自己写的第一个地图-reduce程序

　　　　　　今天尝试自己写了一个map-reduce程序，感觉不错．程序完成的是wordcount的功能，代码贴出来纪念一下：

package com.wjy.hadoop;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class MyFirstHadoopTest extends Configured implements Tool{

	public static class MyMapper extends Mapper<LongWritable,Text,Text,IntWritable>{
		public void map(LongWritable key,Text value,Context context) throws IOException{
			String str=value.toString();
			try{
				String temp[]=str.split(" ");
				context.write(new Text(temp[0]), new IntWritable(1));
				context.write(new Text(temp[1]), new IntWritable(1));
			}catch(ArrayIndexOutOfBoundsException e){
				return;
			} catch (InterruptedException e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			}
		}
	}
	
	public static class MyReducer extends Reducer<Text,IntWritable,Text,IntWritable>{
		public void reduce (Text key,Iterable<IntWritable> values,Context context){
			int count=0;
			for(IntWritable intNum : values){
				count++;
			}
			try {
				context.write(key, new IntWritable(count));
			} catch (IOException e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			} catch (InterruptedException e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			}
		}
	}
	
	public int run(String[] arg0) throws Exception {
		// TODO Auto-generated method stub
		Configuration conf=getConf();
		Job job=new Job(conf,"com.wjy.hadoop.MyFirstHadoopTest");
		job.setJarByClass(com.wjy.hadoop.MyFirstHadoopTest.class);
		
		FileInputFormat.addInputPath(job, new Path(arg0[0]));
		FileOutputFormat.setOutputPath(job, new Path(arg0[1]));
		
		job.setMapperClass(MyMapper.class);
		job.setReducerClass(MyReducer.class);
		job.setOutputFormatClass(TextOutputFormat.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		
		job.waitForCompletion(true);
		
		return job.isSuccessful()?0:1;
	}
	
	public static void main(String args[]){
		try {
			int res=ToolRunner.run(new Configuration(), new MyFirstHadoopTest(),args);
		} catch (Exception e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}
		
	}
	
}

 ps：在run configuration中设置一下hdfs的路径作为程序的输入和输出：

hdfs://localhost:9000/user/root/in hdfs://localhost:9000/user/root/out

在arguments中填上上面．切记要保证程序运行之前out文件夹不存在．

　　　当然也可以将程序打包成jar文件，执行：

root@wjy-Lenovo:/usr/local/hadoop-0.20.2# bin/hadoop jar /usr/local/FirstHadoopTest.jar in myout

root@wjy-Lenovo:/usr/local/hadoop-0.20.2# bin/hadoop dfs -cat ./myout/*

注意：在eclipse中配置hadoop的时候，DFS Master的端口号设置为core-site.xml文件中fs.default.name指定的端口号．

Ｍap/Reduce Master的端口号设置为：mapred-site.xml文件中mapred.job.tracker指定的端口号．