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Posted to mapreduce-issues@hadoop.apache.org by "Scott Chen (JIRA)" <ji...@apache.org> on 2010/12/01 01:43:17 UTC

[jira] Updated: (MAPREDUCE-1783) Task Initialization should be delayed till when a job can be run

     [ https://issues.apache.org/jira/browse/MAPREDUCE-1783?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Scott Chen updated MAPREDUCE-1783:
----------------------------------

       Resolution: Fixed
    Fix Version/s:     (was: 0.22.0)
                   0.23.0
           Status: Resolved  (was: Patch Available)

I just committed this. Thanks Ram.

> 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: 0.23.0
>
>         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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