资源文件file.txt
hello hadoop
hello word
this is my first hadoop program
分析：一个文档中每行的单词通过空格分割后获取，经过map阶段可以将所有的单词整理成如下形式：
key:hello value:1
key:hadoop value:1
key:hello value:1
key:word value:1
key:this value:1
key:is value:1
key:my value:1
key:first value:1
key:hadoop value:1
key:program value:1
经过hadoop整理后以如下形式输入到Reduce中：
key:hello value:{1,1}
key:hadoop value: {1,1}
key:word value:{1}
key:this value: {1}
key:is value:{1}
key:myvalue: {1}
key:first value:{1}
key:program value:{1}
所以Reduce接受的时候是以Iterable<IntWritable> values最为值。在Reduce中我们就可以将value中的值迭代相加就可以得出该单词出现的次数。
实现：
package com.bwzy.hadoop;import java.io.File;import java.io.IOException;import java.util.Iterator;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.*;import org.apache.hadoop.mapreduce.*;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;public class WordCount extends Configured implements Tool { public static class Map extends org.apache.hadoop.mapreduce.Mapper { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key,Text value,Context context){ String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line);//读取每一行数据，并将该行数据以空格分割（StringTokenizer默认是以空格分割字符串） while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); try { context.write(word, one);//输出给Reduce } catch (IOException e) { e.printStackTrace(); } catch (InterruptedException e) { e.printStackTrace(); } } } } public static class Reduce extends Reducer { public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException{ int sum = 0;//合并每个单词出现的次数 for (IntWritable val : values) { sum+=val.get(); } context.write(key, new IntWritable(sum)); } } public static void main(String[] args) throws Exception { int ret = ToolRunner.run(new WordCount(), args); System.exit(ret); } @Override public int run(String[] arg0) throws Exception { Job job = new Job(getConf()); job.setJobName("wordcount"); job.setOutputKeyClass(Text.class);//key--设置输出格式 job.setOutputValueClass(IntWritable.class);//value--设置输出格式 job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.setInputPaths(job, new Path(arg0[0])); FileOutputFormat.setOutputPath(job, new Path(arg0[1])); boolean success = job.waitForCompletion(true); return success?0:1; }} 运行：
1：将程序打包
选中打包的类-->右击-->Export-->java-->JAR file--填入保存路径-->完成
2：将jar包拷贝到hadoop的目录下。（因为程序中用到来hadoop的jar包）
3：将资源文件（存储这单词的文件,假设在/home/user/Document/file1.txt）上传到定义的hdfs目录下
创建hdfs目录命令（在hadoop已经成功启动的前提下）：hadoop fs -mkdir /自定义/自定义/input
上传本地资源文件到hdfs上：hadop fs -put -copyFromLocal /home/user/Document/file1.txt /自定义/自定义/input
4：运行MapReduce程序：
hadoop jar /home/user/hadoop-1.0.4/WordCount.jar com.bwzy.hadoop.WordCount /自定义/自定义/input /自定义/自定义/output
说明：hadoop运行后会自动创建/自定义/自定义/output目录，在该目录下会有两个文件，其中一个文件中存放来MapReduce运行的结果。如果重新运行该程序，需要将/自定义/自定义/output目录删除，否则系统认为该结果已经存在了。
5：运行的结果为
hello 2
hadoop 2
word 1
this 1
is 1
my 1
first 1
program 1