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https://issues.apache.org/jira/browse/SPARK-15861?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15328245#comment-15328245
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Bryan Cutler commented on SPARK-15861:
--------------------------------------
[~gbow...@fastmail.co.uk]
{{mapPartitions}} expects a function the takes an iterator as input then
outputs an iterable sequence, and your function in the example is actually
providing this. I think what is going on here is your function will map the
iterator to a numpy array, that internally will be something like
{noformat}array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]){noformat} for the first
partition, then {{collect}} will iterate over that sequence and return each
element, which will also be a numpy array, so you get {noformat}array([0, 1, 2,
3, 4]), array([5, 6, 7, 8, 9])) {noformat} for the first 2 elements and so on..
I believe this is working as it is supposed to, and in general,
{{mapPartitions}} will not usually give the same result as {{map}} - it will
fail if the function does not return a valid sequence. The documentation could
perhaps be a little clearer in that regard.
> 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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