MapReduce中的WordCount编写

MapReduce中主要有三大类Mapper,Reduce,Drive,它们类似八股文一样有自己的格式

pom.xml:

        
            junit
            junit
            RELEASE
        
        
            org.apache.logging.log4j
            log4j-core
            2.8.2
        
        
            org.apache.hadoop
            hadoop-common
            2.7.2
        
        
            org.apache.hadoop
            hadoop-client
            2.7.2
        
        
            org.apache.hadoop
            hadoop-hdfs
            2.7.2
        
    

    
    
        
            
                maven-compiler-plugin
                2.3.2
                
                    1.8
                    1.8
                
            
            
                maven-assembly-plugin
                
                    
                        jar-with-dependencies
                    
                    
                        
                            com.hadoop.wordcount.WordCountDriver
                        
                    
                
                
                    
                        make-assembly
                        package
                        
                            single
                        
                    
                
            
        
    

Mapper：

package com.hadoop.wordcount;


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

import java.io.IOException;

//map阶段
//Mapper<>定义输入和输出
public class WordCountMapper extends Mapper {
    Text k = new Text();
    IntWritable v = new IntWritable(1);

//    自定义map    LongWritable key     Text value是map输入的数据
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//      获取数据进行转换类型
        String line = value.toString();
//       切分单词
        String[] words = line.split(" ");
//       循环写出,遍历数组
        for (String word : words) {
            k.set(word);
            context.write(k,v);
        }
    }
}

Reduce:

package com.hadoop.wordcount;

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

import java.io.IOException;

public class WordCountReduce extends Reducer {
    IntWritable v = new IntWritable();
    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        int sum = 0;

        //累加求和
        for (IntWritable value : values) {
            sum += value.get();
        }
        v.set(sum);
        context.write(key,v);
    }
}

Drive:

package com.hadoop.wordcount;

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.mapred.lib.CombineSequenceFileInputFormat;
import org.apache.hadoop.mapreduce.Job;
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job对象
        Configuration conf = new Configuration();//job对象的信息
        Job job = Job.getInstance(conf);
        //2.获取jar存储位置
        job.setJarByClass(WordCountDriver.class);
        //3.关联map和reduce类
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReduce.class);
        //4.指定mapper输出数据kv值类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        //5.指定最终输出数据的kv值类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        //6.指定job的输入原始文件所在的目录
        FileInputFormat.setInputPaths(job,new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));
        //7.指定job的输出结果所在的目录
        //job.submit();
        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
}

打包成jar在集群上运行，先在pom.xml中导入导包工具，在进行以下操作：