一.设计分析

1.Map过程:并行读取文本,对读取的单词进行map操作,每个词都以<key,value>形式生成
2.Reduce操作是对map的结果进行排序合并最后得出词频

二.代码开发

1.新建maven工程,添加依赖

<dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.6.0</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-hdfs</artifactId>
      <version>2.6.0</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.6.0</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>2.6.0</version>
    </dependency>
    <dependency>
      <groupId>commons-logging</groupId>
      <artifactId>commons-logging</artifactId>
      <version>1.2</version>
    </dependency>

2.编写Mapper类

package hadoop.mapreduce;

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;

/**
 * @author sunyong
 * @date 2020/07/01
 * @description
 * KEYIN:输入的key类型
 * VALUEIN:输入的value类型
 * KEYOUT:输出的key类型
 * VALUEOUT:输出的value类型
 */
public class WCMapper extends Mapper<LongWritable, Text,Text, IntWritable> {
    Text k = new Text();
    IntWritable v = new IntWritable(1);
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
      //1.将文本转化成字符串
        String line = value.toString();
      //2.将字符串切割
        String[] words = line.split("\\s+");
      //3.循环遍历,将每一个单词写出去
        for (String word : words) {
            k.set(word);
            context.write(k,v);
        }
    }
}

3.编写Reduce类

package hadoop.mapreduce;

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

import java.io.IOException;

/**
 * @author sunyong
 * @date 2020/07/01
 * @description
 * KEYIN:reduce端输入的key类型,即map端输出的key类型
 * VALUEIN:reduce输入的value类型,即map端输出的value类型
 * KEYOUT:reduce输出的key类型
 * VALUEOUT:reduce输出的value类型
 */
public class WCReducer extends Reducer< Text,IntWritable,Text, IntWritable> {
    IntWritable v = new IntWritable();
    int sum;
    @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))
        //对迭代器进行累加求和
        //sum必须赋值为0初始化,因为reduce方法是每个键都会执行一次
        sum=0;
        for (IntWritable count : values) {
            sum+=count.get();
        }
        v.set(sum);
        //将key和value进行写出
        context.write(key,v);
    }
}

4.编写Driver类执行Job

package hadoop.mapreduce;

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;

/**
 * @author sunyong
 * @date 2020/07/01
 * @description
 */
public class WCDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //1.创建配置文件,创建Job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf,"wordcount");

        //2.设置jar的位置,参数为本类类名.class
        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输出端的key,value类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        //5.设置reduce输出的key,value类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //6.设置输入和输出路径,输入的是本地自己建的txt文件,会输出一个test目录
        FileInputFormat.setInputPaths(job,new Path("F:\\sunyong\\Java\\codes\\javaToHdfs\\download\\a.txt"));
        FileOutputFormat.setOutputPath(job,new Path("test"));

        //7.提交程序运行
        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
}

5.执行会在本工程目录出现一个test目录打开目录中的part-r-00000文件即统计词频文件,如下:

在这里插入图片描述

6.在hadoop中运行

1)修改Driver类中输入输出路径:

 		//6.设置输入输出路径
        FileInputFormat.setInputPaths(job,new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));

2)打jar包将jar包上传到hadoop的lib目录下

3)将测试数据上传到hdfs目录中:

hdfs dfs -mkdir /input,hdfs dfs -put /tmp/test.txt /input/

4)提交MapReduce作业运行: (注意如果存在output目录需要先删除)

hadoop jar /opt/install/hadoop/lib/javaToHdfs.jar hadoop.mapreduce.WCDriver /input/test.txt /output

5)查看作业输出结果,如下图所示:

hdfs dfs -text /output/part-*
在这里插入图片描述

MapReduce实现WordCount词频统计

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