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[SPARK-39347][SS] Bug fix for time window calculation when event time < 0 #39843

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[SPARK-39347][SS] Bug fix for time window calculation when event time < 0 #39843

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lazy val version
WweiL Jan 30, 2023
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WweiL Jan 30, 2023
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fix comment error
WweiL Jan 30, 2023
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fix comment error
WweiL Jan 30, 2023
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WweiL Feb 1, 2023
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WweiL Feb 1, 2023
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WweiL Feb 3, 2023
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Feb 3, 2023
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WweiL Feb 3, 2023
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10 changes: 6 additions & 4 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveTimeWindows.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.analysis

import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, AttributeReference, Cast, CreateNamedStruct, Expression, GetStructField, IsNotNull, Literal, PreciseTimestampConversion, SessionWindow, Subtract, TimeWindow, WindowTime}
import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, AttributeReference, CaseWhen, Cast, CreateNamedStruct, Expression, GetStructField, IsNotNull, LessThan, Literal, PreciseTimestampConversion, SessionWindow, Subtract, TimeWindow, WindowTime}
import org.apache.spark.sql.catalyst.plans.logical.{Expand, Filter, LogicalPlan, Project}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.catalyst.trees.TreePattern.{SESSION_WINDOW, TIME_WINDOW, WINDOW_TIME}
Expand Down Expand Up @@ -49,7 +49,8 @@ object TimeWindowing extends Rule[LogicalPlan] {
* The windows are calculated as below:
* maxNumOverlapping <- ceil(windowDuration / slideDuration)
* for (i <- 0 until maxNumOverlapping)
* lastStart <- timestamp - (timestamp - startTime + slideDuration) % slideDuration
* remainder <- (timestamp - startTime) % slideDuration
* lastStart <- timestamp - ((remainder < 0) ? remainder + slideDuration : remainder)
* windowStart <- lastStart - i * slideDuration
* windowEnd <- windowStart + windowDuration
* return windowStart, windowEnd
Expand Down Expand Up @@ -103,8 +104,9 @@ object TimeWindowing extends Rule[LogicalPlan] {

def getWindow(i: Int, dataType: DataType): Expression = {
val timestamp = PreciseTimestampConversion(window.timeColumn, dataType, LongType)
val lastStart = timestamp - (timestamp - window.startTime
+ window.slideDuration) % window.slideDuration
val remainder = (timestamp - window.startTime) % window.slideDuration
val lastStart = timestamp - CaseWhen(Seq((LessThan(remainder, 0),
remainder + window.slideDuration)), Some(remainder))
val windowStart = lastStart - i * window.slideDuration
val windowEnd = windowStart + window.windowDuration

Expand Down
36 changes: 36 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala
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Expand Up @@ -314,6 +314,42 @@ class DataFrameTimeWindowingSuite extends QueryTest with SharedSparkSession {
Row("1970-01-01 00:00:05", "1970-01-01 00:00:15", 2))
)
}

val df3 = Seq(
("1969-12-31 00:00:02", 1),
("1969-12-31 00:00:12", 2)).toDF("time", "value")
val df4 = Seq(
(LocalDateTime.parse("1969-12-31T00:00:02"), 1),
(LocalDateTime.parse("1969-12-31T00:00:12"), 2)).toDF("time", "value")

Seq(df3, df4).foreach { df =>
checkAnswer(
df.select(window($"time", "10 seconds", "10 seconds", "5 seconds"), $"value")
.orderBy($"window.start".asc)
.select($"window.start".cast(StringType), $"window.end".cast(StringType), $"value"),
Seq(
Row("1969-12-30 23:59:55", "1969-12-31 00:00:05", 1),
Row("1969-12-31 00:00:05", "1969-12-31 00:00:15", 2))
)
}

val df5 = Seq(
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@HeartSaVioR HeartSaVioR Feb 2, 2023

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nit: is this test case testing other edge case than above one? Otherwise let's remove this.

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@WweiL WweiL Feb 3, 2023

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Right this is actually testing some other cases, below I put year 1968 not 1969. The 1969 test case tests when window start is less than 0 but window end is greater than 0. I added this case to test when both start and end are less than 0.

("1968-12-31 00:00:02", 1),
("1968-12-31 00:00:12", 2)).toDF("time", "value")
val df6 = Seq(
(LocalDateTime.parse("1968-12-31T00:00:02"), 1),
(LocalDateTime.parse("1968-12-31T00:00:12"), 2)).toDF("time", "value")

Seq(df5, df6).foreach { df =>
checkAnswer(
df.select(window($"time", "10 seconds", "10 seconds", "5 seconds"), $"value")
.orderBy($"window.start".asc)
.select($"window.start".cast(StringType), $"window.end".cast(StringType), $"value"),
Seq(
Row("1968-12-30 23:59:55", "1968-12-31 00:00:05", 1),
Row("1968-12-31 00:00:05", "1968-12-31 00:00:15", 2))
)
}
}

test("multiple time windows in a single operator throws nice exception") {
Expand Down