hadoop学习笔记（九）：MapReduce程序的编写

一、MapReduce主要继承两个父类：

Map

 protected void map(KEY key,VALUE value,Context context) throws IOException,InterruptedException{
 }

Reduce

 1 protected void reduce(KEY key,Iterable<VALUE> values,Context context) throws IOException,InterruptedException{
 2 }

二、使用代码实现WordCount：

 package com.laowang.mapreduce;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.IntWritable;
 import org.apache.hadoop.io.LongWritable;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.mapreduce.Job;
 import org.apache.hadoop.mapreduce.Mapper;
 import org.apache.hadoop.mapreduce.Reducer;
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

 import java.io.IOException;
 import java.util.StringTokenizer;

 public class MR {
     /**
      * @author laowang
      * @version v1.0.0
      * @apiNote Mapper
      * @since 2018/4/27 10:44
      * <p>
      * KEYIN, VALUEIN, KEYOUT, VALUEOUT  输入key类型，输入value类型，输出KEY类型，输出value类型
      */
     static class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
         private final static IntWritable ONE = new IntWritable(1);
         private Text word = new Text();

         @Override
         protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
             //获取每一行的数据
             String lineStr = value.toString();
             //以 空格、/t、/n、/r、/f 分割
             StringTokenizer stringTokenizer = new StringTokenizer(lineStr);
             //遍历
             while (stringTokenizer.hasMoreTokens()) {
                 //获取截取后的每一个字符串
                 String wordValue = stringTokenizer.nextToken();
                 //拼接到word里面去
                 word.set(wordValue);
                 //写入到输出中
                 context.write(word, ONE);
             }
         }
     }

     /**
      * @author laowang
      * @version v1.0.0
      * @apiNote Reducer
      * @since 2018/4/27 10:44
      * <p>
      * KEYIN, VALUEIN, KEYOUT, VALUEOUT  输入key类型，输入value类型，输出KEY类型，输出value类型
      */
     static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
         private IntWritable result = new IntWritable();
         @Override
         protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
             int sum = 0;
             for (IntWritable value : values) {
                 sum += value.get();
             }
             result.set(sum);
             context.write(key, result);
         }
     }

     /**
      * @author laowang
      * @version v1.0.0
      * @apiNote Client
      * @since 2018/4/27 10:47
      */
     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
         //获取配置信息
         Configuration configuration = new Configuration();
         //创建job
         Job job = new Job(configuration,"wc");
         //设置JOB运行的类
         job.setJarByClass(MR.class);
         //设置Mapper和Reducer
         job.setMapperClass(MyMapper.class);
         job.setReducerClass(MyReducer.class);
         //设置输入和输出路径
         FileInputFormat.addInputPath(job,new Path(args[0]));
         FileOutputFormat.setOutputPath(job,new Path(args[1]));
         //设置输出key和value的类型
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(IntWritable.class);
         //提交job
         boolean b = job.waitForCompletion(true);
         //结束程序
         System.exit(b ? 0 : 1);
     }
 }