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https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=499536&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-499536
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ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Oct/20 17:12
Start Date: 12/Oct/20 17:12
Worklog Time Spent: 10m
Work Description: lukecwik commented on pull request #13069:
URL: https://github.com/apache/beam/pull/13069#issuecomment-707242853
> R: @robertwb @lukecwik
>
> PTAL and see if the changes make sense and if there is anything I am
missing.
>
> I am not sure what to do with the .yaml files... The locally modified file
`sdks/python/apache_beam/portability/api/standard_coders.yaml` caused some
python tests fail somehow. I wasn't intended to include that file in this PR
since it is for Java SDk but I ended up reverting the changes and added that
file here. Any guidance?
I don't think you should be creating a copy of standard_coders.yaml inside
the python sdk. I was under the impression that the build process somehow moved
the single standard_coders.yaml file around and made it available to the Python
test but that might only be occurring during import into google.
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Issue Time Tracking
-------------------
Worklog Id: (was: 499536)
Time Spent: 5h 50m (was: 5h 40m)
> GroupIntoBatches with Runner-determined Sharding
> ------------------------------------------------
>
> Key: BEAM-10475
> URL: https://issues.apache.org/jira/browse/BEAM-10475
> Project: Beam
> Issue Type: Improvement
> Components: runner-dataflow
> Reporter: Siyuan Chen
> Assignee: Siyuan Chen
> Priority: P2
> Labels: GCP, performance
> Time Spent: 5h 50m
> Remaining Estimate: 0h
>
> [https://s.apache.org/sharded-group-into-batches|https://s.apache.org/sharded-group-into-batches__]
> Improve the existing Beam transform, GroupIntoBatches, to allow runners to
> choose different sharding strategies depending on how the data needs to be
> grouped. The goal is to help with the situation where the elements to process
> need to be co-located to reduce the overhead that would otherwise be incurred
> per element, while not losing the ability to scale the parallelism. The
> essential idea is to build a stateful DoFn with shardable states.
>
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