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https://issues.apache.org/jira/browse/MAPREDUCE-3473?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13159834#comment-13159834
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Subroto Sanyal commented on MAPREDUCE-3473:
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*mapreduce.map.failures.maxpercent* and *mapreduce.reduce.failures.maxpercent*
hold the percentage of failure tolerance of number of Tasks for Job to handle.
Say in case a map fails and it comes under the tolerance limit then the output
of the mapper is lost(will not be considered for further computation). Same is
with Reducer.
I suggest let user decide the this failure percentage and be ready for such
data loss otherwise, it will come to a surprise to user if the value is set to
non-zero.
Further I feel there won't be any correct default non-zero value for these
configurations. These values depend on user scenarios/use-cases.
> Task failures shouldn't result in Job failures
> -----------------------------------------------
>
> Key: MAPREDUCE-3473
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-3473
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
> Components: tasktracker
> Affects Versions: 0.20.205.0, 0.23.0
> Reporter: Eli Collins
>
> Currently some task failures may result in job failures. Eg a local TT disk
> failure seen in TaskLauncher#run, TaskRunner#run, MapTask#run is visible to
> and can hang the JobClient, causing the job to fail. Job execution should
> always be able to survive a task failure if there are sufficient resources.
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