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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 Mon, 07 Mar 2016 20:50:40 GMT

    [ https://issues.apache.org/jira/browse/FLINK-3322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15183674#comment-15183674
] 

ASF GitHub Bot commented on FLINK-3322:
---------------------------------------

Github user Xazax-hun commented on the pull request:

    https://github.com/apache/flink/pull/1769#issuecomment-193443590
  
    > I would be curious about the soft reference implementation as in DetaIteration cases
I think it is a valid situation that the job needs less and less memory. Please add the licence
header to the manual test file.
    
    This is my first attempt to use a "soft reference pool":
    https://github.com/Xazax-hun/flink/commit/2694910e53b2f86412f2a9c3e4d83cf1705e3c65
    
    I could not measure any performance gain compared to the code before this pull request
as a baseline. Hopefully I will have some extra time tomorrow, so I can further investigate
whether there is something wrong with my first implementation or the approach.


> 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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