Hadoop MapReduce如何进行WordCount自主编译运行

$ jar cvf WordCount.jar test/ 

$ cd /usr/local/hadoop 
$ ./sbin/start-dfs.sh 
$ jps    //检查是否启动NameNode,DataNode等 

$ hadoop fs -put /usr/local/hadoop/etc/hadoop/*.xml input 

$ cd ~/workspace/WordCount/bin   //进入到WordCount.jar所在目录 
$ hadoop jar WordCount.jar test.WordCount input output 
$ hadoop fs -cat output/part-r-00000   //查看输出

$ cd /usr/local/hadoop 
$ ./sbin/stop-dfs.sh 

hadoop jar WordCount.jar WordCount input output 

Exception in thread "main" java.lang.ClassNotFoundException: WordCount 

package test; 
 
import java.io.IOException; 
import java.util.StringTokenizer; 
import org.apache.hadoop.conf.Configuration; 
import org.apache.hadoop.fs.Path; 
import org.apache.hadoop.io.IntWritable; 
import org.apache.hadoop.io.Text; 
import org.apache.hadoop.mapred.JobConf; 
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 org.apache.hadoop.util.GenericOptionsParser; 
 
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 TokenizerMapper extends Mapper<Object, 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();      //Text 实现了BinaryComparable类可以作为key值 
    
    /**  
     * 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(Object key, Text value, Context context) throws IOException, InterruptedException { 
         
        /** 
         * 原始数据： 
         * c++ java hello 
world java hello 
you me too 
map阶段，数据如下形式作为map的输入值：key为偏移量 
0  c++ java hello 
            16 world java hello 
            34 you me too 
 
         */ 
 
            StringTokenizer itr = new StringTokenizer(value.toString());  //得到什么值 
      
            while (itr.hasMoreTokens()) { 
                word.set(itr.nextToken()); 
                context.write(word, one); 
            } 
        } 
    } 
   
    public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { 
        private IntWritable result = new IntWritable(); 
/** 
 * reduce过程是对输入数据解析形成如下格式数据： 
 * (c++ [1]) 
 * (java [1,1]) 
 * (hello [1,1]) 
 * (world [1]) 
 * (you [1]) 
 * (me [1]) 
 * (you [1]) 
 * 供接下来的实现的reduce程序分析数据数据 
 *  
 */ 
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { 
            int sum = 0; 
            for (IntWritable val : values) { 
                sum += val.get(); 
            } 
            result.set(sum); 
            context.write(key, result); 
        } 
    } 
 
    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  
       */   
        Configuration conf = new Configuration(); 
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); 
        //这里需要配置参数即输入和输出的HDFS的文件路径 
        if (otherArgs.length != 2) { 
            System.err.println("Usage: wordcount  "); 
            System.exit(2); 
        } 
        Job job = new Job(conf, "word count");      // Job(Configuration conf, String jobName) 
        job.setJarByClass(WordCount.class); 
    job.setMapperClass(TokenizerMapper.class);  // 为job设置Mapper类  
    job.setCombinerClass(IntSumReducer.class);  // 为job设置Combiner类   
    job.setReducerClass(IntSumReducer.class);   // 为job设置Reduce类    
    job.setOutputKeyClass(Text.class);          // 设置输出key的类型 
    job.setOutputValueClass(IntWritable.class); // 设置输出value的类型 
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));  
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); 
    System.exit(job.waitForCompletion(true) ? 0 : 1); 
    } 
}