Hadoop 4 MapReduce

对单词个数统计的MapReduce的案例

Mapper类：

package main.java.worldClient;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

/**
 * <KEYIN,VALUEIN,KEYOUT,VALUEOUT>
 * 分别对应map输入和输出的key和value对应的数据类型
 * 默认map的输入，key是改行在文件中的偏移量，value是文件中一行的内容
 * @author Lenovo
 *
 */

public class WCMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

	/**
	 * 切分单词，然后输出
	 */
	@Override
	protected void map(LongWritable key, Text value,Mapper<LongWritable, Text, Text, LongWritable>.Context context)
			throws IOException, InterruptedException {
		//获取一行信息
		String line = value.toString();
		String words[] = line.split(" ");
		LongWritable writable = new LongWritable(1);
		for(String word:words){
			//将输出写入context
			//write(a,b)中a与mapper(keyin,valuein,keyout,valueout)的keyout与valueout对应
			context.write(new Text(word), writable);
		}
	}
	
}

　　Reduce类：

package main.java.worldClient;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
/**
 * <KEYIN,VALUEIN,KEYOUT,VALUEOUT>
 * reduce的输入和输出的key和value
 * 输入的key和value肯定和map输出的key和value一致
 * @author Lenovo
 *
 */
public class WCReducer extends Reducer<Text, LongWritable, Text, LongWritable>{

	@Override
	protected void reduce(Text key, Iterable<LongWritable> values,
			Reducer<Text,LongWritable,Text,LongWritable>.Context context)
			throws IOException, InterruptedException {
		int sum = 0;
		Iterator<LongWritable> iter = values.iterator();
		while(iter.hasNext()){
			LongWritable value = iter.next();
			sum += value.get();
		}
		
		context.write(key, new LongWritable(sum));
	}
	
}

　　Runner类：

package main.java.worldClient;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WCRunner {
	public static void main(String[] args) {
		Configuration conf = new Configuration();
		try{
			Job job = Job.getInstance(conf);
			job.setJobName("wc MR");
			job.setJarByClass(WCRunner.class);
			job.setMapperClass(WCMapper.class);
			job.setReducerClass(WCReducer.class);
			
			/*
			 * 如果map和reduce的输出类型一致可以不设置map的输出
			 */
			//map输出的key,value
			job.setMapOutputKeyClass(Text.class);
			job.setMapOutputValueClass(LongWritable.class);
			//reduce输出的key,value
			job.setOutputKeyClass(Text.class);
			job.setOutputValueClass(LongWritable.class);
			
			FileInputFormat.addInputPath(job, new Path(args[0]));
			//输出目录必须不存在
			FileOutputFormat.setOutputPath(job, new Path(args[1]));
			
			job.waitForCompletion(true);
			
		}catch (Exception e) {
			// TODO: handle exception
			e.printStackTrace();
		}
	}
}

　　在windows环境下运行会报控指针错误,目前我正在查找解决方法，所以通过Xshell与Xftp将写好的java导出jar包以及程序需要的文件传到linux虚拟机内(用linux命令将输入文件导入到hadoop的目录下这样会在接下来方便写命令)，在linux下运行测试。hadoop jar找到的jar包为本地jar包无法找hdfs上的jar文件(我自己的理解不知道对不对)

主要步骤：

 1：bin/hadoop fs -mkdir -p /MRTest/input 在hdfs下创建目录

 2：bin/hadoop fs -put ~/WCTest.txt.txt /MRTest/input 将程序需要执行的文件放到input文件夹下

 3：bin/hadoop jar ~/wctest.jar main.java.worldClient.WCRunner /MRTest/input /MRTest/output 运行jar包 其中output必须时不存在的文件目录