Hadoop： MapReduce使用hdfs中的文件

本代码包含功能：获取DataNode名，并写入到HDFS文件系统中的文件hdfs:///copyOftest.c中。
并计数文件hdfs:///copyOftest.c中的wordcount计数，有别于Hadoop的examples中的读取本地文件系统中的文件，这次读取的是HDFS中的文件。

package com.fora;

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hdfs.DistributedFileSystem;
import org.apache.hadoop.hdfs.protocol.DatanodeInfo;
import org.apache.hadoop.io.IntWritable;
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.Mapper.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;


public class FileOperate {
 
    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
        
        init();/*初始化文件*/
           
        Configuration conf = new Configuration();
           
        Job job = new Job(conf, "word count");
        job.setJarByClass(FileOperate.class);
        
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        
               /* set the path of input and output*/
        FileInputFormat.addInputPath(job, new Path("hdfs:///copyOftest.c"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs:///wordcount"));
        
        System.exit(job.waitForCompletion(true) ? 0 : 1);
      }
    
public static class TokenizerMapper 
    extends Mapper{
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        
        StringTokenizer itr = new StringTokenizer(value.toString());
   while (itr.hasMoreTokens()){
     word.set(itr.nextToken());
     context.write(word, one);
   }
 }
}
    
public static class IntSumReducer 
     extends Reducer {
  private IntWritable result = new IntWritable();

  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 init()throws IOException { 
        
        /*copy local file to hdfs*/
        Configuration config = new Configuration();
        FileSystem hdfs = null;
        String  srcFile = "/test.c";
        String  dstFile = "hdfs:///copyOftest.c";
        System.out.print("copy success!\n"); 
   hdfs = FileSystem.get(config);
        Path srcPath = new Path(srcFile);
        Path dstPath = new Path(dstFile);
        hdfs.copyFromLocalFile(srcPath, dstPath); 
        
        String fileName = "hdfs:///copyOftest.c";
        Path path = new Path(fileName);
        FileStatus fileStatus =null;
 
        fileStatus = hdfs.getFileStatus(path); 
        System.out.println(fileStatus.getBlockSize()); 
        
        FileSystem fs = FileSystem.get(config);
        DistributedFileSystem hdfs1 = (DistributedFileSystem) fs;
        DatanodeInfo[] dataNodeStats = hdfs1.getDataNodeStats();
        
        /*create a file on hdfs*/
        Path Outputpath = new Path("hdfs:///output/listOfDatanode");
        FSDataOutputStream outputStream = hdfs.create(Outputpath);        
        
        String[] names = new String[dataNodeStats.length]; 
        for (int i = 0; i < dataNodeStats.length; i++) {
              names[i] = dataNodeStats[i].getHostName();/*get the list of datanodes*/
                          System.out.println(names[i]);
              /*write the list of datanodes to file on hdfs*/
              outputStream.write(names[i].getBytes(), 0, names[i].length());
        } 
    }

}

运行结果：
[root@master bin]# hadoop jar HDFS.jar com.fora.FileOperate
copy success!
67108864
master
slave1
11/07/21 15:45:23 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
11/07/21 15:45:23 INFO input.FileInputFormat: Total input paths to process : 1
11/07/21 15:45:23 INFO mapred.JobClient: Running job: job_201107210917_0003
11/07/21 15:45:24 INFO mapred.JobClient:  map 0% reduce 0%
11/07/21 15:45:31 INFO mapred.JobClient:  map 100% reduce 0%
11/07/21 15:45:43 INFO mapred.JobClient:  map 100% reduce 100%
11/07/21 15:45:45 INFO mapred.JobClient: Job complete: job_201107210917_0003
11/07/21 15:45:45 INFO mapred.JobClient: Counters: 17
11/07/21 15:45:45 INFO mapred.JobClient:   Job Counters 
11/07/21 15:45:45 INFO mapred.JobClient:     Launched reduce tasks=1
11/07/21 15:45:45 INFO mapred.JobClient:     Rack-local map tasks=1
11/07/21 15:45:45 INFO mapred.JobClient:     Launched map tasks=1
11/07/21 15:45:45 INFO mapred.JobClient:   FileSystemCounters
11/07/21 15:45:45 INFO mapred.JobClient:     FILE_BYTES_READ=228
11/07/21 15:45:45 INFO mapred.JobClient:     HDFS_BYTES_READ=126
11/07/21 15:45:45 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=488
11/07/21 15:45:45 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=146
11/07/21 15:45:45 INFO mapred.JobClient:   Map-Reduce Framework
11/07/21 15:45:45 INFO mapred.JobClient:     Reduce input groups=19
11/07/21 15:45:45 INFO mapred.JobClient:     Combine output records=19
11/07/21 15:45:45 INFO mapred.JobClient:     Map input records=8
11/07/21 15:45:45 INFO mapred.JobClient:     Reduce shuffle bytes=228
11/07/21 15:45:45 INFO mapred.JobClient:     Reduce output records=19
11/07/21 15:45:45 INFO mapred.JobClient:     Spilled Records=38
11/07/21 15:45:45 INFO mapred.JobClient:     Map output bytes=211
11/07/21 15:45:45 INFO mapred.JobClient:     Combine input records=22
11/07/21 15:45:45 INFO mapred.JobClient:     Map output records=22
11/07/21 15:45:45 INFO mapred.JobClient:     Reduce input records=19
[root@master bin]# hadoop dfs  -ls /
Found 6 items
-rw-r--r--   1 root supergroup        126 2011-07-21 15:45 /copyOftest.c
-rw-r--r--   1 root supergroup         26 2011-07-21 15:16 /listOfDatanode
drwxr-xr-x   - root supergroup          0 2011-07-21 15:45 /output
-rw-r--r--   1 root supergroup      10400 2011-07-20 16:51 /test.txt
drwxr-xr-x   - root supergroup          0 2011-07-20 16:09 /tmp
drwxr-xr-x   - root supergroup          0 2011-07-21 15:45 /wordcount
[root@master bin]# hadoop dfs -ls /wordcount
Found 2 items
drwxr-xr-x   - root supergroup          0 2011-07-21 15:45 /wordcount/_logs
-rw-r--r--   1 root supergroup        146 2011-07-21 15:45 /wordcount/part-r-00000
[root@master bin]# hadoop dfs -cat /wordcount/part-r-00000
2011-07-21      1
File    1
Hadoop  1
System! 1
a       1
aimed   1
at      1
coping  1
file    3
from    1
from:fora       1
is      1
local   1
system  1
thank   1
the     1
this    2
to      1
you!    1
[root@master bin]#