GitHub user holdenk opened a pull request:
https://github.com/apache/spark/pull/8569
[SPARK-9821][PYSPARK] pyspark-reduceByKey-should-take-a-custom-partitioner
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:
weblogs.map(s => (getFileType(s), 1)).reduceByKey(new
FileTypePartitioner(),_+_)
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:
weblogs.map(lambda s: (getFileType(s),
1)).partitionBy(3,hash_filetype).reduceByKey(lambda v1,v2: v1+v2).collect()
But that defeats the purpose, because I'm shuffling twice instead of once,
so my performance is worse instead of better.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/holdenk/spark
SPARK-9821-pyspark-reduceByKey-should-take-a-custom-partitioner
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/8569.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #8569
----
commit 8d272b3bf84a72c66c1529d2679d465038435f83
Author: Holden Karau <[email protected]>
Date: 2015-09-02T07:27:48Z
Add partitioner function
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