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https://issues.apache.org/jira/browse/SPARK-2494?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14065240#comment-14065240
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Davies Liu commented on SPARK-2494:
-----------------------------------
This bug only happen in cluster mode, so it's can not be reproduced in unit
tests.
In cluster mode (workers on different machines), it will happen:
>>> rdd = sc.parallelize([(None, 1), (None, 2)], 2)
>>> rdd.groupByKey(2).collect()
((None, [1]), (None, [2]))
The same key `None` will be put into different partitions and can not be
aggregated
together.
> Hash of None is different cross machines in CPython
> ---------------------------------------------------
>
> Key: SPARK-2494
> URL: https://issues.apache.org/jira/browse/SPARK-2494
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.0.0, 1.0.1
> Environment: CPython 2.x
> Reporter: Davies Liu
> Priority: Blocker
> Labels: pyspark, shuffle
> Fix For: 1.0.0, 1.0.1
>
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> The hash of None, also tuple with None in it, is different cross machines, so
> the result will be wrong if None appear in the key of partitionBy().
> It should use an portable hash function as the default partition function,
> which generate same hash for all the builtin immutable types, especially
> tuple.
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