Github user Stibbons commented on the issue:
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Ok, abandonning
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Github user HyukjinKwon commented on the issue:
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I do not agree with this change too by the same reason in
https://github.com/apache/spark/pull/14918#issuecomment-250882422.
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Github user holdenk commented on the issue:
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Do we have any usecases or benchmarks for cases where this would be helpful?
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Github user holdenk commented on the issue:
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I'm not sure about its value - since it only applies to equivalent of
parallelize which by definition should be used on relatively small datasets.
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For information, I continue to look at these kind of simple optimisations
that does not cost too much. Python is a pretty slow language, very productive
in term of code writing, but inefficient in
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Rebased
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Indeed, i dont think it will be feasible to propagate the generator up to
the jvm. It would be cool, because when we have the schema there is no need to
iterate several time on the complete sequenc
Github user holdenk commented on the issue:
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So I'm not super sure I understand the point of the refactor - at some
point we are forced to materialize the entire contents of the generator on the
driver anyways to copy to the JVM since parallel
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at last, tests pass ! :)
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