{

        Path inputPath=new Path("e:/mrinput/wordcount");
        Path outputPath=new Path("e:/mroutput/wordcount");

		/*Path inputPath=new Path("/wordcount");
		Path outputPath=new Path("/mroutput/wordcount");*/

        //作为整个Job的配置
        Configuration conf = new Configuration();

		/*conf.set("fs.defaultFS", "hdfs://hadoop101:9000");

		// 在YARN上运行
		conf.set("mapreduce.framework.name", "yarn");
		// RM所在的机器
		conf.set("yarn.resourcemanager.hostname", "hadoop102");*/

        //保证输出目录不存在
        FileSystem fs= FileSystem.get(conf);

        if (fs.exists(outputPath)) {

            fs.delete(outputPath, true);

        }

        // ①创建Job
        Job job = Job.getInstance(conf);

        // 告诉NM运行时，MR中Job所在的Jar包在哪里
        //job.setJar("MapReduce-0.0.1-SNAPSHOT.jar");
        // 将某个类所在地jar包作为job的jar包
        job.setJarByClass(WCDriver.class);


        // 为Job创建一个名字
        job.setJobName("wordcount");

        // ②设置Job
        // 设置Job运行的Mapper，Reducer类型，Mapper,Reducer输出的key-value类型
        job.setMapperClass(WCMapper.class);
        job.setReducerClass(WCReducer.class);

        // Job需要根据Mapper和Reducer输出的Key-value类型准备序列化器，通过序列化器对输出的key-value进行序列化和反序列化
        // 如果Mapper和Reducer输出的Key-value类型一致，直接设置Job最终的输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // 设置输入目录和输出目录
        FileInputFormat.setInputPaths(job, inputPath);
        FileOutputFormat.setOutputPath(job, outputPath);

        // ③运行Job
        job.waitForCompletion(true);


    }