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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: 2.2.2

> 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: 2.2.2
>
>         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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