1.Hadoop_MapReduce WordCount程序编写

Posted 2022-08-04 蒸气awa

一、重写三个方法Mapper、Reducer和Driver

二、对于Mapper

package my.study.mapreduce.wordcount;

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

/*
* KEYIN:,map阶段输入的Key的类型 :LongWritable;
* VALUEIN,map阶段输入value类型:Text(String);
* KEYOUT, map阶段输出的Key类型:Text;
* VALUEOUT, map阶段输出的value类型:IntWritable(Int)
*
* */

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> 

    private Text outK = new Text();
    private IntWritable outV = new IntWritable(1);

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException 

        //1获取一行
        String line = value.toString();

        //2切割
        String[] words = line.split(" ");

        //3循环写出
        for(String word : words)
            //封装
            outK.set(word);
            
            //写出
            context.write(outK,outV);
        

    

三、对于Reducer

package my.study.mapreduce.wordcount;

/*
 * KEYIN:,reduce阶段输入的Key的类型 :Text;
 * VALUEIN,reduce阶段输入value类型:IntWritable;
 * KEYOUT, reduce阶段输出的Key类型:Text;
 * VALUEOUT, reduce阶段输出的value类型:IntWritable(Int)
 *
 * */

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

public class WordCountReducer extends Reducer<Text, IntWritable,Text,IntWritable> 

    private IntWritable outV = new IntWritable();
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException 
        
        int sum = 0;
        //ss,(1,1)
        for (IntWritable value : values) 
            sum += value.get();
        

        outV.set(sum);
        
        //写出
        context.write(key, outV);
    

四、对于driver

package my.study.mapreduce.wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;


public class WordCountDriver 

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException 

        //1获取jop
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //2获取jar包路径
        job.setJarByClass(WordCountDriver.class);

        //3关联mapper和reducer
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);
        //4设置map输出的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        //5设置最终输出的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //6设置输入路径和输出路径
        FileInputFormat.setInputPaths(job, new Path("D:\\\\hadoop\\\\input\\\\wordCount\\\\words.txt"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\\\hadoop\\\\output\\\\wordCount"));

        //7提交job
        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);

    

五、debug调试节点设置

1.map方法：String line = value.toString();

mapper中run方法：setup(context); 和 cleanup(context);

2.reduce方法：int sum  = 0;

Reducer中run方法：setup(context); 和 cleanup(context);

六、集群运行

hadoop jar jar包 copy_Reference 输入路径 输出路径