[
https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=522010&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-522010
]
ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 09/Dec/20 02:36
Start Date: 09/Dec/20 02:36
Worklog Time Spent: 10m
Work Description: udim commented on a change in pull request #13493:
URL: https://github.com/apache/beam/pull/13493#discussion_r538958762
##########
File path: sdks/python/apache_beam/transforms/util.py
##########
@@ -785,7 +788,6 @@ def expand(self, pcoll):
@experimental()
@typehints.with_input_types(Tuple[K, V])
- @typehints.with_output_types(Tuple[K, Iterable[V]])
Review comment:
I don't believe you need infer_output_type if you have the
with_output_types decorator.
Also, I tested this a bit myself and it ended up looking like this:
```py
@typehints.with_input_types(typehints.Tuple[K_i, V_i])
@typehints.with_output_types(typehints.Tuple[ShardedKeyType[K_i],
typehints.Iterable[V_i]])
class WithShardedKey(PTransform):
```
where:
```py
K_i = typehints.TypeVariable('K_i')
V_i = typehints.TypeVariable('V_i')
```
edit: not sure if `with_input_types` needs to change but it looks consistent
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Issue Time Tracking
-------------------
Worklog Id: (was: 522010)
Time Spent: 24h 50m (was: 24h 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: 24h 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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