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https://issues.apache.org/jira/browse/SPARK-9599?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=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
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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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