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https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=500210&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-500210
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ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 13/Oct/20 17:57
Start Date: 13/Oct/20 17:57
Worklog Time Spent: 10m
Work Description: lukecwik commented on pull request #13069:
URL: https://github.com/apache/beam/pull/13069#issuecomment-707911475
> On empty key vs. marker, sorry for going back and forth--let's settle this
before you change the code again.
>
> Taking a step back, the question we want to answer is "do all ShardedKeys
have a shard id" or is the shard id optional. If the former, I think
representing it as a `(bytes, K)` tuple makes sense, but if we ever want to
talk about "not having" a shard id than it should, logically, be
`(Optional[bytes], K)` and we should use an explicit bit rather than have a
"special" value of bytes that means "not there".
Since we expect ShardedKey to be produced by the runner we don't expect the
SDK/user to care so making it required simplifies user & SDK code. The runner
is the only one that may care to differentiate unsharded vs sharded and can use
any representation it wants to do so (e.g. by encoding Optional[bytes] into the
bytes blob). The runners coder/type doesn't have to match the user/SDK type.
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Issue Time Tracking
-------------------
Worklog Id: (was: 500210)
Time Spent: 8h 50m (was: 8h 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: 8h 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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