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From "Konstantin Boudnik (JIRA)" <j...@apache.org>
Subject [jira] Commented: (MAPREDUCE-1783) Task Initialization should be delayed till when a job can be run
Date Tue, 15 Feb 2011 21:09:05 GMT

    [ https://issues.apache.org/jira/browse/MAPREDUCE-1783?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12995004#comment-12995004
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Konstantin Boudnik commented on MAPREDUCE-1783:
-----------------------------------------------

I have fixed it.

> Task Initialization should be delayed till when a job can be run
> ----------------------------------------------------------------
>
>                 Key: MAPREDUCE-1783
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-1783
>             Project: Hadoop Map/Reduce
>          Issue Type: Improvement
>          Components: contrib/fair-share
>    Affects Versions: 0.20.1
>            Reporter: Ramkumar Vadali
>            Assignee: Ramkumar Vadali
>             Fix For: 0.22.0, 0.23.0
>
>         Attachments: 0001-Pool-aware-job-initialization.patch, 0001-Pool-aware-job-initialization.patch.1,
MAPREDUCE-1783.patch, submit-mapreduce-1783.patch
>
>
> The FairScheduler task scheduler uses PoolManager to impose limits on the number of jobs
that can be running at a given time. However, jobs that are submitted are initiaiized immediately
by EagerTaskInitializationListener by calling JobInProgress.initTasks. This causes the job
split file to be read into memory. The split information is not needed until the number of
running jobs is less than the maximum specified. If the amount of split information is large,
this leads to unnecessary memory pressure on the Job Tracker.
> To ease memory pressure, FairScheduler can use another implementation of JobInProgressListener
that is aware of PoolManager limits and can delay task initialization until the number of
running jobs is below the maximum.

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