1、自己准备一个数据量比较小的txt文件

然后将其上传到虚拟机本地：

之后上传到hdfs里面：

2、编写代码

1、引入相关依赖

<dependencies>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>3.3.0</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.3.0</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-hdfs -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>3.3.0</version>
            <scope>test</scope>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-core -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>3.3.0</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/junit/junit -->
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
            <scope>test</scope>
        </dependency>

    </dependencies>

2、编写mapper类

package org.example;

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

import java.io.IOException;

public class WordCountMapper extends Mapper<LongWritable,Text,Text,LongWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        Text text=new Text();
        LongWritable longWritable=new LongWritable();

        String[] split=value.toString().split(",");

        for(String word:split){
            text.set(word);
            longWritable.set(1);
            context.write(text,longWritable);
        }
    }
}

3、编写reduce类

package org.example;

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

import java.io.IOException;

public class WordCountReducer extends Reducer<Text, LongWritable,Text,LongWritable> {
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        long count=0;

        for(LongWritable value:values){
            count+=value.get();
        }

        context.write(key,new LongWritable(count));
    }
}

4、编写主类代码

package org.example;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class JobMain extends Configured implements Tool {

    @Override
    public int run(String[] strings) throws Exception {
        //创建一个job任务对象
        Job job=Job.getInstance(super.getConf(),"wordCount");

        //指定文件读取的路径和对象
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path("hdfs://node1:2181/wordcount"));

        //指定map阶段的处理方式和数据类型
        job.setMapperClass(WordCountMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //指定reduce阶段的处理方式和数据类型
        job.setReducerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        //设置输出类型
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("hdfs://node1:2181/wordcount"));

        //等待任务结束
        boolean b1=job.waitForCompletion(true);
        return b1?0:1;
    }

    public static void main(String[] args) throws Exception {
        Configuration configuration=new Configuration();
        //启动job任务
        int run= ToolRunner.run(configuration,new JobMain(),args);

        System.exit(run);
    }
}

3、代码运行

1、进行代码打包

clean之后package：

在target下面看到jar包啦：

将jar包上传到虚拟机本地：

使用rz -E命令在本地查找jar包并上传；

然后使用hadoop命令进行使用：

hadoop jar testHadoop1006-1.0-SNAPSHOT.jar org.example.JobMain

然后运行即可；