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

    }

}