WordCount源码注解

WordCount是Hadoop官方提供的一个测试示例，用于词频统计，非常适合初学者学习。
查看源码：
解压hadoop发行版（如hadoop 2.6.4）的压缩包，在目录“hadoop-2.6.4\share\hadoop\mapreduce\sources"中找到hadoop-mapreduce-examples-2.6.4-sources.jar文件，解压。然后在解压后的目录中“org/apache/hadoop/examples”中找到WordCount文件。

注解供参考：

/**
 * 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.examples;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

  //Part1：Mapper模块
  public static class TokenizerMapper 
       extends Mapper<Object, Text, Text, IntWritable>{    //继承Mapper，同时设置输入输出键值对格式
    
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
      
    //【核心】与实际业务逻辑挂钩，由开发者自行编写
    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);    //输出键值对
      }
    }
  }
  
  //Part2:Reducer模块
  public static class IntSumReducer 
       extends Reducer<Text,IntWritable,Text,IntWritable> {		//继承Reducer，同时设置输入输出键值对格式
    private IntWritable result = new IntWritable();

    //【核心】与实际业务逻辑挂钩，由开发者自行编写
    public void reduce(Text key, Iterable<IntWritable> values, 
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {  
        sum += val.get();       //键相同的，把其列表值累加起来
      }
      result.set(sum);
      context.write(key, result);   //输出键值对
    }
  }

  //Part3:应用程序Driver
  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();   	 //初始化相关Hadoop配置
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length < 2) {
      System.err.println("Usage: wordcount  [...] ");
      System.exit(2);
    }
    
    Job job = new Job(conf, "word count");    		//新建job并设置主类，“word count”为MapReduce任务名
    job.setJarByClass(WordCount.class);
    
    //设置Mapper、Combiner、Reducer
    job.setMapperClass(TokenizerMapper.class);		//必选
    job.setCombinerClass(IntSumReducer.class);   	//可选
    job.setReducerClass(IntSumReducer.class);		//必选
    
    //设置输出键值对格式
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    
    //设置输入和输出路径
    for (int i = 0; i < otherArgs.length - 1; ++i) {
      FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
    }
    FileOutputFormat.setOutputPath(job,
      new Path(otherArgs[otherArgs.length - 1]));
    
    //提交MapReduce任务运行，并等待运行结束
    System.exit(job.waitForCompletion(true) ? 0 : 1);    //固定写法
  }
}