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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-20426) OneForOneStreamManager occupies too much memory.
Date Mon, 24 Apr 2017 12:55:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-20426?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-20426:
------------------------------------

    Assignee:     (was: Apache Spark)

> 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* objects. 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 metadatas(i.e. StreamState).



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