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https://issues.apache.org/jira/browse/MAPREDUCE-7148?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16639249#comment-16639249
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Wang Yan edited comment on MAPREDUCE-7148 at 10/5/18 4:50 AM:
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[~ozawa] thank you very much for your comment! I can think of one dirty way of
checking exception stack trace and make string comparison such as :
exception.getClass().getSimpleName().equals("DSQuotaExceededException"). But I
am not sure what is a clean way.
was (Author: tiana528):
[~ozawa] thank you very much for your comment! I can think of one dirty way of
checking exception stack trace and make string comparison such as :
exception.getClass().getSimpleName().equals("DSQuotaExceededException"). But I
am not sure what is a clean way.
> Fast fail jobs when exceeds dfs quota limitation
> ------------------------------------------------
>
> Key: MAPREDUCE-7148
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-7148
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
> Components: task
> Affects Versions: 2.7.0, 2.8.0, 2.9.0
> Environment: hadoop 2.7.3
> Reporter: Wang Yan
> Priority: Major
> Attachments: MAPREDUCE-7148.001.patch
>
>
> We are running hive jobs with a DFS quota limitation per job(3TB). If a job
> hits DFS quota limitation, the task that hit it will fail and there will be a
> few task reties before the job actually fails. The retry is not very helpful
> because the job will always fail anyway. In some worse cases, we have a job
> which has a single reduce task writing more than 3TB to HDFS over 20 hours,
> the reduce task exceeds the quota limitation and retries 4 times until the
> job fails in the end thus consuming a lot of unnecessary resource. This
> ticket aims at providing the feature to let a job fail fast when it writes
> too much data to the DFS and exceeds the DFS quota limitation. The fast fail
> feature is introduced in MAPREDUCE-7022 and MAPREDUCE-6489 .
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