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https://issues.apache.org/jira/browse/SPARK-5785?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Josh Rosen resolved SPARK-5785.
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Resolution: Fixed
Fix Version/s: 1.3.0
Issue resolved by pull request 4629
[https://github.com/apache/spark/pull/4629]
> Pyspark does not support narrow dependencies
> --------------------------------------------
>
> Key: SPARK-5785
> URL: https://issues.apache.org/jira/browse/SPARK-5785
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Reporter: Imran Rashid
> Fix For: 1.3.0
>
>
> joins (& cogroups etc.) are always considered to have "wide" dependencies in
> pyspark, they are never narrow. This can cause unnecessary shuffles. eg.,
> this simple job should shuffle rddA & rddB once each, but it also will do a
> third shuffle of the unioned data:
> {code}
> rddA = sc.parallelize(range(100)).map(lambda x: (x,x)).partitionBy(64)
> rddB = sc.parallelize(range(100)).map(lambda x: (x,x)).partitionBy(64)
> joined = rddA.join(rddB)
> joined.count()
> >>> rddA._partitionFunc == rddB._partitionFunc
> True
> {code}
> (Or the docs should somewhere explain that this feature is missing from
> pyspark.)
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