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https://issues.apache.org/jira/browse/SPARK-9599?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14653746#comment-14653746
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Zoltán Zvara commented on SPARK-9599:
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What I can think of is a new, guided partitioning in case when an in-progress
shuffle write indicates that a few buckets will raise on OOM error with high
probability, or will result in a very slow execution in case the user-operator
on grouped keys is expensive. In other words, you might not get an OOM, but a
few very slow tasks.
My first idea is to track the distribution of keys while shuffle-write is in
progress. When we identify that with high probability there will be an OOM or a
very slow execution, just use the currently captured key-distribution to
construct a new partitioner that would partition evenly.
> 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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