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https://issues.apache.org/jira/browse/SPARK-15861?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15331975#comment-15331975
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Bryan Cutler commented on SPARK-15861:
--------------------------------------
If you change your function to this
{noformat}
def to_np(data):
return [np.array(list(data))]
{noformat}
I think you would get what you expect, but this is probably not a good way to
go about it. I feel like you should be aggregating your lists into numpy
arrays instead, but someone else might know better.
> pyspark mapPartitions with none generator functions / functors
> --------------------------------------------------------------
>
> Key: SPARK-15861
> URL: https://issues.apache.org/jira/browse/SPARK-15861
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.6.1
> Reporter: Greg Bowyer
> Priority: Minor
>
> Hi all, it appears that the method `rdd.mapPartitions` does odd things if it
> is fed a normal subroutine.
> For instance, lets say we have the following
> {code}
> rows = range(25)
> rows = [rows[i:i+5] for i in range(0, len(rows), 5)]
> rdd = sc.parallelize(rows, 2)
> def to_np(data):
> return np.array(list(data))
> rdd.mapPartitions(to_np).collect()
> ...
> [array([0, 1, 2, 3, 4]),
> array([5, 6, 7, 8, 9]),
> array([10, 11, 12, 13, 14]),
> array([15, 16, 17, 18, 19]),
> array([20, 21, 22, 23, 24])]
> rdd.mapPartitions(to_np, preservePartitioning=True).collect()
> ...
> [array([0, 1, 2, 3, 4]),
> array([5, 6, 7, 8, 9]),
> array([10, 11, 12, 13, 14]),
> array([15, 16, 17, 18, 19]),
> array([20, 21, 22, 23, 24])]
> {code}
> This basically makes the provided function that did return act like the end
> user called {code}rdd.map{code}
> I think that maybe a check should be put in to call
> {code}inspect.isgeneratorfunction{code}
> ?
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