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https://issues.apache.org/jira/browse/BEAM-12351?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17382622#comment-17382622
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Beam JIRA Bot commented on BEAM-12351:
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This issue is P2 but has been unassigned without any comment for 60 days so it
has been labeled "stale-P2". If this issue is still affecting you, we care!
Please comment and remove the label. Otherwise, in 14 days the issue will be
moved to P3.
Please see https://beam.apache.org/contribute/jira-priorities/ for a detailed
explanation of what these priorities mean.
> combine should be parallelizable in many cases
> ----------------------------------------------
>
> Key: BEAM-12351
> URL: https://issues.apache.org/jira/browse/BEAM-12351
> Project: Beam
> Issue Type: Improvement
> Components: dsl-dataframe, sdk-py-core
> Reporter: Brian Hulette
> Priority: P2
> Labels: dataframe-api, stale-P2
>
> Relevant discussion:
> https://lists.apache.org/thread.html/r9e7d9527eb1d4c9c097c91c010a25dabf4a5f8053d50dc3b6d90d36a%40%3Cdev.beam.apache.org%3E
> Currently we require Singleton partitioning for combine() because func
> *might* operate on the full dataset, but in many cases func is actually an
> elementwise method. We should detect this when possible (e.g. when func is an
> np.ufunc), and/or provide a flag to let the user indicate the function is
> elementwise.
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