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https://issues.apache.org/jira/browse/BEAM-12351?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17549286#comment-17549286
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Danny McCormick commented on BEAM-12351:
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This issue has been migrated to https://github.com/apache/beam/issues/20892
> 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: P3
> Labels: dataframe-api
>
> 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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