package com.felix;

 import java.io.IOException;

 import java.util.Iterator;

 import java.util.StringTokenizer;

 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.mapred.FileInputFormat;

 import org.apache.hadoop.mapred.FileOutputFormat;

 import org.apache.hadoop.mapred.JobClient;

 import org.apache.hadoop.mapred.JobConf;

 import org.apache.hadoop.mapred.MapReduceBase;

 import org.apache.hadoop.mapred.Mapper;

 import org.apache.hadoop.mapred.OutputCollector;

 import org.apache.hadoop.mapred.Reducer;

 import org.apache.hadoop.mapred.Reporter;

 import org.apache.hadoop.mapred.TextInputFormat;

 import org.apache.hadoop.mapred.TextOutputFormat;

 /**

  *

  * 描述：WordCount explains by Felix

  * @author Hadoop Dev Group

  */

 public class WordCount

 {

     /**

      * MapReduceBase类:实现了Mapper和Reducer接口的基类（其中的方法只是实现接口，而未作任何事情）

      * Mapper接口：

      * WritableComparable接口：实现WritableComparable的类可以相互比较。所有被用作key的类应该实现此接口。

      * Reporter 则可用于报告整个应用的运行进度，本例中未使用。

      *

      */

     public static class Map extends MapReduceBase implements

             Mapper<LongWritable, Text, Text, IntWritable>

     {

         /**

          * LongWritable, IntWritable, Text 均是 Hadoop 中实现的用于封装 Java 数据类型的类，这些类实现了WritableComparable接口，

          * 都能够被串行化从而便于在分布式环境中进行数据交换，你可以将它们分别视为long,int,String 的替代品。

          */

         private final static IntWritable one = new IntWritable(1);

         private Text word = new Text();

         /**

          * Mapper接口中的map方法：

          * void map(K1 key, V1 value, OutputCollector<K2,V2> output, Reporter reporter)

          * 映射一个单个的输入k/v对到一个中间的k/v对

          * 输出对不需要和输入对是相同的类型，输入对可以映射到0个或多个输出对。

          * OutputCollector接口：收集Mapper和Reducer输出的<k,v>对。

          * OutputCollector接口的collect(k, v)方法:增加一个(k,v)对到output

          */

         public void map(LongWritable key, Text value,

                 OutputCollector<Text, IntWritable> output, Reporter reporter)

                 throws IOException

         {

             String line = value.toString();

             StringTokenizer tokenizer = new StringTokenizer(line);

             while (tokenizer.hasMoreTokens())

             {

                 word.set(tokenizer.nextToken());

                 output.collect(word, one);

             }

         }

     }

     public static class Reduce extends MapReduceBase implements

             Reducer<Text, IntWritable, Text, IntWritable>

     {

         public void reduce(Text key, Iterator<IntWritable> values,

                 OutputCollector<Text, IntWritable> output, Reporter reporter)

                 throws IOException

         {

             int sum = 0;

             while (values.hasNext())

             {

                 sum += values.next().get();

             }

             output.collect(key, new IntWritable(sum));

         }

     }

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

     {

         /**

          * JobConf：map/reduce的job配置类，向hadoop框架描述map-reduce执行的工作

          * 构造方法：JobConf()、JobConf(Class exampleClass)、JobConf(Configuration conf)等

          */

         JobConf conf = new JobConf(WordCount.class);

         conf.setJobName("wordcount");           //设置一个用户定义的job名称

         conf.setOutputKeyClass(Text.class);    //为job的输出数据设置Key类

         conf.setOutputValueClass(IntWritable.class);   //为job输出设置value类

         conf.setMapperClass(Map.class);         //为job设置Mapper类

         conf.setCombinerClass(Reduce.class);      //为job设置Combiner类

         conf.setReducerClass(Reduce.class);        //为job设置Reduce类

         conf.setInputFormat(TextInputFormat.class);    //为map-reduce任务设置InputFormat实现类

         conf.setOutputFormat(TextOutputFormat.class);  //为map-reduce任务设置OutputFormat实现类

         /**

          * InputFormat描述map-reduce中对job的输入定义

          * setInputPaths():为map-reduce job设置路径数组作为输入列表

          * setInputPath()：为map-reduce job设置路径数组作为输出列表

          */

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

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

         JobClient.runJob(conf);         //运行一个job

     }

 }