| /* |
| * 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.wayang.java.operators.graph; |
| |
| import java.util.HashMap; |
| import java.util.Map; |
| import java.util.function.BiFunction; |
| import org.apache.wayang.basic.data.Tuple2; |
| import org.apache.wayang.basic.operators.PageRankOperator; |
| import org.apache.wayang.core.optimizer.OptimizationContext; |
| 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.util.Tuple; |
| import org.apache.wayang.java.channels.CollectionChannel; |
| import org.apache.wayang.java.channels.StreamChannel; |
| import org.apache.wayang.java.execution.JavaExecutor; |
| import org.apache.wayang.java.operators.JavaExecutionOperator; |
| |
| import java.util.Collection; |
| import java.util.Collections; |
| import java.util.Iterator; |
| import java.util.List; |
| import java.util.Spliterators; |
| import java.util.stream.Stream; |
| import java.util.stream.StreamSupport; |
| |
| /** |
| * Java implementation of the {@link PageRankOperator}. |
| */ |
| public class JavaPageRankOperator extends PageRankOperator implements JavaExecutionOperator { |
| |
| public JavaPageRankOperator(int numIterations) { |
| super(numIterations); |
| } |
| |
| public JavaPageRankOperator(PageRankOperator that) { |
| super(that); |
| } |
| |
| @Override |
| public Tuple<Collection<ExecutionLineageNode>, Collection<ChannelInstance>> evaluate( |
| ChannelInstance[] inputs, |
| ChannelInstance[] outputs, |
| JavaExecutor javaExecutor, |
| OptimizationContext.OperatorContext operatorContext) { |
| CollectionChannel.Instance input = (CollectionChannel.Instance) inputs[0]; |
| StreamChannel.Instance output = (StreamChannel.Instance) outputs[0]; |
| |
| final Collection<Tuple2<Long, Long>> edges = input.provideCollection(); |
| final Map<Long, Float> pageRanks = this.pageRank(edges); |
| final Stream<Tuple2<Long, Float>> pageRankStream = pageRanks.entrySet().stream().map(entry -> new Tuple2<>(entry.getKey(), entry.getValue())); |
| |
| output.accept(pageRankStream); |
| |
| return ExecutionOperator.modelQuasiEagerExecution(inputs, outputs, operatorContext); |
| } |
| |
| /** |
| * Execute the PageRank algorithm. |
| * |
| * @param edgeDataSet edges of a graph |
| * @return the page ranks |
| */ |
| //TODO: change for efficient map |
| private Map<Long, Float> pageRank(Collection<Tuple2<Long, Long>> edgeDataSet) { |
| // Get the degress of all vertices and make sure we collect *all* vertices. |
| //TODO: change for efficient map |
| HashMap<Long, Integer> degrees = new HashMap<>(); |
| for (Tuple2<Long, Long> edge : edgeDataSet) { |
| this.adjustOrPutValue(degrees, edge.field0, 1, 1, Integer::sum); |
| this.adjustOrPutValue(degrees, edge.field0, 0, 0, Integer::sum); |
| } |
| int numVertices = degrees.size(); |
| float initialRank = 1f / numVertices; |
| float dampingRank = (1 - this.dampingFactor) / numVertices; |
| |
| // Initialize the rank map. |
| //TODO: change for efficient map |
| HashMap<Long, Float> initialRanks = new HashMap<>(); |
| degrees.forEach( (k, v) -> { |
| initialRanks.putIfAbsent(k, initialRank); |
| }); |
| |
| HashMap<Long, Float> currentRanks = initialRanks; |
| for (int iteration = 0; iteration < this.getNumIterations(); iteration++) { |
| // Add the damping first. |
| //TODO: change for efficient map |
| HashMap<Long, Float> newRanks = new HashMap<Long, Float>(currentRanks.size()); |
| degrees.forEach( (k, v) -> { |
| newRanks.putIfAbsent(k, dampingRank); |
| }); |
| |
| // Now add the other ranks. |
| for (Tuple2<Long, Long> edge : edgeDataSet) { |
| final long sourceVertex = edge.field0; |
| final long targetVertex = edge.field1; |
| final int degree = degrees.get(sourceVertex); |
| final float currentRank = currentRanks.get(sourceVertex); |
| final float partialRank = this.dampingFactor * currentRank / degree; |
| this.adjustOrPutValue(newRanks, targetVertex, partialRank, partialRank, Float::sum); |
| } |
| |
| currentRanks = newRanks; |
| } |
| |
| return currentRanks; |
| } |
| |
| /** |
| * simulate the process on the Trove4j library |
| * @param key key to modify on the map |
| * @param default_value default value in the case of not key |
| * @param correction element to add the array in the case of the key exist |
| */ |
| private <T> void adjustOrPutValue(Map<Long, T> map, Long key, T default_value, T correction, BiFunction<T, T, T> update){ |
| if(map.containsKey(key)){ |
| T value = map.get(key); |
| map.replace(key, update.apply(value, correction) ); |
| }else{ |
| map.put(key, default_value); |
| } |
| } |
| |
| @Override |
| public String getLoadProfileEstimatorConfigurationKey() { |
| return "wayang.java.pagerank.load"; |
| } |
| |
| @Override |
| public List<ChannelDescriptor> getSupportedInputChannels(int index) { |
| assert index == 0; |
| return Collections.singletonList(CollectionChannel.DESCRIPTOR); |
| } |
| |
| @Override |
| public List<ChannelDescriptor> getSupportedOutputChannels(int index) { |
| assert index == 0; |
| return Collections.singletonList(StreamChannel.DESCRIPTOR); |
| } |
| |
| } |