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From "Jason Lowe (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAPREDUCE-5043) Fetch failure processing can cause AM event queue to backup and eventually OOM
Date Sat, 02 Mar 2013 00:45:13 GMT

    [ https://issues.apache.org/jira/browse/MAPREDUCE-5043?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13591157#comment-13591157

Jason Lowe commented on MAPREDUCE-5043:

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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