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https://issues.apache.org/jira/browse/MAPREDUCE-5043?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13591157#comment-13591157
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Jason Lowe commented on MAPREDUCE-5043:
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One approach to fixing this is to have {{TaskAttempt}} provide a cheap
interface for getting just the phase. The fetch failure processing can then
compute the total number of reducers in the shuffle phase *before* iterating
through the maps with fetch failures rather than computing it redundantly for
each map attempt.
> Fetch failure processing can cause AM event queue to backup and eventually OOM
> ------------------------------------------------------------------------------
>
> Key: MAPREDUCE-5043
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-5043
> Project: Hadoop Map/Reduce
> Issue Type: Bug
> Components: mr-am
> Affects Versions: 0.23.7, 2.0.4-beta
> Reporter: Jason Lowe
> Assignee: Jason Lowe
> Priority: Blocker
>
> Saw an MRAppMaster with a 3G heap OOM. Upon investigating another instance
> of it running, we saw the UI in a weird state where the task table and task
> attempt tables in the job overview page weren't consistent. The AM log
> showed the AsyncDispatcher had hundreds of thousands of events in the event
> queue, and jstacks showed it spending a lot of time in fetch failure
> processing. It turns out fetch failure processing is currently *very*
> expensive, with a triple {{for}} loop where the inner loop is calling the
> quite-expensive {{TaskAttempt.getReport}}. That function ends up
> type-converting the entire task report, counters and all, and performing
> locale conversions among other things. It does this for every reduce task in
> the job, for every map task that failed. And when it's done building up the
> large task report, it pulls out one field, the phase, then throws the report
> away.
> While the AM is busy processing fetch failures, tasks attempts are continuing
> to send events to the AM including memory-expensive events like status
> updates which include the counters. These back up in the AsyncDispatcher
> event queue and eventually even an AM with a large heap size will run out of
> memory and crash or expire because it thrashes in garbage collect.
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