使用mapreduce统计文件中所有单词出现的次数

1、将wordcount.txt文本文件上传到/data/目录下，wordcount.txt文件内容如下：

red   black  green  yellow
red blue blue
black big small small   yellow
red red red red
blue 

2、创建一个java maven工程，pom.xml中添加hdfs、mapreduce的引用，如下

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.che</groupId>
  <artifactId>demo</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>demo</name>
  <url>http://maven.apache.org</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>

  <dependencies>
  
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>3.8.1</version>
      <scope>test</scope>
    </dependency>
    
    <dependency>
		<groupId>org.apache.hadoop</groupId>
		<artifactId>hadoop-common</artifactId>
		<version>2.7.0</version>
	</dependency>
	
	<dependency>
		<groupId>org.apache.hadoop</groupId>
		<artifactId>hadoop-hdfs</artifactId>
		<version>2.7.0</version>
	</dependency>
	
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-mapreduce-client-core</artifactId>
        <version>2.7.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>2.7.0</version>
    </dependency>  
    
  </dependencies>
</project>

3、代码如下：

3.1 WordCount Mapper实现类WordCountMapper.java

package com.che.demo.mapreduce;

import java.io.IOException;

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

/**
 * WordCount Mapper实现类
 */
public class WordCountMapper extends Mapper<LongWritable, Text,Text,LongWritable> {
     

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
     
        // 将Text类型的value  转换成 string
        String datas = value.toString();

        // 将这一行用 " " 切分出各个单词
        String[] words = datas.split(" ");

        for (String word : words) {
     
            context.write(new Text(word),new LongWritable(1));
        }

    }
}

3.2 WordCount Reducer实现类WordCountReducer.java

package com.che.demo.mapreduce;

import java.io.IOException;
import java.util.Iterator;

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

/**
 * WordCount Reducer实现类
 */
public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
     

    @Override
    protected void reduce(Text word, Iterable<IntWritable> valuesIterator,Context context)throws IOException, InterruptedException {
     
        int count=0;
        //统计单词数量
        Iterator<IntWritable> iterator = valuesIterator.iterator();
        while(iterator.hasNext()){
     
            iterator.next();
            count++;
        }
        context.write(word, new IntWritable(count));
    }

}

3.3 WordCount Main方法实现类WordCountJob.java

package com.che.demo.mapreduce;

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;

import java.io.IOException;

/**
 * WordCount Main方法实现类
 */
public class WordCountJob {
     
	
	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
     
        
        Configuration conf = new Configuration();
        Job wcjob = Job.getInstance(conf);
        
        wcjob.setJarByClass(WordCountJob.class);

        wcjob.setMapperClass(WordCountMapper.class);
        wcjob.setReducerClass(WordCountReducer.class);

        wcjob.setMapOutputKeyClass(Text.class);
        wcjob.setMapOutputValueClass(LongWritable.class);

        wcjob.setMapOutputKeyClass(Text.class);
        wcjob.setOutputValueClass(LongWritable.class);

        //指定要处理的数据所在的位置
        FileInputFormat.setInputPaths(wcjob,"/user/che/1021001/input");
        //指定处理完成之后的结果所保存的位置 
        FileOutputFormat.setOutputPath(wcjob, new Path("/user/che/1021001/output"));

        // 向yarn集群提交这个job
        boolean res = wcjob.waitForCompletion(true);
        System.out.println(res?0:1);
    }

}

3.4 在eclipse中将项目工程打成一个jar包，操作如下图

然后工程target目录就会多出一个jar文件如下图

3.5 将demo-0.0.1-SNAPSHOT.jar重命名为demo.jar后，上传到centos7上的/data/目录下

3.6 将centos7中的/data/wordcount.txt文件上传hdfs上的/user/che/1021001/input目录下

hdfs dfs -put /data/wordcount.txt /user/che/1021001/input

3.7 使用hadoop jar命令执行

hadoop jar /data/demo.jar com.che.demo.mapreduce.WordCountJob

3.8 查看输出结果，输出结果在/user/che/1021001/output