rkhachatryan commented on code in PR #21981: URL: https://github.com/apache/flink/pull/21981#discussion_r1117292622
########## flink-runtime/src/main/java/org/apache/flink/runtime/scheduler/adaptive/allocator/StateSizeEstimates.java: ########## @@ -0,0 +1,129 @@ +/* + * 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.scheduler.adaptive.allocator; + +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.runtime.OperatorIDPair; +import org.apache.flink.runtime.checkpoint.CompletedCheckpoint; +import org.apache.flink.runtime.checkpoint.OperatorState; +import org.apache.flink.runtime.executiongraph.ExecutionGraph; +import org.apache.flink.runtime.executiongraph.ExecutionJobVertex; +import org.apache.flink.runtime.jobgraph.JobVertexID; +import org.apache.flink.runtime.jobgraph.OperatorID; +import org.apache.flink.runtime.state.KeyedStateHandle; + +import javax.annotation.Nullable; + +import java.util.Collections; +import java.util.Map; +import java.util.Optional; +import java.util.Set; +import java.util.stream.Collectors; +import java.util.stream.Stream; + +import static java.util.stream.Collectors.toMap; + +/** Managed Keyed State size estimates used to make scheduling decisions. */ +class StateSizeEstimates { + private final Map<JobVertexID, Long> averages; + + public StateSizeEstimates() { + this(Collections.emptyMap()); + } + + public StateSizeEstimates(Map<JobVertexID, Long> averages) { + this.averages = averages; + } + + public Optional<Long> estimate(JobVertexID jobVertexId) { + return Optional.ofNullable(averages.get(jobVertexId)); + } + + static StateSizeEstimates empty() { + return new StateSizeEstimates(); + } + + static StateSizeEstimates fromGraph(@Nullable ExecutionGraph executionGraph) { + return Optional.ofNullable(executionGraph) + .flatMap(graph -> Optional.ofNullable(graph.getCheckpointCoordinator())) + .flatMap(coordinator -> Optional.ofNullable(coordinator.getCheckpointStore())) + .flatMap(store -> Optional.ofNullable(store.getLatestCheckpoint())) + .map( + cp -> + build( + fromCompletedCheckpoint(cp), + mapVerticesToOperators(executionGraph))) + .orElse(empty()); + } + + private static StateSizeEstimates build( + Map<OperatorID, Long> sizePerOperator, + Map<JobVertexID, Set<OperatorID>> verticesToOperators) { + Map<JobVertexID, Long> verticesToSizes = + verticesToOperators.entrySet().stream() + .collect( + toMap(Map.Entry::getKey, e -> size(e.getValue(), sizePerOperator))); + return new StateSizeEstimates(verticesToSizes); + } + + private static long size(Set<OperatorID> ids, Map<OperatorID, Long> sizes) { + return ids.stream() + .mapToLong(key -> sizes.getOrDefault(key, 0L)) + .boxed() + .reduce(Long::sum) + .orElse(0L); + } + + private static Map<JobVertexID, Set<OperatorID>> mapVerticesToOperators( + ExecutionGraph executionGraph) { + return executionGraph.getAllVertices().entrySet().stream() + .collect(toMap(Map.Entry::getKey, e -> getOperatorIDS(e.getValue()))); + } + + private static Set<OperatorID> getOperatorIDS(ExecutionJobVertex v) { + return v.getOperatorIDs().stream() + .map(OperatorIDPair::getGeneratedOperatorID) + .collect(Collectors.toSet()); + } + + private static Map<OperatorID, Long> fromCompletedCheckpoint(CompletedCheckpoint cp) { + Stream<Map.Entry<OperatorID, OperatorState>> states = + cp.getOperatorStates().entrySet().stream(); + Map<OperatorID, Long> estimates = + states.collect( + toMap(Map.Entry::getKey, e -> estimateKeyGroupStateSize(e.getValue()))); + return estimates; + } + + private static long estimateKeyGroupStateSize(OperatorState state) { + Stream<KeyedStateHandle> handles = + state.getSubtaskStates().values().stream() + .flatMap(s -> s.getManagedKeyedState().stream()); + Stream<Tuple2<Long, Integer>> sizeAndCount = + handles.map( + h -> + Tuple2.of( + h.getStateSize(), + h.getKeyGroupRange().getNumberOfKeyGroups())); + Optional<Tuple2<Long, Integer>> totalSizeAndCount = + sizeAndCount.reduce( + (left, right) -> Tuple2.of(left.f0 + right.f0, left.f1 + right.f1)); + Optional<Long> average = totalSizeAndCount.filter(t2 -> t2.f1 > 0).map(t2 -> t2.f0 / t2.f1); + return average.orElse(0L); Review Comment: >one large state + one small state (this is IMO quite common, e.g. buffer + watermark hold) I think we are thinking about aggregation in different dimensions: above is multiple states inside a single operator; or the operators of the same SlotSharingGroup. In both cases, their state size is summed up (either in state handle, or in `StateLocalitySlotAssigner.calculateScore` while merging the score for the same `AllocationID`). I was more thinking about aggregation across different subtasks. > I'm wondering whether something along these lines holds (for RocksDB): "The rescaling of the Flink job is as slow as the rescaling of the single largest state it contains." That's true (except for embarassingly parallel jobs - and even there we'll wait for checkpoint to emit data). -- 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: issues-unsubscr...@flink.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
