参考http://www.cnblogs.com/xia520pi/archive/2012/05/16/2504205.html
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 {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{ //继承org.apache.hadoop.mapreduce包中Mapper类，并重写其map方法
private final static IntWritable one = new IntWritable(1); //Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>
private Text word = new Text();
public void map(Object key, Text value, Context context) //Called once for each key/value pair in the input split
throws IOException, InterruptedException { //value值存储的是文本文件中的一行（以回车符为行结束标记），而key值为该行的首字母相对于文本文件的首地址的偏移量
StringTokenizer itr = new StringTokenizer(value.toString()); //拆分成单词
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one); //输出<word，1>
}
}
}
//系统自动对map结果进行排序等处理，reduce输入例 （asd,1-1-1)
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> { //Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,Context context)
throws IOException, InterruptedException { //reducer输入为Map过程输出，<key,values>中key为单个单词，而values是对应单词的计数值
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println(“Usage: wordcount <in> <out>”);
System.exit(2);
}
Job job = new Job(conf, “word count”);
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class); //setMapperClass：设置Mapper，默认为IdentityMapper
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);//setReducerClass：设置Reducer，默认为IdentityReducer
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//FileInputFormat.addInputPath：设置输入文件的路径，可以是一个文件，一个路径，一个通配符。可以被调用多次添加多个路径
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//FileOutputFormat.setOutputPath：设置输出文件的路径，在job运行前此路径不应该存在
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
//setInputFormat：设置map的输入格式，默认为TextInputFormat，key为LongWritable, value为Text
setNumMapTasks：设置map任务的个数，此设置通常不起作用，map任务的个数取决于输入的数据所能分成的input split的个数
setMapRunnerClass：设置MapRunner, map task是由MapRunner运行的，默认为MapRunnable，其功能为读取input split的一个个record，依次调用Mapper的map函数
setMapOutputKeyClass和setMapOutputValueClass：设置Mapper的输出的key-value对的格式
setOutputKeyClass和setOutputValueClass：设置Reducer的输出的key-value对的格式
setPartitionerClass和setNumReduceTasks：设置Partitioner，默认为HashPartitioner，其根据key的hash值来决定进入哪个partition，每个partition被一个reduce task处理，所以partition的个数等于reduce task的个数
setOutputFormat：设置任务的输出格式，默认为TextOutputFormat