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https://issues.apache.org/jira/browse/BEAM-11881?focusedWorklogId=567140&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-567140
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ASF GitHub Bot logged work on BEAM-11881:
-----------------------------------------

                Author: ASF GitHub Bot
            Created on: 16/Mar/21 17:45
            Start Date: 16/Mar/21 17:45
    Worklog Time Spent: 10m 
      Work Description: aromanenko-dev edited a comment on pull request #14135:
URL: https://github.com/apache/beam/pull/14135#issuecomment-800473198


   Well, my point is that with many small commits it's very difficult to keep a 
git history of atomic and independent commits (ideally, every commit has to be 
easy to revert in case if it's needed). Also, we tend to add a Jira prefix to 
commit message to make it easier for a search in the future. Imho, ideally, we 
have to have only **one** "well tested" commit per one Jira despite the number 
of lines and changes (of course there always will be exceptions).


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Issue Time Tracking
-------------------

    Worklog Id:     (was: 567140)
    Time Spent: 7h  (was: 6h 50m)

> DataFrame subpartitioning order is incorrect
> --------------------------------------------
>
>                 Key: BEAM-11881
>                 URL: https://issues.apache.org/jira/browse/BEAM-11881
>             Project: Beam
>          Issue Type: Bug
>          Components: sdk-py-core
>            Reporter: Brian Hulette
>            Assignee: Brian Hulette
>            Priority: P2
>              Labels: dataframe-api
>             Fix For: 2.30.0
>
>          Time Spent: 7h
>  Remaining Estimate: 0h
>
> Currently we've defined
> Nothing() < Index([i]) < Index([i,j]) < .. < Index() < Singleton()
> s.t. Singleton is a subpartitoning of Index, is a subpartitioning of 
> Index([i,j]), but this is incorrect. The order should be 
> Singleton() < Index([i]) < Index([i,j]) < .. < Index() < Nothing()
> s.t. every other partitioning is a subpartitioning of Singleton. This is 
> logical, since Singleton will collect the largest amount of data on a single 
> node, partitioning by a single index will be alittle more distributed, and 
> partitioning by the full Index() will be the most distribtued.



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