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https://issues.apache.org/jira/browse/MAPREDUCE-7148?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16639232#comment-16639232
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Tsuyoshi Ozawa commented on MAPREDUCE-7148:
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[~tiana528], thanks you for suggesting the awesome feature. If I understand
correctly, you suggest that deterministic failures, in this case DFS quota
limitation, should not retry the task again and again. I think the feature
itself is useful.
At the patch-level design, hadoop-mapreduce-client-app should not depend on
hadoop-hdfs directly. Is it possible to rewrite it with the exception at
FileSystem-level API?
cc: [[email protected]] do you have any idea to handle this kind of quota
error without breaking FileSystem abstraction? One possible solution is to
handle and parse an error message via FileSystem-API's IOException provided
from dfs' QuotaExceeded exception, but it must be dirty.
> 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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