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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2015/08/11 17:51:45 UTC
[jira] [Updated] (SPARK-9821) pyspark reduceByKey should allow a
custom partitioner
[ https://issues.apache.org/jira/browse/SPARK-9821?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen updated SPARK-9821:
-----------------------------
Priority: Minor (was: Major)
Issue Type: Improvement (was: Bug)
I expect that indeed it's just the API hasn't caught up. This is still relevant for master/1.5 I imagine?
> 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
>
> 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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