nyingping opened a new pull request, #36737: URL: https://github.com/apache/spark/pull/36737
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If there is a discussion in the mailing list, please add the link. --> Fix bug that Generate wrong time window when (timestamp-startTime) % slideDuration < 0 The original time window generation rule ``` lastStart <- timestamp - (timestamp - startTime + slideDuration) % slideDuration ``` change like this ``` remainder <- (timestamp - startTime) % slideDuration lastStart <- if (remainder < 0) timestamp - remainder - slideDuration else timestamp - remainder ``` reference: [https://github.com/apache/flink/pull/18982](https://github.com/apache/flink/pull/18982) ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> Since the generation strategy of the sliding window in PR [#35362](https://github.com/apache/spark/pull/35362) is changed to the current one, and that leads to a new problem. A window generation error occurs when the time required to process the recorded data is negative and the modulo value between the time and window length is less than 0. In the current test cases, this bug does not thorw up. [ test("negative timestamps")](https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala#L299) ``` val df1 = Seq( ("1970-01-01 00:00:02", 1), ("1970-01-01 00:00:12", 2)).toDF("time", "value") val df2 = Seq( (LocalDateTime.parse("1970-01-01T00:00:02"), 1), (LocalDateTime.parse("1970-01-01T00:00:12"), 2)).toDF("time", "value") Seq(df1, df2).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), $"value"), Seq( Row("1969-12-31 23:59:55", "1970-01-01 00:00:05", 1), Row("1970-01-01 00:00:05", "1970-01-01 00:00:15", 2)) ) } ``` The timestamp of the above test data is not negative, and the value modulo the window length is not negative, so it can be passes the test case. An exception occurs when the timestamp becomes something like this. ``` val df3 = Seq( ("1969-12-31 00:00:02", 1), ("1969-12-31 00:00:12", 2)).toDF("time", "value") val df4 = Seq( (LocalDateTime.parse("1969-12-31T00:00:02"), 1), (LocalDateTime.parse("1969-12-31T00:00:12"), 2)).toDF("time", "value") Seq(df3, df4).foreach { df => checkAnswer( df.select(window($"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)) ) } ``` run and get unexpected result: ``` == Results == !== Correct Answer - 2 == == Spark Answer - 2 == !struct<> struct<CAST(window.start AS STRING):string,CAST(window.end AS STRING):string,value:int> ![1969-12-30 23:59:55,1969-12-31 00:00:05,1] [1969-12-31 00:00:05,1969-12-31 00:00:15,1] ![1969-12-31 00:00:05,1969-12-31 00:00:15,2] [1969-12-31 00:00:15,1969-12-31 00:00:25,2] ``` ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as the documentation fix. If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master. If no, write 'No'. --> No ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. If benchmark tests were added, please run the benchmarks in GitHub Actions for the consistent environment, and the instructions could accord to: https://spark.apache.org/developer-tools.html#github-workflow-benchmarks. --> Add new unit test. **benchmark result** oldlogic[#18364](https://github.com/apache/spark/pull/18364) VS 【fix version】 ``` Running benchmark: tumbling windows Running case: old logic Stopped after 407 iterations, 10012 ms Running case: new logic Stopped after 615 iterations, 10007 ms Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Windows 10 10.0 Intel64 Family 6 Model 158 Stepping 10, GenuineIntel tumbling windows: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ old logic 17 25 9 580.1 1.7 1.0X new logic 15 16 2 680.8 1.5 1.2X Running benchmark: sliding windows Running case: old logic Stopped after 10 iterations, 10296 ms Running case: new logic Stopped after 15 iterations, 10391 ms Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Windows 10 10.0 Intel64 Family 6 Model 158 Stepping 10, GenuineIntel sliding windows: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ old logic 1000 1030 19 10.0 100.0 1.0X new logic 668 693 21 15.0 66.8 1.5X ``` Fixed version than PR [#38069](https://github.com/apache/spark/pull/35362) lost a bit of the performance. -- This is an automated message from the Apache Git Service. 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