原始数据:

Map阶段

1.每次读一行数据,
2.拆分每行数据,
3.每个单词碰到一次写个1

<0, “hello tom”>

<10, “hello jerry”>

<22, “hello kitty”>

<34, “hello world”>

<46, “hello tom”>

点击查看代码

/**
 * @ClassName:WordCountReduce
 * @Description:TODO
 * @author:Li Wei Ning
 * @Date:2022/4/28 10:55
 */

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

import java.io.IOException;

/**
 * Text  数据类型：字符串类型 String
 * IntWritable reduce阶段的输入类型 int
 * Text reduce阶段的输出数据类型 String类型
 * IntWritable 输出词频个数 Int型
 * @author 暖阳
 */
public class WordCountReduce extends Reducer<Text, IntWritable,Text,IntWritable> {
    /**
     * key 输入的 键
     * value 输入的 值
     * context 上下文对象,用于输出键值对
     */
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context){
        try {
            int sum=0;
            for(IntWritable val:values){
                sum+=val.get();
            }
            context.write(key,new IntWritable(sum));
        }catch (Exception e){
            e.printStackTrace();
        }finally {
            System.out.println("成功!!!");
        }
    }
}

reduce阶段

1.把单词对应的那些1
2遍历
3求和
<hello, {1,1,1,1,1}>

<jerry, {1}>

<kitty, {1}>

<tom, {1,1}>

<world, {1}>

点击查看代码

/**
 * @ClassName:WordCountReduce
 * @Description:TODO
 * @author:Li Wei Ning
 * @Date:2022/4/28 10:55
 */

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

import java.io.IOException;

/**
 * Text  数据类型：字符串类型 String
 * IntWritable reduce阶段的输入类型 int
 * Text reduce阶段的输出数据类型 String类型
 * IntWritable 输出词频个数 Int型
 * @author 暖阳
 */
public class WordCountReduce extends Reducer<Text, IntWritable,Text,IntWritable> {
    /**
     * key 输入的 键
     * value 输入的 值
     * context 上下文对象,用于输出键值对
     */
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context){
        try {
            int sum=0;
            for(IntWritable val:values){
                sum+=val.get();
            }
            context.write(key,new IntWritable(sum));
        }catch (Exception e){
            e.printStackTrace();
        }finally {
            System.out.println("成功!!!");
        }
    }
}

整合合并

点击查看代码

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;

/**
 * @ClassName:WordCount
 * @Description:TODO
 * @author:Li Wei Ning
 * @Date:2022/4/28 10:54
 */
public class WordCountTest {
    public static void main(String[] args) {

        try {
            /*定义配置*/
            Configuration config = new Configuration();
            /* config.set("fs.defaultFS", "hdfs://192.168.47.128:9000");*/

            /*定义一个工作任务,用于套接map和reduce两个阶段*/
            Job job = Job.getInstance(config);

            /* 定义工作任务用map*/
            job.setMapperClass(WordCountMap.class);
            /*定义map的输出key*/
            job.setMapOutputKeyClass(Text.class);
            /*定义map的输出value*/
            job.setMapOutputValueClass(IntWritable.class);

            /*定义map的文件路径*/
            Path srcPath = new Path("C:/Users/暖阳/Desktop/123.txt");
            /*定义map的输入文件*/
            FileInputFormat.setInputPaths(job,srcPath);

            /* 定义reduce用哪个类*/
            job.setReducerClass(WordCountReduce.class);
            /*指定reduce的输出key*/
            job.setOutputKeyClass(Text.class);
            /*指定reduce的输出value*/
            job.setOutputValueClass(IntWritable.class);

            /* 定义主类*/
            job.setJarByClass(WordCountTest.class);

            /*定义reduce的输出文件路径*/
            Path outPath = new Path("C:/Users/暖阳/Desktop/WordCountTest");
            /*输出最终结果文件路径*/
            FileOutputFormat.setOutputPath(job, outPath);

            /*提交job并关闭程序*/
            System.exit(job.waitForCompletion(true) ? 0 : 1);

        }catch (Exception e){
            e.printStackTrace();
        }finally {
            System.out.println("最终的");
        }
    }
}