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https://issues.apache.org/jira/browse/BEAM-10475?focusedWorklogId=500363&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-500363
]
ASF GitHub Bot logged work on BEAM-10475:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 14/Oct/20 00:56
Start Date: 14/Oct/20 00:56
Worklog Time Spent: 10m
Work Description: nehsyc commented on a change in pull request #13069:
URL: https://github.com/apache/beam/pull/13069#discussion_r504339524
##########
File path: sdks/python/apache_beam/coders/coder_impl.py
##########
@@ -1365,3 +1366,38 @@ def estimate_size(self, value, nested=False):
# type: (Any, bool) -> int
value_size = self._value_coder.estimate_size(value)
return get_varint_size(value_size) + value_size
+
+
+class ShardedKeyCoderImpl(StreamCoderImpl):
+ """For internal use only; no backwards-compatibility guarantees.
+
+ A coder for sharded user keys.
+
+ The encoding and decoding should follow the order:
+ length of shard id byte string
+ shard id byte string
+ encoded user key
+ """
+ def __init__(self, key_coder_impl):
+ self._shard_id_coder_impl = LengthPrefixCoderImpl(BytesCoderImpl())
Review comment:
`BytesCoder` simply copies the value to the stream and it doesn't
automatically prepend a length prefix
([code](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/coders/coder_impl.py#L491)).
While in Java the `ByteArrayCoder` does prepend the length.
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Issue Time Tracking
-------------------
Worklog Id: (was: 500363)
Time Spent: 9h 20m (was: 9h 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: 9h 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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