运用MapReduce实现wordCount

一、idea环境配置

要使用MapReduce来进行单词计数，需要导入需要的jar包，所以我们这里使用Maven来建工程，可以通过配置文件参数来自动引入所需要的jar包，下面是配置文件poem.xml的参数：

<?xml version="1.0" encoding="UTF-8"?>

<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>
//创建Maven工程时建的组名和工程名
  <groupId>cn.kgc</groupId>
  <artifactId>hdfsTest2</artifactId>
  <version>1.0-SNAPSHOT</version>

  <name>hdfsTest2</name>
  <!-- FIXME change it to the project's website -->
  <url>http://www.example.com</url>

  <properties>
  //这里使用Maven1.8版本
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
  </properties>

  <dependencies>
    <dependency>
    //junit使用4.12版本
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.12</version>
      <scope>test</scope>
    </dependency>
    <dependency>
    //hadoop2.6.0版本的jar包
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.6.0</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
    <dependency>
    //client的jar包
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.6.0</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-hdfs -->
    <dependency>
    //hdfs的jar包
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-hdfs</artifactId>
      <version>2.6.0</version>
    </dependency>
    <dependency>
    //log日志的jar包
      <groupId>commons-logging</groupId>
      <artifactId>commons-logging</artifactId>
      <version>1.2</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-auth</artifactId>
      <version>2.6.0</version>
    </dependency>
    <dependency>
    //MapReduce的jar包
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>2.6.0</version>
    </dependency>
    <dependency>
    //hadoop的测试工具包
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
      <version>2.6.0</version>
    </dependency>
  </dependencies>
  </project>

配置成功后，等待下载jar包。

二、MapReduce的简单工作流程

1.将文本信息转换成字符串类型
2.用split方法以空格将字符串分割存到数组里
3.以每一个单词作为map的key，单词出现的次数作为map的value
4.Combine是优化的步骤，在mapper阶段就对value的值进行计算，每一个分片只返回单个大的value，而不再是都是1的value
5.Suffle/sort阶段是对单词进行排序并且对所有分片的value值进行再次统计
6.reduce阶段对上一阶段的value值做最后的计算，输出单词对应出现的次数。

三、代码编写

1.Mapper类

package kb07.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;

public class WCMapper extends Mapper<LongWritable, Text,Text, IntWritable> {
    Text k = new Text();
    IntWritable v = new IntWritable(1); //value的值出现一个赋一个1
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();  //1.将文本转化成字符串
        String[]words =  line.split("\\s+");  //2.将字符串切割
        for (String word : words) {  //3.将每一个单词写出
            k.set(word);
            context.write(k,v);
        }
    }
}

2.Reducer类

package cn.kgc.mr.wc;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;

/**
 * KEYIN:reduce端输入的Key类型，即map端输出的key类型
 * VALEINP:reduce端输入的value类型，即map端输出的value类型
 * KEYOUT：reduce端输出的key类型
 * VALUEOUT：reduce端输出的value类型
 */
public class WCReducer extends Reducer<Text, IntWritable,Text,IntWritable> {
    int sum ;
    IntWritable v =  new IntWritable();
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        //reduce端接收到的类型大概是这样的  (wish,(1,1,1,1,1,1,1,1))
        sum = 0;
        //对迭代器进行累加求和
        for (IntWritable count : values) {
            sum += count.get();
        }
        //将key和value进行写出
        v.set(sum);
        context.write(key,v);
    }
}

3.Driver类

package kb07.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.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;

public class WCDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
    	//1.创建配置文件
        Configuration configuration=new Configuration();
        Job job =Job.getInstance(configuration,"wordcount");
        
		//2.设置jar的位置
        job.setJarByClass(WCDriver.class);
        
		//3.设置Map和Reduce的位置
        job.setMapperClass(WCMapper.class);
        job.setReducerClass(WCReducer.class);
        
        //4.设置map输出的key,value的类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        
		//5.设置reduce的输出key，value类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        
		 //6.设置输入输出路径
        FileInputFormat.setInputPaths(job,new Path("file:///D:\\test\\idea datas\\hdfsTest2\\data\\wcinput\\wc.txt"));
        FileOutputFormat.setOutputPath(job,new Path("file:///D:\\test\\idea datas\\hdfsTest2\\data\\wcoutput"));
        
		//7.提交程序运行
        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
}

四、idea运行结果

输入文件：

输出结果：