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https://issues.apache.org/jira/browse/HADOOP-4018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12632088#action_12632088
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Amar Kamat commented on HADOOP-4018:
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Owen,
I think Dhruba's concern here is of many small/avg sized jobs collectively
overloading the jobtracker, see
[here|https://issues.apache.org/jira/browse/HADOOP-4018?focusedCommentId=12625505#action_12625505].
Capping individual jobs might not help as all the jobs will accumulate in JT's
memory and bring it down. I think some kind of local capping, global capping
and smart scheduling/initialization might help. But I agree that in the long
term we need to model the memory better but for now simple heuristics might
work.
> limit memory usage in jobtracker
> --------------------------------
>
> Key: HADOOP-4018
> URL: https://issues.apache.org/jira/browse/HADOOP-4018
> Project: Hadoop Core
> Issue Type: Bug
> Components: mapred
> Reporter: dhruba borthakur
> Assignee: dhruba borthakur
> Attachments: maxSplits.patch, maxSplits2.patch, maxSplits3.patch,
> maxSplits4.patch, maxSplits5.patch, maxSplits6.patch, maxSplits7.patch
>
>
> We have seen instances when a user submitted a job with many thousands of
> mappers. The JobTracker was running with 3GB heap, but it was still not
> enough to prevent memory trashing from Garbage collection; effectively the
> Job Tracker was not able to serve jobs and had to be restarted.
> One simple proposal would be to limit the maximum number of tasks per job.
> This can be a configurable parameter. Is there other things that eat huge
> globs of memory in job Tracker?
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