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https://issues.apache.org/jira/browse/SPARK-20426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15978317#comment-15978317
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Sean Owen commented on SPARK-20426:
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That's not in the NodeManager, right? isn't that application memory?
It's not clear to me that's "too large" but I'm not familiar with the code. But
that's what you need to argue here.
> 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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