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Posted to issues@spark.apache.org by "Xun REN (JIRA)" <ji...@apache.org> on 2018/02/13 14:46:00 UTC
[jira] [Commented] (SPARK-21501) Spark shuffle index cache size
should be memory based
[ https://issues.apache.org/jira/browse/SPARK-21501?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16362424#comment-16362424 ]
Xun REN commented on SPARK-21501:
---------------------------------
Hi guys,
Could you tell me how to figure out how many memory the NM with spark shuffle service has used ? And how to know a spark job has used how many reducers ?
Because I have the same problem recently and I want to get a list of spark jobs by sorting by number of reducers.
Thanks.
Regards,
Xun REN.
> Spark shuffle index cache size should be memory based
> -----------------------------------------------------
>
> Key: SPARK-21501
> URL: https://issues.apache.org/jira/browse/SPARK-21501
> Project: Spark
> Issue Type: Bug
> Components: Shuffle
> Affects Versions: 2.1.0
> Reporter: Thomas Graves
> Assignee: Sanket Reddy
> Priority: Major
> Fix For: 2.3.0
>
>
> Right now the spark shuffle service has a cache for index files. It is based on a # of files cached (spark.shuffle.service.index.cache.entries). This can cause issues if people have a lot of reducers because the size of each entry can fluctuate based on the # of reducers.
> We saw an issues with a job that had 170000 reducers and it caused NM with spark shuffle service to use 700-800MB or memory in NM by itself.
> We should change this cache to be memory based and only allow a certain memory size used. When I say memory based I mean the cache should have a limit of say 100MB.
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