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https://issues.apache.org/jira/browse/BEAM-11267?focusedWorklogId=512601&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-512601
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ASF GitHub Bot logged work on BEAM-11267:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 16/Nov/20 21:22
Start Date: 16/Nov/20 21:22
Worklog Time Spent: 10m
Work Description: je-ik commented on a change in pull request #13353:
URL: https://github.com/apache/beam/pull/13353#discussion_r524597532
##########
File path:
runners/flink/src/main/java/org/apache/beam/runners/flink/translation/wrappers/streaming/WorkItemKeySelector.java
##########
@@ -49,6 +52,6 @@ public ByteBuffer
getKey(WindowedValue<SingletonKeyedWorkItem<K, V>> value) thro
@Override
public TypeInformation<ByteBuffer> getProducedType() {
- return new GenericTypeInfo<>(ByteBuffer.class);
+ return new CoderTypeInformation<>(FlinkKeyUtils.ByteBufferCoder.of(),
pipelineOptions.get());
Review comment:
I would think that the partitioning is ensured by the
`reinterpretAsKeyedStream` and will therefore be preserved from the previous
shuffle phase. Is this not enough?
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Issue Time Tracking
-------------------
Worklog Id: (was: 512601)
Time Spent: 1h (was: 50m)
> Remove unnecessary reshuffle for stateful ParDo after keyed operation
> ---------------------------------------------------------------------
>
> Key: BEAM-11267
> URL: https://issues.apache.org/jira/browse/BEAM-11267
> Project: Beam
> Issue Type: Improvement
> Components: runner-flink
> Reporter: David Morávek
> Assignee: David Morávek
> Priority: P3
> Time Spent: 1h
> Remaining Estimate: 0h
>
> When we have stateful pardo after GBK / Combine, we can safely assume that
> partitioning remains consistent and we can safe an extra shuffle.
> The use case for this are user defined timers / datadriven triggers. This
> code path is stressed for example by
> org.apache.beam.sdk.transforms.ParDoTest.TimerTests#testGbkFollowedByUserTimers.
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