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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2015/08/04 16:34:05 UTC
[jira] [Comment Edited] (SPARK-9599) Dynamic partitioning based on
key-distribution
[ https://issues.apache.org/jira/browse/SPARK-9599?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14653723#comment-14653723 ]
Sean Owen edited comment on SPARK-9599 at 8/4/15 2:33 PM:
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For example, in the case of groupByKey, how would anything know a key mapped to many values before performing a shuffle anyway?
EDIT: err, I mean a distributed count operation, which isn't trivial but not a full shuffle I suppose. Interesting, not sure if there are subtle reasons this is hard to work around or not, because now the very partitioning is a function of all values in the parent.
was (Author: srowen):
For example, in the case of groupByKey, how would anything know a key mapped to many values before performing a shuffle anyway?
> Dynamic partitioning based on key-distribution
> ----------------------------------------------
>
> Key: SPARK-9599
> URL: https://issues.apache.org/jira/browse/SPARK-9599
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle, Spark Core
> Affects Versions: 1.4.1
> Reporter: Zoltán Zvara
>
> When - for example - using {{groupByKey}} operator with default {{HashPartitioner}}, there might be a case when heavy keys get partitioned into the same bucket, later raising an OOM error at the result partition. A domain-based partitioner might not be able to help, when the outstanding key-distribution changes from time to time (for example while dealing with data streams).
> Spark should identify these situations and change the partitioning accordingly when a partitioning would raise an OOM later.
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