Java tutorial
/** * 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.hadoop.mapreduce.lib.chain; import org.apache.hadoop.classification.InterfaceAudience; import org.apache.hadoop.classification.InterfaceStability; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.chain.Chain.ChainBlockingQueue; import java.io.IOException; /** * The ChainReducer class allows to chain multiple Mapper classes after a * Reducer within the Reducer task. * * <p> * For each record output by the Reducer, the Mapper classes are invoked in a * chained (or piped) fashion. The output of the reducer becomes the input of * the first mapper and output of first becomes the input of the second, and so * on until the last Mapper, the output of the last Mapper will be written to * the task's output. * </p> * <p> * The key functionality of this feature is that the Mappers in the chain do not * need to be aware that they are executed after the Reducer or in a chain. This * enables having reusable specialized Mappers that can be combined to perform * composite operations within a single task. * </p> * <p> * Special care has to be taken when creating chains that the key/values output * by a Mapper are valid for the following Mapper in the chain. It is assumed * all Mappers and the Reduce in the chain use matching output and input key and * value classes as no conversion is done by the chaining code. * </p> * <p> Using the ChainMapper and the ChainReducer classes is possible to * compose Map/Reduce jobs that look like <code>[MAP+ / REDUCE MAP*]</code>. And * immediate benefit of this pattern is a dramatic reduction in disk IO. </p> * <p> * IMPORTANT: There is no need to specify the output key/value classes for the * ChainReducer, this is done by the setReducer or the addMapper for the last * element in the chain. * </p> * ChainReducer usage pattern: * <p> * * <pre> * ... * Job = new Job(conf); * .... * * Configuration reduceConf = new Configuration(false); * ... * ChainReducer.setReducer(job, XReduce.class, LongWritable.class, Text.class, * Text.class, Text.class, true, reduceConf); * * ChainReducer.addMapper(job, CMap.class, Text.class, Text.class, * LongWritable.class, Text.class, false, null); * * ChainReducer.addMapper(job, DMap.class, LongWritable.class, Text.class, * LongWritable.class, LongWritable.class, true, null); * * ... * * job.waitForCompletion(true); * ... * </pre> */ @InterfaceAudience.Public @InterfaceStability.Stable public class ChainReducer<KEYIN, VALUEIN, KEYOUT, VALUEOUT> extends Reducer<KEYIN, VALUEIN, KEYOUT, VALUEOUT> { /** * Sets the {@link Reducer} class to the chain job. * * <p> * The key and values are passed from one element of the chain to the next, by * value. For the added Reducer the configuration given for it, * <code>reducerConf</code>, have precedence over the job's Configuration. * This precedence is in effect when the task is running. * </p> * <p> * IMPORTANT: There is no need to specify the output key/value classes for the * ChainReducer, this is done by the setReducer or the addMapper for the last * element in the chain. * </p> * * @param job * the job * @param klass * the Reducer class to add. * @param inputKeyClass * reducer input key class. * @param inputValueClass * reducer input value class. * @param outputKeyClass * reducer output key class. * @param outputValueClass * reducer output value class. * @param reducerConf * a configuration for the Reducer class. It is recommended to use a * Configuration without default values using the * <code>Configuration(boolean loadDefaults)</code> constructor with * FALSE. */ public static void setReducer(Job job, Class<? extends Reducer> klass, Class<?> inputKeyClass, Class<?> inputValueClass, Class<?> outputKeyClass, Class<?> outputValueClass, Configuration reducerConf) { job.setReducerClass(ChainReducer.class); job.setOutputKeyClass(outputKeyClass); job.setOutputValueClass(outputValueClass); Chain.setReducer(job, klass, inputKeyClass, inputValueClass, outputKeyClass, outputValueClass, reducerConf); } /** * Adds a {@link Mapper} class to the chain reducer. * * <p> * The key and values are passed from one element of the chain to the next, by * value For the added Mapper the configuration given for it, * <code>mapperConf</code>, have precedence over the job's Configuration. This * precedence is in effect when the task is running. * </p> * <p> * IMPORTANT: There is no need to specify the output key/value classes for the * ChainMapper, this is done by the addMapper for the last mapper in the * chain. * </p> * * @param job * The job. * @param klass * the Mapper class to add. * @param inputKeyClass * mapper input key class. * @param inputValueClass * mapper input value class. * @param outputKeyClass * mapper output key class. * @param outputValueClass * mapper output value class. * @param mapperConf * a configuration for the Mapper class. It is recommended to use a * Configuration without default values using the * <code>Configuration(boolean loadDefaults)</code> constructor with * FALSE. */ public static void addMapper(Job job, Class<? extends Mapper> klass, Class<?> inputKeyClass, Class<?> inputValueClass, Class<?> outputKeyClass, Class<?> outputValueClass, Configuration mapperConf) throws IOException { job.setOutputKeyClass(outputKeyClass); job.setOutputValueClass(outputValueClass); Chain.addMapper(false, job, klass, inputKeyClass, inputValueClass, outputKeyClass, outputValueClass, mapperConf); } private Chain chain; protected void setup(Context context) { chain = new Chain(false); chain.setup(context.getConfiguration()); } public void run(Context context) throws IOException, InterruptedException { setup(context); // if no reducer is set, just do nothing if (chain.getReducer() == null) { return; } int numMappers = chain.getAllMappers().size(); // if there are no mappers in chain, run the reducer if (numMappers == 0) { chain.runReducer(context); return; } // add reducer and all mappers with proper context ChainBlockingQueue<Chain.KeyValuePair<?, ?>> inputqueue; ChainBlockingQueue<Chain.KeyValuePair<?, ?>> outputqueue; // add reducer outputqueue = chain.createBlockingQueue(); chain.addReducer(context, outputqueue); // add all mappers except last one for (int i = 0; i < numMappers - 1; i++) { inputqueue = outputqueue; outputqueue = chain.createBlockingQueue(); chain.addMapper(inputqueue, outputqueue, context, i); } // add last mapper chain.addMapper(outputqueue, context, numMappers - 1); // start all threads chain.startAllThreads(); // wait for all threads chain.joinAllThreads(); } }