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https://issues.apache.org/jira/browse/SPARK-4927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14358958#comment-14358958
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Ilya Ganelin edited comment on SPARK-4927 at 3/12/15 6:50 PM:
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Hi Sean - I have a code snippet that reproduced this. Let me send it to you in
a bit - I don't have the means to run 1.3 in a cluster.
Realized that I already had that code snippet posted. Running the above code
doesn't reproduce the issue?
was (Author: ilganeli):
Hi Sean - I have a code snippet that reproduced this. Let me send it to you in
a bit - I don't have the means to run 1.3 in a cluster.
Sent with Good (www.good.com)
> 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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