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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-3322) MemoryManager creates too much GC pressure with iterative jobs
Date Wed, 09 Mar 2016 17:43:40 GMT

    [ https://issues.apache.org/jira/browse/FLINK-3322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15187493#comment-15187493

ASF GitHub Bot commented on FLINK-3322:

Github user Xazax-hun commented on the pull request:

    I think the soft references solution is not worth investigating, and I agree that the
best way to solve the problem is to make the operators smarter for the iterative jobs. Do
you want to merge this pull request to temporarily solve the problem until the other solution
is materialized? In case the answer is no, I think I might give this Jira back to someone
else to be able to focus on serialization (in case the community accept me to work on that.)

> MemoryManager creates too much GC pressure with iterative jobs
> --------------------------------------------------------------
>                 Key: FLINK-3322
>                 URL: https://issues.apache.org/jira/browse/FLINK-3322
>             Project: Flink
>          Issue Type: Bug
>          Components: Distributed Runtime
>    Affects Versions: 1.0.0
>            Reporter: Gabor Gevay
>            Assignee: Gabor Horvath
>            Priority: Critical
>             Fix For: 1.0.0
> When taskmanager.memory.preallocate is false (the default), released memory segments
are not added to a pool, but the GC is expected to take care of them. This puts too much pressure
on the GC with iterative jobs, where the operators reallocate all memory at every superstep.
> See the following discussion on the mailing list:
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/Memory-manager-behavior-in-iterative-jobs-tt10066.html
> Reproducing the issue:
> https://github.com/ggevay/flink/tree/MemoryManager-crazy-gc
> The class to start is malom.Solver. If you increase the memory given to the JVM from
1 to 50 GB, performance gradually degrades by more than 10 times. (It will generate some lookuptables
to /tmp on first run for a few minutes.) (I think the slowdown might also depend somewhat
on taskmanager.memory.fraction, because more unused non-managed memory results in rarer GCs.)

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