前面我们学习了相关的理论，接下来，我们开始去解决开头提出的问题

（五）Tool接口改进代码

（1）新建Maven项目YarnDemo，并设置pom.xml如下：

<?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>

    <groupId>com.atguigu.hadoop</groupId>

    <artifactId>yarn_tool_test</artifactId>

    <version>1.0-SNAPSHOT</version>

    <dependencies>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-client</artifactId>

            <version>3.1.3</version>

        </dependency>

    </dependencies>

</project>

（2）新建一个包，名字为com.yarn。

（3）在这个包下创建类WordCountDriver并实现Tool接口：

package com.example.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 org.apache.hadoop.util.Tool;

import org.apache.hadoop.util.ToolRunner;

import java.io.IOException;

import java.util.Arrays;

//mapreduce: 分布式计算框架

// 提交任务给hadoop集群执行

// 它要做7件事 hadoop jar mc8.js com.example.mapreduce.WordCountDriver /要处理的文件夹 /结果路径

public class WordCountDriverTool implements Tool {

    private Configuration conf;

    public static void main(String[] args) throws Exception {

        switch(args[0]) {

            case "wordcount":

                int run = ToolRunner.run(new Configuration(), new WordCountDriverTool(), Arrays.copyOfRange(args,1, args.length));

                System.exit(run);

                break;

            default:

                throw new RuntimeException("no such tool" +args[0]);

        }

    }

    @Override

    public int run(String[] args) throws Exception {

        // 连接到hadoop集群

        // conf.set("fs.defaultFS", "hdfs://hadoop100:8020");

        Job job = Job.getInstance(conf);

        // 2. 关联本地的jar包

        job.setJarByClass(WordCountDriverTool.class);

        // 3. 关联Mapper和Reducer

        job.setMapperClass(WordCountMapper.class);

        job.setReducerClass(WordCountReducer.class);

        // 4. 设置Map的键值对泛型

        job.setMapOutputKeyClass(Text.class);

        job.setMapOutputValueClass(LongWritable.class);

        // 5. 设置Reduce的键值对泛型

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(LongWritable.class);

        // 6. 设置输入路径（D:\vm\wcinput）和输出路径（D:\vm\output01）

        FileInputFormat.setInputPaths(job, new Path(args[0]));

        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 7. 提交job,根据返回值设置程序退出code

        return job.waitForCompletion(true) ? 0 : 1;

    }

    @Override

    public void setConf(Configuration configuration) {

        this.conf = configuration;

    }

    @Override

    public Configuration getConf() {

        return conf;

    }

}

（4）重新打包生成jar，假设名称为MapReduceDemo1-1.0-SNAPSHOT.jar

（5）上传到集群的节点上。

（6）运行jar

hadoop jar MapReduceDemo1-1.0-SNAPSHOT.jar com.example.mapreduce.WordCountDriverTool wordcount  /wcinput /a1

（7）不使用-D参数，测试运行。

接下来，使用-D参数运行效果。

hadoop jar MapReduceDemo1-1.0-SNAPSHOT.jar com.example.mapreduce.WordCountDriverTool wordcount -Dmapreduce.job.queue.name=test /wcinput /a2

（8）运行结果如下