huyuanfeng2018 commented on code in PR #879:
URL:
https://github.com/apache/flink-kubernetes-operator/pull/879#discussion_r1759654296
##########
flink-autoscaler/src/main/java/org/apache/flink/autoscaler/JobVertexScaler.java:
##########
@@ -389,15 +407,61 @@ protected static int scale(
return newParallelism;
}
- // When the shuffle type of vertex inputs contains keyBy or vertex is
a source, we try to
- // adjust the parallelism such that it divides the maxParallelism
without a remainder
- // => data is evenly spread across subtasks
- for (int p = newParallelism; p <= maxParallelism / 2 && p <=
upperBound; p++) {
- if (maxParallelism % p == 0) {
+ int numKeyGroupsOrPartitions = maxParallelism;
+ int upperBoundForAlignment;
+ if (numPartitions <= 0) {
+ upperBoundForAlignment = Math.min(maxParallelism / 2, upperBound);
Review Comment:
Well, you are right, the specific entrance is here
[KeyGroupStreamPartitioner](https://github.com/apache/flink/blob/release-1.19/flink-streaming-java/src/main/java/org/apache/flink/streaming/runtime/partitioner/KeyGroupStreamPartitioner.java#L63)In
this case, We can unify our logic.
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