hadoop wordcount

本文介绍了一个使用Hadoop MapReduce框架实现的基本单词计数应用。该应用包括Mapper类用于将输入文本分解为单词并为每个单词分配计数值1;Reducer类汇总中间结果并输出最终的单词计数。
package com.hadoop.mapreduce.wordcount_client;

import java.awt.RenderingHints.Key;
import java.io.IOException;

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

public class WCMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
	
	private Text keyout = new Text();
	private IntWritable valueout = new IntWritable(1); 

	@Override
	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
		
		String line = value.toString();
		String[] words = line.split(" ");
		for(String word : words){
			keyout.set(word);;
			context.write(keyout, valueout);
		}
	}

	
	
}

package com.hadoop.mapreduce.wordcount_client;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class WCReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

	private IntWritable valueOut = new IntWritable();
	
	@Override
	protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		int sum = 0;
		for(IntWritable value : values){
			sum += value.get();
		}
		valueOut.set(sum);
		context.write(key, valueOut);
	}

	
	
}

package com.hadoop.mapreduce.wordcount_client;

import java.io.IOException;

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

public class WCDriver {

	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{
		
		Configuration configuration = new Configuration();
		
		Job job = Job.getInstance(configuration);
		
		FileInputFormat.addInputPath(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		
		job.setJarByClass(WCDriver.class);
		
		job.setMapperClass(WCMapper.class);
		job.setReducerClass(WCReducer.class);
		
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		
		boolean res = job.waitForCompletion(true);
		
		System.exit(res ? 0 : 1);
	}
	
}
bin/yarn jar wordcount.jar com.hadoop.mapreduce.wordcount_client.WCDriver /README.txt /output
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