wanglijie95 commented on code in PR #21111:
URL: https://github.com/apache/flink/pull/21111#discussion_r1014852386
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flink-runtime/src/main/java/org/apache/flink/runtime/scheduler/adaptivebatch/AdaptiveBatchScheduler.java:
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@@ -149,13 +159,45 @@ protected void startSchedulingInternal() {
super.startSchedulingInternal();
}
+ public boolean updateTaskExecutionState(TaskExecutionState
taskExecutionState) {
+ updateResultPartitionBytesMetrics(taskExecutionState.getIOMetrics());
Review Comment:
> Looks to me it's not right because the data produced by an execution
vertex can change if
You are right. It looks like we have to record the subpartition bytes for
each subpartition (no aggregation), then we can reset the bytes when a
partition is reset.
However, once all partitions are finished, we can convert the subpartiton
bytes to aggregated value(to reduce the space usage). Because the distribution
of source splits does not affect the distribution of data consumed by
downstream tasks of ALL_TO_ALL edges(Hashing or Rebalancing, we do not consider
rare cases such as custom partitions here).
> Because `taskExecutionState.getExecutionState() ==
ExecutionState.FINISHED` does not mean the task truly transitions to FINISHED
at JM side.
Yes, we should check whether the state update was successful.
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