1996fanrui commented on code in PR #21443:
URL: https://github.com/apache/flink/pull/21443#discussion_r1042919428
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flink-streaming-java/src/main/java/org/apache/flink/streaming/runtime/tasks/StreamTask.java:
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@@ -1614,6 +1617,15 @@
List<RecordWriter<SerializationDelegate<StreamRecord<OUT>>>> createRecordWriters
return recordWriters;
}
+ private static void
replaceForwardPartitionerIfConsumerParallelismDoesNotMatch(
+ Environment environment, NonChainedOutput streamOutput) {
+ if (streamOutput.getPartitioner() instanceof ForwardPartitioner
+ && streamOutput.getConsumerParallelism()
+ !=
environment.getTaskInfo().getNumberOfParallelSubtasks()) {
+ streamOutput.setPartitioner(new RescalePartitioner<>());
Review Comment:
Thanks for your feedback. In most scenarios, `RescalePartitioner` works
well. However, I'm not sure if it's generic.
For example, a job has 2 vertexes, the parallelism of them are 200, the
partitioner is `ForwardPartitioner`. Autoscaling found that the performance of
vertex2 is poor, so increase the parallelism of vertex2 from 200 to 300, and
the parallelism of vertex1 doesn't be changed.
If using the `RescalePartitioner`, there are 100 subtasks of vertex1 will
send data to one downstream subtask, and the rest 100 subtasks of vertex1 will
send data to 2 downstream subtasks.
The first half of the source may still have lag.
Not sure if it will happen.
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