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https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=524653&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-524653
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ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 15/Dec/20 19:04
Start Date: 15/Dec/20 19:04
Worklog Time Spent: 10m
Work Description: udim commented on pull request #13493:
URL: https://github.com/apache/beam/pull/13493#issuecomment-745500507
> Python is hard :\ Kept getting `inherit-non-class` error with
`with_metaclass` imported from `future.utils` and ended up using
`six.with_metaclass` instead. Also added some doc string for
`ShardedKeyTypeConstraint`.
I would rather not use `six`, since it's for compatibility between Python
2.x and 3.x, and Beam doesn't support 2.x any more. I could help debug this
further if you wish.
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Issue Time Tracking
-------------------
Worklog Id: (was: 524653)
Time Spent: 26h 20m (was: 26h 10m)
> 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: 26h 20m
> 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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