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* Licensed to the Apache Software Foundation (ASF) under one
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* 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,
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* See the License for the specific language governing permissions and
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*/
package org.apache.wayang.spark.operators;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import java.util.Optional;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.wayang.basic.data.Tuple2;
import org.apache.wayang.basic.operators.JoinOperator;
import org.apache.wayang.core.api.Configuration;
import org.apache.wayang.core.function.TransformationDescriptor;
import org.apache.wayang.core.optimizer.OptimizationContext;
import org.apache.wayang.core.optimizer.costs.LoadProfileEstimator;
import org.apache.wayang.core.optimizer.costs.LoadProfileEstimators;
import org.apache.wayang.core.plan.wayangplan.ExecutionOperator;
import org.apache.wayang.core.platform.ChannelDescriptor;
import org.apache.wayang.core.platform.ChannelInstance;
import org.apache.wayang.core.platform.lineage.ExecutionLineageNode;
import org.apache.wayang.core.types.DataSetType;
import org.apache.wayang.core.util.Tuple;
import org.apache.wayang.spark.channels.RddChannel;
import org.apache.wayang.spark.compiler.FunctionCompiler;
import org.apache.wayang.spark.execution.SparkExecutor;
/**
* Spark implementation of the {@link JoinOperator}.
*/
public class SparkJoinOperator<InputType0, InputType1, KeyType>
extends JoinOperator<InputType0, InputType1, KeyType>
implements SparkExecutionOperator {
/**
* Creates a new instance.
*/
public SparkJoinOperator(DataSetType<InputType0> inputType0,
DataSetType<InputType1> inputType1,
TransformationDescriptor<InputType0, KeyType> keyDescriptor0,
TransformationDescriptor<InputType1, KeyType> keyDescriptor1) {
super(keyDescriptor0, keyDescriptor1, inputType0, inputType1);
}
/**
* Copies an instance (exclusive of broadcasts).
*
* @param that that should be copied
*/
public SparkJoinOperator(JoinOperator<InputType0, InputType1, KeyType> that) {
super(that);
}
@Override
public Tuple<Collection<ExecutionLineageNode>, Collection<ChannelInstance>> evaluate(
ChannelInstance[] inputs,
ChannelInstance[] outputs,
SparkExecutor sparkExecutor,
OptimizationContext.OperatorContext operatorContext) {
assert inputs.length == this.getNumInputs();
assert outputs.length == this.getNumOutputs();
final RddChannel.Instance input0 = (RddChannel.Instance) inputs[0];
final RddChannel.Instance input1 = (RddChannel.Instance) inputs[1];
final RddChannel.Instance output = (RddChannel.Instance) outputs[0];
final JavaRDD<InputType0> inputRdd0 = input0.provideRdd();
final JavaRDD<InputType1> inputRdd1 = input1.provideRdd();
FunctionCompiler compiler = sparkExecutor.getCompiler();
final PairFunction<InputType0, KeyType, InputType0> keyExtractor0 = compiler.compileToKeyExtractor(this.keyDescriptor0);
final PairFunction<InputType1, KeyType, InputType1> keyExtractor1 = compiler.compileToKeyExtractor(this.keyDescriptor1);
JavaPairRDD<KeyType, InputType0> pairStream0 = inputRdd0.mapToPair(keyExtractor0);
JavaPairRDD<KeyType, InputType1> pairStream1 = inputRdd1.mapToPair(keyExtractor1);
final JavaPairRDD<KeyType, scala.Tuple2<InputType0, InputType1>> outputPair =
pairStream0.<InputType1>join(pairStream1, sparkExecutor.getNumDefaultPartitions());
this.name(outputPair);
// convert from scala tuple to wayang tuple
final JavaRDD<Tuple2<InputType0, InputType1>> outputRdd = outputPair
.map(new TupleConverter<>());
this.name(outputRdd);
output.accept(outputRdd, sparkExecutor);
return ExecutionOperator.modelLazyExecution(inputs, outputs, operatorContext);
}
@Override
protected ExecutionOperator createCopy() {
return new SparkJoinOperator<>(this.getInputType0(), this.getInputType1(),
this.getKeyDescriptor0(), this.getKeyDescriptor1());
}
/**
* Migrates {@link scala.Tuple2} to {@link Tuple2}.
* <p><i>TODO: See, if we can somehow dodge all this conversion, which is likely to happen a lot.</i></p>
*/
private static class TupleConverter<InputType0, InputType1, KeyType>
implements Function<scala.Tuple2<KeyType, scala.Tuple2<InputType0, InputType1>>, Tuple2<InputType0, InputType1>> {
@Override
public Tuple2<InputType0, InputType1> call(scala.Tuple2<KeyType, scala.Tuple2<InputType0, InputType1>> scalaTuple) throws Exception {
return new Tuple2<>(scalaTuple._2._1, scalaTuple._2._2);
}
}
@Override
public String getLoadProfileEstimatorConfigurationKey() {
return "wayang.spark.join.load";
}
@Override
public Optional<LoadProfileEstimator> createLoadProfileEstimator(Configuration configuration) {
final Optional<LoadProfileEstimator> optEstimator =
SparkExecutionOperator.super.createLoadProfileEstimator(configuration);
LoadProfileEstimators.nestUdfEstimator(optEstimator, this.keyDescriptor0, configuration);
LoadProfileEstimators.nestUdfEstimator(optEstimator, this.keyDescriptor1, configuration);
return optEstimator;
}
@Override
public List<ChannelDescriptor> getSupportedInputChannels(int index) {
assert index <= this.getNumInputs() || (index == 0 && this.getNumInputs() == 0);
return Arrays.asList(RddChannel.UNCACHED_DESCRIPTOR, RddChannel.CACHED_DESCRIPTOR);
}
@Override
public List<ChannelDescriptor> getSupportedOutputChannels(int index) {
assert index <= this.getNumOutputs() || (index == 0 && this.getNumOutputs() == 0);
return Collections.singletonList(RddChannel.UNCACHED_DESCRIPTOR);
}
@Override
public boolean containsAction() {
return false;
}
}