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https://issues.apache.org/jira/browse/SPARK-22471?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Felix Cheung updated SPARK-22471:
---------------------------------
Target Version/s: 2.2.1 (was: 2.2.2)
Fix Version/s: (was: 2.2.2)
2.2.1
> SQLListener consumes much memory causing OutOfMemoryError
> ---------------------------------------------------------
>
> Key: SPARK-22471
> URL: https://issues.apache.org/jira/browse/SPARK-22471
> Project: Spark
> Issue Type: Bug
> Components: SQL, Web UI
> Affects Versions: 2.2.0
> Environment: Spark 2.2.0, Linux
> Reporter: Arseniy Tashoyan
> Assignee: Arseniy Tashoyan
> Labels: memory-leak, sql
> Fix For: 2.2.1
>
> Attachments: SQLListener_retained_size.png,
> SQLListener_stageIdToStageMetrics_retained_size.png
>
> Original Estimate: 72h
> Remaining Estimate: 72h
>
> _SQLListener_ may grow very large when Spark runs complex multi-stage
> requests. The listener tracks metrics for all stages in
> __stageIdToStageMetrics_ hash map. _SQLListener_ has some means to cleanup
> this hash map regularly, but this is not enough. Precisely, the method
> _trimExecutionsIfNecessary_ ensures that __stageIdToStageMetrics_ does not
> have metrics for very old data; this method runs on each execution completion.
> However, if an execution has many stages, _SQLListener_ keeps adding new
> entries to __stageIdToStageMetrics_ without calling
> _trimExecutionsIfNecessary_. The hash map may grow to enormous size.
> Strictly speaking, it is not a memory leak, because finally
> _trimExecutionsIfNecessary_ cleans the hash map. However, the driver program
> has high odds to crash with OutOfMemoryError (and it does).
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