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MapReduce实战之NLineInputFormat使用案例


1)需求:根据每个输入文件的行数来规定输出多少个切片。例如每三行放入一个切片中。

2)输入数据:

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3)输出结果:

Number of splits:4

4)代码实现:

(1)编写mapper

package com.atguigu.mapreduce.nline;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

 

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

      

       private Text k = new Text();

       private LongWritable v = new LongWritable(1);

      

       @Override

       protected void map(LongWritable key, Text value, Context context)

                     throws IOException, InterruptedException {

             

              // 1 获取一行

        final String line = value.toString();

       

        // 2 切割

        final String[] splited = line.split(" ");

       

        // 3 循环写出

        for (int i = 0; i < splited.length; i++) {

              

               k.set(splited[i]);

              

            context.write(k, v);

        }

       }

}

(2)编写Reducer

package com.atguigu.mapreduce.nline;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

 

public class NLineReducer extends Reducer<Text, LongWritable, Text, LongWritable>{

      

       LongWritable v = new LongWritable();

      

       @Override

       protected void reduce(Text key, Iterable<LongWritable> values,

                     Context context) throws IOException, InterruptedException {

             

        long count = 0l;

        // 1 汇总

        for (LongWritable value : values) {

            count += value.get();

        } 

       

        v.set(count);

       

        // 2 输出

        context.write(key, v);

       }

}

(3)编写driver

package com.atguigu.mapreduce.nline;

import java.io.IOException;

import java.net.URISyntaxException;

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.input.NLineInputFormat;

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

 

public class NLineDriver {

      

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

             

              // 获取job对象

              Configuration configuration = new Configuration();

        Job job = Job.getInstance(configuration);

       

        // 设置每个切片InputSplit中划分三条记录

        NLineInputFormat.setNumLinesPerSplit(job, 3);

         

        // 使用NLineInputFormat处理记录数

        job.setInputFormatClass(NLineInputFormat.class); 

         

        // 设置jar包位置,关联mapper和reducer

        job.setJarByClass(NLineDriver.class); 

        job.setMapperClass(NLineMapper.class); 

        job.setReducerClass(NLineReducer.class); 

       

        // 设置map输出kv类型

        job.setMapOutputKeyClass(Text.class); 

        job.setMapOutputValueClass(LongWritable.class); 

       

        // 设置最终输出kv类型

        job.setOutputKeyClass(Text.class); 

        job.setOutputValueClass(LongWritable.class); 

         

        // 设置输入输出数据路径

        FileInputFormat.setInputPaths(job, new Path(args[0])); 

        FileOutputFormat.setOutputPath(job, new Path(args[1])); 

         

        // 提交job

        job.waitForCompletion(true); 

       }

}

5)结果查看

(1)输入数据

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banzhang ni hao

xihuan hadoop banzhang dc

banzhang ni hao

xihuan hadoop banzhang dc

banzhang ni hao

xihuan hadoop banzhang dc

banzhang ni hao

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xihuan hadoop banzhang dc

(2)输出结果的切片数:

MapReduce实战之NLineInputFormat使用案例_NLineInputFormat使用案例

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