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https://issues.apache.org/jira/browse/SPARK-16589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15545239#comment-15545239
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Maciej Szymkiewicz commented on SPARK-16589:
--------------------------------------------
Not actively so if you want to give it a shot go ahead.
I investigated a little bit deeper and tried to fix it closer to the sources
but it ended in a hell full of special cases which makes me think that we
should never expose data requiring `CartesianDeserializer` directly (there is
also a SPARK-16589).
> Chained cartesian produces incorrect number of records
> ------------------------------------------------------
>
> Key: SPARK-16589
> URL: https://issues.apache.org/jira/browse/SPARK-16589
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.4.0, 1.5.0, 1.6.0, 2.0.0
> Reporter: Maciej Szymkiewicz
>
> Chaining cartesian calls in PySpark results in the number of records lower
> than expected. It can be reproduced as follows:
> {code}
> rdd = sc.parallelize(range(10), 1)
> rdd.cartesian(rdd).cartesian(rdd).count()
> ## 355
> rdd.cartesian(rdd).cartesian(rdd).distinct().count()
> ## 251
> {code}
> It looks like it is related to serialization. If we reserialize after initial
> cartesian:
> {code}
> rdd.cartesian(rdd)._reserialize(BatchedSerializer(PickleSerializer(),
> 1)).cartesian(rdd).count()
> ## 1000
> {code}
> or insert identity map:
> {code}
> rdd.cartesian(rdd).map(lambda x: x).cartesian(rdd).count()
> ## 1000
> {code}
> it yields correct results.
>
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