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https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=499665&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-499665
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ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Oct/20 21:37
Start Date: 12/Oct/20 21:37
Worklog Time Spent: 10m
Work Description: nehsyc commented on pull request #13069:
URL: https://github.com/apache/beam/pull/13069#issuecomment-707357737
> Regarding the yaml issues, I would like to see what the Python failures
were. If it's just that this coder is not implemented, perhaps simply
implementing it in Python as well would be the simplest.
Yeah the failure is that the coder is not implemented. I have a separate
workspace for python. If it's okay to put python and java changes together I
can merge those changes to this PR.
> As for using the empty byte string vs. an explicit marker, I don't think
the extra byte in savings is significant. I think the bigger question is
whether logically we would want to allow the empty byte string as a shard key
(and if it would complicate code/the contract to explicitly avoid it). If it's
OK, it should be included as an example and test.
Empty is different from null. We could 1) allow null value and add a
constraint on non-null shard id to be non-empty, or 2) not allow null value and
assume empty bytes to be non-existing/default shard id. Adding an indicator
would avoid the the constraint or assumption on the user side I think.
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Issue Time Tracking
-------------------
Worklog Id: (was: 499665)
Time Spent: 6h 20m (was: 6h 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: 6h 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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