[
https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=519909&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-519909
]
ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 03/Dec/20 22:54
Start Date: 03/Dec/20 22:54
Worklog Time Spent: 10m
Work Description: nehsyc commented on a change in pull request #13474:
URL: https://github.com/apache/beam/pull/13474#discussion_r535706100
##########
File path: sdks/python/apache_beam/coders/coders.py
##########
@@ -1485,6 +1486,21 @@ def as_cloud_object(self, coders_context=None):
],
}
+ def to_type_hint(self):
+ from apache_beam.typehints import sharded_key_type
+ return sharded_key_type.ShardedKeyTypeConstraint(
+ self._key_coder.to_type_hint())
+
+ @staticmethod
+ def from_type_hint(typehint, registry):
+ from apache_beam.typehints import sharded_key_type
+ if isinstance(typehint, sharded_key_type.ShardedKeyTypeConstraint):
+ return ShardedKeyCoder(registry.get_coder(typehint.key_type))
+ else:
+ raise ValueError((
Review comment:
Done.
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Issue Time Tracking
-------------------
Worklog Id: (was: 519909)
Time Spent: 20h 50m (was: 20h 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: 20h 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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