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https://issues.apache.org/jira/browse/MAPREDUCE-2905?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Todd Lipcon updated MAPREDUCE-2905:
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Attachment: mr-2905.txt
How does this patch look, Jeff? I took a slightly different approach and simply
passed the number of already-assigned tasks in as part of the API. It breaks
the LoadManager API, but in a very simple way, and it's only very rarely
extended (and by expert users).
This made the changes in CapBasedLoadManager much simpler.
If you're able to perform the manual sleep-job test to verify this, that would
be awesome.
> CapBasedLoadManager incorrectly allows assignment when assignMultiple is true
> (was: assignmultiple per job)
> -----------------------------------------------------------------------------------------------------------
>
> Key: MAPREDUCE-2905
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-2905
> Project: Hadoop Map/Reduce
> Issue Type: Bug
> Components: contrib/fair-share
> Affects Versions: 0.20.2
> Reporter: Jeff Bean
> Attachments: MR-2905.10-13-2011, MR-2905.patch, MR-2905.patch.2,
> mr-2905.txt, screenshot-1.jpg
>
>
> We encountered a situation where in the same cluster, large jobs benefit from
> mapred.fairscheduler.assignmultiple, but small jobs with small numbers of
> mappers do not: the mappers all clump to fully occupy just a few nodes, which
> causes those nodes to saturate and bottleneck. The desired behavior is to
> spread the job across more nodes so that a relatively small job doesn't
> saturate any node in the cluster.
> Testing has shown that setting mapred.fairscheduler.assignmultiple to false
> gives the desired behavior for small jobs, but is unnecessary for large jobs.
> However, since this is a cluster-wide setting, we can't properly tune.
> It'd be nice if jobs can set a param similar to
> mapred.fairscheduler.assignmultiple on submission to better control the task
> distribution of a particular job.
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