Diana Carroll created SPARK-9821:
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Summary: pyspark reduceByKey should allow a custom partitioner
Key: SPARK-9821
URL: https://issues.apache.org/jira/browse/SPARK-9821
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 1.3.0
Reporter: Diana Carroll
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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