package org.apache.hadoop.examples;
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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;
public class MyWordCount
{
//main方法
public static void main(String[] args) throws Exception
{
//初始化Conf 连接到HDFS
Configuration conf = new Configuration();
conf.set("fs.defaultFS", "hdfs://localhost:9000/user/root");
//指定输入输出目录
String[] otherArgs = new String[]{"/user/root/input","/user/root/output"};
Path path = new Path(otherArgs[1]);
//如果输出路径已存在则删除
FileSystem fileSystem = path.getFileSystem(conf);
if (fileSystem.exists(new Path(otherArgs[1])))
{
fileSystem.delete(new Path(otherArgs[1]),true);
}
//如果不是一个输入一个输出路径,则报错
if(otherArgs.length < 2)
{
System.err.println("Usage: wordcount <in> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "word count"); //Job(Configuration conf, String jobName) 设置job名称
job.setJarByClass(MyWordCount.class);
job.setMapperClass(MyWordCount.TokenizerMapper.class); //为job设置Mapper类
job.setCombinerClass(MyWordCount.IntSumReducer.class); //为job设置Combiner类
job.setReducerClass(MyWordCount.IntSumReducer.class); //为job设置Reduce类
job.setOutputKeyClass(Text.class); //设置输出key的类型
job.setOutputValueClass(IntWritable.class); //设置输出value的类型
FileInputFormat.addInputPath(job, new Path(otherArgs[0])); //为map-reduce任务设置InputFormat实现类 设置输入路径
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); //为map-reduce任务设置OutputFormat实现类 设置输出路径
System.exit(job.waitForCompletion(true)?0:1);
}
//Map类，继承自Mapper类--一个抽象类
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>
{
//每个单词都在Context中写入1（频次）
private static final IntWritable one = new IntWritable(1);
//Text 实现了BinaryComparable类可以作为key值
private Text word = new Text();
public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException
{
StringTokenizer itr = new StringTokenizer(value.toString()); //得到什么值 StringTokenizer是分割String串的方法
//如果itr还有下一个分割的值
while(itr.hasMoreTokens())
{
//word为Text类型，要用set方法定义值
this.word.set(itr.nextToken());
//写入context（上下文，传给Reduce节点）
context.write(this.word, one);
}
}
}
//Reduce类，继承自Reducer类--一个抽象类
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable>
{
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException
{
int sum = 0;
IntWritable val;
//对于每一个相同的key值即word，计算所有节点传入的频次和
for(Iterator i = values.iterator(); i.hasNext(); sum += val.get())
{
val = (IntWritable)i.next();
}
this.result.set(sum);
//key为word，result为频次
context.write(key, this.result);
}
}
}
代码有很清晰的注释，看不懂的话可以评论给我，input目录文件及运行结果output目录如下：
