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Posted to reviews@spark.apache.org by "sahnib (via GitHub)" <gi...@apache.org> on 2024/04/12 23:50:54 UTC

[PR] [SPARK-47840][SS] Disable foldable propagation across Streaming Aggregate/Join nodes [spark]

sahnib opened a new pull request, #46035:
URL: https://github.com/apache/spark/pull/46035

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   ### What changes were proposed in this pull request?
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   Streaming queries with Union of 2 data streams followed by an Aggregate (groupBy) can produce incorrect results if the grouping key is a constant literal for micro-batch duration.
   
   The query produces incorrect results because the query optimizer recognizes the literal value in the grouping key as foldable and replaces the grouping key expression with the actual literal value. This optimization is correct for batch queries. However Streaming queries also read information from StateStore, and the output contains both the results from StateStore (computed in previous microbatches) and data from input sources (computed in this microbatch). The HashAggregate node after StateStore always reads grouping key value as the optimized literal (as the grouping key expression is optimized into a literal by the optimizer). This ends up replacing keys in StateStore with the literal value resulting in incorrect output. 
   
   See an example logical and physical plan below for a query performing a union on 2 data streams, followed by a groupBy. Note that the name#4 expression has been optimized to ds1. The Streaming query Aggregate adds StateStoreSave node as child of HashAggregate, however any grouping key read from StateStore will still be read as ds1 due to the optimization. 
   
   
   ### Optimized Logical Plan
   
   ```
   === Applying Rule org.apache.spark.sql.catalyst.optimizer.FoldablePropagation ===
   
   === Old Plan ===
   
   WriteToMicroBatchDataSource MemorySink, eb67645e-30fc-41a8-8006-35bb7649c202, Complete, 0
   +- Aggregate [name#4], [name#4, count(1) AS count#31L]
      +- Project [ds1 AS name#4]
     	+- StreamingDataSourceV2ScanRelation[value#1] MemoryStreamDataSource
   
   
   === New Plan ===
   
   WriteToMicroBatchDataSource MemorySink, eb67645e-30fc-41a8-8006-35bb7649c202, Complete, 0
   +- Aggregate [ds1], [ds1 AS name#4, count(1) AS count#31L]
      +- Project [ds1 AS name#4]
     	+- StreamingDataSourceV2ScanRelation[value#1] MemoryStreamDataSource
   
   
   ====
   ```
   
   
   ### Corresponding Physical Plan
   
   ```
   WriteToDataSourceV2 MicroBatchWrite[epoch: 0, writer: org.apache.spark.sql.execution.streaming.sources.MemoryStreamingWrite@2b4c6242], org.apache.spark.sql.execution.datasources.v2.DataSourceV2Strategy$$Lambda$3143/1859075634@35709d26
   +- HashAggregate(keys=[ds1#39], functions=[finalmerge_count(merge count#38L) AS count(1)#30L], output=[name#4, count#31L])
      +- StateStoreSave [ds1#39], state info [ checkpoint = file:/tmp/streaming.metadata-e470782a-18a3-463c-9e61-3a10d0bdf180/state, runId = 4dedecca-910c-4518-855e-456702617414, opId = 0, ver = 0, numPartitions = 5], Complete, 0, 0, 2
     	+- HashAggregate(keys=[ds1#39], functions=[merge_count(merge count#38L) AS count#38L], output=[ds1#39, count#38L])
        	+- StateStoreRestore [ds1#39], state info [ checkpoint = file:/tmp/streaming.metadata-e470782a-18a3-463c-9e61-3a10d0bdf180/state, runId = 4dedecca-910c-4518-855e-456702617414, opId = 0, ver = 0, numPartitions = 5], 2
           	+- HashAggregate(keys=[ds1#39], functions=[merge_count(merge count#38L) AS count#38L], output=[ds1#39, count#38L])
              	+- HashAggregate(keys=[ds1 AS ds1#39], functions=[partial_count(1) AS count#38L], output=[ds1#39, count#38L])
                 	+- Project
                    	+- MicroBatchScan[value#1] MemoryStreamDataSource
   
   ```
   
   This PR disables foldable propagation across Streaming Aggregate/Join nodes in the logical plan. 
   
   ### 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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   Changes are needed to ensure that Streaming queries with literal value for grouping key/join key produce correct results. 
   
   ### Does this PR introduce _any_ user-facing change?
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   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.
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   No
   
   ### How was this patch tested?
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   Added `sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingQueryOptimizationCorrectnessSuite.scala` testcase.
   
   ```
   
   [info] Run completed in 54 seconds, 150 milliseconds.
   [info] Total number of tests run: 9
   [info] Suites: completed 1, aborted 0
   [info] Tests: succeeded 9, failed 0, canceled 0, ignored 0, pending 0
   [info] All tests passed.
   
   ```
   
   ### Was this patch authored or co-authored using generative AI tooling?
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   No


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Re: [PR] [SPARK-47840][SS] Disable foldable propagation across Streaming Aggregate/Join nodes [spark]

Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.

cloud-fan commented on PR #46035:
URL: https://github.com/apache/spark/pull/46035#issuecomment-2056640353

   Shall we revisit all the rules that match Aggregate/Join? It's a good point that streaming aggregate/join also reads data from state and we can't optimize it with the query plan of the current micro-batch.


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