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https://issues.apache.org/jira/browse/SPARK-10781?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16738932#comment-16738932
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nxet edited comment on SPARK-10781 at 1/10/19 2:52 AM:
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I met the same problem as some empty sequence files cause the failure of the
whole job,but by MR can run
normally(mapreduce.map.failures.maxpercent,mapreduce.reduce.failures.maxpercent),the
following is my source files:
_116.1 M 348.3 M /20181226/1545753600402.lzo_deflate_
_97.0 M 290.9 M /20181226/1545754236750.lzo_deflate_
_113.3 M 339.8 M /20181226/1545754856515.lzo_deflate_
_126.5 M 379.5 M /20181226/1545753600402.lzo_deflate_
_92.9 M 278.6 M /20181226/1545754233009.lzo_deflate_
_117.7 M 353.2 M /20181226/1545754850857.lzo_deflate_
_0 M 0 M /20181226/1545755455381.lzo_deflate_
_0 M 0 M /20181226/1545756056457.lzo_deflate_
was (Author: nxet):
I met the same problem as some empty sequence files cause the failure of the
whole job,but by MR can run
normally(mapreduce.map.failures.maxpercent,mapreduce.reduce.failures.maxpercent),the
following is my source files:
_116.1 M 348.3 M /20181226/1545753600402.lzo_deflate
97.0 M 290.9 M /20181226/1545754236750.lzo_deflate
113.3 M 339.8 M /20181226/1545754856515.lzo_deflate
126.5 M 379.5 M /20181226/1545753600402.lzo_deflate
92.9 M 278.6 M /20181226/1545754233009.lzo_deflate
117.7 M 353.2 M /20181226/1545754850857.lzo_deflate
0 M 0 M /20181226/1545755455381.lzo_deflate
0 M 0 M /20181226/1545756056457.lzo_deflate_
> Allow certain number of failed tasks and allow job to succeed
> -------------------------------------------------------------
>
> Key: SPARK-10781
> URL: https://issues.apache.org/jira/browse/SPARK-10781
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 1.5.0
> Reporter: Thomas Graves
> Priority: Major
> Attachments: SPARK_10781_Proposed_Solution.pdf
>
>
> MapReduce has this config mapreduce.map.failures.maxpercent and
> mapreduce.reduce.failures.maxpercent which allows for a certain percent of
> tasks to fail but the job to still succeed.
> This could be a useful feature in Spark also if a job doesn't need all the
> tasks to be successful.
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