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Posted to issues@spark.apache.org by "XiDuo You (Jira)" <ji...@apache.org> on 2023/10/07 01:52:00 UTC
[jira] [Commented] (SPARK-45443) Revisit TableCacheQueryStage to avoid replicated InMemoryRelation materialization
[ https://issues.apache.org/jira/browse/SPARK-45443?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17772729#comment-17772729 ]
XiDuo You commented on SPARK-45443:
-----------------------------------
hi [~erenavsarogullari] , it seems that, it depends on the behavior of rdd cache. Say, what happens if we materialize a cached rdd twice at the same time ? There are some race condition in block manager per rdd partition so it makes things slow. BTW, what's the behavior before we have TableCacheQueryStage ? Does not it have this issue ?
> Revisit TableCacheQueryStage to avoid replicated InMemoryRelation materialization
> ---------------------------------------------------------------------------------
>
> Key: SPARK-45443
> URL: https://issues.apache.org/jira/browse/SPARK-45443
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.5.0
> Reporter: Eren Avsarogullari
> Priority: Major
> Attachments: IMR Materialization - Stage 2.png, IMR Materialization - Stage 3.png
>
>
> TableCacheQueryStage is created per InMemoryTableScanExec by AdaptiveSparkPlanExec and it materializes InMemoryTableScanExec output (cached RDD) to provide runtime stats in order to apply AQE optimizations into remaining physical plan stages. TableCacheQueryStage materializes InMemoryTableScanExec eagerly by submitting job per TableCacheQueryStage instance. For example, if there are 2 TableCacheQueryStage instances referencing same IMR instance (cached RDD) and first InMemoryTableScanExec' s materialization takes longer, following logic will return false (inMemoryTableScan.isMaterialized => false) and this may cause replicated IMR materialization. This behavior can be more visible when cached RDD size is high.
> [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/QueryStageExec.scala#L281]
> Would like to get community feedback. Thanks in advance.
> cc [~ulysses] [~cloud_fan]
> *Sample Query to simulate the problem:*
> // Both join legs uses same IMR instance
> {code:java}
> import spark.implicits._
> val arr = (1 to 12).map { i => {
> val index = i % 5
> (index, s"Employee_$index", s"Department_$index")
> }
> }
> val df = arr.toDF("id", "name", "department")
> .filter('id >= 0)
> .sort("id")
> .groupBy('id, 'name, 'department)
> .count().as("count")
> df.persist()
> val df2 = df.sort("count").filter('count <= 2)
> val df3 = df.sort("count").filter('count >= 3)
> val df4 = df2.join(df3, Seq("id", "name", "department"), "fullouter")
> df4.show() {code}
> *Physical Plan:*
> {code:java}
> == Physical Plan ==
> AdaptiveSparkPlan (31)
> +- == Final Plan ==
> CollectLimit (21)
> +- * Project (20)
> +- * SortMergeJoin FullOuter (19)
> :- * Sort (10)
> : +- * Filter (9)
> : +- TableCacheQueryStage (8), Statistics(sizeInBytes=210.0 B, rowCount=5)
> : +- InMemoryTableScan (1)
> : +- InMemoryRelation (2)
> : +- AdaptiveSparkPlan (7)
> : +- HashAggregate (6)
> : +- Exchange (5)
> : +- HashAggregate (4)
> : +- LocalTableScan (3)
> +- * Sort (18)
> +- * Filter (17)
> +- TableCacheQueryStage (16), Statistics(sizeInBytes=210.0 B, rowCount=5)
> +- InMemoryTableScan (11)
> +- InMemoryRelation (12)
> +- AdaptiveSparkPlan (15)
> +- HashAggregate (14)
> +- Exchange (13)
> +- HashAggregate (4)
> +- LocalTableScan (3) {code}
> *Stages DAGs materializing the same IMR instance:*
> !IMR Materialization - Stage 2.png|width=303,height=134!
> !IMR Materialization - Stage 3.png|width=303,height=134!
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