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https://issues.apache.org/jira/browse/SPARK-9821?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Davies Liu resolved SPARK-9821.
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Resolution: Fixed
Fix Version/s: 1.6.0
Issue resolved by pull request 8569
[https://github.com/apache/spark/pull/8569]
> pyspark reduceByKey should allow a custom partitioner
> -----------------------------------------------------
>
> Key: SPARK-9821
> URL: https://issues.apache.org/jira/browse/SPARK-9821
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 1.3.0
> Reporter: Diana Carroll
> Priority: Minor
> Fix For: 1.6.0
>
>
> In Scala, I can supply a custom partitioner to reduceByKey (and other
> aggregation/repartitioning methods like aggregateByKey and combinedByKey),
> but as far as I can tell from the Pyspark API, there's no way to do the same
> in Python.
> Here's an example of my code in Scala:
> {code}weblogs.map(s => (getFileType(s), 1)).reduceByKey(new
> FileTypePartitioner(),_+_){code}
> But I can't figure out how to do the same in Python. The closest I can get
> is to call repartition before reduceByKey like so:
> {code}weblogs.map(lambda s: (getFileType(s),
> 1)).partitionBy(3,hash_filetype).reduceByKey(lambda v1,v2:
> v1+v2).collect(){code}
> But that defeats the purpose, because I'm shuffling twice instead of once, so
> my performance is worse instead of better.
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