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[SPARK-57847][SQL] Support the TIME data type in approx_count_distinct_for_intervals #56934

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[SPARK-57847][SQL] Support the TIME data type in approx_count_distinct_for_intervals #56934

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[SPARK-57847][SQL] Support the TIME data type in approx_count_distinc…
yadavay-amzn Jul 1, 2026
cc26b38
[SPARK-57847][SQL] Use realistic time-of-day magnitudes in approx_cou…
yadavay-amzn Jul 1, 2026
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8 changes: 4 additions & 4 deletions ...org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ case class ApproxCountDistinctForIntervals(

override def inputTypes: Seq[AbstractDataType] = {
Seq(TypeCollection(NumericType, TimestampType, DateType, TimestampNTZType,
YearMonthIntervalType, DayTimeIntervalType), ArrayType)
YearMonthIntervalType, DayTimeIntervalType, AnyTimeType), ArrayType)
}

// Mark as lazy so that endpointsExpression is not evaluated during tree transformation.
Expand All @@ -90,7 +90,7 @@ case class ApproxCountDistinctForIntervals(
} else {
endpointsExpression.dataType match {
case ArrayType(_: NumericType | DateType | TimestampType | TimestampNTZType |
_: AnsiIntervalType, _) =>
_: AnsiIntervalType | _: AnyTimeType, _) =>
if (endpoints.length < 2) {
DataTypeMismatch(
errorSubClass = "WRONG_NUM_ENDPOINTS",
Expand All @@ -100,7 +100,7 @@ case class ApproxCountDistinctForIntervals(
}
case inputType =>
val requiredElemTypes = toSQLType(TypeCollection(
NumericType, DateType, TimestampType, TimestampNTZType, AnsiIntervalType))
NumericType, DateType, TimestampType, TimestampNTZType, AnsiIntervalType, AnyTimeType))
DataTypeMismatch(
errorSubClass = "UNEXPECTED_INPUT_TYPE",
messageParameters = Map(
Expand Down Expand Up @@ -144,7 +144,7 @@ case class ApproxCountDistinctForIntervals(
.toDouble(value.asInstanceOf[PhysicalNumericType#InternalType])
case _: DateType | _: YearMonthIntervalType =>
value.asInstanceOf[Int].toDouble
case TimestampType | TimestampNTZType | _: DayTimeIntervalType =>
case TimestampType | TimestampNTZType | _: DayTimeIntervalType | _: AnyTimeType =>
value.asInstanceOf[Long].toDouble
}

Expand Down
49 changes: 45 additions & 4 deletions ...pache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervalsSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
package org.apache.spark.sql.catalyst.expressions.aggregate

import java.sql.{Date, Timestamp}
import java.time.LocalDateTime
import java.time.{LocalDateTime, LocalTime}

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.InternalRow
Expand All @@ -44,7 +44,7 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
messageParameters = Map(
"paramIndex" -> ordinalNumber(0),
"requiredType" -> ("(\"NUMERIC\" or \"TIMESTAMP\" or \"DATE\" or \"TIMESTAMP_NTZ\"" +
" or \"INTERVAL YEAR TO MONTH\" or \"INTERVAL DAY TO SECOND\")"),
" or \"INTERVAL YEAR TO MONTH\" or \"INTERVAL DAY TO SECOND\" or \"TIME\")"),
"inputSql" -> "\"a\"",
"inputType" -> toSQLType(dataType)
)
Expand Down Expand Up @@ -92,7 +92,7 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
errorSubClass = "UNEXPECTED_INPUT_TYPE",
messageParameters = Map(
"paramIndex" -> ordinalNumber(1),
"requiredType" -> "ARRAY OF (\"NUMERIC\" or \"DATE\" or \"TIMESTAMP\" or \"TIMESTAMP_NTZ\" or \"ANSI INTERVAL\")",
"requiredType" -> "ARRAY OF (\"NUMERIC\" or \"DATE\" or \"TIMESTAMP\" or \"TIMESTAMP_NTZ\" or \"ANSI INTERVAL\" or \"TIME\")",
"inputSql" -> "\"array(foobar)\"",
"inputType" -> "\"ARRAY<STRING>\"")))
// scalastyle:on line.size.limit
Expand Down Expand Up @@ -230,7 +230,9 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
(intRecords.map(DateTimeUtils.toJavaTimestamp(_)),
intEndpoints.map(DateTimeUtils.toJavaTimestamp(_)), TimestampType),
(intRecords.map(DateTimeUtils.microsToLocalDateTime(_)),
intEndpoints.map(DateTimeUtils.microsToLocalDateTime(_)), TimestampNTZType)
intEndpoints.map(DateTimeUtils.microsToLocalDateTime(_)), TimestampNTZType),
(intRecords.map(i => LocalTime.ofNanoOfDay(i.toLong)),
intEndpoints.map(i => LocalTime.ofNanoOfDay(i.toLong)), TimeType())
)

inputs.foreach { case (records, endpoints, dataType) =>
Expand All @@ -241,6 +243,7 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
case d: Date => DateTimeUtils.fromJavaDate(d)
case t: Timestamp => DateTimeUtils.fromJavaTimestamp(t)
case ldt: LocalDateTime => DateTimeUtils.localDateTimeToMicros(ldt)
case lt: LocalTime => DateTimeUtils.localTimeToNanos(lt)
case _ => r
}
input.update(0, value)
Expand All @@ -253,6 +256,44 @@ class ApproxCountDistinctForIntervalsSuite extends SparkFunSuite {
}
}

test("TIME type with realistic nanos-of-day magnitudes") {
// Realistic time-of-day values in nanos: midnight, 06:00, 12:00, 18:00, near max
// LocalTime.MAX is 23:59:59.999999999 = 86_399_999_999_999 nanos
val midnight = 0L
val sixAm = 6L * 3600L * 1000000000L // 21_600_000_000_000
val noon = 12L * 3600L * 1000000000L // 43_200_000_000_000
val sixPm = 18L * 3600L * 1000000000L // 64_800_000_000_000
val nearMax = 86399999999999L // 23:59:59.999999999

val endpoints = Array(midnight, sixAm, noon, sixPm, nearMax)
.map(n => LocalTime.ofNanoOfDay(n))

// Generate distinct values per interval using minute-granularity nanos.
// [midnight, 6AM): 100 distinct minutes (00:00 .. 01:39)
// [6AM, noon): 80 distinct minutes (06:00 .. 07:19)
// [noon, 6PM): 60 distinct minutes (12:00 .. 12:59)
// [6PM, nearMax]: 50 distinct values including edge nearMax
val minuteNanos = 60L * 1000000000L
val interval1 = (0 until 100).map(i => midnight + i * minuteNanos)
val interval2 = (0 until 80).map(i => sixAm + i * minuteNanos)
val interval3 = (0 until 60).map(i => noon + i * minuteNanos)
val interval4 = (0 until 49).map(i => sixPm + i * minuteNanos) :+ nearMax

val allNanos = interval1 ++ interval2 ++ interval3 ++ interval4

val (aggFunc, input, buffer) = createEstimator(endpoints, TimeType())
allNanos.foreach { n =>
input.update(0, n)
aggFunc.update(buffer, input)
}

// 4 intervals: [midnight,6AM), [6AM,noon), [noon,6PM), [6PM,nearMax]
checkNDVs(
ndvs = aggFunc.eval(buffer).asInstanceOf[ArrayData].toLongArray(),
expectedNdvs = Array(100, 80, 60, 50),
rsd = aggFunc.relativeSD)
}

private def checkNDVs(ndvs: Array[Long], expectedNdvs: Array[Long], rsd: Double): Unit = {
assert(ndvs.length == expectedNdvs.length)
for (i <- ndvs.indices) {
Expand Down