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https://issues.apache.org/jira/browse/SPARK-20426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15978345#comment-15978345
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jin xing edited comment on SPARK-20426 at 4/21/17 9:00 AM:
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That's inside NodeManager(not application memory). With
*spark.shuffle.service.enabled=true* , we can run external shuffle service,
which will be inside NodeManager's process. But the code is from Spark project
:)
was (Author: jinxing6...@126.com):
That's inside NodeManager(not application memory). With
*spark.shuffle.service.enabled=true* , we can run external shuffle service,
which will be inside NodeManager's process. But the code is from Spark project )
> OneForOneStreamManager occupies too much memory.
> ------------------------------------------------
>
> Key: SPARK-20426
> URL: https://issues.apache.org/jira/browse/SPARK-20426
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle
> Affects Versions: 2.1.0
> Reporter: jin xing
> Attachments: screenshot-1.png, screenshot-2.png
>
>
> Spark jobs are running on yarn cluster in my warehouse. We enabled the
> external shuffle service(*--conf spark.shuffle.service.enabled=true*).
> Recently NodeManager runs OOM now and then. Dumping heap memory, we find that
> *OneFroOneStreamManager*'s footprint is huge. NodeManager is configured with
> 5G heap memory. While *OneForOneManager* costs 2.5G and there are 5503233
> *FileSegmentManagedBuffer*. Is there any suggestions to avoid this other than
> just keep increasing NodeManager's memory? Is it possible to stop
> *registerStream* in OneForOneStreamManager? Thus we don't need to cache so
> many metadata(i.e. StreamState).
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