MapReduce开发自己的wordcount程序

MapReduce开发自己的wordcount程序

1、Mapper阶段

2、Reducer阶段

3、主程序job阶段

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1、Mapper阶段

package demo.wc;

import java.io.IOException;

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


//                                           k1            v1     k2        v2
public class WordCountMapper extends Mapper<LongWritable, Text, Text, LongWritable>{


@Override
protected void map(LongWritable k1, Text v1, Context context)
throws IOException, InterruptedException {
/*
* context代表Map的上下文
* 上文:HDFS
* 下文是:Reducer
*/
//数据: I love Beijing
String data = v1.toString();

//分词
String[] words = data.split(" ");

//输出:  k2     v2
for(String w:words){
context.write(new Text(w), new LongWritable(1));
}
}


}

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2、Reducer阶段

package demo.wc;

import java.io.IOException;

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


//                                              k3     v3          k4       v4
public class WordCountReducer extends Reducer<Text, LongWritable, Text, LongWritable> {


@Override
protected void reduce(Text k3, Iterable<LongWritable> v3,Context context) throws IOException, InterruptedException {
/*
* context 代表Reducer上下文
* 上文:mapper
* 下文:HDFS
*/
long total = 0;
for(LongWritable l:v3){
total = total + l.get();
}

//输出  k4     v4
context.write(k3, new LongWritable(total));
}
}

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3、主程序job阶段

//创建一个job = mapper + reducer

//指定job的入口

//指定任务的mapper和输出数据类型

//指定任务的reducer和输出数据类型

//指定输入的路径和输出的路径

//执行任务



package demo.wc;

import java.io.IOException;

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 WordCountMain {


public static void main(String[] args) throws Exception {
//创建一个job = mapper + reducer
Job job = Job.getInstance(new Configuration());
//指定job的入口
job.setJarByClass(WordCountMain.class);

//指定任务的mapper和输出数据类型
job.setMapperClass(WordCountMapper.class);
job.setMapOutputKeyClass(Text.class); //指定k2的类型
job.setMapOutputValueClass(LongWritable.class);//指定v2的数据类型

//指定任务的reducer和输出数据类型
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);//指定k4的类型
job.setOutputValueClass(LongWritable.class);//指定v4的类型

//指定输入的路径和输出的路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));

//执行任务
job.waitForCompletion(true);
}
}