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Date: Fri, 21 May 2010 12:11:20 -0400 (EDT)
From: "Ramkumar Vadali (JIRA)" <jira@apache.org>
To: mapreduce-issues@hadoop.apache.org
Subject: [jira] Updated: (MAPREDUCE-1783) Task Initialization should be
 delayed till when a job can be run
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     [ https://issues.apache.org/jira/browse/MAPREDUCE-1783?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ramkumar Vadali updated MAPREDUCE-1783:
---------------------------------------

    Attachment: submit-mapreduce-1783.patch

Formatted patch, this should work.

> 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
>             Fix For: 0.22.0
>
>         Attachments: 0001-Pool-aware-job-initialization.patch, 0001-Pool-aware-job-initialization.patch.1, 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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