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https://issues.apache.org/jira/browse/SPARK-4927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14359294#comment-14359294
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Ilya Ganelin commented on SPARK-4927:
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Are you running over yarn? My theory is that the memory usage has to do with
data movement between nodes.
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> Spark does not clean up properly during long jobs.
> ---------------------------------------------------
>
> Key: SPARK-4927
> URL: https://issues.apache.org/jira/browse/SPARK-4927
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.1.0
> Reporter: Ilya Ganelin
>
> On a long running Spark job, Spark will eventually run out of memory on the
> driver node due to metadata overhead from the shuffle operation. Spark will
> continue to operate, however with drastically decreased performance (since
> swapping now occurs with every operation).
> The spark.cleanup.tll parameter allows a user to configure when cleanup
> happens but the issue with doing this is that it isn’t done safely, e.g. If
> this clears a cached RDD or active task in the middle of processing a stage,
> this ultimately causes a KeyNotFoundException when the next stage attempts to
> reference the cleared RDD or task.
> There should be a sustainable mechanism for cleaning up stale metadata that
> allows the program to continue running.
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