mxm commented on code in PR #847:
URL: 
https://github.com/apache/flink-kubernetes-operator/pull/847#discussion_r1667341534


##########
flink-autoscaler/src/main/java/org/apache/flink/autoscaler/IntermediateScalingResult.java:
##########
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.autoscaler;
+
+import org.apache.flink.runtime.jobgraph.JobVertexID;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/** Class for storing intermediate scaling results. */
+public class IntermediateScalingResult {
+
+    private final Map<JobVertexID, ScalingSummary> scalingSummaries;
+    private final List<JobVertexID> bottlenecks;
+
+    private double backpropagationScaleFactor = 1.0;
+
+    public IntermediateScalingResult() {
+        scalingSummaries = new HashMap<>();
+        bottlenecks = new ArrayList<>();
+    }
+
+    void addScalingSummary(JobVertexID vertex, ScalingSummary scalingSummary) {
+        scalingSummaries.put(vertex, scalingSummary);
+    }
+
+    void addBottleneckVertex(JobVertexID bottleneck, double factor) {
+        bottlenecks.add(bottleneck);
+        backpropagationScaleFactor = Math.min(backpropagationScaleFactor, 
factor);

Review Comment:
   I think Gyula has a point. The current implementation works for simple jobs 
but there are many jobs with more complex uncorrelated branches which would 
lead to unnecessary scale downs or prevent upscales (if scale down is 
disabled). Using the output ratios would allow us to precisely feed back the 
bottleneck ratios and avoid any accidental backpropagation. 



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