gyfora commented on code in PR #847: URL: https://github.com/apache/flink-kubernetes-operator/pull/847#discussion_r1667035712
########## 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 we might also have to consider the output ratios when propagating the bottleneck backwards . So technically speaking if we want to be completely precise we can do this in a single pass if we start computing the target rates from the sinks. Once the actual scaled rate is computed we have to propagate the diff compared to the original one back based on the output ratio to the upstream tasks. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
