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From "Chen He (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (YARN-5140) NM usercache fill up with burst of jobs leading to rapid temp IO FS fill up and potentially NM outage
Date Tue, 03 Oct 2017 01:16:00 GMT

    [ https://issues.apache.org/jira/browse/YARN-5140?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16189144#comment-16189144

Chen He commented on YARN-5140:

Hi [~okalinin], this is a interesting issue. According to the description, if I understand
correctly, could we avoid multiple NM crash if we reduce the "yarn.nodemanager.localizer.cache.cleanup.interval-ms"
and increasing "yarn.nodemanager.localizer.cache.target-size-mb"?

> NM usercache fill up with burst of jobs leading to rapid temp IO FS fill up and potentially
NM outage
> -----------------------------------------------------------------------------------------------------
>                 Key: YARN-5140
>                 URL: https://issues.apache.org/jira/browse/YARN-5140
>             Project: Hadoop YARN
>          Issue Type: Bug
>          Components: nodemanager
>    Affects Versions: 2.7.0
>         Environment: Linux RHEL 6.7, Hadoop 2.7.0
>            Reporter: Oleksandr Kalinin
>            Priority: Minor
> A burst or rapid rate of submitted jobs with substantial NM usercache resource localization
footprint may lead to rapid fill up of the NM local temporary IO FS (/tmp by default) with
negative consequences in terms of stability.
> The core issue seems to be the fact that NM continues to localize the resources beyond
the maximum local cache size (yarn.nodemanager.localizer.cache.target-size-mb , default 10G).
Since maximum local cache size is effectively not taken into account when localizing new resources
(note that default cache cleanup interval is 10 min controlled by yarn.nodemanager.localizer.cache.cleanup.interval-ms),
this basically leads to sort of self-destruction scenario : once /tmp FS utilization reaches
the threshold of 90%, NM will automatically de-register from RM, effectively leading to NM
> This issue may offline many NMs simultaneously at the same time and thus is quite critical
in terms of platform stability.

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