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Posted to issues@beam.apache.org by "Brian Hulette (Jira)" <ji...@apache.org> on 2021/05/17 18:56:00 UTC

[jira] [Created] (BEAM-12351) combine should be parallelizable in many cases

Brian Hulette created BEAM-12351:
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             Summary: 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: sdk-py-core
            Reporter: Brian Hulette


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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