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package com.bigdata;import org.apache.hadoop.io.LongWritable;import java.io.IOException;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.task.reduce.MapOutput;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;import com.bigdata.hadoop_MapReduce2.WCMapper.MapReduceReducer;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.io.Text;public class hadoop_MapReduce2 extends Configured implements Tool {
   //  step 1 :Mapper class    public static class WCMapper extends Mapper
   
    {
           private Text MapOutputkey = new Text();//      出现一次就记录一次        private IntWritable mapOutputValue = new IntWritable(1);        @Override        public void map(LongWritable key, Text value,Context context)throws IOException, InterruptedException {//      读取文件的每一件,将Text类型装换为String类型        String linevalue = value.toString();//      分割单词,以空格分割        String[]strs=linevalue.split(" ");//      分割之后将单词从数组中一个个拿出来，组成
    
     ,比如
     
              for (String str : strs) {//          设置key的输出            MapOutputkey.set(str);//          map输出            context.write(MapOutputkey,mapOutputValue);        }}       //  step 2 :Reducer class    public static class MapReduceReducer extends Reducer
      
       {
           private IntWritable outputValue = new IntWritable();        protected void reduce(Text key, Iterable
       
         values,Context context) throws IOException, InterruptedException {        int sum = 0;//      对值进行迭代累加        for(IntWritable value : values) {//          total            sum+=value.get();        }//      set output value        outputValue.set(sum);//      最终输出        context.write(key, outputValue);        }    }    }//  step 3 :Driver    public int run(String[] args)throws Exception{//      获取集群中的相关配置信息        Configuration configuration = this.getConf();//      创建一个job任务        Job job=Job.getInstance(configuration,this.getClass().getSimpleName());//      整个MapReduce程序运行的入口,或者叫jar包的入口，jar具体运行的是哪个类        job.setJarByClass(this.getClass());;//      设置Job//      input输入,输入的路径        Path inpath =new Path(args[0]);        FileInputFormat.addInputPath(job, inpath);//      output输出，输出路径        Path outpath = new Path(args[1]);        FileOutputFormat.setOutputPath(job, outpath);//      设置mapper        job.setMapperClass(WCMapper.class);        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(IntWritable.class);//      设置Reduce        job.setReducerClass(MapReduceReducer.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);//      提交Job-》YARN        boolean isSuccess = job.waitForCompletion(true);        return isSuccess ? 0:1;    }    public static void main(String[] args)throws Exception {        Configuration configuration = new Configuration();        //run job        int status = ToolRunner.run(configuration, new hadoop_MapReduce2(), args);        //exit program        System.exit(status);/*   args = new String[] {                "hdfs://hadoop3:9000/wc.input",                "hdfs://hadoop3:9000/output"        };        int status =new hadoop_MapReduce2().run(args);//      关闭        System.exit(status);    }*/    }}
       
      
     
    
   

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