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[FLINK-36607][table-planner] Introduce AdaptiveJoinProcessor to injec…
…t adaptive join node.
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.../java/org/apache/flink/table/planner/plan/nodes/exec/processor/AdaptiveJoinProcessor.java
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.flink.table.planner.plan.nodes.exec.processor; | ||
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| import org.apache.flink.configuration.JobManagerOptions; | ||
| import org.apache.flink.table.api.TableConfig; | ||
| import org.apache.flink.table.api.TableException; | ||
| import org.apache.flink.table.api.config.OptimizerConfigOptions; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.AdaptiveJoinExecNode; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.ExecEdge; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.ExecNode; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.ExecNodeGraph; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.InputProperty; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.InputProperty.DistributionType; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.batch.BatchExecAdaptiveJoin; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.batch.BatchExecExchange; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.stream.StreamExecNode; | ||
| import org.apache.flink.table.planner.plan.nodes.exec.visitor.AbstractExecNodeExactlyOnceVisitor; | ||
| import org.apache.flink.table.planner.plan.utils.OperatorType; | ||
| import org.apache.flink.table.planner.utils.TableConfigUtils; | ||
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| import java.util.ArrayList; | ||
| import java.util.List; | ||
| import java.util.stream.Collectors; | ||
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| import static org.apache.flink.table.planner.plan.nodes.exec.InputProperty.DistributionType.KEEP_INPUT_AS_IS; | ||
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| /** | ||
| * A {@link ExecNodeGraphProcessor} which replace the qualified join nodes into adaptive join nodes. | ||
| */ | ||
| public class AdaptiveJoinProcessor implements ExecNodeGraphProcessor { | ||
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| @Override | ||
| public ExecNodeGraph process(ExecNodeGraph execGraph, ProcessorContext context) { | ||
| if (execGraph.getRootNodes().get(0) instanceof StreamExecNode) { | ||
| throw new TableException("AdaptiveJoin does not support streaming jobs."); | ||
| } | ||
| if (!isAdaptiveJoinEnabled(context)) { | ||
| return execGraph; | ||
| } | ||
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| AbstractExecNodeExactlyOnceVisitor visitor = | ||
| new AbstractExecNodeExactlyOnceVisitor() { | ||
| @Override | ||
| protected void visitNode(ExecNode<?> node) { | ||
| visitInputs(node); | ||
| // AdaptiveJoin conversion should be avoided when there is a | ||
| // KEEP_INPUT_AS_IS constraint downstream. And we don't need to check all | ||
| // downstream nodes of the join, because the KEEP_INPUT_AS_IS constraint | ||
| // will be bound to BatchExecExchange, which will be the direct downstream | ||
| // node of the join. | ||
| if (shouldKeepInputAsIs(node.getInputProperties())) { | ||
| return; | ||
| } | ||
| for (int i = 0; i < node.getInputEdges().size(); ++i) { | ||
| ExecEdge edge = node.getInputEdges().get(i); | ||
| ExecNode<?> newNode = tryReplaceWithAdaptiveJoinNode(edge.getSource()); | ||
| node.replaceInputEdge( | ||
| i, | ||
| ExecEdge.builder() | ||
| .source(newNode) | ||
| .target(node) | ||
| .shuffle(edge.getShuffle()) | ||
| .exchangeMode(edge.getExchangeMode()) | ||
| .build()); | ||
| } | ||
| } | ||
| }; | ||
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| List<ExecNode<?>> newRootNodes = | ||
| execGraph.getRootNodes().stream() | ||
| .map( | ||
| node -> { | ||
| node = tryReplaceWithAdaptiveJoinNode(node); | ||
| node.accept(visitor); | ||
| return node; | ||
| }) | ||
| .collect(Collectors.toList()); | ||
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| return new ExecNodeGraph(execGraph.getFlinkVersion(), newRootNodes); | ||
| } | ||
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| private ExecNode<?> tryReplaceWithAdaptiveJoinNode(ExecNode<?> node) { | ||
| // For AdaptiveJoin to be converted, its upstream input must ensure: | ||
| // 1. Data distribution is by Hash. | ||
| // 2. No upstream nodes require KEEP_INPUT_AS_IS (achieved by inserting BatchExecExchange). | ||
| if (!(areAllInputsHashShuffle(node)) | ||
| || shouldKeepUpstreamExchangeInputAsIs(node.getInputEdges())) { | ||
| return node; | ||
| } | ||
| ExecNode<?> newNode = node; | ||
| if (node instanceof AdaptiveJoinExecNode | ||
| && ((AdaptiveJoinExecNode) node).canBeTransformedToAdaptiveJoin()) { | ||
| BatchExecAdaptiveJoin adaptiveJoin = ((AdaptiveJoinExecNode) node).toAdaptiveJoinNode(); | ||
| replaceInputEdge(adaptiveJoin, node); | ||
| newNode = adaptiveJoin; | ||
| } | ||
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| return newNode; | ||
| } | ||
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| private boolean shouldKeepInputAsIs(List<InputProperty> inputProperties) { | ||
| return inputProperties.stream() | ||
| .anyMatch( | ||
| inputProperty -> | ||
| inputProperty.getRequiredDistribution().getType() | ||
| == KEEP_INPUT_AS_IS); | ||
| } | ||
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| // If KEEP_INPUT_AS_IS constraint exists on an operator, it will always show on its upstream | ||
| // BatchExecExchange. | ||
| private boolean shouldKeepUpstreamExchangeInputAsIs(List<ExecEdge> inputEdges) { | ||
| return inputEdges.stream() | ||
| .filter(execEdge -> execEdge.getSource() instanceof BatchExecExchange) | ||
| .map(execEdge -> (BatchExecExchange) execEdge.getSource()) | ||
| .anyMatch(exchange -> shouldKeepInputAsIs(exchange.getInputProperties())); | ||
| } | ||
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| private boolean isAdaptiveJoinEnabled(ProcessorContext context) { | ||
| TableConfig tableConfig = context.getPlanner().getTableConfig(); | ||
| boolean isAdaptiveJoinEnabled = | ||
| tableConfig.get( | ||
| OptimizerConfigOptions | ||
| .TABLE_OPTIMIZER_ADAPTIVE_BROADCAST_JOIN_STRATEGY) | ||
| != OptimizerConfigOptions.AdaptiveBroadcastJoinStrategy.NONE | ||
| && !TableConfigUtils.isOperatorDisabled( | ||
| tableConfig, OperatorType.BroadcastHashJoin); | ||
| JobManagerOptions.SchedulerType schedulerType = | ||
| context.getPlanner() | ||
| .getExecEnv() | ||
| .getConfig() | ||
| .getSchedulerType() | ||
| .orElse(JobManagerOptions.SchedulerType.AdaptiveBatch); | ||
| boolean isAdaptiveBatchSchedulerEnabled = | ||
| schedulerType == JobManagerOptions.SchedulerType.AdaptiveBatch; | ||
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| return isAdaptiveJoinEnabled && isAdaptiveBatchSchedulerEnabled; | ||
| } | ||
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| private boolean areAllInputsHashShuffle(ExecNode<?> node) { | ||
| for (InputProperty inputProperty : node.getInputProperties()) { | ||
| if (inputProperty.getRequiredDistribution().getType() != DistributionType.HASH) { | ||
| return false; | ||
| } | ||
| } | ||
| return true; | ||
| } | ||
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| private void replaceInputEdge(ExecNode<?> newNode, ExecNode<?> originalNode) { | ||
| List<ExecEdge> inputEdges = new ArrayList<>(); | ||
| for (int i = 0; i < originalNode.getInputEdges().size(); ++i) { | ||
| ExecEdge edge = originalNode.getInputEdges().get(i); | ||
| inputEdges.add( | ||
| ExecEdge.builder() | ||
| .source(edge.getSource()) | ||
| .target(newNode) | ||
| .shuffle(edge.getShuffle()) | ||
| .exchangeMode(edge.getExchangeMode()) | ||
| .build()); | ||
| } | ||
| newNode.setInputEdges(inputEdges); | ||
| } | ||
| } |
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