nyingping opened a new pull request, #36737:
URL: https://github.com/apache/spark/pull/36737

   
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   ### What changes were proposed in this pull request?
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   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?
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   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.
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   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.
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   No
   
   ### How was this patch tested?
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   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.


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