + + # Client + client = Client(id="client-1") + + # Server + endpoint (CPU → RAM → I/O) + endpoint = Endpoint( + endpoint_name="ep-1", + probability=1.0, + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, # 1 ms + {"kind": "ram", "step_operation": {"necessary_ram": 100}}, # 100 MB + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.100}}, # 100 ms + ], + ) + server = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[endpoint], + ) + + # Network edges (3 ms mean, exponential) + e_gen_client = Edge( + id="gen-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e_client_srv = Edge( + id="client-srv", + source="client-1", + target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + e_srv_client = Edge( + id="srv-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ) + + # Simulation settings + settings = SimulationSettings( + total_simulation_time=500, + sample_period_s=0.05, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + # Assemble payload with events + payload = ( + AsyncFlow() + .add_generator(generator) + .add_client(client) + .add_servers(server) + .add_edges(e_gen_client, e_client_srv, e_srv_client) + .add_simulation_settings(settings) + # Events + .add_network_spike( + event_id="ev-spike-1", + edge_id="client-srv", + t_start=120.0, + t_end=240.0, + spike_s=0.020, # 20 ms spike + ) + ).build_payload() + + # Run + env = simpy.Environment() + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + return results + + +def main() -> None: + # Build & run + res = build_and_run() + + # Print concise latency summary + print(res.format_latency_stats()) + + # Prepare output dir + script_dir = Path(__file__).parent + out_dir = script_dir / "single_server_plot" + out_dir.mkdir(parents=True, exist_ok=True) + + # Dashboard (latency + throughput) + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + res.plot_base_dashboard(axes[0], axes[1]) + fig.tight_layout() + dash_path = out_dir / "event_inj_single_server_dashboard.png" + fig.savefig(dash_path) + print(f"Saved: {dash_path}") + + # Per-server plots + for sid in res.list_server_ids(): + # Ready queue + f1, a1 = plt.subplots(figsize=(10, 5)) + res.plot_single_server_ready_queue(a1, sid) + f1.tight_layout() + p1 = out_dir / f"event_inj_single_server_ready_queue_{sid}.png" + f1.savefig(p1) + print(f"Saved: {p1}") + + # I/O queue + f2, a2 = plt.subplots(figsize=(10, 5)) + res.plot_single_server_io_queue(a2, sid) + f2.tight_layout() + p2 = out_dir / f"event_inj_single_server_io_queue_{sid}.png" + f2.savefig(p2) + print(f"Saved: {p2}") + + # RAM usage + f3, a3 = plt.subplots(figsize=(10, 5)) + res.plot_single_server_ram(a3, sid) + f3.tight_layout() + p3 = out_dir / f"event_inj_single_server_ram_{sid}.png" + f3.savefig(p3) + print(f"Saved: {p3}") + + +if __name__ == "__main__": + main() diff --git a/examples/builder_input/event_injection/single_server_plot/event_inj_single_server_dashboard.png b/examples/builder_input/event_injection/single_server_plot/event_inj_single_server_dashboard.png new file mode 100644 index 0000000000000000000000000000000000000000..1a81453274d2017f8af3f7bc2779856490aa8a2b GIT binary patch literal 146570 zcmb@uc{JDS_cs1Qh9X1CoSDj;%wtJJMI=*Yp6ARng(y>$DN#sNBr;|mLdXzho-${i zXP$j`KHu|M&-1MHtlu9$>p15uyx;HF>wevP?`vPzweQC_RTPgOqdSIS*m0$+^6D6d 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a/examples/yaml_input/event_injections/lb_two_servers.py b/examples/yaml_input/event_injections/lb_two_servers.py new file mode 100644 index 0000000..a2b666c --- /dev/null +++ b/examples/yaml_input/event_injections/lb_two_servers.py @@ -0,0 +1,78 @@ +""" +Run the YAML scenario with LB + 2 servers and export charts. + +Scenario file: + data/lb_two_servers_events.yml + +Outputs (saved in subfolder next to this script): + - dashboard PNG (latency + throughput) + - per-server PNGs: ready queue, I/O queue, RAM +""" + +from __future__ import annotations + +from pathlib import Path +import matplotlib.pyplot as plt +import simpy + +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner + + +def main() -> None: + """Defines paths, runs the simulation, and generates all outputs.""" + # --- 1. Define paths --- + script_dir = Path(__file__).parent + yaml_path = script_dir.parent / "data" / "event_inj_lb.yml" + + out_dir = script_dir / "lb_two_servers_plots" + out_dir.mkdir(exist_ok=True) # create if missing + + output_base_name = "lb_two_servers_events" + + if not yaml_path.exists(): + msg = f"YAML configuration file not found: {yaml_path}" + raise FileNotFoundError(msg) + + # --- 2. Run the simulation --- + env = simpy.Environment() + runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) + results: ResultsAnalyzer = runner.run() + + # --- 3. Dashboard (latency + throughput) --- + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + results.plot_base_dashboard(axes[0], axes[1]) + fig.tight_layout() + dash_path = out_dir / f"{output_base_name}_dashboard.png" + fig.savefig(dash_path) + print(f"Saved: {dash_path}") + + # --- 4. Per-server plots --- + for sid in results.list_server_ids(): + # Ready queue + f1, a1 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ready_queue(a1, sid) + f1.tight_layout() + p1 = out_dir / f"{output_base_name}_ready_queue_{sid}.png" + f1.savefig(p1) + print(f"Saved: {p1}") + + # I/O queue + f2, a2 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_io_queue(a2, sid) + f2.tight_layout() + p2 = out_dir / f"{output_base_name}_io_queue_{sid}.png" + f2.savefig(p2) + print(f"Saved: {p2}") + + # RAM usage + f3, a3 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ram(a3, sid) + f3.tight_layout() + p3 = out_dir / f"{output_base_name}_ram_{sid}.png" + f3.savefig(p3) + print(f"Saved: {p3}") + + +if __name__ == "__main__": + main() diff --git a/examples/yaml_input/event_injections/lb_two_servers_plots/lb_two_servers_events_dashboard.png b/examples/yaml_input/event_injections/lb_two_servers_plots/lb_two_servers_events_dashboard.png new file mode 100644 index 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Define File Paths --- + script_dir = Path(__file__).parent # same folder as this file + yaml_path = script_dir.parent / "data" / "event_inj_single_server.yml" + output_base_name = "event_inj_single_server" # prefix for output files + + if not yaml_path.exists(): + msg = f"YAML configuration file not found: {yaml_path}" + raise FileNotFoundError(msg) + + # Create/ensure the output directory: + out_dir = script_dir / "single_server_plot" + out_dir.mkdir(parents=True, exist_ok=True) + + # --- 2. Run the Simulation --- + env = simpy.Environment() + runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) + results: ResultsAnalyzer = runner.run() + + # --- 3. Dashboard (latency + throughput) --- + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + results.plot_base_dashboard(axes[0], axes[1]) + fig.tight_layout() + dash_path = out_dir / f"{output_base_name}_dashboard.png" + fig.savefig(dash_path) + print(f"Saved: {dash_path}") + + # --- 4. Per-server plots --- + for sid in results.list_server_ids(): + # Ready queue + f1, a1 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ready_queue(a1, sid) + f1.tight_layout() + p1 = out_dir / f"{output_base_name}_ready_queue_{sid}.png" + f1.savefig(p1) + print(f"Saved: {p1}") + + # I/O queue + f2, a2 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_io_queue(a2, sid) + f2.tight_layout() + p2 = out_dir / f"{output_base_name}_io_queue_{sid}.png" + f2.savefig(p2) + print(f"Saved: {p2}") + + # RAM usage + f3, a3 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ram(a3, sid) + f3.tight_layout() + p3 = out_dir / f"{output_base_name}_ram_{sid}.png" + f3.savefig(p3) + print(f"Saved: {p3}") + + +if __name__ == "__main__": + main() diff --git a/examples/yaml_input/event_injections/single_server_plot/event_inj_single_server_dashboard.png b/examples/yaml_input/event_injections/single_server_plot/event_inj_single_server_dashboard.png new file mode 100644 index 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exist_ok=True) + + yaml_path = script_dir.parent / "data" / "two_servers_lb.yml" if not yaml_path.exists(): raise FileNotFoundError(f"YAML configuration not found: {yaml_path}") @@ -49,8 +52,8 @@ def main() -> None: results.plot_latency_distribution(axes_dash[0]) results.plot_throughput(axes_dash[1]) fig_dash.tight_layout() - out_dashboard = script_dir / "lb_dashboard.png" - fig_dash.savefig(out_dashboard) + out_dashboard = out_dir / "lb_dashboard.png" + fig_dash.savefig(out_dashboard, bbox_inches="tight") print(f"🖼️ Dashboard saved to: {out_dashboard}") # ---- Per-server metrics: one figure per server (Ready | I/O | RAM) ---- @@ -61,7 +64,7 @@ def main() -> None: results.plot_single_server_ram(axes[2], sid) fig_row.suptitle(f"Server metrics — {sid}", y=1.04, fontsize=14) fig_row.tight_layout() - out_path = script_dir / f"lb_server_{sid}_metrics.png" + out_path = out_dir / f"lb_server_{sid}_metrics.png" fig_row.savefig(out_path, bbox_inches="tight") print(f"🖼️ Server metrics for 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zK(ZdRu#dYI5GUIwo7|TwX=sQOTKcs4l9GZHY`ws!cmQ1dh3u>P@QAoAbr>enHLiWX z?ytZ7rmXj{A69}5!YTT`m+PDNl`r-~MRkg{+yK)Hum+x&w>}!^GZ3f(#G4c^e>9<%(Nl4G9w-B+@(T)yBvH=miq&LAg zG&Gn~g{KtObU03JciQ|9iPh{SbtxN*dEAMuwziQ_?Q-6gD_2~*&Y((wYD4gq*&}&k zD$*uM9RP_)NJ$ZfH_7n}YAp!-X7S>+3YosVxOPcv?i5McchTNq&9)&hQ)iICNWI_t2Vz5 zWuH=(>GrGc?;-jc#zPD4R5D?-5znPy^4$i&#*wXuM{u9$MQ^YBY18q#6Y~5~_SC{w zlZ4=vAwT@!P?tken Ghx`NR&W5-E diff --git a/pyproject.toml b/pyproject.toml index aea9845..ca8f555 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "asyncflow-sim" -version = "0.1.0" +version = "0.1.1" description = "Digital-twin simulator for distributed async systems. Build what-if scenarios and quantify capacity, latency and throughput offline, before you deploy." authors = ["Gioele Botta"] readme = "README.md" diff --git a/src/asyncflow/builder/asyncflow_builder.py b/src/asyncflow/builder/asyncflow_builder.py index f6d2cea..ef33e7b 100644 --- a/src/asyncflow/builder/asyncflow_builder.py +++ b/src/asyncflow/builder/asyncflow_builder.py @@ -4,6 +4,8 @@ from typing import Self +from asyncflow.config.constants import EventDescription +from asyncflow.schemas.events.injection import End, EventInjection, Start from asyncflow.schemas.payload import SimulationPayload from asyncflow.schemas.settings.simulation import SimulationSettings from asyncflow.schemas.topology.edges import Edge @@ -28,6 +30,7 @@ def __init__(self) -> None: self._edges: list[Edge] | None = None self._sim_settings: SimulationSettings | None = None self._load_balancer: LoadBalancer | None = None + self._events: list[EventInjection] = [] def add_generator(self, rqs_generator: RqsGenerator) -> Self: """Method to instantiate the generator""" @@ -88,6 +91,55 @@ def add_load_balancer(self, load_balancer: LoadBalancer) -> Self: self._load_balancer = load_balancer return self + # --------------------------------------------------------------------- # + # Events # + # --------------------------------------------------------------------- # + + def add_network_spike( + self, + *, + event_id: str, + edge_id: str, + t_start: float, + t_end: float, + spike_s: float, + ) -> Self: + """Convenience: add a NETWORK_SPIKE on a given edge.""" + event = EventInjection( + event_id=event_id, + target_id=edge_id, + start=Start( + kind=EventDescription.NETWORK_SPIKE_START, + t_start=t_start, + spike_s=spike_s, + ), + end=End( + kind=EventDescription.NETWORK_SPIKE_END, + t_end=t_end, + ), + ) + + self._events.append(event) + return self + + def add_server_outage( + self, + *, + event_id: str, + server_id: str, + t_start: float, + t_end: float, + ) -> Self: + """Convenience: add a SERVER_DOWN → SERVER_UP window for a server.""" + event = EventInjection( + event_id=event_id, + target_id=server_id, + start=Start(kind=EventDescription.SERVER_DOWN, t_start=t_start), + end=End(kind=EventDescription.SERVER_UP, t_end=t_end), + ) + self._events.append(event) + return self + def build_payload(self) -> SimulationPayload: """Method to build the payload for the simulation""" if self._generator is None: @@ -121,6 +173,7 @@ def build_payload(self) -> SimulationPayload: "rqs_input": self._generator, "topology_graph": graph, "sim_settings": self._sim_settings, + "events": self._events or None, }) diff --git a/src/asyncflow/components/__init__.py b/src/asyncflow/components/__init__.py index 2f40f69..12e9e9e 100644 --- a/src/asyncflow/components/__init__.py +++ b/src/asyncflow/components/__init__.py @@ -1,7 +1,7 @@ """Public components: re-exports Pydantic schemas (topology).""" from __future__ import annotations -from asyncflow.schemas.event.injection import EventInjection +from asyncflow.schemas.events.injection import EventInjection from asyncflow.schemas.topology.edges import Edge from asyncflow.schemas.topology.endpoint import Endpoint from asyncflow.schemas.topology.nodes import ( diff --git a/src/asyncflow/runtime/actors/edge.py b/src/asyncflow/runtime/actors/edge.py index ee2131d..63c8f45 100644 --- a/src/asyncflow/runtime/actors/edge.py +++ b/src/asyncflow/runtime/actors/edge.py @@ -6,7 +6,7 @@ waits the sampled delay (and any resource wait) before delivering the message to the target node's inbox. """ -from collections.abc import Generator +from collections.abc import Container, Generator, Mapping from typing import TYPE_CHECKING import numpy as np @@ -23,25 +23,40 @@ from asyncflow.schemas.common.random_variables import RVConfig - class EdgeRuntime: """definining the logic to handle the edges during the simulation""" - def __init__( + def __init__( # Noqa: PLR0913 self, *, env: simpy.Environment, edge_config: Edge, + + # ------------------------------------------------------------ + # ATTRIBUTES FROM THE OBJECT EVENTINJECTIONRUNTIME + # We do not want to pass the full object EventInjectionRuntime + # we pass only the two structure necessary to add the spike + # in the case the edge is affected by increase latency + # We initiate both objects to None to dont break the API + # of SimulationRunner + + edge_spike: Mapping[str, float] | None = None, # read-only view + edges_affected: Container[str] | None = None, # membership only + # ------------------------------------------------------------- + rng: np.random.Generator | None = None, target_box: simpy.Store, settings: SimulationSettings, + ) -> None: """Definition of the instance attributes""" self.env = env self.edge_config = edge_config + self.edges_spike = edge_spike + self.edges_affected = edges_affected self.target_box = target_box self.rng = rng or np.random.default_rng() - self.setting = settings + self.settings = settings self._edge_enabled_metrics = build_edge_metrics( settings.enabled_sample_metrics, ) @@ -73,7 +88,24 @@ def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: self._concurrent_connections +=1 transit_time = general_sampler(random_variable, self.rng) - yield self.env.timeout(transit_time) + + + # Logic to add if exists the event injection for the given edge + spike = 0.0 + if ( + self.edges_spike + and self.edges_affected + and self.edge_config.id in self.edges_affected + ): + spike = self.edges_spike.get(self.edge_config.id, 0.0) + + # we do not use max(0.0, effective since) the transite time + # is positive, this condition is guaranteed from the pydantic + # validation + + effective = transit_time + spike + yield self.env.timeout(effective) + state.record_hop( SystemEdges.NETWORK_CONNECTION, diff --git a/src/asyncflow/runtime/actors/load_balancer.py b/src/asyncflow/runtime/actors/load_balancer.py index 498fb18..343cd84 100644 --- a/src/asyncflow/runtime/actors/load_balancer.py +++ b/src/asyncflow/runtime/actors/load_balancer.py @@ -1,16 +1,17 @@ """Definition of the node represented by the LB in the simulation""" + +from collections import OrderedDict from collections.abc import Generator -from typing import TYPE_CHECKING +from typing import ( + TYPE_CHECKING, +) import simpy -from asyncflow.config.constants import LbAlgorithmsName, SystemNodes +from asyncflow.config.constants import SystemNodes from asyncflow.runtime.actors.edge import EdgeRuntime -from asyncflow.runtime.actors.routing.lb_algorithms import ( - least_connections, - round_robin, -) +from asyncflow.runtime.actors.routing.lb_algorithms import LB_TABLE from asyncflow.schemas.topology.nodes import LoadBalancer if TYPE_CHECKING: @@ -26,29 +27,38 @@ def __init__( *, env: simpy.Environment, lb_config: LoadBalancer, - out_edges: list[EdgeRuntime] | None, + + # We use an OrderedDict because, for the RR algorithm, + # we rotate elements in O(1) by moving the selected key to the end. + # An OrderedDict also lets us remove an element by key in O(1) + # without implementing a custom doubly linked list + hashmap. + # Keys are the unique edge IDs that connect the LB to the servers. + # If multiple LBs are present, the SimulationRunner assigns + # the correct dict to each LB. Removals/insertions are performed + # by the EventInjectionRuntime. + + lb_out_edges: OrderedDict[str, EdgeRuntime], lb_box: simpy.Store, ) -> None: """ Descriprion of the instance attributes for the class Args: - env (simpy.Environment): env of the simulation - lb_config (LoadBalancer): input to define the lb in the runtime - rqs_state (RequestState): state of the simulation - out_edges (list[EdgeRuntime]): list of edges that connects lb with servers - lb_box (simpy.Store): store to add the state - + env (simpy.Environment): Simulation environment. + lb_config (LoadBalancer): LB configuration for the runtime. + out_edges (OrderedDict[str, EdgeRuntime]): Edges connecting + the LB to servers. + lb_box (simpy.Store): Queue (mailbox) from which the LB + consumes request states. """ self.env = env self.lb_config = lb_config - self.out_edges = out_edges + self.lb_out_edges = lb_out_edges self.lb_box = lb_box - self._round_robin_index: int = 0 + def _forwarder(self) -> Generator[simpy.Event, None, None]: """Updtate the state before passing it to another node""" - assert self.out_edges is not None while True: state: RequestState = yield self.lb_box.get() # type: ignore[assignment] @@ -58,14 +68,7 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]: self.env.now, ) - if self.lb_config.algorithms == LbAlgorithmsName.ROUND_ROBIN: - out_edge, self._round_robin_index = round_robin( - self.out_edges, - self._round_robin_index, - ) - else: - out_edge = least_connections(self.out_edges) - + out_edge = LB_TABLE[self.lb_config.algorithms](self.lb_out_edges) out_edge.transport(state) def start(self) -> simpy.Process: diff --git a/src/asyncflow/runtime/actors/routing/lb_algorithms.py b/src/asyncflow/runtime/actors/routing/lb_algorithms.py index 46078f7..47f950d 100644 --- a/src/asyncflow/runtime/actors/routing/lb_algorithms.py +++ b/src/asyncflow/runtime/actors/routing/lb_algorithms.py @@ -1,30 +1,45 @@ """algorithms to simulate the load balancer during the simulation""" +from collections import OrderedDict +from collections.abc import Callable - +from asyncflow.config.constants import LbAlgorithmsName from asyncflow.runtime.actors.edge import EdgeRuntime -def least_connections(list_edges: list[EdgeRuntime]) -> EdgeRuntime: - """We send the state to the edge with less concurrent connections""" - concurrent_connections = [edge.concurrent_connections for edge in list_edges] - - idx_min = concurrent_connections.index(min(concurrent_connections)) - - return list_edges[idx_min] - -def round_robin(edges: list[EdgeRuntime], idx: int) -> tuple[EdgeRuntime, int]: +def least_connections( + edges: OrderedDict[str, EdgeRuntime], + ) -> EdgeRuntime: + """Return the edge with the fewest concurrent connections""" + # Here we use a O(n) operation, considering the amount of edges + # for the average simulation it should be ok, however, in the + # future we might consider to implement an heap structure to + # reduce the time complexity, especially if we will see + # during the Montecarlo analysis not good performances + name = min(edges, key=lambda k: edges[k].concurrent_connections) + return edges[name] + +def round_robin( + edges: OrderedDict[str, EdgeRuntime], + ) -> EdgeRuntime: """ We send states to different server in uniform way by - rotating the list of edges that should transport the state - to the correct server, we rotate the index and not the list - to avoid aliasing since the list is shared by many components + rotating the ordered dict, given the pydantic validation + we don't have to manage the edge case where the dict + is empty """ - idx %= len(edges) - chosen = edges[idx] - idx = (idx + 1) % len(edges) - return chosen, idx + # we use iter next creating all time a new iterator + # to be sure that we return always the first element + key, value = next(iter(edges.items())) + edges.move_to_end(key) + + return value +LB_TABLE: dict[LbAlgorithmsName, + Callable[[OrderedDict[str, EdgeRuntime]], EdgeRuntime]] = { + LbAlgorithmsName.LEAST_CONNECTIONS: least_connections, + LbAlgorithmsName.ROUND_ROBIN: round_robin, +} diff --git a/src/asyncflow/runtime/actors/server.py b/src/asyncflow/runtime/actors/server.py index 0572956..b1d204f 100644 --- a/src/asyncflow/runtime/actors/server.py +++ b/src/asyncflow/runtime/actors/server.py @@ -113,27 +113,34 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 # CPU & RAM SCHEDULING # # RAM FIRST, CPU LATER - # - The request reserves its full working set (total_ram) before - # acquiring any CPU core. If memory isn't available, it stays - # queued and leaves cores free for other requests. + # - The request reserves its full working set (total_ram) BEFORE + # trying to acquire a CPU core. If memory is not available, the + # request blocks on RAM and never enters the CPU/ready flow. # - # LAZY-CPU LOCK - # - A core token is acquired only at the FIRST CPU step - # (`if not core_locked`) and held for all consecutive CPU steps. - # - As soon as an I/O step is encountered, the core is released - # (`CPU.put(1)`) and remains free until the next CPU step, - # which will re-acquire it. + # LAZY CPU-LOCK (CPU bursts) + # - A CPU token is acquired only at the FIRST CPU step of a burst + # (`if not core_locked`). Consecutive CPU steps reuse the same token. + # - As soon as an I/O step is encountered, the token is released + # (`CPU.put(1)`) and remains free until the next CPU step. + # + # READY QUEUE (new definition) + # - The ready queue tracks ONLY requests waiting for a core. + # - Increment ready when `CPU.get(1)` is NOT immediately satisfied + # (`event.triggered == False`). + # - Decrement ready right after the `yield cpu_req` succeeds, + # i.e. when the core is actually granted. + # - A request currently executing on a core (`core_locked=True`) + # is NOT counted in ready. # # WHY THIS IS REALISTIC - # Prevents “core-hogging” during long I/O awaits. - # Avoids redundant get/put calls for consecutive CPU steps - # (one token for the entire sequence). - # Mirrors a real Python async server: the GIL/worker thread is - # held only during CPU-bound code and released on each await. + # - Prevents “core-hogging” during long I/O waits (the core is released). + # - Avoids redundant get/put calls on consecutive CPU steps. + # - Mirrors a real async server: a worker thread/GIL is held only + # during CPU-bound code and released on each `await` (I/O). # # END OF HANDLER - # - If we still hold the core at the end (`core_locked == True`), - # we put it back, then release the reserved RAM. + # - If the request still holds a core at the end, release it. + # - Then release the reserved RAM. # ------------------------------------------------------------------ # Ask the necessary ram to the server @@ -142,36 +149,49 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 self._ram_in_use += total_ram - # Initial conditions of the server a rqs a priori is not in any queue + # Initial conditions of the server: a rqs a priori is not in any queue # and it does not occupy a core until it started to be elaborated + # these are local variable so they are created for every request. core_locked = False is_in_io_queue = False - is_in_ready_queue = False + waiting_cpu = False - # --- Step Execution: Process CPU and IO operations --- - # EDGE CASE - # First-step I/O - # A request could (in theory) start with an I/O step. In that case - # it doesn't hold any core; we enter the - # `not core_locked and not is_in_io_queue` branch and add +1 - # to the I/O queue without touching the ready queue. + # --- Step Execution: CPU & I/O dynamics --- + # + # EDGE CASE: First-step I/O + # - An endpoint can start with an I/O step: in that case the request + # holds no core (`core_locked=False`) and enters the I/O queue. + # Ready queue is unaffected. This is not realistic since the first + # step is usually related to the parsing of the requests however + # we prevent this case, since right now we dont have a pydantic + # validation to ensure the first step is CPU bounderd # # Consecutive I/O steps - # The second I/O sees `is_in_io_queue == True`, so it does NOT - # increment again—no double counting. + # - The second (and later) I/O step sees is_in_io_queue=True, so it + # does NOT increment again → no double-counting in I/O. # - # Transition CPU → I/O → CPU - # - CPU step: `core_locked` becomes True, +1 ready queue - # - I/O step: core is put back, -1 ready queue, +1 I/O queue - # - Next CPU step: core is acquired, -1 I/O queue, +1 ready queue + # Transition CPU → I/O → CPU (with new ready semantics) + # - CPU step: + # * If no core is held, create cpu_req = CPU.get(1). + # * If cpu_req.triggered == False → the request is waiting → ready += 1. + # * After yield cpu_req (core granted) → ready -= 1, set core_locked=True. + # * A request currently executing on a core is NOT in ready. + # - I/O step: + # * If holding a core, release it (`CPU.put(1)`) and enter I/O queue (+1). + # * If already in I/O (consecutive step), do nothing (no double-counting). + # * Ready queue is never touched here. + # - Next CPU step: + # * Leave I/O queue (if counted) and repeat CPU acquisition logic. + # * If acquisition is not immediate, enter ready until the event resolves. # # Endpoint completion - # If `core_locked == True` we were in the ready queue (-1) - # Otherwise we were in the I/O queue (-1) - # In both cases we clear the local flags so no “ghost” entries - # remain in the global counters. - # ------------------------------------------------------------------ + # - If core_locked=True → release the core. + # - If is_in_io_queue=True → leave the I/O queue. + # - waiting_cpu should be False by now; if not, remove from ready defensively. + # - Invariant: the request must not remain counted in any queue once finished. + # ------------------------------------------------------------------- + for step in selected_endpoint.steps: @@ -183,19 +203,28 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 # in this configuration we are asking the cpu just in the # first one - if not core_locked: - core_locked = True + if is_in_io_queue: + is_in_io_queue = False + self._el_io_queue_len -= 1 - if is_in_io_queue: - is_in_io_queue = False - self._el_io_queue_len -= 1 + if not core_locked: + # simpy create an event and if it can be satisfied is triggered + cpu_req = self.server_resources[ServerResourceName.CPU.value].get(1) - if not is_in_ready_queue: - is_in_ready_queue = True + # no trigger ready queue without execution + if not cpu_req.triggered: + waiting_cpu = True self._el_ready_queue_len += 1 + # at this point wait for the cpu + yield cpu_req - yield self.server_resources[ServerResourceName.CPU.value].get(1) + # here the cpu is free + if waiting_cpu: + waiting_cpu = False + self._el_ready_queue_len -= 1 + + core_locked = True cpu_time = step.step_operation[StepOperation.CPU_TIME] # Execute the step giving back the control to the simpy env @@ -204,42 +233,36 @@ def _handle_request( # noqa: PLR0915, PLR0912, C901 # since the object is of an Enum class we check if the step.kind # is one member of enum elif step.kind in EndpointStepIO: + # define the io time io_time = step.step_operation[StepOperation.IO_WAITING_TIME] - # Same here with the boolean if we have multiple I/O steps - # we release the core just the first time if the previous step - # was a cpu bound step - - if not core_locked and not is_in_io_queue: - is_in_io_queue = True - self._el_io_queue_len += 1 - if core_locked: - # if the core is locked in the function it means that for sure - # we had a cpu bound step so the if statement will be always - # satisfy and we have to remove one element from the ready queue - - if is_in_ready_queue: - is_in_ready_queue = False - self._el_ready_queue_len -= 1 + # release the core coming from a cpu step + yield self.server_resources[ServerResourceName.CPU.value].put(1) + core_locked = False if not is_in_io_queue: is_in_io_queue = True self._el_io_queue_len += 1 + elif not is_in_io_queue: + is_in_io_queue = True + self._el_io_queue_len += 1 - yield self.server_resources[ServerResourceName.CPU.value].put(1) - core_locked = False - yield self.env.timeout(io_time) # Wait without holding a CPU core - + yield self.env.timeout(io_time) if core_locked: - is_in_ready_queue = False - self._el_ready_queue_len -= 1 yield self.server_resources[ServerResourceName.CPU.value].put(1) - else: + core_locked = False + + if is_in_io_queue: is_in_io_queue = False self._el_io_queue_len -= 1 + if waiting_cpu: + waiting_cpu = False + self._el_ready_queue_len -= 1 + + if total_ram: self._ram_in_use -= total_ram diff --git a/src/asyncflow/runtime/events/injection.py b/src/asyncflow/runtime/events/injection.py new file mode 100644 index 0000000..753c5d3 --- /dev/null +++ b/src/asyncflow/runtime/events/injection.py @@ -0,0 +1,254 @@ +""" +Centralized runtime object to inject events into the simulation. +This covers, for example, deterministic network latency spikes on edges and +scheduled server outages over a defined time window. +""" +from collections import OrderedDict +from collections.abc import Generator +from typing import cast + +import simpy + +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.schemas.events.injection import EventInjection +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.nodes import Server + +# Helpers to distinguish when the event start and when the event finish +START_MARK = "start" +END_MARK = "end" + +# definition of indexes for the tuple to be assigned for the timeline +TIME = 0 +EVENT_ID = 1 +TARGET_ID = 2 +START_END = 3 + +class EventInjectionRuntime: + """ + Runtime container responsible for applying event effects. + It ingests validated EventInjection objects plus the current topology + (edges and servers) and exposes the state needed to activate/deactivate + event effects during the simulation. + """ + + def __init__( + self, + *, + events: list[EventInjection] | None, + edges: list[Edge], + env: simpy.Environment, + servers: list[Server], + # This is initiated in the simulation runner to understand + # the process there are extensive comments in that file + lb_out_edges: OrderedDict[str, EdgeRuntime], + ) -> None: + """ + Definition of the attributes of the instance for + the object EventInjectionRuntime + + Args: + events (list[EventInjection]): input data of all events + edges (list[Edge]): input data for the edges + env (simpy.Environment): simpy env for the simulation + servers (list[Server]): input data of the server + lb_out_edges: OrderedDict[str, EdgeRuntime]: + ordered dict to handle server events + + """ + self.events = events + self.edges = edges + self.env = env + self.servers = servers + self.lb_out_edges = lb_out_edges + + # Nested mapping for edge spikes: + # edges_events: Dict[event_id, Dict[edge_id, float]] + # The outer key is the globally unique event_id. The inner mapping + # uses the target edge_id. Because multiple events can apply to the + # same edge, we use event_id as the primary key. The inner value + # stores the spike amplitude (in seconds) to apply while the event + # is active. It is necessary to compute superoposition of spike + # on the same target + + self._edges_events: dict[str, dict[str, float]] = {} + + # --------------------------------------------------------- + # THE FOLLOWING TWO INSTANCES ARE THE ONE NECESSARY TO ADD + # THE SPIKE DURING THE SIMULATION AND THEY WILL BE USED + # IN THE EDGERUNTIME CLASS + # --------------------------------------------------------- + + # definition of a dictionary that will be useful in the + # Edge runtime to track the cumulative spike for a given edge + # alone, we need this in combination with the above nested map + # becuase as we said since we allow the superposition of spike + # we need the nested map to calculate the correct cumulative + # spike, in this way in the edge runtime we will need just + # the information of the edge id to have the correct cumulative + # spike + # The idea is a nested map to calculate and a suitable dict + # to be imported in edge runtime that with just the information + # of the edge id is able to assign the correct delay even + # with superposition of spike + + self._edges_spike: dict[str, float] = {} + + # We need a set for a fast lookup to determine if a given edge + # identifid with its own id is affected by an event + + self._edges_affected: set[str] = set() + + # --------------------------------------------------------------- + + # Definition of timeline object, they represent a time + # ordered list of tuple that we will use to iterate to track + # and inject the events in the simulation + + self._edges_timeline: list[tuple[float, str, str, str]] = [] + self._servers_timeline: list[tuple[float, str, str, str]] = [] + + # No events we do not have to do any operation + if not self.events: + return + + # Set for a fast lookup to fill the nested map and + self._servers_ids = {server.id for server in self.servers} + self._edges_ids = {edge.id for edge in self.edges} + + for event in self.events: + start_event = ( + event.start.t_start, event.event_id, event.target_id, START_MARK, + ) + end_event = ( + event.end.t_end, event.event_id, event.target_id, END_MARK, + ) + + if event.target_id in self._edges_ids: + spike = event.start.spike_s + assert spike is not None + self._edges_events.setdefault( + event.event_id, + {})[event.target_id] = spike + + self._edges_timeline.append(start_event) + self._edges_timeline.append(end_event) + self._edges_affected.add(event.target_id) + elif event.target_id in self._servers_ids: + self._servers_timeline.append(start_event) + self._servers_timeline.append(end_event) + + # Order the two timeline with lambda functions + self._edges_timeline.sort( + key=lambda e: ( + e[TIME], e[START_END] == START_MARK, e[EVENT_ID], e[TARGET_ID], + ), + ) + self._servers_timeline.sort( + key=lambda e: ( + e[TIME], e[START_END] == START_MARK, e[EVENT_ID], e[TARGET_ID], + ), + ) + + # This function is useful to assign to connect the server id + # that will be down to the edge runtime that we have to remove + # from the ordered dict + + # Build reverse index: server_id -> (edge_id, EdgeRuntime) + self._edge_by_server: dict[str, tuple[str, EdgeRuntime]] = {} + + for edge_id, edge_runtime in lb_out_edges.items(): + # Each EdgeRuntime has an associated Edge config. + # The .edge_config.target corresponds to the server_id. + server_id = edge_runtime.edge_config.target + self._edge_by_server[server_id] = (edge_id, edge_runtime) + + + def _assign_edges_spike(self) -> Generator[simpy.Event, None, None]: + """ + Function to manage the assignment of the cumulative spikes + during the simulation. + The timeline contains absolute timestamps (seconds since t=0). + SimPy expects relative waits, so we advance by dt = t_event - last_t. + After waiting up to the event time, we apply the state change. + END comes before START at identical timestamps thanks to sorting. + """ + last_t: float = float(self.env.now) # usually 0.0 at start + + for event in self._edges_timeline: + # Explicit type for mypy + t: float = cast("float", event[TIME]) + event_id: str = cast("str", event[EVENT_ID]) + edge_id: str = cast("str", event[TARGET_ID]) + mark: str = cast("str", event[START_END]) + + dt: float = t - last_t + if dt > 0.0: + yield self.env.timeout(dt) + last_t = t + + # Apply the effect at the instant when the event start + if mark == START_MARK: + current = self._edges_spike.get(edge_id, 0.0) + delta = self._edges_events[event_id][edge_id] + self._edges_spike[edge_id] = current + delta + else: # END_MARK + current = self._edges_spike.get(edge_id, 0.0) + delta = self._edges_events[event_id][edge_id] + self._edges_spike[edge_id] = current - delta + + + def _assign_server_state(self) -> Generator[simpy.Event, None, None]: + last_t: float = float(self.env.now) + for ev in self._servers_timeline: + t = cast("float", ev[TIME]) + server_id = cast("str", ev[TARGET_ID]) + mark = cast("str", ev[START_END]) + + dt = t - last_t + if dt > 0.0: + yield self.env.timeout(dt) + last_t = t + + edge_info = self._edge_by_server.get(server_id) + if not edge_info: + continue + edge_id, edge_runtime = edge_info + + if mark == START_MARK: + # server DOWN: remove edge from the ordered dict + self.lb_out_edges.pop(edge_id, None) + else: + # server UP: put the edge server lb + # back in the ordered dict with the + # policy to move it at the end + self.lb_out_edges[edge_id] = edge_runtime + self.lb_out_edges.move_to_end(edge_id) + + + + + + def start(self) -> tuple[simpy.Process, simpy.Process]: + """Start both edge-spike and server-outage timelines.""" + p1 = self.env.process(self._assign_edges_spike()) + p2 = self.env.process(self._assign_server_state()) + return p1, p2 + + @property + def edges_spike(self) -> dict[str, float]: + """ + Expose the value of the private dict, this will be + used in the edge runtime to determine the current, + if exist, network spike + """ + return self._edges_spike + + @property + def edges_affected(self) -> set[str]: + """ + Expose the value of the private set, this will be + used in the edge runtime to determine the current, + if exist, network spike + """ + return self._edges_affected diff --git a/src/asyncflow/runtime/simulation_runner.py b/src/asyncflow/runtime/simulation_runner.py index 5d112ae..08ae0df 100644 --- a/src/asyncflow/runtime/simulation_runner.py +++ b/src/asyncflow/runtime/simulation_runner.py @@ -1,7 +1,11 @@ """Components to run the whole simulation given specific input data""" +from __future__ import annotations + +from collections import OrderedDict from itertools import chain from pathlib import Path +from types import MappingProxyType from typing import TYPE_CHECKING, Protocol, cast import numpy as np @@ -16,11 +20,13 @@ from asyncflow.runtime.actors.load_balancer import LoadBalancerRuntime from asyncflow.runtime.actors.rqs_generator import RqsGeneratorRuntime from asyncflow.runtime.actors.server import ServerRuntime +from asyncflow.runtime.events.injection import EventInjectionRuntime from asyncflow.schemas.payload import SimulationPayload if TYPE_CHECKING: from collections.abc import Iterable + from asyncflow.schemas.events.injection import EventInjection from asyncflow.schemas.topology.edges import Edge from asyncflow.schemas.topology.nodes import ( Client, @@ -63,18 +69,51 @@ def __init__( # instantiation of object needed to build nodes for the runtime phase self.servers: list[Server] = simulation_input.topology_graph.nodes.servers self.client: Client = simulation_input.topology_graph.nodes.client + self.events: list[EventInjection] | None = None self.rqs_generator: RqsGenerator = simulation_input.rqs_input self.lb: LoadBalancer | None = None self.simulation_settings = simulation_input.sim_settings self.edges: list[Edge] = simulation_input.topology_graph.edges self.rng = np.random.default_rng() - # Object needed to start the simualation + # Object needed to start the simulation self._servers_runtime: dict[str, ServerRuntime] = {} self._client_runtime: dict[str, ClientRuntime] = {} self._rqs_runtime: dict[str, RqsGeneratorRuntime] = {} - self._lb_runtime: dict[str, LoadBalancerRuntime] = {} + # right now we allow max one LB per simulation so we don't need a dict + self._lb_runtime: LoadBalancerRuntime | None = None self._edges_runtime: dict[tuple[str, str], EdgeRuntime] = {} + self._events_runtime: EventInjectionRuntime | None = None + + # Initialization of the OrderedDict used for event injection. + # This structure allows us to temporarily shut down servers by removing + # their edges from the load balancer during the simulation. The choice + # of OrderedDict is motivated by its mutability and O(1) operations for + # both removal (by key) and moving an element to the end. + # + # Advantages of this approach: + # 1) We allocate a single shared object in memory. + # 2) The same object is aliased in both LoadBalancerRuntime and + # EventInjectionRuntime, so updates are reflected dynamically. + # + # Workflow: + # - Instantiate the OrderedDict here. + # - Remove edges (LB→server connections) in EventInjectionRuntime + # when a server goes down. + # - Pass the same OrderedDict to LoadBalancerRuntime, which will + # apply its algorithm (RR, LeastConnections) only to the currently + # available edges. + # + # Notes: + # - Pydantic ensures that at least one server remains available, so the + # condition "all servers down" is not allowed. + # - Shutting down a server by cutting its edge reduces the number of + # SimPy processes to manage, because we skip transport to a down + # server entirely. + # - This also avoids extra conditions or policies on the server side to + # check whether the server is up or down. + + self._lb_out_edges: OrderedDict[str, EdgeRuntime] = OrderedDict() # ------------------------------------------------------------------ # @@ -144,7 +183,7 @@ def _build_load_balancer(self) -> None: """ Build given the input data the load balancer runtime we will use a dict because we may have multiple load balancer and we - will be usefull to assign outer edges + will be useful to assign outer edges """ # Topologies without a LB are perfectly legal (e.g. the “minimal” # integration test). Early-return instead of asserting. @@ -153,14 +192,16 @@ def _build_load_balancer(self) -> None: self.lb = self.simulation_input.topology_graph.nodes.load_balancer - self._lb_runtime[self.lb.id] = LoadBalancerRuntime( + self._lb_runtime = LoadBalancerRuntime( env=self.env, lb_config=self.lb, - out_edges= [], + lb_out_edges = self._lb_out_edges, lb_box=self._make_inbox(), ) + + def _build_edges(self) -> None: """Initialization of the edges runtime dictionary from the input data""" # We need to merge all previous dictionary for the nodes to assign @@ -168,9 +209,11 @@ def _build_edges(self) -> None: all_nodes: dict[str, object] = { **self._servers_runtime, **self._client_runtime, - **self._lb_runtime, **self._rqs_runtime, -} + } + + if self._lb_runtime is not None: + all_nodes[self._lb_runtime.lb_config.id] = self._lb_runtime for edge in self.edges: @@ -186,6 +229,7 @@ def _build_edges(self) -> None: msg = f"Unknown runtime for {edge.target!r}" raise TypeError(msg) + self._edges_runtime[(edge.source, edge.target)] = ( EdgeRuntime( env=self.env, @@ -208,18 +252,49 @@ def _build_edges(self) -> None: edge.target) ] elif isinstance(source_object, LoadBalancerRuntime): - assert source_object.out_edges is not None - source_object.out_edges.append(self._edges_runtime[( - edge.source, - edge.target, - ) - ]) - + self._lb_out_edges[edge.id] = ( + self._edges_runtime[(edge.source, edge.target)] + ) else: msg = f"Unknown runtime for {edge.source!r}" raise TypeError(msg) + + def _build_events(self) -> None: + """ + Function to centralize the events logic: with this function + we attach all events to the components affected + """ + if not self.simulation_input.events or self._events_runtime is not None: + return + + self.events = self.simulation_input.events + self._events_runtime = EventInjectionRuntime( + events=self.events, + edges=self.edges, + env=self.env, + servers=self.servers, + lb_out_edges=self._lb_out_edges, + ) + + # container only readable + edges_affected_view = self._events_runtime.edges_affected + + # only readable map + edges_spike_view = MappingProxyType(self._events_runtime.edges_spike) + + # We assign the two objects to all edges even though there are no + # events affecting them, this case is managed in the EdgeRuntime + # In the future we may control here if an edge is affected from an + # event this should have some advantage at the level of ram + + for edge in self._edges_runtime.values(): + edge.edges_affected = edges_affected_view + edge.edges_spike = edges_spike_view + + + # ------------------------------------------------------------------ # # RUN phase # # ------------------------------------------------------------------ # @@ -242,7 +317,7 @@ def _start_all_processes(self) -> None: self._rqs_runtime.values(), self._client_runtime.values(), self._servers_runtime.values(), - self._lb_runtime.values(), + ([] if self._lb_runtime is None else [self._lb_runtime]), ) # Here we are saying to mypy that those object are of @@ -250,6 +325,7 @@ def _start_all_processes(self) -> None: for rt in cast("Iterable[Startable]", runtimes): rt.start() + def _start_metric_collector(self) -> None: """One coroutine that snapshots RAM / queues / connections.""" SampledMetricCollector( @@ -259,6 +335,14 @@ def _start_metric_collector(self) -> None: sim_settings=self.simulation_settings, ).start() + + def _start_events(self) -> None: + """Function to start the process to build events""" + if self._events_runtime is not None: + self._events_runtime.start() + + + # ------------------------------------------------------------------ # # Public entry-point # # ------------------------------------------------------------------ # @@ -273,7 +357,11 @@ def run(self) -> ResultsAnalyzer: # 2. WIRE self._build_edges() + # 3 ATTACH EVENTS TO THE COMPONENTS + self._build_events() + # 3. START ALL COROUTINES + self._start_events() self._start_all_processes() self._start_metric_collector() @@ -296,7 +384,7 @@ def from_yaml( *, env: simpy.Environment, yaml_path: str | Path, - ) -> "SimulationRunner": + ) -> SimulationRunner: """ Quick helper so that integration tests & CLI can do: diff --git a/src/asyncflow/schemas/event/injection.py b/src/asyncflow/schemas/events/injection.py similarity index 100% rename from src/asyncflow/schemas/event/injection.py rename to src/asyncflow/schemas/events/injection.py diff --git a/src/asyncflow/schemas/payload.py b/src/asyncflow/schemas/payload.py index 4b593b0..cd5cf7d 100644 --- a/src/asyncflow/schemas/payload.py +++ b/src/asyncflow/schemas/payload.py @@ -3,7 +3,7 @@ from pydantic import BaseModel, field_validator, model_validator from asyncflow.config.constants import EventDescription -from asyncflow.schemas.event.injection import EventInjection +from asyncflow.schemas.events.injection import EventInjection from asyncflow.schemas.settings.simulation import SimulationSettings from asyncflow.schemas.topology.graph import TopologyGraph from asyncflow.schemas.workload.rqs_generator import RqsGenerator @@ -200,3 +200,55 @@ def ensure_not_all_servers_are_down_simultaneously( return model + @model_validator(mode="after") # type: ignore[arg-type] + def forbid_overlapping_server_outages( + cls, # noqa: N805 + model: "SimulationPayload", + ) -> "SimulationPayload": + """ + Forbid overlapping SERVER_DOWN intervals targeting the same server. + + Rationale: + - Keeps runtime simple (no reference counting). + - Allows back-to-back windows (END at t and START at t) thanks to sorting + END before START at the same timestamp. + """ + events = model.events + if not events: + return model + + servers_ids = {s.id for s in model.topology_graph.nodes.servers} + + # Build per-server timelines with (time, kind) marks only for server outages + per_server: dict[str, list[tuple[float, str]]] = {} + for ev in events: + if ( + ev.target_id in servers_ids + and ev.start.kind == EventDescription.SERVER_DOWN + ): + per_server.setdefault( + ev.target_id, []).append((ev.start.t_start, "start"), + ) + per_server[ev.target_id].append((ev.end.t_end, "end")) + + # Sweep-line per server: sort by (time, END first), ensure active<=1 + for srv_id, timeline in per_server.items(): + if not timeline: + continue + # END before START at same t + timeline.sort(key=lambda x: (x[0], x[1] == "start")) + active = 0 + for t, mark in timeline: + if mark == "end": + if active > 0: + active -= 1 + else: # START + if active >= 1: + msg = (f"Overlapping events for server '{srv_id}' at t={t:.6f};" + " server outage windows must not overlap.") + raise ValueError(msg) + active += 1 + + return model + + diff --git a/src/asyncflow/schemas/topology/nodes.py b/src/asyncflow/schemas/topology/nodes.py index d742421..5ada69b 100644 --- a/src/asyncflow/schemas/topology/nodes.py +++ b/src/asyncflow/schemas/topology/nodes.py @@ -141,6 +141,8 @@ class TopologyNodes(BaseModel): servers: list[Server] client: Client + # Right now we accept just one LB, in the future we + # will change this load_balancer: LoadBalancer | None = None @model_validator(mode="after") # type: ignore[arg-type] diff --git a/tests/conftest.py b/tests/conftest.py index b0fd5de..6834764 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,7 @@ """Pytest configuration file for setting up test fixtures and plugins.""" - import pytest +import simpy from numpy.random import Generator as NpGenerator from numpy.random import default_rng @@ -152,3 +152,11 @@ def payload_base( sim_settings=sim_settings, ) + +# --------------------------------------------------------------------------- # +# Simpy env # +# --------------------------------------------------------------------------- # +@pytest.fixture +def env() -> simpy.Environment: + """Return a fresh SimPy environment per test.""" + return simpy.Environment() diff --git a/tests/integration/event_injection/lb_two_servers.py b/tests/integration/event_injection/lb_two_servers.py new file mode 100644 index 0000000..4272719 --- /dev/null +++ b/tests/integration/event_injection/lb_two_servers.py @@ -0,0 +1,113 @@ +"""Integration test: LB with two servers and concurrent event injections. + +Topology: + + rqs-1 → client-1 → lb-1 → {srv-1, srv-2} + srv-* → client-1 + +Events: +- NETWORK_SPIKE on 'client-to-lb' in [0.20, 0.35]. +- SERVER_DOWN/UP on 'srv-1' in [0.40, 0.55]. + +Assertions: +- Simulation completes. +- Latency stats and throughput exist. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import simpy + +from asyncflow.config.constants import Distribution, EventDescription, LatencyKey +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.events.injection import EventInjection +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( + Client, + LoadBalancer, + Server, + ServerResources, + TopologyNodes, +) +from asyncflow.schemas.workload.rqs_generator import RqsGenerator + +if TYPE_CHECKING: + from asyncflow.metrics.analyzer import ResultsAnalyzer + + +def _server(sid: str) -> Server: + return Server(id=sid, server_resources=ServerResources(), endpoints=[]) + + +def _edge(eid: str, src: str, tgt: str, mean: float = 0.002) -> Edge: + return Edge( + id=eid, + source=src, + target=tgt, + latency=RVConfig(mean=mean, distribution=Distribution.POISSON), + ) + + +def test_lb_two_servers_with_events_end_to_end() -> None: + """Round-robin LB with events; check that KPIs are produced.""" + env = simpy.Environment() + rqs = RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=1.0), + avg_request_per_minute_per_user=RVConfig(mean=2.0), + user_sampling_window=10.0, + ) + sim = SimulationSettings(total_simulation_time=0.8) + + client = Client(id="client-1") + lb = LoadBalancer(id="lb-1") + srv1 = _server("srv-1") + srv2 = _server("srv-2") + + edges = [ + _edge("gen-to-client", "rqs-1", "client-1"), + _edge("client-to-lb", "client-1", "lb-1"), + _edge("lb-to-srv1", "lb-1", "srv-1"), + _edge("lb-to-srv2", "lb-1", "srv-2"), + _edge("srv1-to-client", "srv-1", "client-1"), + _edge("srv2-to-client", "srv-2", "client-1"), + ] + nodes = TopologyNodes(servers=[srv1, srv2], client=client, load_balancer=lb) + topo = TopologyGraph(nodes=nodes, edges=edges) + + events = [ + EventInjection( + event_id="spike", + target_id="client-to-lb", + start={ + "kind": EventDescription.NETWORK_SPIKE_START, + "t_start": 0.20, + "spike_s": 0.02, + }, + end={"kind": EventDescription.NETWORK_SPIKE_END, "t_end": 0.35}, + ), + EventInjection( + event_id="outage-srv1", + target_id="srv-1", + start={"kind": EventDescription.SERVER_DOWN, "t_start": 0.40}, + end={"kind": EventDescription.SERVER_UP, "t_end": 0.55}, + ), + ] + + payload = SimulationPayload(rqs_input=rqs, topology_graph=topo, sim_settings=sim) + payload.events = events + + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + + stats = results.get_latency_stats() + assert stats + assert stats[LatencyKey.TOTAL_REQUESTS] > 0 + ts, rps = results.get_throughput_series() + assert len(ts) == len(rps) > 0 diff --git a/tests/integration/event_injection/single_server.py b/tests/integration/event_injection/single_server.py new file mode 100644 index 0000000..1698305 --- /dev/null +++ b/tests/integration/event_injection/single_server.py @@ -0,0 +1,108 @@ +"""Integration test: single server with edge spike and server outage. + +Topology: + + rqs-1 → client-1 → lb-1 → srv-1 + srv-1 → client-1 + +Events: +- NETWORK_SPIKE on 'client-to-lb' during a small window. +- SERVER_DOWN/UP on 'srv-1' during a small window. + +Assertions focus on end-to-end KPIs; the fine-grained event sequencing is +covered by unit tests in the event injection suite. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import simpy + +from asyncflow.config.constants import Distribution, EventDescription, LatencyKey +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.events.injection import EventInjection +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( + Client, + LoadBalancer, + Server, + ServerResources, + TopologyNodes, +) +from asyncflow.schemas.workload.rqs_generator import RqsGenerator + +if TYPE_CHECKING: + from asyncflow.metrics.analyzer import ResultsAnalyzer + + +def _server(sid: str) -> Server: + return Server(id=sid, server_resources=ServerResources(), endpoints=[]) + + +def _edge(eid: str, src: str, tgt: str, mean: float = 0.002) -> Edge: + return Edge( + id=eid, + source=src, + target=tgt, + latency=RVConfig(mean=mean, distribution=Distribution.POISSON), + ) + + +def test_single_server_with_spike_and_outage_end_to_end() -> None: + """Run with both edge spike and server outage; verify KPIs exist.""" + env = simpy.Environment() + rqs = RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=1.0), + avg_request_per_minute_per_user=RVConfig(mean=2.0), + user_sampling_window=10.0, + ) + sim = SimulationSettings(total_simulation_time=1.0) + + client = Client(id="client-1") + lb = LoadBalancer(id="lb-1") + srv = _server("srv-1") + + edges = [ + _edge("gen-to-client", "rqs-1", "client-1"), + _edge("client-to-lb", "client-1", "lb-1"), + _edge("lb-to-srv1", "lb-1", "srv-1"), + _edge("srv1-to-client", "srv-1", "client-1"), + ] + nodes = TopologyNodes(servers=[srv], client=client, load_balancer=lb) + topo = TopologyGraph(nodes=nodes, edges=edges) + + # Events in a short (but disjoint) schedule to avoid cross-process ties + events = [ + EventInjection( + event_id="spike", + target_id="client-to-lb", + start={ + "kind": EventDescription.NETWORK_SPIKE_START, + "t_start": 0.2, + "spike_s": 0.01, + }, + end={"kind": EventDescription.NETWORK_SPIKE_END, "t_end": 0.4}, + ), + EventInjection( + event_id="outage", + target_id="srv-1", + start={"kind": EventDescription.SERVER_DOWN, "t_start": 0.5}, + end={"kind": EventDescription.SERVER_UP, "t_end": 0.7}, + ), + ] + + payload = SimulationPayload(rqs_input=rqs, topology_graph=topo, sim_settings=sim) + payload.events = events + + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + + stats = results.get_latency_stats() + assert stats + assert stats[LatencyKey.TOTAL_REQUESTS] > 0 diff --git a/tests/integration/load_balancer/test_lb_basic.py b/tests/integration/load_balancer/test_lb_basic.py new file mode 100644 index 0000000..293f5ef --- /dev/null +++ b/tests/integration/load_balancer/test_lb_basic.py @@ -0,0 +1,130 @@ +"""Integration test: one LB and two servers (round-robin by default). + +We build a minimal but functional topology: + + rqs-1 → client-1 → lb-1 → {srv-1, srv-2} + srv-* → client-1 + +Assertions: +- Simulation completes without error. +- Latency stats and throughput time-series are non-empty. +- Sampled metrics include edge/server series. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import simpy + +from asyncflow.config.constants import ( + Distribution, + EndpointStepCPU, + LatencyKey, + SampledMetricName, + StepOperation, +) +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.endpoint import ( + Endpoint, + Step, +) +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( + Client, + LoadBalancer, + Server, + ServerResources, + TopologyNodes, +) +from asyncflow.schemas.workload.rqs_generator import RqsGenerator + +if TYPE_CHECKING: + from asyncflow.metrics.analyzer import ResultsAnalyzer + + +def _server(server_id: str) -> Server: + """Minimal server with a single CPU-bound endpoint.""" + ep = Endpoint( + endpoint_name="get", + steps=[ + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_operation={StepOperation.CPU_TIME: 0.001}, + ), + ], + ) + return Server( + id=server_id, + server_resources=ServerResources(), # defaults are fine + endpoints=[ep], + ) + + +def _edge(eid: str, src: str, tgt: str, mean: float = 0.001) -> Edge: + """Low-latency edge to keep tests fast/deterministic enough.""" + return Edge( + id=eid, + source=src, + target=tgt, + latency=RVConfig(mean=mean, distribution=Distribution.POISSON), + ) + + +def test_lb_two_servers_end_to_end_smoke() -> None: + """Run end-to-end with LB and two servers; check basic KPIs exist.""" + env = simpy.Environment() + + # Stronger workload to avoid empty stats due to randomness: + # ~5 active users generating ~60 rpm each → ~5 rps expected. + rqs = RqsGenerator( + id="rqs-1", + avg_active_users=RVConfig(mean=5.0), + avg_request_per_minute_per_user=RVConfig(mean=60.0), + user_sampling_window=5.0, + ) + # Horizon must be >= 5 (schema), use a bit more to accumulate samples. + sim = SimulationSettings(total_simulation_time=8.0) + + # Topology: rqs→client→lb→srv{1,2} and back srv→client + client = Client(id="client-1") + lb = LoadBalancer(id="lb-1") + + srv1 = _server("srv-1") + srv2 = _server("srv-2") + + edges = [ + _edge("gen-to-client", "rqs-1", "client-1"), + _edge("client-to-lb", "client-1", "lb-1"), + _edge("lb-to-srv1", "lb-1", "srv-1"), + _edge("lb-to-srv2", "lb-1", "srv-2"), + _edge("srv1-to-client", "srv-1", "client-1"), + _edge("srv2-to-client", "srv-2", "client-1"), + ] + nodes = TopologyNodes(servers=[srv1, srv2], client=client, load_balancer=lb) + topo = TopologyGraph(nodes=nodes, edges=edges) + + payload = SimulationPayload(rqs_input=rqs, topology_graph=topo, sim_settings=sim) + + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + + # Assertions: latency and throughput are present + stats = results.get_latency_stats() + assert stats + assert stats[LatencyKey.TOTAL_REQUESTS] > 0 + assert stats[LatencyKey.MEAN] > 0.0 + + ts, rps = results.get_throughput_series() + assert len(ts) == len(rps) > 0 + assert any(val > 0 for val in rps) + + sampled = results.get_sampled_metrics() + assert SampledMetricName.RAM_IN_USE in sampled + assert sampled[SampledMetricName.RAM_IN_USE] + assert SampledMetricName.EDGE_CONCURRENT_CONNECTION in sampled + assert sampled[SampledMetricName.EDGE_CONCURRENT_CONNECTION] diff --git a/tests/integration/single_server/test_int_single_server.py b/tests/integration/single_server/test_int_single_server.py index 4d55310..efb1ef9 100644 --- a/tests/integration/single_server/test_int_single_server.py +++ b/tests/integration/single_server/test_int_single_server.py @@ -12,6 +12,7 @@ from typing import TYPE_CHECKING +import numpy as np import pytest from asyncflow.config.constants import LatencyKey, SampledMetricName @@ -26,7 +27,19 @@ # --------------------------------------------------------------------------- # @pytest.mark.integration def test_single_server_happy_path(runner: SimulationRunner) -> None: - """Run the simulation and ensure that *something* was processed.""" + """Run the simulation and ensure that *something* was processed. + + Make the test deterministic and sufficiently loaded so at least one request + is generated and measured. + """ + # Deterministic RNG for the whole runner + runner.rng = np.random.default_rng(0) + + # Increase horizon and load to avoid zero-request realizations + runner.simulation_settings.total_simulation_time = 30 + runner.rqs_generator.avg_active_users.mean = 5.0 + runner.rqs_generator.avg_request_per_minute_per_user.mean = 30.0 + results: ResultsAnalyzer = runner.run() # ── Latency stats must exist ─────────────────────────────────────────── @@ -43,10 +56,8 @@ def test_single_server_happy_path(runner: SimulationRunner) -> None: # ── Sampled metrics must include *one* server and *one* edge ─────────── sampled = results.get_sampled_metrics() - # Server RAM & queues assert SampledMetricName.RAM_IN_USE in sampled assert sampled[SampledMetricName.RAM_IN_USE], "Server RAM time-series missing." - # Edge concurrent-connection metric assert SampledMetricName.EDGE_CONCURRENT_CONNECTION in sampled - assert sampled[SampledMetricName.EDGE_CONCURRENT_CONNECTION], "Edge metric missing." + diff --git a/tests/system/test_sys_ev_inj_lb_two_servers.py b/tests/system/test_sys_ev_inj_lb_two_servers.py new file mode 100644 index 0000000..c9c4dd1 --- /dev/null +++ b/tests/system/test_sys_ev_inj_lb_two_servers.py @@ -0,0 +1,237 @@ +"""System test: client + LB + 2 servers with edge spike and server outage. + +Topology: + generator → client → lb-1 → {srv-1, srv-2} → client + +Endpoint on both servers: + CPU(1 ms) → RAM(64 MB) → IO(10 ms) + +Edges (baseline): + exponential latency ~ 2-3 ms per hop. + +Events injected: + - NETWORK_SPIKE on edge 'lb-srv-1': +50 ms, t ∈ [2.0, 12.0] s + - SERVER_DOWN on 'srv-2': t ∈ [5.0, 20.0] s + +Checks: +- mean latency with events > baseline by a safe margin; +- throughput stays > 30% of baseline (LB still routes to srv-1), + and not more than +5% above baseline; +- sampled metrics present for both servers. +""" + +from __future__ import annotations + +import os +import random +from typing import TYPE_CHECKING + +import numpy as np +import pytest +import simpy + +from asyncflow import AsyncFlow +from asyncflow.components import Client, Edge, Endpoint, LoadBalancer, Server +from asyncflow.config.constants import LatencyKey +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator + +if TYPE_CHECKING: + from asyncflow.metrics.analyzer import ResultsAnalyzer + from asyncflow.schemas.payload import SimulationPayload + +pytestmark = [ + pytest.mark.system, + pytest.mark.skipif( + os.getenv("ASYNCFLOW_RUN_SYSTEM_TESTS") != "1", + reason=( + "System tests disabled " + "(set ASYNCFLOW_RUN_SYSTEM_TESTS=1 to run)." + ), + ), +] + +SEED = 7778 +# LB re-routing and stochasticity can raise throughput +REL_TOL_TPUT_UPPER = 0.15 # allow up to +15% increase; +REL_TOL_TPUT_LOWER = 0.30 # must keep at least 30% throughput + + +def _seed_all(seed: int = SEED) -> None: + """Seed Python, NumPy, and hashing for reproducibility.""" + random.seed(seed) + np.random.seed(seed) # noqa: NPY002 + os.environ["PYTHONHASHSEED"] = str(seed) + + +def _build_payload(*, with_events: bool) -> SimulationPayload: + """Build payload for client + LB + two servers; optionally add events.""" + # Workload: ~26.7 rps (80 users * 20 rpm / 60). + gen = RqsGenerator( + id="rqs-1", + avg_active_users={"mean": 80}, + avg_request_per_minute_per_user={"mean": 20}, + user_sampling_window=60, + ) + client = Client(id="client-1") + lb = LoadBalancer(id="lb-1", algorithm="round_robin") + + ep = Endpoint( + endpoint_name="/api", + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, + {"kind": "ram", "step_operation": {"necessary_ram": 64}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ], + ) + + srv1 = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[ep], + ) + srv2 = Server( + id="srv-2", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[ep], + ) + + # Edges: generator→client, client→lb, lb→srv-{1,2}, srv-{1,2}→client. + edges = [ + Edge( + id="gen-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="client-lb", + source="client-1", + target="lb-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="lb-srv-1", + source="lb-1", + target="srv-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="lb-srv-2", + source="lb-1", + target="srv-2", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="srv1-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="srv2-client", + source="srv-2", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + ] + + settings = SimulationSettings( + total_simulation_time=100.0, # >= 5 s + sample_period_s=0.05, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + flow = ( + AsyncFlow() + .add_generator(gen) + .add_client(client) + .add_load_balancer(lb) + .add_servers(srv1, srv2) + .add_edges(*edges) + .add_simulation_settings(settings) + ) + + if with_events: + # Edge spike on lb→srv-1: +50 ms between 2s and 12s. + flow = flow.add_network_spike( + event_id="edge-spike-1", + edge_id="lb-srv-1", + t_start=2.0, + t_end=12.0, + spike_s=0.050, + ) + + # Server outage on srv-2 between 5s and 20s. + flow = flow.add_server_outage( + event_id="srv2-outage", + server_id="srv-2", + t_start=5.0, + t_end=20.0, + ) + + return flow.build_payload() + + +def _run(payload: SimulationPayload) -> ResultsAnalyzer: + """Run one simulation and return the analyzer.""" + env = simpy.Environment() + runner = SimulationRunner(env=env, simulation_input=payload) + return runner.run() + + +def test_lb_two_servers_spike_and_outage() -> None: + """LB keeps serving via srv-1; latency rises; throughput remains non-zero.""" + _seed_all(SEED) + + # Baseline + res_base = _run(_build_payload(with_events=False)) + stats_base = res_base.get_latency_stats() + assert stats_base, "Expected non-empty latency stats (baseline)." + mean_base = float(stats_base.get(LatencyKey.MEAN, 0.0)) + assert mean_base > 0.0 + + # With events (spike on lb→srv-1 and outage on srv-2) + _seed_all(SEED) + res_evt = _run(_build_payload(with_events=True)) + stats_evt = res_evt.get_latency_stats() + assert stats_evt, "Expected non-empty latency stats (events)." + mean_evt = float(stats_evt.get(LatencyKey.MEAN, 0.0)) + assert mean_evt > 0.0 + + # Expect a noticeable increase in mean latency with events. + # Spike is +50 ms for 10 s out of 40 s on half of LB routes ≈ few ms avg. + assert mean_evt >= mean_base + 0.003 + + # Throughput should remain within reasonable bounds: + # not zero (LB routes to srv-1), and not spuriously higher than baseline. + _, rps_base = res_base.get_throughput_series() + _, rps_evt = res_evt.get_throughput_series() + + assert rps_base, "No throughput series produced (baseline)." + assert rps_evt, "No throughput series produced (events)." + + rps_mean_base = float(np.mean(rps_base)) + rps_mean_evt = float(np.mean(rps_evt)) + denom = max(rps_mean_base, 1e-9) + + # Lower bound: at least 30% of baseline throughput. + assert (rps_mean_evt / denom) >= REL_TOL_TPUT_LOWER + # Upper bound: at most +5% above baseline. + assert (abs(rps_mean_evt - rps_mean_base) / denom) <= REL_TOL_TPUT_UPPER + + # Sampled metrics present for both servers. + sampled = res_evt.get_sampled_metrics() + for key in ("ready_queue_len", "event_loop_io_sleep", "ram_in_use"): + assert key in sampled + assert "srv-1" in sampled[key] + assert "srv-2" in sampled[key] + assert len(sampled[key]["srv-1"]) > 0 + assert len(sampled[key]["srv-2"]) > 0 diff --git a/tests/system/test_sys_ev_inj_single_server.py b/tests/system/test_sys_ev_inj_single_server.py new file mode 100644 index 0000000..e1132c3 --- /dev/null +++ b/tests/system/test_sys_ev_inj_single_server.py @@ -0,0 +1,199 @@ +"""System test: single server with a deterministic network spike on an edge. + +Topology: + generator → client → srv-1 → client + +Endpoint: + CPU(1 ms) → RAM(64 MB) → IO(10 ms) + +Edges (baseline): + exponential latency ~ 2-3 ms per hop. + +Event injected: + NETWORK_SPIKE on edge 'client-srv' adding +50 ms between t=[0.5, 2.5] s. + +Checks: +- mean latency with spike > mean latency without spike by a safe margin; +- throughput stays roughly similar (the spike increases latency, not λ); +- sampled metrics present. + +This test runs *two* short simulations (same seed): + (A) baseline (no events) + (B) with edge spike +Then compares their metrics. +""" + +from __future__ import annotations + +import os +import random +from typing import TYPE_CHECKING + +import numpy as np +import pytest +import simpy + +from asyncflow import AsyncFlow +from asyncflow.components import Client, Edge, Endpoint, Server +from asyncflow.config.constants import LatencyKey +from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator + +if TYPE_CHECKING: + from asyncflow.metrics.analyzer import ResultsAnalyzer + from asyncflow.schemas.payload import SimulationPayload + +pytestmark = [ + pytest.mark.system, + pytest.mark.skipif( + os.getenv("ASYNCFLOW_RUN_SYSTEM_TESTS") != "1", + reason=( + "System tests disabled " + "(set ASYNCFLOW_RUN_SYSTEM_TESTS=1 to run)." + ), + ), +] + +SEED = 4240 +REL_TOL_TPUT = 0.20 # throughput should be within ±20% + + +def _seed_all(seed: int = SEED) -> None: + """Seed Python, NumPy, and hashing for reproducibility.""" + random.seed(seed) + np.random.seed(seed) # noqa: NPY002 + os.environ["PYTHONHASHSEED"] = str(seed) + + +def _build_payload(*, with_spike: bool) -> SimulationPayload: + """Build a single-server payload; optionally inject an edge spike.""" + # Workload: ~26.7 rps (80 users * 20 rpm / 60). + gen = RqsGenerator( + id="rqs-1", + avg_active_users={"mean": 80}, + avg_request_per_minute_per_user={"mean": 20}, + user_sampling_window=60, + ) + client = Client(id="client-1") + + ep = Endpoint( + endpoint_name="/api", + steps=[ + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, + {"kind": "ram", "step_operation": {"necessary_ram": 64}}, + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.010}}, + ], + ) + srv = Server( + id="srv-1", + server_resources={"cpu_cores": 1, "ram_mb": 2048}, + endpoints=[ep], + ) + + # Edges: baseline exponential latencies around a few milliseconds. + edges = [ + Edge( + id="gen-client", + source="rqs-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + Edge( + id="client-srv", + source="client-1", + target="srv-1", + latency={"mean": 0.002, "distribution": "exponential"}, + ), + Edge( + id="srv-client", + source="srv-1", + target="client-1", + latency={"mean": 0.003, "distribution": "exponential"}, + ), + ] + + # Simulation horizon covers the whole spike window. + settings = SimulationSettings( + total_simulation_time=100.0, # >= 5 (schema lower bound) + sample_period_s=0.05, + enabled_sample_metrics=[ + "ready_queue_len", + "event_loop_io_sleep", + "ram_in_use", + "edge_concurrent_connection", + ], + enabled_event_metrics=["rqs_clock"], + ) + + flow = ( + AsyncFlow() + .add_generator(gen) + .add_client(client) + .add_servers(srv) + .add_edges(*edges) + .add_simulation_settings(settings) + ) + + if with_spike: + # Add +50 ms to client→server between t=[0.5, 2.5] seconds. + flow = flow.add_network_spike( + event_id="net-spike-1", + edge_id="client-srv", + t_start=0.5, + t_end=2.5, + spike_s=0.050, + ) + + return flow.build_payload() + + +def _run(payload: SimulationPayload) -> ResultsAnalyzer: + """Run one simulation and return the analyzer.""" + env = simpy.Environment() + runner = SimulationRunner(env=env, simulation_input=payload) + return runner.run() + + +def test_edge_latency_spike_increases_mean_latency() -> None: + """The injected edge spike must measurably increase mean latency.""" + _seed_all(SEED) + + # Baseline. + res_base = _run(_build_payload(with_spike=False)) + stats_base = res_base.get_latency_stats() + assert stats_base, "Expected non-empty latency stats (baseline)." + mean_base = float(stats_base.get(LatencyKey.MEAN, 0.0)) + assert mean_base > 0.0 + + # With spike. + _seed_all(SEED) # identical workload chronology, only edge differs + res_spike = _run(_build_payload(with_spike=True)) + stats_spike = res_spike.get_latency_stats() + assert stats_spike, "Expected non-empty latency stats (spike)." + mean_spike = float(stats_spike.get(LatencyKey.MEAN, 0.0)) + assert mean_spike > 0.0 + + # The spike window covers part of the horizon and adds +50 ms on + # the client→server hop; expect a noticeable average increase. + assert mean_spike >= mean_base * 1.02 + + # Throughput should remain roughly similar (spike adds latency, not λ). + _, rps_base = res_base.get_throughput_series() + _, rps_spike = res_spike.get_throughput_series() + + assert rps_base, "No throughput series produced (baseline)." + assert rps_spike, "No throughput series produced (spike)." + + rps_mean_base = float(np.mean(rps_base)) + rps_mean_spike = float(np.mean(rps_spike)) + denom = max(rps_mean_base, 1e-9) + rel_diff = abs(rps_mean_spike - rps_mean_base) / denom + assert rel_diff <= REL_TOL_TPUT + + # Basic sampled metrics should be present. + sampled = res_spike.get_sampled_metrics() + for key in ("ready_queue_len", "event_loop_io_sleep", "ram_in_use"): + assert key in sampled + assert "srv-1" in sampled[key] + assert len(sampled[key]["srv-1"]) > 0 diff --git a/tests/unit/runtime/actors/test_load_balancer.py b/tests/unit/runtime/actors/test_load_balancer.py index 1905543..94c372d 100644 --- a/tests/unit/runtime/actors/test_load_balancer.py +++ b/tests/unit/runtime/actors/test_load_balancer.py @@ -2,9 +2,9 @@ from __future__ import annotations +from collections import OrderedDict from typing import TYPE_CHECKING, cast -import pytest import simpy from asyncflow.config.constants import LbAlgorithmsName, SystemNodes @@ -15,7 +15,6 @@ from asyncflow.runtime.actors.edge import EdgeRuntime - # --------------------------------------------------------------------------- # # Dummy objects (lightweight test doubles) # # --------------------------------------------------------------------------- # @@ -23,7 +22,7 @@ class DummyState: """Tiny substitute for ``RequestState`` - only ``history`` is needed.""" def __init__(self) -> None: - """Instance of the state history""" + """Inizialization of the attributes""" self.history: list[str] = [] def record_hop(self, comp_type: SystemNodes, comp_id: str, _: float) -> None: @@ -35,27 +34,21 @@ class DummyEdge: """Stub that mimics just the pieces `LoadBalancerRuntime` relies on.""" def __init__(self, edge_id: str, concurrent: int = 0) -> None: - """Instance for the dummy edge""" + """Inizialization of the attributes""" self.edge_config = type("Cfg", (), {"id": edge_id}) self.concurrent_connections = concurrent self.received: list[DummyState] = [] # Signature compatible with EdgeRuntime.transport def transport(self, state: DummyState) -> None: - """Collect the state for later assertions.""" + """Function to simulate the transport""" self.received.append(state) # --------------------------------------------------------------------------- # -# Fixtures # +# Helpers # # --------------------------------------------------------------------------- # -@pytest.fixture -def env() -> simpy.Environment: - """Return a fresh SimPy environment per test.""" - return simpy.Environment() - - -def _make_lb_runtime( +def make_lb_runtime( env: simpy.Environment, algorithm: LbAlgorithmsName, edges: list[DummyEdge], @@ -64,26 +57,30 @@ def _make_lb_runtime( lb_cfg = LoadBalancer( id="lb-1", algorithms=algorithm, - server_covered={e.edge_config.id for e in edges}, # type: ignore[attr-defined] + server_covered={e.edge_config.id for e in edges}, # type: ignore[attr-defined] ) inbox: simpy.Store = simpy.Store(env) + + # Build the OrderedDict[id -> DummyEdge] + od: OrderedDict[str, DummyEdge] = OrderedDict((e.edge_config.id, e) for e in edges) # type: ignore[attr-defined] + lb = LoadBalancerRuntime( env=env, lb_config=lb_cfg, - # ② cast DummyEdge list to the expected interface type - out_edges=cast("list[EdgeRuntime]", edges), + lb_out_edges=cast("OrderedDict[str, EdgeRuntime]", od), lb_box=inbox, ) lb.start() return lb + # --------------------------------------------------------------------------- # # Tests # # --------------------------------------------------------------------------- # def test_round_robin_rotation(env: simpy.Environment) -> None: """Three requests, two edges ⇒ order must be edge-0, edge-1, edge-0.""" edge0, edge1 = DummyEdge("srv-A"), DummyEdge("srv-B") - lb = _make_lb_runtime(env, LbAlgorithmsName.ROUND_ROBIN, [edge0, edge1]) + lb = make_lb_runtime(env, LbAlgorithmsName.ROUND_ROBIN, [edge0, edge1]) for _ in range(3): lb.lb_box.put(DummyState()) @@ -103,7 +100,7 @@ def test_least_connections_picks_lowest(env: simpy.Environment) -> None: busy = DummyEdge("busy", concurrent=10) idle = DummyEdge("idle", concurrent=1) - lb = _make_lb_runtime(env, LbAlgorithmsName.LEAST_CONNECTIONS, [busy, idle]) + lb = make_lb_runtime(env, LbAlgorithmsName.LEAST_CONNECTIONS, [busy, idle]) lb.lb_box.put(DummyState()) env.run() @@ -112,20 +109,23 @@ def test_least_connections_picks_lowest(env: simpy.Environment) -> None: assert not busy.received -def test_start_raises_if_no_edges(env: simpy.Environment) -> None: - """`start()` followed by `env.run()` with `out_edges=None` must assert.""" +def test_no_edges_is_noop(env: simpy.Environment) -> None: + """ + With an empty mapping of lb_out_edges, starting the LB and running the env + should be a no-op (no assertions raised). + """ lb_cfg = LoadBalancer( - id="lb-bad", + id="lb-empty", algorithms=LbAlgorithmsName.ROUND_ROBIN, server_covered=set(), ) lb = LoadBalancerRuntime( env=env, lb_config=lb_cfg, - out_edges=None, + lb_out_edges=cast("OrderedDict[str, EdgeRuntime]", OrderedDict()), lb_box=simpy.Store(env), ) lb.start() - with pytest.raises(AssertionError): - env.run() + # No events in the env; this should simply return without error. + env.run() diff --git a/tests/unit/runtime/actors/test_server.py b/tests/unit/runtime/actors/test_server.py index 4c915ac..f5ff2ef 100644 --- a/tests/unit/runtime/actors/test_server.py +++ b/tests/unit/runtime/actors/test_server.py @@ -1,18 +1,18 @@ -"""Unit-tests for ServerRuntime concurrency, resource handling and metrics. +"""Unit tests for ServerRuntime concurrency, resources, and metrics. -Each test spins up an isolated SimPy environment containing: +Each test spins up an isolated SimPy environment with: * one ServerRuntime * one mock edge with zero-latency delivery (InstantEdge) * an inbox (simpy.Store) for incoming requests -* a sink (simpy.Store) that receives the request after the server +* a sink (simpy.Store) receiving the RequestState after the server -The server exposes: - RAM = 1024 MB, CPU cores = 2 -and a single endpoint with the step sequence: - RAM(128 MB) ➜ CPU(5 ms) ➜ I/O(20 ms). +Default server: + RAM = 1024 MB, CPU cores = 2 +Default endpoint: + RAM(128 MB) → CPU(5 ms) → I/O(20 ms) -All timings are in **seconds** because SimPy's clock is unit-agnostic. +All timings are in seconds (SimPy is unit-agnostic). """ from __future__ import annotations @@ -34,77 +34,92 @@ from asyncflow.runtime.rqs_state import RequestState from asyncflow.schemas.settings.simulation import SimulationSettings from asyncflow.schemas.topology.endpoint import Endpoint, Step -from asyncflow.schemas.topology.nodes import ( - Server, - ServerResources, -) +from asyncflow.schemas.topology.nodes import Server, ServerResources if TYPE_CHECKING: - - from collections.abc import Generator + from collections.abc import Generator, Iterable # ---------------------------------------------------------------------------# # Helpers # # ---------------------------------------------------------------------------# + + class InstantEdge: """Stub EdgeRuntime with zero latency and no drops.""" def __init__(self, env: simpy.Environment, sink: simpy.Store) -> None: - """Attribute""" + """Store environment and sink.""" self._env = env self._sink = sink def transport(self, state: RequestState) -> simpy.Process: - """Transport function""" + """Schedule the zero-latency delivery.""" return self._env.process(self._deliver(state)) - def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]: - """Deliver function""" + def _deliver( + self, state: RequestState, + ) -> Generator[simpy.Event, None, None]: + """Put the state into the sink immediately.""" yield self._sink.put(state) +def _mk_endpoint(steps: Iterable[Step]) -> Endpoint: + """Build a single endpoint with the provided steps.""" + return Endpoint(endpoint_name="/predict", steps=list(steps)) + + +def _default_steps() -> tuple[Step, Step, Step]: + """RAM → CPU → I/O default pipeline.""" + return ( + Step( + kind=EndpointStepRAM.RAM, + step_operation={StepOperation.NECESSARY_RAM: 128}, + ), + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_operation={StepOperation.CPU_TIME: 0.005}, + ), + Step( + kind=EndpointStepIO.DB, + step_operation={StepOperation.IO_WAITING_TIME: 0.020}, + ), + ) + + def _make_server_runtime( env: simpy.Environment, + *, + cpu_cores: int = 2, + ram_mb: int = 1024, + steps: Iterable[Step] | None = None, ) -> tuple[ServerRuntime, simpy.Store]: """Return a (ServerRuntime, sink) ready for injection tests.""" - # Resources - res_spec = ServerResources(cpu_cores=2, ram_mb=1024) + res_spec = ServerResources(cpu_cores=cpu_cores, ram_mb=ram_mb) containers = build_containers(env, res_spec) - # Endpoint: RAM → CPU → I/O - endpoint = Endpoint( - endpoint_name="/predict", - steps=[ - Step( - kind=EndpointStepRAM.RAM, - step_operation={StepOperation.NECESSARY_RAM: 128}, - ), - Step( - kind=EndpointStepCPU.CPU_BOUND_OPERATION, - step_operation={StepOperation.CPU_TIME: 0.005}, - ), - Step( - kind=EndpointStepIO.DB, - step_operation={StepOperation.IO_WAITING_TIME: 0.020}, - ), - ], + endpoint = _mk_endpoint(steps if steps is not None else _default_steps()) + server_cfg = Server( + id="api_srv", + endpoints=[endpoint], + server_resources=res_spec, ) - server_cfg = Server(id="api_srv", endpoints=[endpoint], server_resources=res_spec) - inbox: simpy.Store = simpy.Store(env) sink: simpy.Store = simpy.Store(env) edge = InstantEdge(env, sink) - settings = SimulationSettings(total_simulation_time=1900, sample_period_s=0.1) + settings = SimulationSettings( + total_simulation_time=60, + sample_period_s=0.01, + ) runtime = ServerRuntime( env=env, server_resources=containers, server_config=server_cfg, - out_edge=edge, # type: ignore[arg-type] + out_edge=edge, # type: ignore[arg-type] server_box=inbox, settings=settings, rng=default_rng(seed=0), @@ -113,8 +128,10 @@ def _make_server_runtime( # ---------------------------------------------------------------------------# -# Tests # +# Tests (ready queue = only requests waiting for a CPU core) # # ---------------------------------------------------------------------------# + + def test_ram_is_released_at_end() -> None: """RAM tokens must return to capacity once the request finishes.""" env = simpy.Environment() @@ -129,52 +146,217 @@ def test_ram_is_released_at_end() -> None: assert len(sink.items) == 1 -def test_cpu_core_held_only_during_cpu_step() -> None: - """Exactly one core is busy during the CPU-bound window (0 5ms).""" +def test_cpu_core_held_only_during_cpu_step_single_request() -> None: + """Single request with 2 cores holds a core only during CPU time.""" env = simpy.Environment() - server, _ = _make_server_runtime(env) + server, _ = _make_server_runtime(env, cpu_cores=2) cpu = server.server_resources["CPU"] server.server_box.put(RequestState(id=2, initial_time=0.0)) server.start() - env.run(until=0.004) # mid-CPU step - assert cpu.level == 1 # 2-1 + # Mid CPU step (5 ms total). + env.run(until=0.003) + # One core in use: level = 2 - 1 = 1 + assert cpu.level == 1 + # No ready-wait, acquisition was immediate. + assert server.ready_queue_len == 0 + assert server.io_queue_len == 0 - env.run(until=0.006) # after CPU step + # After CPU step, during I/O. + env.run(until=0.008) assert cpu.level == 2 # released + assert server.ready_queue_len == 0 + assert server.io_queue_len == 1 # now in I/O + # End. + env.run() + assert server.ready_queue_len == 0 + assert server.io_queue_len == 0 -def test_ready_and_io_queue_counters() -> None: - """ready_queue_len and io_queue_len should toggle as CPU⇄I/O phases alternate.""" + +def test_ready_increases_only_when_cpu_contention_exists() -> None: + """With 1 core and overlap, the second request waits in ready.""" env = simpy.Environment() - server, _ = _make_server_runtime(env) + server, _ = _make_server_runtime(env, cpu_cores=1) + + # First request at t=0.0 + server.server_box.put(RequestState(id=10, initial_time=0.0)) + # Second overlaps during the first CPU window. + server.server_box.put(RequestState(id=11, initial_time=0.001)) - server.server_box.put(RequestState(id=3, initial_time=0.0)) server.start() - # 1) before start queues are empty + # During first CPU, second should be in ready. + env.run(until=0.004) + assert server.ready_queue_len == 1 + + # After first CPU is done, second should start CPU → ready back to 0. + env.run(until=0.0065) assert server.ready_queue_len == 0 - assert server.io_queue_len == 0 - # 2) during CPU (0 5ms) ready queue+1 - env.run(until=0.003) - assert server.ready_queue_len == 1 + env.run() + assert server.ready_queue_len == 0 assert server.io_queue_len == 0 - # 3) during I/O (5 25ms) ready 0, io+1 - env.run(until=0.010) + +def test_consecutive_io_steps_do_not_double_count() -> None: + """Two consecutive I/O steps count as a single presence in I/O queue.""" + env = simpy.Environment() + + steps = ( + Step( + kind=EndpointStepRAM.RAM, + step_operation={StepOperation.NECESSARY_RAM: 64}, + ), + Step( + kind=EndpointStepIO.DB, + step_operation={StepOperation.IO_WAITING_TIME: 0.010}, + ), + # Use another valid I/O category (e.g., CACHE) to simulate consecutive I/O. + Step( + kind=EndpointStepIO.CACHE, + step_operation={StepOperation.IO_WAITING_TIME: 0.015}, + ), + ) + server, _ = _make_server_runtime(env, steps=steps) + + server.server_box.put(RequestState(id=20, initial_time=0.0)) + server.start() + + # During first I/O. + env.run(until=0.005) + assert server.io_queue_len == 1 + + # Still I/O during second consecutive I/O step; stays 1. + env.run(until=0.020) + assert server.io_queue_len == 1 + + env.run() + assert server.io_queue_len == 0 assert server.ready_queue_len == 0 + + +def test_first_step_io_enters_io_queue_without_touching_ready() -> None: + """First-step I/O enters I/O queue and leaves ready untouched.""" + env = simpy.Environment() + + steps = ( + Step( + kind=EndpointStepRAM.RAM, + step_operation={StepOperation.NECESSARY_RAM: 64}, + ), + # Valid I/O category for first-step I/O (e.g., WAIT). + Step( + kind=EndpointStepIO.WAIT, + step_operation={StepOperation.IO_WAITING_TIME: 0.010}, + ), + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_operation={StepOperation.CPU_TIME: 0.005}, + ), + ) + server, _ = _make_server_runtime(env, steps=steps, cpu_cores=1) + + server.server_box.put(RequestState(id=30, initial_time=0.0)) + server.start() + + # During first I/O window. + env.run(until=0.005) assert server.io_queue_len == 1 + assert server.ready_queue_len == 0 + + # When switching to CPU: with a single request, acquisition is immediate. + env.run(until=0.012) + assert server.ready_queue_len == 0 + + env.run() + assert server.io_queue_len == 0 + assert server.ready_queue_len == 0 + + +def test_cpu_burst_reuses_single_token_no_extra_ready() -> None: + """Consecutive CPU steps reuse the same token; no extra ready bumps.""" + env = simpy.Environment() + + steps = ( + Step( + kind=EndpointStepRAM.RAM, + step_operation={StepOperation.NECESSARY_RAM: 64}, + ), + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_operation={StepOperation.CPU_TIME: 0.004}, + ), + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_operation={StepOperation.CPU_TIME: 0.004}, + ), + ) + server, _ = _make_server_runtime(env, steps=steps, cpu_cores=1) + cpu = server.server_resources["CPU"] + + server.server_box.put(RequestState(id=40, initial_time=0.0)) + server.start() + + # During first CPU step. + env.run(until=0.002) + assert cpu.level == 0 # 1 core total, 1 in use + assert server.ready_queue_len == 0 + + # During second CPU step (same token). + env.run(until=0.006) + assert cpu.level == 0 + assert server.ready_queue_len == 0 + + env.run() + assert cpu.level == 1 + assert server.ready_queue_len == 0 + assert server.io_queue_len == 0 + + +def test_ram_gating_blocks_before_ready() -> None: + """When RAM is scarce, blocks on RAM and must NOT inflate ready.""" + env = simpy.Environment() + + # Respect ServerResources(min RAM = 256). + # Endpoint needs 256 MB → second request waits on RAM (not in ready). + steps = ( + Step( + kind=EndpointStepRAM.RAM, + step_operation={StepOperation.NECESSARY_RAM: 256}, + ), + Step( + kind=EndpointStepCPU.CPU_BOUND_OPERATION, + step_operation={StepOperation.CPU_TIME: 0.005}, + ), + Step( + kind=EndpointStepIO.DB, + step_operation={StepOperation.IO_WAITING_TIME: 0.020}, + ), + ) + server, _ = _make_server_runtime( + env, + cpu_cores=2, + ram_mb=256, + steps=steps, + ) + + server.server_box.put(RequestState(id=50, initial_time=0.0)) + server.server_box.put(RequestState(id=51, initial_time=0.0)) + server.start() + + # Shortly after start: first runs; second is blocked on RAM, not in ready. + env.run(until=0.002) + assert server.ready_queue_len == 0 - # 4) completed both back to 0 env.run() assert server.ready_queue_len == 0 assert server.io_queue_len == 0 def test_enabled_metrics_dict_populated() -> None: - """ServerRuntime must create lists for every mandatory sampled metric.""" + """ServerRuntime creates lists for every mandatory sampled metric.""" env = simpy.Environment() server, _ = _make_server_runtime(env) diff --git a/tests/unit/runtime/events/test_injection_edges.py b/tests/unit/runtime/events/test_injection_edges.py new file mode 100644 index 0000000..1bb76a6 --- /dev/null +++ b/tests/unit/runtime/events/test_injection_edges.py @@ -0,0 +1,296 @@ +"""Step-by-step tests for edge spike handling in EventInjectionRuntime.""" + +from __future__ import annotations + +from collections import OrderedDict +from typing import TYPE_CHECKING + +import pytest + +from asyncflow.config.constants import EventDescription +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.events.injection import ( + END_MARK, + START_MARK, + EventInjectionRuntime, +) +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.events.injection import EventInjection +from asyncflow.schemas.topology.edges import Edge + +if TYPE_CHECKING: + import simpy + + +# ----------------------------- Helpers ------------------------------------- # + +def _edge(edge_id: str, source: str, target: str) -> Edge: + """Minimal edge with negligible latency.""" + return Edge(id=edge_id, source=source, target=target, latency=RVConfig(mean=0.001)) + + +def _spike_event( + *, event_id: str, edge_id: str, t_start: float, t_end: float, spike_s: float, +) -> EventInjection: + """NETWORK_SPIKE event for a specific edge.""" + return EventInjection( + event_id=event_id, + target_id=edge_id, + start={ + "kind": EventDescription.NETWORK_SPIKE_START, + "t_start": t_start, + "spike_s": spike_s, + }, + end={"kind": EventDescription.NETWORK_SPIKE_END, "t_end": t_end}, + ) + + +def _drain_zero_time(env: simpy.Environment) -> None: + """Consume *all* events scheduled at the current time (typically t=0)""" + while env.peek() == env.now: + env.step() + +# ----------------------------- Tests (edge spike) --------------------------- # + +def test_single_spike_start_and_end_step_by_step(env: simpy.Environment) -> None: + """Single spike: +0.5 at t=1.0, gives 0.0 a t=3.0.""" + edges = [_edge("edge-1", "A", "B")] + ev = _spike_event( + event_id="ev1", edge_id="edge-1", t_start=1.0, t_end=3.0, spike_s=0.5, + ) + + rt = EventInjectionRuntime( + events=[ev], + edges=edges, + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + rt.start() + + # Drena tutti gli 'start events' a t=0 dei processi registrati + _drain_zero_time(env) + + # Ora il prossimo evento deve essere a 1.0 (START) + assert env.peek() == pytest.approx(1.0) + + # Step @1.0 → applica START(ev1) + env.step() + assert env.now == pytest.approx(1.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.5) + + # Prossimo evento a 3.0 (END) + assert env.peek() == pytest.approx(3.0) + + # Step @3.0 → applica END(ev1) + env.step() + assert env.now == pytest.approx(3.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.0) + + +def test_spike_superposition_on_same_edge(env: simpy.Environment) -> None: + """Due spike sovrapposti si sommano nell'intervallo comune.""" + edges = [_edge("edge-1", "A", "B")] + ev1 = _spike_event( + event_id="ev1", edge_id="edge-1", t_start=1.0, t_end=4.0, spike_s=0.3, + ) + ev2 = _spike_event( + event_id="ev2", edge_id="edge-1", t_start=2.0, t_end=3.0, spike_s=0.2, + ) + + rt = EventInjectionRuntime( + events=[ev1, ev2], + edges=edges, + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + rt.start() + + _drain_zero_time(env) # next should be 1.0 + env.step() # @1.0 START ev1 + assert env.now == pytest.approx(1.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.3) + + env.step() # @2.0 START ev2 + assert env.now == pytest.approx(2.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.5) + + env.step() # @3.0 END ev2 + assert env.now == pytest.approx(3.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.3) + + env.step() # @4.0 END ev1 + assert env.now == pytest.approx(4.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.0) + + +def test_end_before_start_at_same_timestamp(env: simpy.Environment) -> None: + """A t=5.0 devono avvenire END(evA) poi START(evB), spike finale 0.6.""" + edges = [_edge("edge-1", "X", "Y")] + ev_a = _spike_event( + event_id="evA", edge_id="edge-1", t_start=1.0, t_end=5.0, spike_s=0.4, + ) + ev_b = _spike_event( + event_id="evB", edge_id="edge-1", t_start=5.0, t_end=6.0, spike_s=0.6, + ) + + rt = EventInjectionRuntime( + events=[ev_a, ev_b], + edges=edges, + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + rt.start() + + _drain_zero_time(env) + env.step() + assert env.now == pytest.approx(1.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.4) + + env.step() + assert env.now == pytest.approx(5.0) + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.6) + + +def test_only_targeted_edges_are_marked_affected(env: simpy.Environment) -> None: + """Solo l'edge con evento è marcato e riceve spike.""" + edges = [_edge("edge-1", "A", "B"), _edge("edge-2", "A", "C")] + ev = _spike_event( + event_id="ev1", edge_id="edge-1", t_start=1.0, t_end=2.0, spike_s=0.4, + ) + + rt = EventInjectionRuntime( + events=[ev], + edges=edges, + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + rt.start() + + assert "edge-1" in rt.edges_affected + assert "edge-2" not in rt.edges_affected + + _drain_zero_time(env) # next 1.0 + env.step() # @1.0 START ev1 + assert rt.edges_spike.get("edge-1", 0.0) == pytest.approx(0.4) + assert rt.edges_spike.get("edge-2", 0.0) == pytest.approx(0.0) + + +def test_internal_timeline_order_at_same_time(env: simpy.Environment) -> None: + """Controllo diretto della timeline: a 5.0 → [END, START].""" + edges = [_edge("edge-1", "S", "T")] + ev_a = _spike_event( + event_id="a", edge_id="edge-1", t_start=1.0, t_end=5.0, spike_s=0.4, + ) + ev_b = _spike_event( + event_id="b", edge_id="edge-1", t_start=5.0, t_end=6.0, spike_s=0.6, + ) + + rt = EventInjectionRuntime( + events=[ev_a, ev_b], + edges=edges, + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + + times_at_5 = [tpl for tpl in rt._edges_timeline if tpl[0] == 5.0] # noqa: SLF001 + assert len(times_at_5) == 2 + marks_at_5 = [tpl[3] for tpl in times_at_5] + assert marks_at_5 == [END_MARK, START_MARK] + + +def test_no_events_is_noop(env: simpy.Environment) -> None: + """When events=None, the runtime should not alter any edge state.""" + edges = [_edge("e1", "A", "B")] + + inj = EventInjectionRuntime( + events=None, + edges=edges, + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + # Should start without scheduling edge changes. + inj.start() + + assert inj.edges_affected == set() + assert inj.edges_spike == {} + + +def test_end_then_multiple_starts_same_timestamp(env: simpy.Environment) -> None: + """At the same timestamp, END must be applied before multiple STARTs. + + Scenario on edge 'E': + ev1: +0.4 active [1, 5] + ev2: +0.3 active [5, 6] + ev3: +0.2 active [5, 7] + + At t=5.0: + - END(ev1) applies first (removes +0.4 → 0.0), + - then START(ev2) and START(ev3) (+0.3 + 0.2), + Final spike at t=5.0 is +0.5. + """ + e = _edge("E", "S", "T") + ev1 = _spike_event( + event_id="ev1", edge_id=e.id, t_start=1.0, t_end=5.0, spike_s=0.4, + ) + ev2 = _spike_event( + event_id="ev2", edge_id=e.id, t_start=5.0, t_end=6.0, spike_s=0.3, + ) + ev3 = _spike_event( + event_id="ev3", edge_id=e.id, t_start=5.0, t_end=7.0, spike_s=0.2, + ) + + inj = EventInjectionRuntime( + events=[ev1, ev2, ev3], + edges=[e], + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + inj.start() + + _drain_zero_time(env) + + # @1.0 → START ev1 → +0.4 + env.step() + assert env.now == pytest.approx(1.0) + assert inj.edges_spike[e.id] == pytest.approx(0.4) + + # @5.0 → END ev1, then START ev2 & START ev3 → 0.0 + 0.3 + 0.2 = 0.5 + env.step() + assert env.now == pytest.approx(5.0) + assert inj.edges_spike[e.id] == pytest.approx(0.5) + + +def test_zero_time_batch_draining_makes_first_event_visible( + env: simpy.Environment) -> None: + """After start(), draining zero-time events reveals the first real timestamp. + + Without draining, the next scheduled item may still be an activation at t=0. + After draining, the next event should be the edge spike START (e.g., 1.0s). + """ + e = _edge("E", "S", "T") + ev = _spike_event(event_id="ev", edge_id=e.id, t_start=1.0, t_end=2.0, spike_s=0.1) + + inj = EventInjectionRuntime( + events=[ev], + edges=[e], + env=env, + servers=[], + lb_out_edges=OrderedDict[str, EdgeRuntime](), + ) + inj.start() + + # Drain zero-time activations so the next event is 1.0s. + _drain_zero_time(env) + assert env.peek() == pytest.approx(1.0) + + # Step to 1.0s and confirm activation. + env.step() + assert env.now == pytest.approx(1.0) + assert inj.edges_spike[e.id] == pytest.approx(0.1) diff --git a/tests/unit/runtime/events/test_injection_servers.py b/tests/unit/runtime/events/test_injection_servers.py new file mode 100644 index 0000000..7347fb3 --- /dev/null +++ b/tests/unit/runtime/events/test_injection_servers.py @@ -0,0 +1,133 @@ +"""Server-outage tests for EventInjectionRuntime (using real `Server` model).""" + +from __future__ import annotations + +from collections import OrderedDict +from typing import TYPE_CHECKING + +import pytest +import simpy + +from asyncflow.config.constants import EventDescription +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.events.injection import EventInjectionRuntime +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.events.injection import EventInjection +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.nodes import Server, ServerResources + +if TYPE_CHECKING: + from asyncflow.schemas.settings.simulation import SimulationSettings + + +# --------------------------------------------------------------------------- # +# Helpers # +# --------------------------------------------------------------------------- # + +def _edge(edge_id: str, source: str, target: str) -> Edge: + """Create a minimal LB→server edge with negligible latency.""" + return Edge( + id=edge_id, + source=source, + target=target, + latency=RVConfig(mean=0.001), + ) + + +def _srv(server_id: str) -> Server: + """Create a minimal, fully-typed Server instance for tests.""" + return Server( + id=server_id, + server_resources=ServerResources(), # uses defaults + endpoints=[], # empty list is valid + ) + +def _srv_event( + server_id: str, ev_id: str, t_start: float, t_end: float) -> EventInjection: + """Create a SERVER_DOWN/UP event for the given server id.""" + return EventInjection( + event_id=ev_id, + target_id=server_id, + start={"kind": EventDescription.SERVER_DOWN, "t_start": t_start}, + end={"kind": EventDescription.SERVER_UP, "t_end": t_end}, + ) + + +def _drain_zero_time(env: simpy.Environment) -> None: + """Consume all events scheduled at the current time (typically t=0).""" + while env.peek() == env.now: + env.step() + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # + +def test_outage_removes_and_restores_edge_order( + env: simpy.Environment, sim_settings: SimulationSettings, +) -> None: + """DOWN removes the LB→server edge; reinserts it at the end (OrderedDict policy)""" + # Two distinct LB→server edges + lb_e1 = _edge("lb-e1", "lb-1", "srv-1") + lb_e2 = _edge("lb-e2", "lb-1", "srv-2") + + er1 = EdgeRuntime( + env=env, edge_config=lb_e1, target_box=simpy.Store(env), settings=sim_settings, + ) + er2 = EdgeRuntime( + env=env, edge_config=lb_e2, target_box=simpy.Store(env), settings=sim_settings, + ) + + lb_out = OrderedDict[str, EdgeRuntime]([("lb-e1", er1), ("lb-e2", er2)]) + + outage = _srv_event("srv-1", "ev-out", 5.0, 7.0) + servers = [_srv("srv-1"), _srv("srv-2")] + + inj = EventInjectionRuntime( + events=[outage], edges=[], env=env, servers=servers, lb_out_edges=lb_out, + ) + inj.start() + + _drain_zero_time(env) + assert list(lb_out.keys()) == ["lb-e1", "lb-e2"] + + # @5.0 → remove lb-e1 + env.step() + assert env.now == pytest.approx(5.0) + assert list(lb_out.keys()) == ["lb-e2"] + assert "lb-e1" not in lb_out + + # @7.0 → reinsert lb-e1 at the end + env.step() + assert env.now == pytest.approx(7.0) + assert list(lb_out.keys()) == ["lb-e2", "lb-e1"] + assert lb_out["lb-e1"] is er1 + + +def test_outage_for_server_not_in_lb_is_noop( + env: simpy.Environment, sim_settings: SimulationSettings, +) -> None: + """DOWN/UP for a server with no LB edges should not change the LB mapping.""" + lb_e2 = _edge("lb-e2", "lb-1", "srv-2") + er2 = EdgeRuntime( + env=env, edge_config=lb_e2, target_box=simpy.Store(env), settings=sim_settings) + lb_out = OrderedDict[str, EdgeRuntime]([("lb-e2", er2)]) + + outage = _srv_event("srv-3", "ev-out", 5.0, 6.0) # srv-3 not in LB + inj = EventInjectionRuntime( + events=[outage], + edges=[], + env=env, + servers=[_srv("srv-2"), _srv("srv-3")], + lb_out_edges=lb_out, + ) + inj.start() + + _drain_zero_time(env) + assert list(lb_out.keys()) == ["lb-e2"] + + env.step() # @5.0 + assert list(lb_out.keys()) == ["lb-e2"] + + env.step() # @6.0 + assert list(lb_out.keys()) == ["lb-e2"] diff --git a/tests/unit/runtime/events/test_injection_servers_edges.py b/tests/unit/runtime/events/test_injection_servers_edges.py new file mode 100644 index 0000000..966a9f5 --- /dev/null +++ b/tests/unit/runtime/events/test_injection_servers_edges.py @@ -0,0 +1,237 @@ +"""Mixed timeline tests: edge spikes and server outages in the same run.""" + +from __future__ import annotations + +from collections import OrderedDict +from typing import TYPE_CHECKING + +import pytest +import simpy + +from asyncflow.config.constants import EventDescription +from asyncflow.runtime.actors.edge import EdgeRuntime +from asyncflow.runtime.events.injection import EventInjectionRuntime +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.events.injection import EventInjection +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.nodes import Server, ServerResources + +if TYPE_CHECKING: + from asyncflow.schemas.settings.simulation import SimulationSettings + + +# --------------------------------------------------------------------------- # +# Helpers # +# --------------------------------------------------------------------------- # + +def _edge(edge_id: str, source: str, target: str) -> Edge: + """Create a minimal edge with negligible latency.""" + return Edge(id=edge_id, source=source, target=target, latency=RVConfig(mean=0.001)) + + +def _srv(server_id: str) -> Server: + """Create a minimal, fully-typed Server instance for tests.""" + return Server(id=server_id, server_resources=ServerResources(), endpoints=[]) + + +def _spike_event( + *, ev_id: str, + edge_id: str, + t0: float, + t1: float, + spike_s: float, + ) -> EventInjection: + """Build a NETWORK_SPIKE_START → NETWORK_SPIKE_END event for an edge.""" + return EventInjection( + event_id=ev_id, + target_id=edge_id, + start={ + "kind": EventDescription.NETWORK_SPIKE_START, + "t_start": t0, + "spike_s": spike_s, + }, + end={"kind": EventDescription.NETWORK_SPIKE_END, "t_end": t1}, + ) + + +def _srv_event(server_id: str, ev_id: str, t0: float, t1: float) -> EventInjection: + """Build a SERVER_DOWN → SERVER_UP event for a server.""" + return EventInjection( + event_id=ev_id, + target_id=server_id, + start={"kind": EventDescription.SERVER_DOWN, "t_start": t0}, + end={"kind": EventDescription.SERVER_UP, "t_end": t1}, + ) + + +def _drain_zero_time(env: simpy.Environment) -> None: + """Consume all events at the current time (typically t=0).""" + while env.peek() == env.now: + env.step() + + +# --------------------------------------------------------------------------- # +# Tests # +# --------------------------------------------------------------------------- # + +def test_edge_spike_and_server_outage_independent_timelines( + env: simpy.Environment, sim_settings: SimulationSettings, +) -> None: + """Edge spikes evolve independently from server outages on LB edges.""" + # Topology pieces: + net_e = _edge("net-1", "X", "Y") # edge that will receive the spike + lb_e1 = _edge("lb-e1", "lb-1", "srv-1") + lb_e2 = _edge("lb-e2", "lb-1", "srv-2") + + # Edge runtimes only for LB edges (spike is handled by injection runtime state) + er1 = EdgeRuntime( + env=env, + edge_config=lb_e1, + target_box=simpy.Store(env), + settings=sim_settings, + ) + er2 = EdgeRuntime( + env=env, + edge_config=lb_e2, + target_box=simpy.Store(env), + settings=sim_settings, + ) + lb_out = OrderedDict[str, EdgeRuntime]([("lb-e1", er1), ("lb-e2", er2)]) + + # Events: + # - Server outage on srv-1: [1.0, 3.0] → lb-e1 removed at 1.0, reinserted at 3.0. + # - Edge spike on net-1: [2.0, 4.0] → +0.3 during [2.0, 4.0]. + ev_srv = _srv_event("srv-1", "out-1", 1.0, 3.0) + ev_spk = _spike_event( + ev_id="spk-1", edge_id="net-1", t0=2.0, t1=4.0, spike_s=0.3, + ) + + inj = EventInjectionRuntime( + events=[ev_srv, ev_spk], + edges=[net_e], + env=env, + servers=[_srv("srv-1"), _srv("srv-2")], + lb_out_edges=lb_out, + ) + inj.start() + + _drain_zero_time(env) + assert list(lb_out.keys()) == ["lb-e1", "lb-e2"] + assert inj.edges_spike.get("net-1", 0.0) == pytest.approx(0.0) + + # @1.0 → server DOWN (srv-1) → remove lb-e1 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(1.0) + assert list(lb_out.keys()) == ["lb-e2"] + assert "lb-e1" not in lb_out + assert inj.edges_spike.get("net-1", 0.0) == pytest.approx(0.0) + + # @2.0 → spike START on net-1 → +0.3 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(2.0) + assert inj.edges_spike.get("net-1", 0.0) == pytest.approx(0.3) + assert list(lb_out.keys()) == ["lb-e2"] # still down for srv-1 + + # @3.0 → server UP (srv-1) → reinsert lb-e1 at the end + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(3.0) + assert list(lb_out.keys()) == ["lb-e2", "lb-e1"] + assert inj.edges_spike.get("net-1", 0.0) == pytest.approx(0.3) + + # @4.0 → spike END on net-1 → 0.0 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(4.0) + assert inj.edges_spike.get("net-1", 0.0) == pytest.approx(0.0) + + +def test_interleaved_multiple_spikes_with_single_outage( + env: simpy.Environment, sim_settings: SimulationSettings, +) -> None: + """Multiple spikes can interleave with a single outage on a different component.""" + # Components: + net_e = _edge("net-2", "S", "T") + lb_e1 = _edge("lb-e1", "lb-1", "srv-1") + lb_e2 = _edge("lb-e2", "lb-1", "srv-2") + + er1 = EdgeRuntime( + env=env, + edge_config=lb_e1, + target_box=simpy.Store(env), + settings=sim_settings, + ) + er2 = EdgeRuntime( + env=env, + edge_config=lb_e2, + target_box=simpy.Store(env), + settings=sim_settings, + ) + lb_out = OrderedDict[str, EdgeRuntime]([("lb-e1", er1), ("lb-e2", er2)]) + + # Events timeline no equal timestamps across server/edge to + # avoid cross-process order assumptions): + # 1.0 server DOWN (srv-1) → remove lb-e1 + # 2.0 spike A START on net-2 (+0.2) → +0.2 + # 3.0 server UP (srv-1) → reinsert lb-e1 at the end + # 4.0 spike B START on net-2 (+0.1) → +0.3 + # 5.0 spike A END on net-2 → +0.1 + # 6.0 spike B END on net-2 → +0.0 + ev_down = _srv_event("srv-1", "out-1", 1.0, 3.0) + spk_a = _spike_event(ev_id="spk-A", edge_id="net-2", t0=2.0, t1=5.0, spike_s=0.2) + spk_b = _spike_event(ev_id="spk-B", edge_id="net-2", t0=4.0, t1=6.0, spike_s=0.1) + + inj = EventInjectionRuntime( + events=[ev_down, spk_a, spk_b], + edges=[net_e], + env=env, + servers=[_srv("srv-1"), _srv("srv-2")], + lb_out_edges=lb_out, + ) + inj.start() + + _drain_zero_time(env) + assert list(lb_out.keys()) == ["lb-e1", "lb-e2"] + assert inj.edges_spike.get("net-2", 0.0) == pytest.approx(0.0) + + # @1.0 server DOWN → remove lb-e1 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(1.0) + assert list(lb_out.keys()) == ["lb-e2"] + assert inj.edges_spike.get("net-2", 0.0) == pytest.approx(0.0) + + # @2.0 spike A START → +0.2 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(2.0) + assert inj.edges_spike.get("net-2", 0.0) == pytest.approx(0.2) + assert list(lb_out.keys()) == ["lb-e2"] + + # @3.0 server UP → reinsert lb-e1 at the end + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(3.0) + assert list(lb_out.keys()) == ["lb-e2", "lb-e1"] + assert inj.edges_spike.get("net-2", 0.0) == pytest.approx(0.2) + + # @4.0 spike B START → +0.3 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(4.0) + assert inj.edges_spike.get("net-2", 0.0) == pytest.approx(0.3) + + # @5.0 spike A END → +0.1 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(5.0) + assert inj.edges_spike.get("net-2", 0.0) == pytest.approx(0.1) + + # @6.0 spike B END → +0.0 + env.step() + _drain_zero_time(env) + assert env.now == pytest.approx(6.0) + assert inj.edges_spike.get("net-2", 0.0) == pytest.approx(0.0) + diff --git a/tests/unit/runtime/test_simulation_runner.py b/tests/unit/runtime/test_simulation_runner.py index 3a352f5..9ec9299 100644 --- a/tests/unit/runtime/test_simulation_runner.py +++ b/tests/unit/runtime/test_simulation_runner.py @@ -14,14 +14,30 @@ import simpy import yaml +from asyncflow.config.constants import Distribution, EventDescription from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.schemas.common.random_variables import RVConfig +from asyncflow.schemas.events.injection import EventInjection +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.edges import Edge +from asyncflow.schemas.topology.graph import TopologyGraph +from asyncflow.schemas.topology.nodes import ( + Client, + LoadBalancer, + Server, + ServerResources, + TopologyNodes, +) if TYPE_CHECKING: from pathlib import Path from asyncflow.runtime.actors.client import ClientRuntime from asyncflow.runtime.actors.rqs_generator import RqsGeneratorRuntime - from asyncflow.schemas.payload import SimulationPayload + from asyncflow.schemas.settings.simulation import SimulationSettings + from asyncflow.schemas.workload.rqs_generator import RqsGenerator + # --------------------------------------------------------------------------- # @@ -43,7 +59,7 @@ def runner( # --------------------------------------------------------------------------- # -# Builder-level tests # +# Builder-level tests (original) # # --------------------------------------------------------------------------- # def test_build_rqs_generator_populates_dict(runner: SimulationRunner) -> None: """_build_rqs_generator() must register one generator runtime.""" @@ -77,13 +93,13 @@ def test_build_servers_keeps_empty_with_minimal_topology( def test_build_load_balancer_noop_when_absent( runner: SimulationRunner, ) -> None: - """No LB in the payload → builder leaves dict empty.""" + """No LB in the payload → builder leaves runtime as None.""" runner._build_load_balancer() # noqa: SLF001 - assert runner._lb_runtime == {} # noqa: SLF001 + assert runner._lb_runtime is None # noqa: SLF001 # --------------------------------------------------------------------------- # -# Edges builder # +# Edges builder (original) # # --------------------------------------------------------------------------- # def test_build_edges_with_stub_edge(runner: SimulationRunner) -> None: """ @@ -91,14 +107,14 @@ def test_build_edges_with_stub_edge(runner: SimulationRunner) -> None: to the single stub edge (generator → client) present in the minimal topology fixture. """ - runner._build_rqs_generator() # noqa: SLF001 - runner._build_client() # noqa: SLF001 - runner._build_edges() # noqa: SLF001 + runner._build_rqs_generator() # noqa: SLF001 + runner._build_client() # noqa: SLF001 + runner._build_edges() # noqa: SLF001 assert len(runner._edges_runtime) == 1 # noqa: SLF001 # --------------------------------------------------------------------------- # -# from_yaml utility # +# from_yaml utility (original) # # --------------------------------------------------------------------------- # def test_from_yaml_minimal(tmp_path: Path, env: simpy.Environment) -> None: """from_yaml() parses YAML, validates via Pydantic and returns a runner.""" @@ -124,3 +140,141 @@ def test_from_yaml_minimal(tmp_path: Path, env: simpy.Environment) -> None: assert isinstance(runner, SimulationRunner) assert runner.rqs_generator.id == "gen-yaml" assert runner.client.id == "cli-yaml" + + +def _payload_with_lb_one_server_and_edges( + *, + rqs_input: RqsGenerator, + sim_settings: SimulationSettings, +) -> SimulationPayload: + """Build a small payload with LB → server wiring and one net edge.""" + client = Client(id="client-1") + server = Server(id="srv-1", server_resources=ServerResources(), endpoints=[]) + lb = LoadBalancer(id="lb-1") + nodes = TopologyNodes(servers=[server], client=client, load_balancer=lb) + + e_gen_lb = Edge( + id="gen-lb", + source=rqs_input.id, + target=lb.id, + latency=RVConfig(mean=0.001, distribution=Distribution.POISSON), + ) + e_lb_srv = Edge( + id="lb-srv", + source=lb.id, + target=server.id, + latency=RVConfig(mean=0.002, distribution=Distribution.POISSON), + ) + e_net = Edge( + id="net-edge", + source=rqs_input.id, + target=client.id, + latency=RVConfig(mean=0.003, distribution=Distribution.POISSON), + ) + graph = TopologyGraph(nodes=nodes, edges=[e_gen_lb, e_lb_srv, e_net]) + + return SimulationPayload( + rqs_input=rqs_input, + topology_graph=graph, + sim_settings=sim_settings, + ) + + +def test_make_inbox_bound_to_env_and_fifo(runner: SimulationRunner) -> None: + """_make_inbox() binds to runner.env and behaves FIFO.""" + box = runner._make_inbox() # noqa: SLF001 + assert isinstance(box, simpy.Store) + + # Put two items and consume them in order using `run(until=...)`. + env = runner.env + env.run(until=box.put("first")) + env.run(until=box.put("second")) + got1 = env.run(until=box.get()) + got2 = env.run(until=box.get()) + assert got1 == "first" + assert got2 == "second" + + +def test_build_load_balancer_when_present( + env: simpy.Environment, + rqs_input: RqsGenerator, + sim_settings: SimulationSettings, +) -> None: + """_build_load_balancer() should create `_lb_runtime` if LB exists.""" + payload = _payload_with_lb_one_server_and_edges( + rqs_input=rqs_input, sim_settings=sim_settings, + ) + sr = SimulationRunner(env=env, simulation_input=payload) + + sr._build_load_balancer() # noqa: SLF001 + assert sr._lb_runtime is not None # noqa: SLF001 + assert sr._lb_runtime.lb_config.id == "lb-1" # noqa: SLF001 + + +def test_build_edges_populates_lb_out_edges_and_sources( + env: simpy.Environment, + rqs_input: RqsGenerator, + sim_settings: SimulationSettings, +) -> None: + """_build_edges() wires generator→LB and populates `_lb_out_edges`.""" + payload = _payload_with_lb_one_server_and_edges( + rqs_input=rqs_input, sim_settings=sim_settings, + ) + sr = SimulationRunner(env=env, simulation_input=payload) + + sr._build_rqs_generator() # noqa: SLF001 + sr._build_client() # noqa: SLF001 + sr._build_servers() # noqa: SLF001 + sr._build_load_balancer() # noqa: SLF001 + sr._build_edges() # noqa: SLF001 + + assert "lb-srv" in sr._lb_out_edges # noqa: SLF001 + assert len(sr._edges_runtime) >= 2 # noqa: SLF001 + gen_rt = next(iter(sr._rqs_runtime.values())) # noqa: SLF001 + assert gen_rt.out_edge is not None + + +def test_build_events_attaches_shared_views( + env: simpy.Environment, + rqs_input: RqsGenerator, + sim_settings: SimulationSettings, +) -> None: + """_build_events() attaches shared `edges_affected` and `edges_spike` views.""" + payload = _payload_with_lb_one_server_and_edges( + rqs_input=rqs_input, sim_settings=sim_settings, + ) + spike = EventInjection( + event_id="ev-spike", + target_id="net-edge", + start={ + "kind": EventDescription.NETWORK_SPIKE_START, + "t_start": 0.2, + "spike_s": 0.05, + }, + end={"kind": EventDescription.NETWORK_SPIKE_END, "t_end": 0.4}, + ) + outage = EventInjection( + event_id="ev-out", + target_id="srv-1", + start={"kind": EventDescription.SERVER_DOWN, "t_start": 0.1}, + end={"kind": EventDescription.SERVER_UP, "t_end": 0.3}, + ) + payload.events = [spike, outage] + + sr = SimulationRunner(env=env, simulation_input=payload) + sr._build_rqs_generator() # noqa: SLF001 + sr._build_client() # noqa: SLF001 + sr._build_servers() # noqa: SLF001 + sr._build_load_balancer() # noqa: SLF001 + sr._build_edges() # noqa: SLF001 + sr._build_events() # noqa: SLF001 + + assert sr._events_runtime is not None # noqa: SLF001 + events_rt = sr._events_runtime # noqa: SLF001 + + assert "net-edge" in events_rt.edges_affected + for er in sr._edges_runtime.values(): # noqa: SLF001 + assert er.edges_spike is not None + assert er.edges_affected is events_rt.edges_affected + + diff --git a/tests/unit/schemas/test_event_injection.py b/tests/unit/schemas/test_event_injection.py index 36edbaa..5593fd7 100644 --- a/tests/unit/schemas/test_event_injection.py +++ b/tests/unit/schemas/test_event_injection.py @@ -18,7 +18,7 @@ from pydantic import ValidationError from asyncflow.config.constants import EventDescription -from asyncflow.schemas.event.injection import End, EventInjection, Start +from asyncflow.schemas.events.injection import End, EventInjection, Start # --------------------------------------------------------------------------- # Helpers diff --git a/tests/unit/schemas/test_payload.py b/tests/unit/schemas/test_payload.py index 5ae8d55..8547f83 100644 --- a/tests/unit/schemas/test_payload.py +++ b/tests/unit/schemas/test_payload.py @@ -20,7 +20,7 @@ from asyncflow.config.constants import Distribution, EventDescription from asyncflow.schemas.common.random_variables import RVConfig -from asyncflow.schemas.event.injection import End, EventInjection, Start +from asyncflow.schemas.events.injection import End, EventInjection, Start from asyncflow.schemas.payload import SimulationPayload from asyncflow.schemas.topology.edges import Edge from asyncflow.schemas.topology.graph import TopologyGraph @@ -331,3 +331,44 @@ def test_accept_when_never_all_down( ) assert payload.events is not None assert len(payload.events) == 2 + + +def test_server_outage_back_to_back_is_valid( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """Back-to-back outages on the same server (END==START) must be accepted.""" + topo = _topology_with_two_servers_and_edge() + sim_settings.total_simulation_time = 30 # ensure timestamps are within horizon + + # srv-1: [10, 15] followed immediately by [15, 20] → no overlap + ev_a = _mk_server_window("ev-a", "srv-1", start_t=10.0, end_t=15.0) + ev_b = _mk_server_window("ev-b", "srv-1", start_t=15.0, end_t=20.0) + + payload = SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev_a, ev_b], + ) + assert payload.events is not None + assert len(payload.events) == 2 + + +def test_server_outage_overlap_same_server_is_rejected( + rqs_input: RqsGenerator, sim_settings: SimulationSettings, +) -> None: + """Overlapping outages on the same server must be rejected by validation.""" + topo = _topology_with_two_servers_and_edge() + sim_settings.total_simulation_time = 30 # ensure timestamps are within horizon + + # srv-1: [10, 15] and [14, 20] → overlap in [14, 15] + ev_a = _mk_server_window("ev-a", "srv-1", start_t=10.0, end_t=15.0) + ev_b = _mk_server_window("ev-b", "srv-1", start_t=14.0, end_t=20.0) + + with pytest.raises(ValueError, match=r"Overlapping events for"): + SimulationPayload( + rqs_input=rqs_input, + topology_graph=topo, + sim_settings=sim_settings, + events=[ev_a, ev_b], + ) From 75a025f2cea800b80f827f3db388f5eb716d61e3 Mon Sep 17 00:00:00 2001 From: Gioele Botta <157820569+GioeleB00@users.noreply.github.com> Date: Fri, 29 Aug 2025 16:32:37 +0200 Subject: [PATCH 40/52] Refactor/fixing readme small fixes (#21) * new readme, ci to have pycov badge, comment on server runtime * small changes --- .github/workflows/ci-main.yml | 11 ++ CHANGELOG.MD | 45 +++-- README.md | 161 +++++++----------- ROADMAP.md | 65 +++++++ examples/yaml_input/data/two_servers_lb.yml | 4 +- .../two_servers_plot/lb_dashboard.png | Bin 267501 -> 268965 bytes .../lb_server_srv-1_metrics.png | Bin 104510 -> 109793 bytes .../lb_server_srv-2_metrics.png | Bin 106674 -> 118739 bytes readme_img/lb_dashboard.png | Bin 0 -> 268965 bytes ..._dashboard.png\357\200\272Zone.Identifier" | 2 + readme_img/lb_server_srv-1_metrics.png | Bin 0 -> 109793 bytes ...-1_metrics.png\357\200\272Zone.Identifier" | 2 + readme_img/lb_server_srv-2_metrics.png | Bin 0 -> 118739 bytes ...-2_metrics.png\357\200\272Zone.Identifier" | 2 + readme_img/topology.png | Bin 2285328 -> 2483072 bytes src/asyncflow/runtime/actors/server.py | 4 + 16 files changed, 180 insertions(+), 116 deletions(-) create mode 100644 ROADMAP.md create mode 100644 readme_img/lb_dashboard.png create mode 100644 "readme_img/lb_dashboard.png\357\200\272Zone.Identifier" create mode 100644 readme_img/lb_server_srv-1_metrics.png create mode 100644 "readme_img/lb_server_srv-1_metrics.png\357\200\272Zone.Identifier" create mode 100644 readme_img/lb_server_srv-2_metrics.png create mode 100644 "readme_img/lb_server_srv-2_metrics.png\357\200\272Zone.Identifier" diff --git a/.github/workflows/ci-main.yml b/.github/workflows/ci-main.yml index d49831f..0390a2e 100644 --- a/.github/workflows/ci-main.yml +++ b/.github/workflows/ci-main.yml @@ -52,3 +52,14 @@ jobs: run: | poetry run pytest \ --disable-warnings + --cov=asyncflow \ + --cov-report=term-missing \ + --cov-report=xml + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v4 + with: + files: coverage.xml + flags: tests + fail_ci_if_error: true + diff --git a/CHANGELOG.MD b/CHANGELOG.MD index c7ba4cc..20a987b 100644 --- a/CHANGELOG.MD +++ b/CHANGELOG.MD @@ -1,4 +1,3 @@ - # Changelog All notable changes to this project will be documented in this file. @@ -7,24 +6,39 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). ## \[Unreleased] +### Planned + +* **Network baseline upgrade** (sockets, RAM per connection, keep-alive). +* **New metrics and visualization improvements** (queue wait times, service histograms). +* **Monte Carlo analysis** with confidence intervals. + +--- + +## \[0.1.1] – 2025-08-29 + ### Added -* **Event Injection input schema** (merged on `develop`): +* **Event Injection (runtime-ready):** + + * Declarative events with `start` / `end` markers (server down/up, network spike start/end). + * Runtime scheduler integrated with SimPy, applying events at the right simulation time. + * Deterministic latency **offset handling** for network spikes (phase 1). + +* **Improved Server Model:** - * Declarative events with `start` / `end` markers (server **down/up**, network **spike start/end**). - * Strong validation (Pydantic): + * Refined CPU + I/O handling with clearer queue accounting. + * Ready queue length now explicitly updated on contention. + * I/O queue metrics improved with better protection against mis-counting edge cases. + * Enhanced readability and maintainability in endpoint step execution flow. - * `event_id` uniqueness. - * `target_id` must exist (server or edge). - * Start/end times within the simulation horizon and well-ordered. - * Kind ↔ target compatibility (server events must target servers, network events must target edges). - * Safety guard: **never all servers down simultaneously**. -* Documentation comments and examples for authoring event windows in YAML. +### Documentation -### In Progress +* Expanded examples on event injection in YAML. +* Inline comments clarifying queue management logic. + +### Notes -* **Runtime Event Scheduler**: SimPy process to apply events at scheduled times. -* **Edge spike handling (phase 1)**: deterministic latency **offsets** during spike windows (no changes to base distributions yet). +* This is still an **alpha-series** release, but now supports scenario-driven **event injection** and a more faithful **server runtime model**, paving the way for the upcoming network baseline upgrade. --- @@ -96,11 +110,6 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). * **Python 3.12+** (Linux/macOS/Windows). * Install from PyPI: `pip install asyncflow-sim`. -### Known Limitations (alpha) -* Network: latency + optional drops (no bandwidth/payload/TCP yet). -* Single event loop per server (no multi-process/node). -* Linear endpoint pipelines (no branching/fan-out inside endpoints). -* Stationary workload; very short spikes may be missed if `sample_period_s` is large. diff --git a/README.md b/README.md index 4622987..b084df7 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ -# AsyncFlow — Event-Loop Aware Simulator for Async Distributed Systems +# AsyncFlow: Scenario-Driven Simulator for Async Systems Created and maintained by @GioeleB00. [![PyPI](https://img.shields.io/pypi/v/asyncflow-sim)](https://pypi.org/project/asyncflow-sim/) [![Python](https://img.shields.io/pypi/pyversions/asyncflow-sim)](https://pypi.org/project/asyncflow-sim/) [![License](https://img.shields.io/github/license/AsyncFlow-Sim/AsyncFlow)](LICENSE) -[![Status](https://img.shields.io/badge/status-v0.1.0alpha-orange)](#) +[![codecov](https://codecov.io/gh/AsyncFlow-Sim/AsyncFlow/branch/main/graph/badge.svg)](https://codecov.io/gh/AsyncFlow-Sim/AsyncFlow) [![Ruff](https://img.shields.io/badge/lint-ruff-informational)](https://github.com/astral-sh/ruff) [![Typing](https://img.shields.io/badge/typing-mypy-blueviolet)](https://mypy-lang.org/) [![Tests](https://img.shields.io/badge/tests-pytest-6DA55F)](https://docs.pytest.org/) @@ -14,27 +14,65 @@ Created and maintained by @GioeleB00. ----- -AsyncFlow is a discrete-event simulator for modeling and analyzing the performance of asynchronous, distributed backend systems built with SimPy. You describe your system's topology—its servers, network links, and load balancers—and AsyncFlow simulates the entire lifecycle of requests as they move through it. +**AsyncFlow** is a scenario-driven simulator for **asynchronous distributed backends**. +You don’t “predict the Internet” — you **declare scenarios** (network RTT + jitter, resource caps, failure events) and AsyncFlow shows the operational impact: concurrency, queue growth, socket/RAM pressure, latency distributions. This means you can evaluate architectures before implementation: test scaling strategies, network assumptions, or failure modes without writing production code. -It provides a **digital twin** of your service, modeling not just the high-level architecture but also the low-level behavior of each server's **event loop**, including explicit **CPU work**, **RAM residency**, and **I/O waits**. This allows you to run realistic "what-if" scenarios that behave like production systems rather than toy benchmarks. +At its core, AsyncFlow is **event-loop aware**: + +* **CPU work** blocks the loop, +* **RAM residency** ties up memory until release, +* **I/O waits** free the loop just like in real async frameworks. + +With the new **event injection engine**, you can explore *what-if* dynamics: network spikes, server outages, degraded links, all under your control. + +--- ### What Problem Does It Solve? -Modern async stacks like FastAPI are incredibly performant, but predicting their behavior under real-world load is difficult. Capacity planning often relies on guesswork, expensive cloud-based load tests, or discovering bottlenecks only after a production failure. AsyncFlow is designed to replace that uncertainty with **data-driven forecasting**, allowing you to understand how your system will perform before you deploy a single line of code. +Predicting how an async system will behave under real-world load is notoriously hard. Teams often rely on rough guesses, over-provisioning, or painful production incidents. **AsyncFlow replaces guesswork with scenario-driven simulations**: you declare the conditions (network RTT, jitter, resource limits, injected failures) and observe the consequences on latency, throughput, and resource pressure. + +--- + +### Why Scenario-Driven? *Design Before You Code* + +AsyncFlow doesn’t need your backend to exist. +You can model your architecture with YAML or Python, run simulations, and explore bottlenecks **before writing production code**. +This scenario-driven approach lets you stress-test scaling strategies, network assumptions, and failure modes safely and repeatably. + +--- + +### How Does It Work? -### How Does It Work? An Example Topology +AsyncFlow represents your system as a **directed graph of components**, for example: clients, load balancers, servers—connected by network edges with configurable latency models. Each server is **event-loop aware**: CPU work blocks, RAM stays allocated, and I/O yields the loop, just like real async frameworks. You can define topologies via **YAML** or a **Pythonic builder**. -AsyncFlow models your system as a directed graph of interconnected components. A typical setup might look like this: +![Topology](https://raw.githubusercontent.com/AsyncFlow-Sim/AsyncFlow/main/readme_img/topology.png) -![Topology at a glance](readme_img/topology.png) +Run the simulation and inspect the outputs: + +

        + + Latency + Throughput Dashboard + + + Server 1 Metrics + + + Server 2 Metrics + +

        + + +--- ### What Questions Can It Answer? -By running simulations on your defined topology, you can get quantitative answers to critical engineering questions, such as: +With scenario simulations, AsyncFlow helps answer questions such as: + +* How does **p95 latency** shift if active users double? +* What happens when a **client–server edge** suffers a 20 ms spike for 60 seconds? +* Will a given endpoint pipeline — CPU parse → RAM allocation → DB I/O — still meet its **SLA at 40 RPS**? +* How many sockets and how much RAM will a load balancer need under peak conditions? - * How does **p95 latency** change if active users increase from 100 to 200? - * What is the impact on the system if the **client-to-server network latency** increases by 3ms? - * Will a specific API endpoint—with a pipeline of parsing, RAM allocation, and database I/O—hold its **SLA at a load of 40 requests per second**? --- ## Installation @@ -167,7 +205,7 @@ You’ll get latency stats in the terminal and a PNG with four charts (latency d **Want more?** -For ready-to-run scenarios—including examples using the Pythonic builder and multi-server topologies—check out the `examples/` directory in the repository. +For ready-to-run scenarios including examples using the Pythonic builder and multi-server topologies, check out the `examples/` directory in the repository. ## Development @@ -279,97 +317,28 @@ bash scripts/run_sys_tests.sh Executes **pytest** with a terminal coverage summary (no XML, no slowest list). +## Current Limitations (v0.1.1) +AsyncFlow is still in alpha. The current release has some known limitations that are already on the project roadmap: -## What AsyncFlow Models (v0.1) - -AsyncFlow provides a detailed simulation of your backend system. Here is a high-level overview of the core components it models. For a deeper technical dive into the implementation and design rationale, follow the links to the internal documentation. - -* **Async Event Loop:** Simulates a single-threaded, non-blocking event loop per server. **CPU steps** block the loop, while **I/O steps** are non-blocking, accurately modeling `asyncio` behavior. - * *(Deep Dive: `docs/internals/runtime-and-resources.md`)* - -* **System Resources:** Models finite server resources, including **CPU cores** and **RAM (MB)**. Requests must acquire these resources, creating natural back-pressure and contention when the system is under load. - * *(Deep Dive: `docs/internals/runtime-and-resources.md`)* - -* **Endpoints & Request Lifecycles:** Models server endpoints as a linear sequence of **steps**. Each step is a distinct operation, such as `cpu_bound_operation`, `io_wait`, or `ram` allocation. - * *(Schema Definition: `docs/internals/simulation-input.md`)* - -* **Network Edges:** Simulates the connections between system components. Each edge has a configurable **latency** (drawn from a probability distribution) and an optional **dropout rate** to model packet loss. - * *(Schema Definition: `docs/internals/simulation-input.md` | Runtime Behavior: `docs/internals/runtime-and-resources.md`)* - -* **Stochastic Workload:** Generates user traffic based on a two-stage sampling model, combining the number of active users and their request rate per minute to produce a realistic, fluctuating load (RPS) on the system. - * *(Modeling Details with mathematical explanation and clear assumptions: `docs/internals/requests-generator.md`)* - -* **Metrics & Outputs:** Collects two types of data: **time-series metrics** (e.g., `ready_queue_len`, `ram_in_use`) and **event-based data** (`RqsClock`). This raw data is used to calculate final KPIs like **p95/p99 latency** and **throughput**. - * *(Metric Reference: `docs/internals/metrics`)* - -## Current Limitations (v0.1) - -* Network realism: base latency + optional drops (no bandwidth/payload/TCP yet). -* Single event loop per server: no multi-process/multi-node servers yet. -* Linear endpoint flows: no branching/fan-out within an endpoint. -* No thread-level concurrency; modeling OS threads and scheduler/context switching is out of scope.” -* Stationary workload: no diurnal patterns or feedback/backpressure. -* Sampling cadence: very short spikes can be missed if `sample_period_s` is large. - - -## Roadmap (Order is not indicative of priority) - -This roadmap outlines the key development areas to transform AsyncFlow into a comprehensive framework for statistical analysis and resilience modeling of distributed systems. - -### 1. Monte Carlo Simulation Engine - -**Why:** To overcome the limitations of a single simulation run and obtain statistically robust results. This transforms the simulator from an "intuition" tool into an engineering tool for data-driven decisions with confidence intervals. - -* **Independent Replications:** Run the same simulation N times with different random seeds to sample the space of possible outcomes. -* **Warm-up Period Management:** Introduce a "warm-up" period to be discarded from the analysis, ensuring that metrics are calculated only on the steady-state portion of the simulation. -* **Ensemble Aggregation:** Calculate means, standard deviations, and confidence intervals for aggregated metrics (latency, throughput) across all replications. -* **Confidence Bands:** Visualize time-series data (e.g., queue lengths) with confidence bands to show variability over time. - -### 2. Realistic Service Times (Stochastic Service Times) - -**Why:** Constant service times underestimate tail latencies (p95/p99), which are almost always driven by "slow" requests. Modeling this variability is crucial for a realistic analysis of bottlenecks. - -* **Distributions for Steps:** Allow parameters like `cpu_time` and `io_waiting_time` in an `EndpointStep` to be sampled from statistical distributions (e.g., Lognormal, Gamma, Weibull) instead of being fixed values. -* **Per-Request Sampling:** Each request will sample its own service times independently, simulating the natural variability of a real-world system. - -### 3. Component Library Expansion - -**Why:** To increase the variety and realism of the architectures that can be modeled. - -* **New System Nodes:** - * `CacheRuntime`: To model caching layers (e.g., Redis) with hit/miss logic, TTL, and warm-up behavior. - * `APIGatewayRuntime`: To simulate API Gateways with features like rate-limiting and authentication caching. - * `DBRuntime`: A more advanced model for databases featuring connection pool contention and row-level locking. -* **New Load Balancer Algorithms:** Add more advanced routing strategies (e.g., Weighted Round Robin, Least Response Time). - -### 4. Fault and Event Injection - -**Why:** To test the resilience and behavior of the system under non-ideal conditions, a fundamental use case for Site Reliability Engineering (SRE). - -* **API for Scheduled Events:** Introduce a system to schedule events at specific simulation times, such as: - * **Node Down/Up:** Turn a server off and on to test the load balancer's failover logic. - * **Degraded Edge:** Drastically increase the latency or drop rate of a network link. - * **Error Bursts:** Simulate a temporary increase in the rate of application errors. - -### 5. Advanced Network Modeling +* **Network model** — only base latency + jitter/spikes. + Bandwidth, queuing, and protocol-level details (HTTP/2 streams, QUIC, TLS handshakes) are not yet modeled. -**Why:** To more faithfully model network-related bottlenecks that are not solely dependent on latency. +* **Server model** — single event loop per server. + Multi-process or multi-threaded execution is not yet supported. -* **Bandwidth and Payload Size:** Introduce the concepts of link bandwidth and request/response size to simulate delays caused by data transfer. -* **Retries and Timeouts:** Model retry and timeout logic at the client or internal service level. +* **Endpoint flows** — endpoints are linear pipelines. + Branching/fan-out (e.g. service calls to DB + cache) will be added in future versions. -### 6. Complex Endpoint Flows +* **Workload generation** — stationary workloads only. + No support yet for diurnal patterns, feedback loops, or adaptive backpressure. -**Why:** To model more realistic business logic that does not follow a linear path. +* **Overload policies** — no explicit handling of overload conditions. + Queue caps, deadlines, timeouts, rate limiting, and circuit breakers are not yet implemented. -* **Conditional Branching:** Introduce the ability to have conditional steps within an endpoint (e.g., a different path for a cache hit vs. a cache miss). -* **Fan-out / Fan-in:** Model scenarios where a service calls multiple downstream services in parallel and waits for their responses. +* **Sampling cadence** — very short events may be missed if the `sample_period_s` is too large. -### 7. Backpressure and Autoscaling -**Why:** To simulate the behavior of modern, adaptive systems that react to load. -* **Dynamic Rate Limiting:** Introduce backpressure mechanisms where services slow down the acceptance of new requests if their internal queues exceed a certain threshold. -* **Autoscaling Policies:** Model simple Horizontal Pod Autoscaler (HPA) policies where the number of server replicas increases or decreases based on metrics like CPU utilization or queue length. +📌 See the [ROADMAP](./ROADMAP.md) for planned features and upcoming milestones. diff --git a/ROADMAP.md b/ROADMAP.md new file mode 100644 index 0000000..0fc9666 --- /dev/null +++ b/ROADMAP.md @@ -0,0 +1,65 @@ +# **AsyncFlow Roadmap** + +AsyncFlow is designed as a **scenario-driven simulator for capacity planning**. Its purpose is not to “predict the Internet,” but to give engineers and researchers a way to test how backend systems behave under controlled, reproducible what-if conditions. The roadmap reflects a balance between realism, clarity, and usability: each step extends the tool while keeping its scope transparent and focused. + +--- + +## **1. Network Baseline Upgrade** + +The first milestone is to move beyond a purely abstract latency model and introduce a more realistic network layer. Instead of only attaching a fixed RTT, AsyncFlow will account for socket capacity and per-connection memory usage at each node (servers and load balancers). This brings the simulator closer to operational limits, where resource saturation, rather than bandwidth, becomes the bottleneck. + +**Impact:** users will see how socket pressure and memory constraints affect latency, throughput, and error rates under different scenarios. + +--- + +## **2. Richer Metrics and Visualization** + +Next, the focus shifts to **observability**. The simulator will expose finer metrics such as RAM queue lengths, CPU waiting times, and service durations. Visualizations will be improved with richer charts, event markers, and streamlined dashboards. + +**Impact:** enables clearer attribution of slowdowns whether they stem from CPU contention, memory limits, or network pressure and makes results easier to communicate. + +--- + +## **3. Monte Carlo Analysis** + +Simulations are inherently variable. This milestone adds **multi-run Monte Carlo support**, allowing users to quantify uncertainty in latency, throughput, and utilization metrics. Results will be presented with confidence intervals and bands over time series, turning AsyncFlow into a decision-making tool rather than a single-run experiment. + +**Impact:** supports risk-aware capacity planning by highlighting ranges and probabilities, not just averages. + +--- + +## **4. Databases and Caches** + +Once the core network and metric layers are mature, AsyncFlow will expand into modeling **stateful backends**. Simple but powerful abstractions for databases and caches will be introduced: connection pools, cache hit/miss dynamics, and latency distributions. + +**Impact:** this step unlocks realistic end-to-end scenarios, where system behavior is dominated not just by servers and edges, but by datastore capacity and caching efficiency. + +--- + +## **5. Overload Policies and Resilience** + +With the main components in place, the simulator will introduce **control policies**: queue caps, deadlines, circuit breakers, rate limiting, and similar mechanisms. These features make it possible to test how systems protect themselves under overload, and to compare resilience strategies side by side. + +**Impact:** users will gain insight into not just when a system fails, but how gracefully it degrades. + +--- + +## **6. Reinforcement Learning Playground** + +The final planned milestone is a **research-oriented playground** where AsyncFlow serves as a training and evaluation environment for intelligent load-balancing and autoscaling strategies. With a Gym-like interface, researchers can train RL agents and benchmark them against established baselines in controlled, reproducible conditions. + +**Impact:** bridges capacity planning with modern adaptive control, turning AsyncFlow into both an educational tool and a research testbed. + +--- + +## **Vision** + +At each step, AsyncFlow stays true to its philosophy: **clarity over exhaustiveness, scenarios over prediction**. The roadmap builds toward a platform that is useful across three domains: + +* **Education**, to illustrate principles of latency, concurrency, and resilience. +* **Pre-production planning**, to evaluate system limits before deployment. +* **Research**, to test new algorithms and policies in a safe, transparent environment. + +--- + + diff --git a/examples/yaml_input/data/two_servers_lb.yml b/examples/yaml_input/data/two_servers_lb.yml index 100a46b..6adf5cb 100644 --- a/examples/yaml_input/data/two_servers_lb.yml +++ b/examples/yaml_input/data/two_servers_lb.yml @@ -9,11 +9,11 @@ # Each server runs: CPU(2 ms) → RAM(128 MB) → IO wait(12 ms) # All network links use exponential latency with small means (2–3 ms). # -# Workload targets ~40 rps (120 users × 20 req/min ÷ 60). + rqs_input: id: rqs-1 - avg_active_users: { mean: 120 } + avg_active_users: { mean: 400 } avg_request_per_minute_per_user: { mean: 20 } user_sampling_window: 60 diff --git 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Define File Paths --- - script_dir = Path(__file__).parent # <-- same folder as this file - out_dir = script_dir # <-- save outputs here + script_dir = Path(__file__).parent # same folder as this script + out_dir = script_dir / "single_server_plot" # outputs will go here + out_dir.mkdir(parents=True, exist_ok=True) # create if not exists + yaml_path = script_dir.parent / "data" / "single_server.yml" - output_base_name = "single_server_results" # prefix for all output files + output_base_name = "single_server_results" # prefix for output files if not yaml_path.exists(): raise FileNotFoundError(f"YAML configuration file not found: {yaml_path}") diff --git a/examples/yaml_input/single_server/single_server_plot/single_server_results_dashboard.png b/examples/yaml_input/single_server/single_server_plot/single_server_results_dashboard.png new file mode 100644 index 0000000000000000000000000000000000000000..b54350da076c4ae4544f5d31ad0b4a623a924620 GIT binary patch literal 158132 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z5w=8@S#b4lIiIFwYBoD%i#IM5m=Q%{sIN}RvUnnA&Y^A>t zCYd>+EumR{3Bo`naUH.png + - heavy_event_inj_single_server_io_queue_.png + - heavy_event_inj_single_server_ram_.png +""" + +from __future__ import annotations + +from pathlib import Path + +import matplotlib.pyplot as plt +import simpy + +from asyncflow.metrics.analyzer import ResultsAnalyzer +from asyncflow.runtime.simulation_runner import SimulationRunner + + +def main() -> None: + """Defines paths, runs the simulation, and generates all outputs.""" + # --- 1. Define File Paths --- + script_dir = Path(__file__).parent + yaml_path = script_dir.parent / "data" / "heavy_inj_single_server.yml" + output_base_name = "heavy_inj_single_server" + + if not yaml_path.exists(): + msg = f"YAML configuration file not found: {yaml_path}" + raise FileNotFoundError(msg) + + # Create/ensure the output directory (overwrite files if present). + out_dir = script_dir / "heavy_single_server_plot" + out_dir.mkdir(parents=True, exist_ok=True) + + # --- 2. Run the Simulation --- + env = simpy.Environment() + runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) + results: ResultsAnalyzer = runner.run() + + # --- 3. Dashboard (latency + throughput) --- + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + results.plot_base_dashboard(axes[0], axes[1]) + fig.tight_layout() + dash_path = out_dir / f"{output_base_name}_dashboard.png" + fig.savefig(dash_path) + print(f"Saved: {dash_path}") + + # --- 4. Per-server plots --- + for sid in results.list_server_ids(): + # Ready queue + f1, a1 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ready_queue(a1, sid) + f1.tight_layout() + p1 = out_dir / f"{output_base_name}_ready_queue_{sid}.png" + f1.savefig(p1) + print(f"Saved: {p1}") + + # I/O queue + f2, a2 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_io_queue(a2, sid) + f2.tight_layout() + p2 = out_dir / f"{output_base_name}_io_queue_{sid}.png" + f2.savefig(p2) + print(f"Saved: {p2}") + + # RAM usage + f3, a3 = plt.subplots(figsize=(10, 5)) + results.plot_single_server_ram(a3, sid) + f3.tight_layout() + p3 = out_dir / f"{output_base_name}_ram_{sid}.png" + f3.savefig(p3) + print(f"Saved: {p3}") + + +if __name__ == "__main__": + main() diff --git a/examples/yaml_input/event_injections/heavy_single_server_plot/heavy_inj_single_server_dashboard.png b/examples/yaml_input/event_injections/heavy_single_server_plot/heavy_inj_single_server_dashboard.png new file mode 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be available (via 'py' launcher or python.exe). +# - We do NOT delete an existing .venv; it will be reused if compatible. + +# Strict error handling +$ErrorActionPreference = 'Stop' +Set-StrictMode -Version Latest + +# --- helpers ------------------------------------------------------------------ + +function Write-Info { param([string]$Msg) Write-Host "==> $Msg" } +function Write-Ok { param([string]$Msg) Write-Host "✅ $Msg" -ForegroundColor Green } +function Fail { param([string]$Msg) Write-Error $Msg; exit 1 } + +# Resolve repo root (this script lives in scripts/) +$ScriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path +$RepoRoot = Resolve-Path (Join-Path $ScriptDir '..') + +function Require-Pyproject { + if (-not (Test-Path (Join-Path $RepoRoot 'pyproject.toml'))) { + Fail "pyproject.toml not found at repo root ($RepoRoot)" + } +} + +function Get-PythonPath-3_12Plus { + <# + Try common Windows launchers/executables and return the *actual* Python + interpreter path (sys.executable) for a version >= 3.12. + #> + $candidates = @( + @('py', '-3.13'), + @('py', '-3.12'), + @('py', '-3'), + @('python3.13'), + @('python3.12'), + @('python') + ) + + foreach ($cand in $candidates) { + $exe = $cand[0] + $args = @() + if ($cand.Count -gt 1) { $args = $cand[1..($cand.Count-1)] } + + if (-not (Get-Command $exe -ErrorAction SilentlyContinue)) { continue } + + # Check version + & $exe @args -c "import sys; import sys as s; raise SystemExit(0 if sys.version_info[:2] >= (3,12) else 1)" 2>$null + if ($LASTEXITCODE -ne 0) { continue } + + # Obtain the real interpreter path + $pyPath = & $exe @args -c "import sys; print(sys.executable)" 2>$null + if ($LASTEXITCODE -eq 0 -and $pyPath) { + return $pyPath.Trim() + } + } + + return $null +} + +function Ensure-Poetry { + if (Get-Command poetry -ErrorAction SilentlyContinue) { + poetry --version | Out-Null + return + } + + Write-Info "Poetry not found; attempting installation…" + + # Official installer (recommended by Poetry) + $installer = (Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content + # Pipe installer to Python (stdin) + $pythonToUse = (Get-Command py -ErrorAction SilentlyContinue) ? 'py' : 'python' + $installer | & $pythonToUse - + + # Common locations (make available for current session) + $poetryCandidates = @( + (Join-Path $env:APPDATA 'pypoetry\venv\Scripts'), + (Join-Path $env:USERPROFILE '.local\bin') + ) + foreach ($p in $poetryCandidates) { + if (Test-Path $p) { $env:Path = "$p;$env:Path" } + } + + if (-not (Get-Command poetry -ErrorAction SilentlyContinue)) { + Fail "Poetry installation failed (not on PATH). Close & reopen PowerShell or add the Poetry path to PATH." + } + + poetry --version | Out-Null +} + +function Run-Tests-WithOptionalCoverage { + <# + Try pytest with coverage first; if the plugin is missing, + fall back to plain pytest. Propagate failure if tests fail. + #> + $cmd = { poetry run pytest --cov=src --cov-report=term-missing:skip-covered --cov-report=xml --disable-warnings -q } + try { + & $cmd + if ($LASTEXITCODE -eq 0) { + Write-Ok "Tests (with coverage) PASSED" + return + } + } catch { + # ignore; retry without coverage below + } + + Write-Info "Coverage run failed (likely pytest-cov not installed). Falling back to plain pytest…" + poetry run pytest --disable-warnings -q + if ($LASTEXITCODE -ne 0) { + Fail "Tests FAILED" + } + Write-Ok "Tests PASSED" +} + +# --- main --------------------------------------------------------------------- + +Set-Location $RepoRoot +Require-Pyproject + +$PythonExe = Get-PythonPath-3_12Plus +if (-not $PythonExe) { + Fail "Python >= 3.12 not found. Install Python 3.12+ and re-run." +} +Write-Info ("Using Python: " + (& $PythonExe -V)) + +Ensure-Poetry + +# Make sure Poetry venv lives inside the repo +Write-Info "Configuring Poetry to use in-project virtualenv (.venv)…" +poetry config virtualenvs.in-project true +Write-Ok "Poetry configured to use .venv" + +# Bind Poetry to the chosen interpreter (creates .venv if needed) +poetry env use "$PythonExe" | Out-Null +Write-Ok "Virtualenv ready (.venv)" + +# Policy: always remove lock to avoid conflicts across environments +$lockPath = Join-Path $RepoRoot 'poetry.lock' +if (Test-Path $lockPath) { + Write-Info "Removing poetry.lock for a clean resolution…" + Remove-Item $lockPath -Force + Write-Ok "poetry.lock removed" +} + +# Faster installs and stable headless plotting +$env:PIP_DISABLE_PIP_VERSION_CHECK = '1' +$env:MPLBACKEND = 'Agg' + +Write-Info "Installing project with dev extras…" +poetry install --with dev --no-interaction --no-ansi +Write-Ok "Dependencies installed (dev)" + +Write-Info "Running Ruff (lint)…" +poetry run ruff check src tests +Write-Ok "Ruff PASSED" + +Write-Info "Running MyPy (type-check)…" +poetry run mypy src tests +Write-Ok "MyPy PASSED" + +Write-Info "Running tests (with coverage if available)…" +Run-Tests-WithOptionalCoverage + +Write-Ok "All checks completed SUCCESSFULLY 🎉" diff --git a/scripts/dev_setup.sh b/scripts/dev_setup.sh new file mode 100644 index 0000000..fe97c0c --- /dev/null +++ b/scripts/dev_setup.sh @@ -0,0 +1,135 @@ +# Post-clone developer setup for AsyncFlow (Linux/macOS/WSL). +# +# What it does: +# 1) Ensures Poetry is available (prefers pipx if present; otherwise uses +# the official installer). +# 2) Configures Poetry to create an in-project virtualenv (.venv). +# 3) Removes poetry.lock (fresh dependency resolution by policy). +# 4) Installs the project with dev extras. +# 5) Runs ruff, mypy, and pytest (with coverage if available). +# +# Usage: +# bash scripts/dev_setup.sh +# +# Notes: +# - Run this from anywhere; it will cd to repo root. +# - Requires Python >= 3.12 to be available (python3.12 or python3). +# - We do NOT delete an existing .venv; it will be reused if compatible. + +set -Eeuo pipefail + +# --- helpers ----------------------------------------------------------------- + +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +err() { echo "ERROR: $*" >&2; exit 1; } +info() { echo "==> $*"; } +ok() { echo "✅ $*"; } + +require_pyproject() { + [[ -f "$repo_root/pyproject.toml" ]] || err "pyproject.toml not found at repo root ($repo_root)" +} + +pick_python() { + # Return a python executable >= 3.12 + for cand in python3.13 python3.12 python3; do + if command -v "$cand" >/dev/null 2>&1; then + if "$cand" -c 'import sys; sys.exit(0 if sys.version_info[:2] >= (3,12) else 1)'; then + echo "$cand" + return 0 + fi + fi + done + err "Python >= 3.12 not found. Install python3.12+ and re-run." +} + +ensure_poetry() { + if command -v poetry >/dev/null 2>&1; then + poetry --version || true + return 0 + fi + + info "Poetry not found; attempting installation…" + + if command -v pipx >/dev/null 2>&1; then + pipx install poetry || pipx upgrade poetry || true + else + # Official installer (recommended by Poetry) — installs to ~/.local/bin + curl -sSL https://install.python-poetry.org | python3 - + export PATH="$HOME/.local/bin:$PATH" + fi + + # Ensure poetry is now available on PATH + export PATH="$HOME/.local/bin:$PATH" + command -v poetry >/dev/null 2>&1 || err "Poetry installation failed (not on PATH)." + poetry --version || true +} + +run_tests_with_optional_coverage() { + # Try pytest with coverage first; if plugin missing, fallback to plain pytest. + set +e + poetry run pytest \ + --cov=src \ + --cov-report=term-missing:skip-covered \ + --cov-report=xml \ + --disable-warnings -q + local status=$? + set -e + + if [[ $status -eq 0 ]]; then + ok "Tests (with coverage) PASSED" + return 0 + fi + + info "Coverage run failed (likely pytest-cov not installed). Falling back to plain pytest…" + + poetry run pytest --disable-warnings -q + ok "Tests PASSED" +} + +# --- main -------------------------------------------------------------------- + +cd "$repo_root" +require_pyproject + +PY_BIN="$(pick_python)" +info "Using Python: $("$PY_BIN" -V)" + +ensure_poetry + +# Make sure Poetry venv lives inside the repo +info "Configuring Poetry to use in-project virtualenv (.venv)…" +poetry config virtualenvs.in-project true +ok "Poetry configured to use .venv" + +# Bind Poetry to the chosen interpreter (creates .venv if needed) +poetry env use "$PY_BIN" >/dev/null 2>&1 || true +ok "Virtualenv ready (.venv)" + +# Policy: always remove lock to avoid conflicts across environments +if [[ -f poetry.lock ]]; then + info "Removing poetry.lock for a clean resolution…" + rm -f poetry.lock + ok "poetry.lock removed" +fi + +# Faster installs and stable headless plotting +export PIP_DISABLE_PIP_VERSION_CHECK=1 +export MPLBACKEND=Agg + +info "Installing project with dev extras…" +poetry install --with dev --no-interaction --no-ansi +ok "Dependencies installed (dev)" + +info "Running Ruff (lint)…" +poetry run ruff check src tests +ok "Ruff PASSED" + +info "Running MyPy (type-check)…" +poetry run mypy src tests +ok "MyPy PASSED" + +info "Running tests (with coverage if available)…" +run_tests_with_optional_coverage + +ok "All checks completed SUCCESSFULLY 🎉" diff --git a/scripts/quality_check.ps1 b/scripts/quality_check.ps1 new file mode 100644 index 0000000..74bbd4c --- /dev/null +++ b/scripts/quality_check.ps1 @@ -0,0 +1,14 @@ +# Lint & format with Ruff (applies --fix) and type-check with MyPy. +# Usage: +# .\scripts\quality_check.ps1 + +$ErrorActionPreference = 'Stop' +Set-StrictMode -Version Latest + +# Ruff (lint + auto-fix) +poetry run ruff check src tests --fix + +# MyPy (type-check) +poetry run mypy src tests + +Write-Host "✅ Linting and type-checking completed SUCCESSFULLY" diff --git a/scripts/quality-check.sh b/scripts/quality_check.sh similarity index 92% rename from scripts/quality-check.sh rename to scripts/quality_check.sh index eb2a66f..85c269e 100644 --- a/scripts/quality-check.sh +++ b/scripts/quality_check.sh @@ -1,4 +1,3 @@ -#!/usr/bin/env bash set -euo pipefail # Lint & format with ruff, automatic corrections applied (--fix) diff --git a/scripts/run_sys_tests.ps1 b/scripts/run_sys_tests.ps1 new file mode 100644 index 0000000..aa3630f --- /dev/null +++ b/scripts/run_sys_tests.ps1 @@ -0,0 +1,55 @@ +# Run only system tests (marked @pytest.mark.system) with the required env var. +# Keeps output concise (no XML, no slowest list), shows the usual pytest summary. +# +# Usage: +# .\scripts\run_system_tests.ps1 +# +# Notes: +# - Uses `poetry run` when Poetry + pyproject.toml are present; otherwise falls back to `pytest`. +# - Forces a headless backend for any plots generated during tests. + +Set-StrictMode -Version Latest +$ErrorActionPreference = 'Stop' + +# Resolve repo root +$ScriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path +$RepoRoot = Resolve-Path (Join-Path $ScriptDir '..') + +# Collect test paths (default: tests/system) +if ($args.Count -ge 1) { + $TestPaths = $args +} else { + $TestPaths = @('tests/system') +} + +# Decide runner prefix +$UsePoetry = (Get-Command poetry -ErrorAction SilentlyContinue) -ne $null -and + (Test-Path (Join-Path $RepoRoot 'pyproject.toml')) +$Runner = if ($UsePoetry) { 'poetry run pytest' } else { 'pytest' } + +# Set env vars for this process +$env:MPLBACKEND = if ($env:MPLBACKEND) { $env:MPLBACKEND } else { 'Agg' } +$env:ASYNCFLOW_RUN_SYSTEM_TESTS = '1' + +Push-Location $RepoRoot +try { + Write-Host "==> Running system tests…" + # Clear any configured addopts and run only system-marked tests + $pytestArgs = @( + '-o', 'addopts=', + '-m', 'system', + '--disable-warnings', + '-q' + ) + $TestPaths + + if ($UsePoetry) { + poetry run pytest @pytestArgs + } else { + pytest @pytestArgs + } + + Write-Host "✅ System tests PASSED" +} +finally { + Pop-Location +} diff --git a/scripts/run_sys_tests.sh b/scripts/run_sys_tests.sh new file mode 100644 index 0000000..78ee74b --- /dev/null +++ b/scripts/run_sys_tests.sh @@ -0,0 +1,42 @@ +# Run only system tests (marked @pytest.mark.system) with the required env var. +# Keeps output concise (no XML, no slowest list), shows the usual pytest summary. +# +# Usage: +# bash scripts/run_system_tests.sh +# +# Notes: +# - Uses `poetry run` when Poetry + pyproject.toml are present; otherwise falls back to `pytest`. +# - Forces a headless backend for any plots generated during tests. + +set -Eeuo pipefail + +# Pick test paths (default to tests/system) +if [[ $# -ge 1 ]]; then + TEST_PATHS=("$@") +else + TEST_PATHS=(tests/system) +fi + +REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +RUN_PREFIX="" +if command -v poetry >/dev/null 2>&1 && [[ -f "$REPO_ROOT/pyproject.toml" ]]; then + RUN_PREFIX="poetry run" +fi + +# Headless plotting; enable system tests +export MPLBACKEND="${MPLBACKEND:-Agg}" +export ASYNCFLOW_RUN_SYSTEM_TESTS=1 + +cd "$REPO_ROOT" + +echo "==> Running system tests…" +# Clear any configured addopts and run only system-marked tests +# Keep output short but with the final summary line. +$RUN_PREFIX pytest \ + -o addopts= \ + -m system \ + --disable-warnings \ + -q \ + "${TEST_PATHS[@]}" + +echo "✅ System tests PASSED" diff --git a/scripts/run_tests.ps1 b/scripts/run_tests.ps1 new file mode 100644 index 0000000..86bacfd --- /dev/null +++ b/scripts/run_tests.ps1 @@ -0,0 +1,39 @@ +# Run tests with coverage ONLY (no XML, no durations), showing pytest’s usual summary. +# It also overrides any configured addopts (e.g. durations/xml) via `-o addopts=`. +# +# Usage: +# .\scripts\run_tests.ps1 + +$ErrorActionPreference = 'Stop' +Set-StrictMode -Version Latest + +# Pick test paths +[string[]]$TestPaths = if ($args.Count -ge 1) { $args } else { @('tests') } + +# Resolve repo root (this script lives in scripts/) +$ScriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path +$RepoRoot = Resolve-Path (Join-Path $ScriptDir '..') + +# Use Poetry if available and pyproject exists +$RunWithPoetry = (Get-Command poetry -ErrorAction SilentlyContinue) -and (Test-Path (Join-Path $RepoRoot 'pyproject.toml')) + +# Headless backend if plots are generated during tests +if (-not $env:MPLBACKEND) { $env:MPLBACKEND = 'Agg' } + +Set-Location $RepoRoot + +# Build command +$cmd = @() +if ($RunWithPoetry) { $cmd += @('poetry', 'run') } +$cmd += 'pytest' +$cmd += @( + '-o', 'addopts=', + '--cov=src', + '--cov-report=term', + '--disable-warnings', + '-q' +) +$cmd += $TestPaths + +# Execute +& $cmd diff --git a/scripts/run_tests.sh b/scripts/run_tests.sh new file mode 100644 index 0000000..089d8e4 --- /dev/null +++ b/scripts/run_tests.sh @@ -0,0 +1,34 @@ +# Run tests with coverage ONLY (no XML, no durations), showing pytest’s usual summary. +# It also overrides any configured addopts (e.g. durations/xml) via `-o addopts=`. +# +# Usage: +# bash scripts/run_tests.sh +set -Eeuo pipefail + +# Pick test paths +if [[ $# -ge 1 ]]; then + TEST_PATHS=("$@") +else + TEST_PATHS=(tests) +fi + +REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +RUN_PREFIX="" +if command -v poetry >/dev/null 2>&1 && [[ -f "$REPO_ROOT/pyproject.toml" ]]; then + RUN_PREFIX="poetry run" +fi + +# Headless backend if plots are generated during tests +export MPLBACKEND="${MPLBACKEND:-Agg}" + +cd "$REPO_ROOT" + +# Run pytest with coverage summary in terminal, no xml, no durations, +# and wipe any addopts coming from config files. +$RUN_PREFIX pytest \ + -o addopts= \ + --cov=src \ + --cov-report=term \ + --disable-warnings \ + -q \ + "${TEST_PATHS[@]}" diff --git a/simulation_base.png b/simulation_base.png new file mode 100644 index 0000000000000000000000000000000000000000..d8c8e16a95d051bdfa46196d8562a93b5dd77f9f GIT binary patch literal 123625 zcmeFZXH-;K*EL#zfPr9WMX~{r)PDN;-tXRVf7~(dpIc*eD0Yv`>~8A&l)iU@IVich8rC)*l}@xPU#AMfy$z9#~=Ff3ap$onC 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AsyncFlow scenario with a Load Balancer (2 servers) from YAML and print diagnostics. - -What it does: -- Loads the simulation payload from YAML via `SimulationRunner.from_yaml`. -- Runs the simulation. -- Prints latency stats, 1s-bucket throughput, and a preview of sampled metrics. -- Saves four plots (latency histogram, throughput, server queues, RAM). -- Performs sanity checks (expected vs observed) with simple LB-aware heuristics. - -Usage: - python src/app/example/run_lb_from_yaml.py \ - --yaml src/app/example/data/two_servers_lb.yml +Walkthrough: run a Load-Balanced (2 servers) AsyncFlow scenario from YAML. + +What this script does +--------------------- +1) Loads the SimulationPayload from a YAML file (round-robin LB, 2 identical servers). +2) Runs the simulation via `SimulationRunner`. +3) Prints a concise latency summary to stdout. +4) Saves plots **in the same folder as this script**: + • `lb_dashboard.png` (Latency histogram + Throughput) + • One figure per server with 3 panels: Ready Queue, I/O Queue, RAM usage. + +How to use +---------- +- Put this script and `two_servers_lb.yml` in the same directory. +- Run: `python run_lb_from_yaml.py` """ from __future__ import annotations -from argparse import ArgumentParser from pathlib import Path -from typing import Dict, Iterable, List, Mapping, Tuple - -import matplotlib.pyplot as plt -import numpy as np import simpy +import matplotlib.pyplot as plt -from asyncflow.config.constants import ( # only for basic step-kind/ops inspection - EndpointStepCPU, - EndpointStepIO, - EndpointStepRAM, - LatencyKey, - StepOperation, -) -from asyncflow.metrics.analyzer import ResultsAnalyzer from asyncflow.runtime.simulation_runner import SimulationRunner +from asyncflow.metrics.analyzer import ResultsAnalyzer -# ───────────────────────────────────────────────────────────── -# Pretty printers (same style as your single-server script) -# ───────────────────────────────────────────────────────────── -def print_latency_stats(res: ResultsAnalyzer) -> None: - """Print latency statistics returned by the analyzer.""" - stats: Mapping[LatencyKey, float] = res.get_latency_stats() - print("\n════════ LATENCY STATS ════════") - if not stats: - print("(empty)") - return - - order: List[LatencyKey] = [ - LatencyKey.TOTAL_REQUESTS, - LatencyKey.MEAN, - LatencyKey.MEDIAN, - LatencyKey.STD_DEV, - LatencyKey.P95, - LatencyKey.P99, - LatencyKey.MIN, - LatencyKey.MAX, - ] - for key in order: - if key in stats: - print(f"{key.name:<20} = {stats[key]:.6f}") - - -def print_throughput(res: ResultsAnalyzer) -> None: - """Print 1-second throughput buckets.""" - timestamps, rps = res.get_throughput_series() - print("\n════════ THROUGHPUT (req/sec) ════════") - if not timestamps: - print("(empty)") - return - - for t, rate in zip(timestamps, rps): - print(f"t={t:4.1f}s → {rate:6.2f} rps") - - -def print_sampled_preview(res: ResultsAnalyzer) -> None: - """Print first 5 samples of each sampled metric series.""" - sampled: Dict[str, Dict[str, List[float]]] = res.get_sampled_metrics() - print("\n════════ SAMPLED METRICS ════════") - if not sampled: - print("(empty)") - return - - for metric, series in sampled.items(): - print(f"\n📈 {metric}:") - for entity, vals in series.items(): - head = list(vals[:5]) if vals else [] - print(f" - {entity}: len={len(vals)}, first={head}") - - -# ───────────────────────────────────────────────────────────── -# Plotting -# ───────────────────────────────────────────────────────────── -def save_all_plots(res: ResultsAnalyzer, out_path: Path) -> None: - """Generate the 2x2 plot figure and save it to `out_path`.""" - fig, axes = plt.subplots(2, 2, figsize=(12, 8)) - res.plot_latency_distribution(axes[0, 0]) - res.plot_throughput(axes[0, 1]) - res.plot_server_queues(axes[1, 0]) - res.plot_ram_usage(axes[1, 1]) - fig.tight_layout() - fig.savefig(out_path) - print(f"\n🖼️ Plots saved to: {out_path}") - - -# ───────────────────────────────────────────────────────────── -# Sanity checks (LB-aware, still rough) -# ───────────────────────────────────────────────────────────── - -def run_sanity_checks(runner: SimulationRunner, res: ResultsAnalyzer) -> None: - """ - Sanity checks LB-aware (round-robin): observed vs expected - """ - from asyncflow.config.constants import ( - EndpointStepCPU, EndpointStepIO, EndpointStepRAM, StepOperation, LatencyKey - ) - import numpy as np - - def _mean(arr): - a = np.asarray(list(arr), dtype=float) - return float(a.mean()) if a.size else 0.0 - - # 1) λ - w = runner.simulation_input.rqs_input - lam = float(w.avg_active_users.mean) * float(w.avg_request_per_minute_per_user.mean) / 60.0 - - topo = runner.simulation_input.topology_graph - servers = {s.id: s for s in topo.nodes.servers} - client_id = topo.nodes.client.id - lb = topo.nodes.load_balancer - lb_id = lb.id if lb else None - gen_id = runner.simulation_input.rqs_input.id - - # 2) LB (round_robin -> 1/N) - if lb and lb.server_covered: - covered = [sid for sid in lb.server_covered if sid in servers] - N = max(1, len(covered)) - shares = {sid: 1.0 / N for sid in covered} - else: - only = next(iter(servers.keys())) - shares = {only: 1.0} - - # 3) endpoint totals per server - def endpoint_totals(server): - cpu_s = io_s = ram_mb = 0.0 - for ep in getattr(server, "endpoints", []) or []: - prob = getattr(ep, "probability", 1.0) - for step in ep.steps: - k = step.kind - op = step.step_operation - if isinstance(k, EndpointStepCPU): - cpu_s += prob * float(op[StepOperation.CPU_TIME]) - elif isinstance(k, EndpointStepIO): - io_s += prob * float(op[StepOperation.IO_WAITING_TIME]) - elif isinstance(k, EndpointStepRAM): - ram_mb += prob * float(op[StepOperation.NECESSARY_RAM]) - return cpu_s, io_s, ram_mb - - per_srv = {sid: endpoint_totals(srv) for sid, srv in servers.items()} - - # 4) mappa latencies of edges per role (source,target) - mean_gen_client = 0.0; id_gen_client = None - mean_client_lb = 0.0; id_client_lb = None - mean_lb_srv = {} # sid -> mean - mean_srv_client = {} # sid -> mean - id_lb_srv = {} # sid -> edge_id - id_srv_client = {} # sid -> edge_id - - for e in topo.edges: - s, t, mu = e.source, e.target, float(e.latency.mean) - if s == gen_id and t == client_id: - mean_gen_client = mu; id_gen_client = e.id - elif s == client_id and lb_id and t == lb_id: - mean_client_lb = mu; id_client_lb = e.id - elif lb_id and s == lb_id and t in servers: - mean_lb_srv[t] = mu; id_lb_srv[t] = e.id - elif s in servers and t == client_id: - mean_srv_client[s] = mu; id_srv_client[s] = e.id - - # 5) expected: average latencies - cpu_exp = sum(shares[sid] * per_srv[sid][0] for sid in shares) - io_exp = sum(shares[sid] * per_srv[sid][1] for sid in shares) - net_exp = ( - mean_gen_client + mean_client_lb + - sum(shares[sid] * (mean_lb_srv.get(sid, 0.0) + mean_srv_client.get(sid, 0.0)) for sid in shares) - ) - latency_expected = cpu_exp + io_exp + net_exp - - # 6) observed: throughput & latencies - stats = res.get_latency_stats() - latency_observed = float(stats.get(LatencyKey.MEAN, 0.0)) - _, rps_series = res.get_throughput_series() - rps_observed = _mean(rps_series) - - # 7) expected: RAM e I/O queue as a sum over server - ram_expected = sum((shares[sid] * lam) * (per_srv[sid][0] + per_srv[sid][1]) * per_srv[sid][2] for sid in shares) - ioq_expected = sum((shares[sid] * lam) * per_srv[sid][1] for sid in shares) - - # 8) observed: RAM (sum) and I/O queue sum - sampled = res.get_sampled_metrics() - ram_series = sampled.get("ram_in_use", {}) - ioq_series = sampled.get("event_loop_io_sleep", {}) - ram_observed = sum(_mean(vals) for vals in ram_series.values()) if ram_series else 0.0 - ioq_observed = sum(_mean(vals) for vals in ioq_series.values()) if ioq_series else 0.0 - - # 9) print - REL_TOL = 0.30 - def tick(label, exp, obs): - delta = (abs(obs - exp) / abs(exp)) if exp else 0.0 - icon = "✓" if delta <= REL_TOL else "⚠" - print(f"{icon} {label:<28} expected≈{exp:.3f} observed={obs:.3f} Δ={delta*100:.1f}%") - - print("\n════════ SANITY CHECKS (LB-aware) ════════") - tick("Mean throughput (rps)", lam, rps_observed) - tick("Mean latency (s)", latency_expected, latency_observed) - tick("Mean RAM (MB)", ram_expected, ram_observed) - tick("Mean I/O queue", ioq_expected, ioq_observed) - - # 10) Edge concurrency estimation - edge_conc = sampled.get("edge_concurrent_connection", {}) - if edge_conc: - print("\n— Edge concurrency (LB-aware) —") - means_obs = {eid: _mean(vals) for eid, vals in edge_conc.items()} - - if id_gen_client: - tick(f"edge {id_gen_client}", lam * mean_gen_client, means_obs.get(id_gen_client, 0.0)) - if id_client_lb: - tick(f"edge {id_client_lb}", lam * mean_client_lb, means_obs.get(id_client_lb, 0.0)) - - for sid, p in shares.items(): - lam_i = p * lam - eid = id_lb_srv.get(sid) - if eid: - tick(f"edge {eid}", lam_i * mean_lb_srv.get(sid, 0.0), means_obs.get(eid, 0.0)) - eid = id_srv_client.get(sid) - if eid: - tick(f"edge {eid}", lam_i * mean_srv_client.get(sid, 0.0), means_obs.get(eid, 0.0)) - - # Extra - print("\n— Diagnostics —") - print("λ={:.3f} rps | E[cpu]={:.3f}s E[io]={:.3f}s E[net]≈{:.3f}s | E[RAM/req]={:.1f} MB" - .format(lam, cpu_exp, io_exp, net_exp, sum(shares[sid]*per_srv[sid][2] for sid in shares))) - - - -# ───────────────────────────────────────────────────────────── -# Main -# ───────────────────────────────────────────────────────────── def main() -> None: - """Parse args, run simulation, print/plot, sanity-check (LB topology).""" - parser = ArgumentParser(description="Run AsyncFlow LB scenario from YAML and print outputs + sanity checks.") - parser.add_argument( - "--yaml", - type=Path, - default=Path(__file__).parent.parent / "data" / "two_servers_lb.yml", - help="Path to the simulation YAML file.", - ) - parser.add_argument( - "--out", - type=Path, - default=Path(__file__).parent / "two_servers.png", - help="Path to the output image (plots).", - ) - args = parser.parse_args() - - yaml_path: Path = args.yaml + # Paths (same directory as this script) + script_dir = Path(__file__).parent.parent + yaml_path = script_dir / "data" / "two_servers_lb.yml" if not yaml_path.exists(): - raise FileNotFoundError(f"YAML not found: {yaml_path}") + raise FileNotFoundError(f"YAML configuration not found: {yaml_path}") + # Run the simulation + print(f"🚀 Loading and running simulation from: {yaml_path}") env = simpy.Environment() runner = SimulationRunner.from_yaml(env=env, yaml_path=yaml_path) results: ResultsAnalyzer = runner.run() - - print_latency_stats(results) - print_throughput(results) - print_sampled_preview(results) - - run_sanity_checks(runner, results) - save_all_plots(results, args.out) + print("✅ Simulation finished!") + + # Print concise latency summary + print(results.format_latency_stats()) + + # ---- Plots: dashboard (latency + throughput) ---- + fig_dash, axes_dash = plt.subplots(1, 2, figsize=(14, 5), dpi=160) + results.plot_latency_distribution(axes_dash[0]) + results.plot_throughput(axes_dash[1]) + fig_dash.tight_layout() + out_dashboard = script_dir / "lb_dashboard.png" + fig_dash.savefig(out_dashboard) + print(f"🖼️ Dashboard saved to: {out_dashboard}") + + # ---- Per-server metrics: one figure per server (Ready | I/O | RAM) ---- + for sid in results.list_server_ids(): + fig_row, axes = plt.subplots(1, 3, figsize=(16, 3.8), dpi=160) + results.plot_single_server_ready_queue(axes[0], sid) + results.plot_single_server_io_queue(axes[1], sid) + results.plot_single_server_ram(axes[2], sid) + fig_row.suptitle(f"Server metrics — {sid}", y=1.04, fontsize=14) + fig_row.tight_layout() + out_path = script_dir / f"lb_server_{sid}_metrics.png" + fig_row.savefig(out_path, bbox_inches="tight") + print(f"🖼️ Server metrics for '{sid}' saved to: {out_path}") if __name__ == "__main__": diff --git a/examples/yaml_input/single_server/single_server.png b/examples/yaml_input/single_server/single_server.png deleted file mode 100644 index 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zhWpx~HpWQHt>wT@yVPG-@?@hlWg4Xil^uj-)6~eEa`(cFLdbKXcP&~?OI@Am@I$Ae z4~fyy>rl%6NGUS~sIp-h6%2zRES%5q&op6ZNSnTj0JAcmdoJ_fr$qzs+tAQ(vs{Q= z)tb21D9H|lh4zB=h-#mY5ym0`7qdgrjhUfDD;ad#D~%4F9r8}C%P@vMhrwKufZ0$k zn;p%@qb>S8(HDg*WO3TYG?%WzLVI}imKAH25m88Xa{aHA2vl zPBrzBshp7vnV5Tu>BNeLmU3xova6e#@{;@}uVR)BX`f|c;MmIM;q4Ai$WBdvV7OIwkX6|n5;C}+9(rudn diff --git a/examples/builder_input/single_server/single_server.py b/examples/builder_input/single_server/single_server.py index 6088a12..bb54344 100644 --- a/examples/builder_input/single_server/single_server.py +++ b/examples/builder_input/single_server/single_server.py @@ -1,179 +1,53 @@ #!/usr/bin/env python3 """ -Didactic example: build and run a AsyncFlow scenario **without** YAML, -using the 'builder' (AsyncFlow) to assemble the SimulationPayload. +AsyncFlow builder example — build, run, and visualize a single-server async system. -Scenario reproduced (same as the previous YAML): +Topology (single server) generator ──edge──> client ──edge──> server ──edge──> client -Load: - ~100 active users, 20 req/min each. - -Server: - 1 CPU core, 2GB RAM, endpoint with steps: - CPU(1ms) → RAM(100MB) → IO(100ms) - -Network: - 3ms mean (exponential) latency on each edge. - -What this script does: - 1) Build Pydantic models (generator, client, server, edges, settings). - 2) Compose the final SimulationPayload via AsyncFlow (builder pattern). - 3) Run the simulation with SimulationRunner. - 4) Print latency stats, throughput timeline, and a sampled-metrics preview. - 5) (Optional) Visualize the topology with Matplotlib. - -Run: - python run_with_builder.py +Load model + ~100 active users, 20 requests/min each (Poisson-like aggregate). + +Server model + 1 CPU core, 2 GB RAM + Endpoint pipeline: CPU(1 ms) → RAM(100 MB) → I/O wait (100 ms) + Semantics: + - CPU step blocks the event loop + - RAM step holds a working set until request completion + - I/O step is non-blocking (event-loop friendly) + +Network model + Each edge has exponential latency with mean 3 ms. + +Outputs + - Prints latency statistics to stdout + - Saves a 2×2 PNG in the same directory as this script: + [0,0] Latency histogram (with mean/P50/P95/P99) + [0,1] Throughput (with mean/P95/max overlays) + [1,0] Ready queue for the first server + [1,1] RAM usage for the first server """ from __future__ import annotations from pathlib import Path -from typing import Iterable, List, Mapping - -import numpy as np import simpy +import matplotlib.pyplot as plt -# ── AsyncFlow domain imports ─────────────────────────────────────────────────── -from asyncflow.builder.asyncflow_builder import AsyncFlow +# Public AsyncFlow API (builder) +from asyncflow import AsyncFlow +from asyncflow.components import Client, Server, Edge, Endpoint +from asyncflow.settings import SimulationSettings +from asyncflow.workload import RqsGenerator + +# Runner + Analyzer from asyncflow.runtime.simulation_runner import SimulationRunner from asyncflow.metrics.analyzer import ResultsAnalyzer -from asyncflow.schemas.payload import SimulationPayload -from asyncflow.schemas.workload.rqs_generator import RqsGenerator -from asyncflow.schemas.settings.simulation import SimulationSettings -from asyncflow.schemas.topology.endpoint import Endpoint -from asyncflow.schemas.topology.nodes import ( - Client, - Server, -) -from asyncflow.schemas.topology.edges import Edge - -from asyncflow.config.constants import LatencyKey, SampledMetricName - - -# ───────────────────────────────────────────────────────────── -# Pretty printers (compact, readable output) -# ───────────────────────────────────────────────────────────── -def print_latency_stats(res: ResultsAnalyzer) -> None: - """Print latency statistics calculated by the analyzer.""" - stats: Mapping[LatencyKey, float] = res.get_latency_stats() - print("\n════════ LATENCY STATS ════════") - if not stats: - print("(empty)") - return - - order: List[LatencyKey] = [ - LatencyKey.TOTAL_REQUESTS, - LatencyKey.MEAN, - LatencyKey.MEDIAN, - LatencyKey.STD_DEV, - LatencyKey.P95, - LatencyKey.P99, - LatencyKey.MIN, - LatencyKey.MAX, - ] - for key in order: - if key in stats: - print(f"{key.name:<20} = {stats[key]:.6f}") -def print_throughput(res: ResultsAnalyzer) -> None: - """Print the 1-second throughput buckets.""" - timestamps, rps = res.get_throughput_series() - print("\n════════ THROUGHPUT (req/sec) ════════") - if not timestamps: - print("(empty)") - return - - for t, rate in zip(timestamps, rps): - print(f"t={t:4.1f}s → {rate:6.2f} rps") - - -def print_sampled_preview(res: ResultsAnalyzer) -> None: - """ - Print a small preview for each sampled metric series (first 5 values). - This helps verify that sampler pipelines are running. - """ - sampled = res.get_sampled_metrics() - print("\n════════ SAMPLED METRICS (preview) ════════") - if not sampled: - print("(empty)") - return - - for metric, series in sampled.items(): - metric_name = ( - metric.name if isinstance(metric, SampledMetricName) else str(metric) - ) - print(f"\n📈 {metric_name}:") - for entity, vals in series.items(): - head = list(vals[:5]) if vals else [] - print(f" - {entity}: len={len(vals)}, first={head}") - - -# ───────────────────────────────────────────────────────────── -# Tiny helpers for sanity checks (optional) -# ───────────────────────────────────────────────────────────── -def _mean(series: Iterable[float]) -> float: - """Numerically stable mean for a generic float iterable.""" - arr = np.asarray(list(series), dtype=float) - return float(np.mean(arr)) if arr.size else 0.0 - - -def run_sanity_checks( - runner: SimulationRunner, - res: ResultsAnalyzer, -) -> None: - """ - Back-of-the-envelope checks to compare rough expectations vs observations. - These are intentionally simplistic approximations. - """ - print("\n════════ SANITY CHECKS (rough) ════════") - w = runner.simulation_input.rqs_input - lam_rps = ( - float(w.avg_active_users.mean) - * float(w.avg_request_per_minute_per_user.mean) - / 60.0 - ) - - # Observed throughput - _, rps_series = res.get_throughput_series() - rps_observed = _mean(rps_series) - print(f"• Mean throughput (rps) expected≈{lam_rps:.3f} " - f"observed={rps_observed:.3f}") - - # A few sampled signals (RAM, queues) just to show they are populated. - sampled = res.get_sampled_metrics() - ram_series = sampled.get(SampledMetricName.RAM_IN_USE, {}) - ioq_series = sampled.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) - ready_series = sampled.get(SampledMetricName.READY_QUEUE_LEN, {}) - - ram_mean = _mean([_mean(v) for v in ram_series.values()]) if ram_series else 0.0 - ioq_mean = _mean([_mean(v) for v in ioq_series.values()]) if ioq_series else 0.0 - ready_mean = ( - _mean([_mean(v) for v in ready_series.values()]) if ready_series else 0.0 - ) - - print(f"• Mean RAM in use (MB) observed={ram_mean:.3f}") - print(f"• Mean I/O queue length observed={ioq_mean:.3f}") - print(f"• Mean ready queue length observed={ready_mean:.3f}") - - -# ───────────────────────────────────────────────────────────── -# Build the same scenario via AsyncFlow (builder) -# ───────────────────────────────────────────────────────────── -def build_payload_with_builder() -> SimulationPayload: - """ - Construct the SimulationPayload programmatically using the builder. - - This mirrors the YAML: - - Generator (100 users, 20 rpm each) - - Client - - One server with a single endpoint (CPU → RAM → IO) - - Three edges with exponential latency (3ms mean) - - Simulation settings: 500s total, sample period 50ms - """ - # 1) Request generator +def build_and_run() -> ResultsAnalyzer: + """Build the scenario via the Pythonic builder and run the simulation.""" + # Workload (generator) generator = RqsGenerator( id="rqs-1", avg_active_users={"mean": 100}, @@ -181,50 +55,48 @@ def build_payload_with_builder() -> SimulationPayload: user_sampling_window=60, ) - # 2) Client + # Client client = Client(id="client-1") - # 3) Server (1 CPU core, 2GB RAM) with one endpoint and three steps - # We let Pydantic coerce nested dicts for the endpoint steps. + # Server + endpoint (CPU → RAM → I/O) endpoint = Endpoint( - endpoint_name="ep-1", + endpoint_name="/api", probability=1.0, steps=[ - {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, - {"kind": "ram", "step_operation": {"necessary_ram": 100}}, - {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.1}}, + {"kind": "initial_parsing", "step_operation": {"cpu_time": 0.001}}, # 1 ms + {"kind": "ram", "step_operation": {"necessary_ram": 100}}, # 100 MB + {"kind": "io_wait", "step_operation": {"io_waiting_time": 0.100}}, # 100 ms ], ) - server = Server( - id="srv-1", + id="app-1", server_resources={"cpu_cores": 1, "ram_mb": 2048}, endpoints=[endpoint], ) - # 4) Edges: exponential latency with 3ms mean (same as YAML) + # Network edges (3 ms mean, exponential) e_gen_client = Edge( - id="gen-to-client", + id="gen-client", source="rqs-1", target="client-1", latency={"mean": 0.003, "distribution": "exponential"}, ) - e_client_server = Edge( - id="client-to-server", + e_client_app = Edge( + id="client-app", source="client-1", - target="srv-1", + target="app-1", latency={"mean": 0.003, "distribution": "exponential"}, ) - e_server_client = Edge( - id="server-to-client", - source="srv-1", + e_app_client = Edge( + id="app-client", + source="app-1", target="client-1", latency={"mean": 0.003, "distribution": "exponential"}, ) - # 5) Simulation settings + # Simulation settings settings = SimulationSettings( - total_simulation_time=500, + total_simulation_time=300, sample_period_s=0.05, enabled_sample_metrics=[ "ready_queue_len", @@ -235,59 +107,56 @@ def build_payload_with_builder() -> SimulationPayload: enabled_event_metrics=["rqs_clock"], ) - # 6) Assemble the payload via the builder (AsyncFlow). - # The builder will validate the final structure on build. - flow = ( + # Assemble payload with the builder + payload = ( AsyncFlow() .add_generator(generator) .add_client(client) .add_servers(server) - .add_edges(e_gen_client, e_client_server, e_server_client) + .add_edges(e_gen_client, e_client_app, e_app_client) .add_simulation_settings(settings) - ) + ).build_payload() - return flow.build_payload() + # Run + env = simpy.Environment() + runner = SimulationRunner(env=env, simulation_input=payload) + results: ResultsAnalyzer = runner.run() + return results -# ───────────────────────────────────────────────────────────── -# Main entry-point -# ───────────────────────────────────────────────────────────── def main() -> None: - """ - Build → wire → run the simulation, then print diagnostics. - Mirrors run_from_yaml.py but uses the builder to construct the input. - Also saves a 2x2 plot figure (latency, throughput, server queues, RAM). - """ - env = simpy.Environment() - payload = build_payload_with_builder() + # Build & run + res = build_and_run() - runner = SimulationRunner(env=env, simulation_input=payload) - results: ResultsAnalyzer = runner.run() + # Print concise latency summary + print(res.format_latency_stats()) + + # Prepare figure in the same folder as this script + script_dir = Path(__file__).parent + out_path = script_dir / "builder_service_plots.png" + + # 2×2: Latency | Throughput | Ready (first server) | RAM (first server) + fig, axes = plt.subplots(2, 2, figsize=(12, 8), dpi=160) - # Human-friendly diagnostics - print_latency_stats(results) - print_throughput(results) - print_sampled_preview(results) + # Top row + res.plot_latency_distribution(axes[0, 0]) + res.plot_throughput(axes[0, 1]) - # Optional sanity checks (very rough) - run_sanity_checks(runner, results) + # Bottom row — first server, if present + sids = res.list_server_ids() + if sids: + sid = sids[0] + res.plot_single_server_ready_queue(axes[1, 0], sid) + res.plot_single_server_ram(axes[1, 1], sid) + else: + for ax in (axes[1, 0], axes[1, 1]): + ax.text(0.5, 0.5, "No servers", ha="center", va="center") + ax.axis("off") - # Save plots (2x2 figure), same layout as in the YAML-based example - try: - from matplotlib import pyplot as plt # noqa: PLC0415 + fig.tight_layout() + fig.savefig(out_path) + print(f"Plots saved to: {out_path}") - fig, axes = plt.subplots(2, 2, figsize=(12, 8)) - results.plot_latency_distribution(axes[0, 0]) - results.plot_throughput(axes[0, 1]) - results.plot_server_queues(axes[1, 0]) - results.plot_ram_usage(axes[1, 1]) - fig.tight_layout() - out_path = Path(__file__).parent / "single_server.png" - fig.savefig(out_path) - print(f"\n🖼️ Plots saved to: {out_path}") - except Exception as exc: # Matplotlib not installed or plotting failed - print(f"\n[plotting skipped] {exc!r}") - if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/examples/yaml_input/data/two_servers_lb.yml b/examples/yaml_input/data/two_servers_lb.yml index 9b71943..100a46b 100644 --- a/examples/yaml_input/data/two_servers_lb.yml +++ b/examples/yaml_input/data/two_servers_lb.yml @@ -1,96 +1,71 @@ -# AsyncFlow SimulationPayload (LB + 2 servers, realistic steps) +# AsyncFlow SimulationPayload — Load Balancer + 2 identical app servers +# +# Topology: +# generator → client → LB → srv-1 +# └→ srv-2 +# srv-1 → client +# srv-2 → client +# +# Each server runs: CPU(2 ms) → RAM(128 MB) → IO wait(12 ms) +# All network links use exponential latency with small means (2–3 ms). +# +# Workload targets ~40 rps (120 users × 20 req/min ÷ 60). rqs_input: - id: "rqs-1" - avg_active_users: - mean: 120 - avg_request_per_minute_per_user: - mean: 20 + id: rqs-1 + avg_active_users: { mean: 120 } + avg_request_per_minute_per_user: { mean: 20 } user_sampling_window: 60 topology_graph: nodes: - client: - id: "client-1" + client: { id: client-1 } + + load_balancer: + id: lb-1 + algorithms: round_robin + server_covered: [srv-1, srv-2] servers: - - id: "srv-1" - server_resources: - cpu_cores: 1 - ram_mb: 1024 + - id: srv-1 + server_resources: { cpu_cores: 1, ram_mb: 2048 } endpoints: - - endpoint_name: "/api" + - endpoint_name: /api steps: - - kind: "initial_parsing" - step_operation: - cpu_time: 0.002 - - kind: "ram" - step_operation: - necessary_ram: 64 - - kind: "io_wait" - step_operation: - io_waiting_time: 0.012 + - kind: initial_parsing + step_operation: { cpu_time: 0.002 } # 2 ms CPU (blocks event loop) + - kind: ram + step_operation: { necessary_ram: 128 } # 128 MB working set + - kind: io_wait + step_operation: { io_waiting_time: 0.012 } # 12 ms non-blocking I/O - - id: "srv-2" - server_resources: - cpu_cores: 2 - ram_mb: 2048 + - id: srv-2 + server_resources: { cpu_cores: 1, ram_mb: 2048 } endpoints: - - endpoint_name: "/api" + - endpoint_name: /api steps: - - kind: "ram" - step_operation: - necessary_ram: 96 - - kind: "io_db" - step_operation: - io_waiting_time: 0.020 - - kind: "cpu_bound_operation" # <-- was 'final_processing' (invalid) - step_operation: - cpu_time: 0.001 - - load_balancer: - id: "lb-1" - algorithms: "round_robin" - server_covered: ["srv-1", "srv-2"] + - kind: initial_parsing + step_operation: { cpu_time: 0.002 } + - kind: ram + step_operation: { necessary_ram: 128 } + - kind: io_wait + step_operation: { io_waiting_time: 0.012 } edges: - - id: "gen-client" - source: "rqs-1" - target: "client-1" - latency: { mean: 0.003, distribution: "exponential" } - - - id: "client-lb" - source: "client-1" - target: "lb-1" - latency: { mean: 0.002, distribution: "exponential" } - - - id: "lb-srv1" - source: "lb-1" - target: "srv-1" - latency: { mean: 0.002, distribution: "exponential" } - - - id: "lb-srv2" - source: "lb-1" - target: "srv-2" - latency: { mean: 0.002, distribution: "exponential" } - - - id: "srv1-client" - source: "srv-1" - target: "client-1" - latency: { mean: 0.003, distribution: "exponential" } - - - id: "srv2-client" - source: "srv-2" - target: "client-1" - latency: { mean: 0.003, distribution: "exponential" } + - { id: gen-client, source: rqs-1, target: client-1, latency: { mean: 0.003, distribution: exponential } } + - { id: client-lb, source: client-1, target: lb-1, latency: { mean: 0.002, distribution: exponential } } + - { id: lb-srv1, source: lb-1, target: srv-1, latency: { mean: 0.002, distribution: exponential } } + - { id: lb-srv2, source: lb-1, target: srv-2, latency: { mean: 0.002, distribution: exponential } } + - { id: srv1-client, source: srv-1, target: client-1, latency: { mean: 0.003, distribution: exponential } } + - { id: srv2-client, source: srv-2, target: client-1, latency: { mean: 0.003, distribution: exponential } } sim_settings: total_simulation_time: 600 - sample_period_s: 0.02 + sample_period_s: 0.05 enabled_sample_metrics: - - "ready_queue_len" - - "event_loop_io_sleep" - - "ram_in_use" - - "edge_concurrent_connection" + - ready_queue_len + - event_loop_io_sleep + - ram_in_use + - edge_concurrent_connection enabled_event_metrics: - - "rqs_clock" + - rqs_clock diff --git a/examples/yaml_input/lb_dashboard.png b/examples/yaml_input/lb_dashboard.png new file mode 100644 index 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Run: - python run_with_pybuilder.py + python run_with_builder.py """ from __future__ import annotations @@ -36,18 +36,18 @@ import simpy # ── AsyncFlow domain imports ─────────────────────────────────────────────────── -from asyncflow.pybuilder.input_builder import AsyncFlow +from asyncflow.builder.asyncflow_builder import AsyncFlow from asyncflow.runtime.simulation_runner import SimulationRunner from asyncflow.metrics.analyzer import ResultsAnalyzer -from asyncflow.schemas.full_simulation_input import SimulationPayload -from asyncflow.schemas.rqs_generator_input import RqsGeneratorInput -from asyncflow.schemas.simulation_settings_input import SimulationSettings -from asyncflow.schemas.system_topology.endpoint import Endpoint -from asyncflow.schemas.system_topology.full_system_topology import ( +from asyncflow.schemas.payload import SimulationPayload +from asyncflow.schemas.workload.rqs_generator import RqsGenerator +from asyncflow.schemas.settings.simulation import SimulationSettings +from asyncflow.schemas.topology.endpoint import Endpoint +from asyncflow.schemas.topology.nodes import ( Client, - Edge, Server, ) +from asyncflow.schemas.topology.edges import Edge from asyncflow.config.constants import LatencyKey, SampledMetricName @@ -160,9 +160,9 @@ def run_sanity_checks( # ───────────────────────────────────────────────────────────── -# Build the same scenario via AsyncFlow (pybuilder) +# Build the same scenario via AsyncFlow (builder) # ───────────────────────────────────────────────────────────── -def build_payload_with_pybuilder() -> SimulationPayload: +def build_payload_with_builder() -> SimulationPayload: """ Construct the SimulationPayload programmatically using the builder. @@ -174,7 +174,7 @@ def build_payload_with_pybuilder() -> SimulationPayload: - Simulation settings: 500s total, sample period 50ms """ # 1) Request generator - generator = RqsGeneratorInput( + generator = RqsGenerator( id="rqs-1", avg_active_users={"mean": 100}, avg_request_per_minute_per_user={"mean": 20}, @@ -255,11 +255,11 @@ def build_payload_with_pybuilder() -> SimulationPayload: def main() -> None: """ Build → wire → run the simulation, then print diagnostics. - Mirrors run_from_yaml.py but uses the pybuilder to construct the input. + Mirrors run_from_yaml.py but uses the builder to construct the input. Also saves a 2x2 plot figure (latency, throughput, server queues, RAM). """ env = simpy.Environment() - payload = build_payload_with_pybuilder() + payload = build_payload_with_builder() runner = SimulationRunner(env=env, simulation_input=payload) results: ResultsAnalyzer = runner.run() @@ -283,7 +283,7 @@ def main() -> None: results.plot_ram_usage(axes[1, 1]) fig.tight_layout() - out_path = Path(__file__).parent / "single_server_pybuilder.png" + out_path = Path(__file__).parent / "single_server_builder.png" fig.savefig(out_path) print(f"\n🖼️ Plots saved to: {out_path}") except Exception as exc: # Matplotlib not installed or plotting failed diff --git a/examples/single_server_yml.png b/examples/single_server_yml.png index 560a4b3e3eabe2e0b8408c3e322656deb770d7af..76c21154dc9eed6dfb7d453c1e103dc40e659502 100644 GIT binary patch literal 166693 zcmcG$byQYc*Ef6t5()^?9U>(y-O?&bsgyJ#NT-05NJ^I=N+_tHbc1w*NU3x;N`rKL zbL+Xi&-1+F8{Z%A_{QN3^#b-@d#$Me|+4KK?7x_zVAc^+Bf56{R z?i}a8u7J~!$B0SxuVI{-8i+y<{qHSso~~Q{|2B-^tgs7AibhSr7YrUMr}s|2OE8#@ 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