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https://issues.apache.org/jira/browse/SPARK-9599?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14653723#comment-14653723
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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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