Mapperreduce的wordCount原理

wordcount原理：

1.mapper（Object key,Object value ,Context contex）阶段

2.从数据源读取一行数据传递给mapper函数的value

3.处理数据并将处理结果输出到reduce中去

String line = value.toString（）；

String[] words = line.split(" ");

context.write(word,1)

4.reduce（Object key ,List<value> values ,Context context）阶段

遍历values累加技术结果，并将数据输出

context.write(word,1)

代码示例：

Mapper类：

package com.hadoop.mr;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
/**
 * Mapper <Long, String, String, Long>
 * Mapper<LongWritable, Text, Text, LongWritable>//hadoop对上边的数据类型进行了封装
 *  LongWritable（Long）:偏移量
 *  Text（String）：输入数据的数据类型
 *  Text（String）:输出数据的key的数据类型
 *  LongWritable（Long）:输出数据的key的数据类型
 * @author shiwen
 */
public class WordCountMapper extends Mapper<LongWritable, Text, Text, LongWritable>{
    @Override
    protected void map(LongWritable key, Text value,
            Mapper<LongWritable, Text, Text, LongWritable>.Context context)
            throws IOException, InterruptedException {
        //1.读取一行
        String line = value.toString();
        //2.分割单词
        String[] words = line.split(" ");
        //3.统计单词
        for(String word : words){
            //4.输出统计
            context.write(new Text(word), new LongWritable(1));
        }
    }
}

reduce类

package com.hadoop.mr;

import java.io.IOException;

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

public class WordCountReduce 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 {

        long count = 0;
        //1.遍历vlues统计数据
        for(LongWritable value : values){
            count += value.get();
        }
        //输出统计
        context.write(key, new LongWritable(count));

    }

}

运行类：

package com.hadoop.mr;

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

import com.sun.jersey.core.impl.provider.entity.XMLJAXBElementProvider.Text;

public class WordCountRunner {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //1.创建配置对象
        Configuration config = new Configuration();
        //2.Job对象
        Job job = new Job(config);

        //3.设置mapperreduce所在的jar包
        job.setJarByClass(WordCountRunner.class);

        //4.设置mapper的类
        job.setMapOutputKeyClass(WordCountMapper.class);
        //5.设置reduce的类
        job.setReducerClass(WordCountReduce.class);

        //6.设置reduce输入的key的数据类型
        job.setOutputKeyClass(Text.class);
        //7.设置reduce输出的value的数据类型
        job.setOutputValueClass(LongWritable.class);

        //8.设置输入的文件位置
        FileInputFormat.setInputPaths(job, new Path("hdfs://192.168.1.10:9000/input"));
        //9.设置输出的文件位置
        FileOutputFormat.setOutputPath(job, new Path("hdfs://192.168.1.10:9000/input"));

        //10.将任务提交给集群
        job.waitForCompletion(true);

    }

}