AHeise commented on a change in pull request #13735: URL: https://github.com/apache/flink/pull/13735#discussion_r518163988
########## File path: flink-runtime/src/main/java/org/apache/flink/runtime/checkpoint/ChannelRescalerRepartitioner.java ########## @@ -0,0 +1,88 @@ +/* + * 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.runtime.checkpoint; + +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.runtime.io.network.api.writer.ChannelStateRescaler; + +import org.apache.flink.shaded.guava18.com.google.common.collect.Iterables; + +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Set; +import java.util.function.Function; +import java.util.stream.Collectors; +import java.util.stream.IntStream; + +/** + * A repartitioner that assigns the same channel state to multiple subtasks according to some mapping. + * + * <p>The replicated data will then be filtered before processing the record. + * + * <p>Note that channel mappings are cached for the same parallelism changes. + */ +public class ChannelRescalerRepartitioner<T> implements OperatorStateRepartitioner<T> { + private final ChannelStateRescaler channelStateRescaler; + private final Map<Tuple2<Integer, Integer>, Map<Integer, Set<Integer>>> newToOldMappingCache = new HashMap<>(2); + + public ChannelRescalerRepartitioner(ChannelStateRescaler channelStateRescaler) { + this.channelStateRescaler = channelStateRescaler; + } + + private static <T> List<T> getOldState(List<List<T>> previousParallelSubtaskStates, Set<Integer> oldIndexes) { + switch (oldIndexes.size()) { + case 0: + return Collections.emptyList(); + case 1: + return previousParallelSubtaskStates.get(Iterables.getOnlyElement(oldIndexes)); + default: + return oldIndexes.stream() + .flatMap(oldIndex -> previousParallelSubtaskStates.get(oldIndex).stream()) + .collect(Collectors.toList()); + } + } + + protected Map<Integer, Set<Integer>> createNewToOldMapping(int oldParallelism, int newParallelism) { + return IntStream.range(0, newParallelism).boxed(). + collect(Collectors.toMap( + Function.identity(), + channelIndex -> channelStateRescaler.getOldChannels( + channelIndex, + oldParallelism, + newParallelism))); + } + + @Override + public List<List<T>> repartitionState( + List<List<T>> previousParallelSubtaskStates, + int oldParallelism, + int newParallelism) { + final Map<Integer, Set<Integer>> newToOldMapping = getNewToOldMapping(oldParallelism, newParallelism); + return IntStream.range(0, newParallelism) + .mapToObj(newIndex -> getOldState(previousParallelSubtaskStates, newToOldMapping.get(newIndex))) + .collect(Collectors.toList()); Review comment: It's good feedback if stuff is confusing. I'll offer an alternative that avoids using streams: ``` List<List<T>> repartitioned = new ArrayList<>(); for (int newIndex = 0; newIndex < newParallelism; newIndex++) { repartitioned.add(getOldState(previousParallelSubtaskStates, newToOldSubtasksMapping.get(newIndex))); } return repartitioned; ``` ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
