单独编译运行hadoop自带wordcount例子

1、首先要找到wordcount的源代码，在http://svn.apache.org/repos/asf/hadoop/中，用svn客户端check out下来，找到svn.apache.org_repos_asf\hadoop\hadoop-mapreduce-project\hadoop-mapreduce-examples\src\main\java\org\apache\hadoop\examples\WordCount.java

2、创建文件夹并且把wordcount文件拷出来：

mkdir playground

mkdir playground/src

mkdir playground/classes

cp src/examples/org/apache/hadoop/examples/WordCount.java playground/src/WordCount.java

3、在hadoop框架中编译和执行这个副本

javac -classpath hadoop-core-1.0.1.jar:lib/commons-cli-1.2.jar -d playground/classes/ playground/src/WordCount.java

 

jar -cvf playground/wordcount.jar -C playground/classes/ .

 

1. 标明清单(manifest)  
2. 增加：org/(读入= 0) (写出= 0)(存储了 0%)  
3. 增加：org/apache/(读入= 0) (写出= 0)(存储了 0%)  
4. 增加：org/apache/hadoop/(读入= 0) (写出= 0)(存储了 0%)  
5. 增加：org/apache/hadoop/examples/(读入= 0) (写出= 0)(存储了 0%)  
6. 增加：org/apache/hadoop/examples/WordCount.class(读入= 1911) (写出= 996)(压缩了 47%)  
7. 增加：org/apache/hadoop/examples/WordCount$IntSumReducer.class(读入= 1789) (写出= 746)(压缩了 58%)  
8. 增加：org/apache/hadoop/examples/WordCount$TokenizerMapper.class(读入= 1903) (写出= 819)(压缩了 56%) 

 

4、运行你的程序，出现如下信息说明执行成功：

$ bin/hadoop jar playground/wordcount.jar org.apache.hadoop.examples.WordCount input my_output

11/12/05 21:33:30 INFO input.FileInputFormat: Total input paths to process : 1
11/12/05 21:33:31 INFO mapred.JobClient: Running job: job_201111281334_0014
11/12/05 21:33:32 INFO mapred.JobClient:  map 0% reduce 0%
11/12/05 21:33:41 INFO mapred.JobClient:  map 100% reduce 0%
11/12/05 21:33:53 INFO mapred.JobClient:  map 100% reduce 100%
11/12/05 21:33:55 INFO mapred.JobClient: Job complete: job_201111281334_0014
11/12/05 21:33:55 INFO mapred.JobClient: Counters: 17
11/12/05 21:33:55 INFO mapred.JobClient:   Job Counters
11/12/05 21:33:55 INFO mapred.JobClient:     Launched reduce tasks=1
11/12/05 21:33:55 INFO mapred.JobClient:     Launched map tasks=1
11/12/05 21:33:55 INFO mapred.JobClient:     Data-local map tasks=1
11/12/05 21:33:55 INFO mapred.JobClient:   FileSystemCounters
11/12/05 21:33:55 INFO mapred.JobClient:     FILE_BYTES_READ=25190
11/12/05 21:33:55 INFO mapred.JobClient:     HDFS_BYTES_READ=44253
11/12/05 21:33:55 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=50412
11/12/05 21:33:55 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=17876
11/12/05 21:33:55 INFO mapred.JobClient:   Map-Reduce Framework
11/12/05 21:33:55 INFO mapred.JobClient:     Reduce input groups=1857
11/12/05 21:33:55 INFO mapred.JobClient:     Combine output records=1857
11/12/05 21:33:55 INFO mapred.JobClient:     Map input records=734
11/12/05 21:33:55 INFO mapred.JobClient:     Reduce shuffle bytes=25190
11/12/05 21:33:55 INFO mapred.JobClient:     Reduce output records=1857
11/12/05 21:33:55 INFO mapred.JobClient:     Spilled Records=3714
11/12/05 21:33:55 INFO mapred.JobClient:     Map output bytes=73129
11/12/05 21:33:55 INFO mapred.JobClient:     Combine input records=7696
11/12/05 21:33:55 INFO mapred.JobClient:     Map output records=7696
11/12/05 21:33:55 INFO mapred.JobClient:     Reduce input records=1857

5、查看结果，在文件系统的my_output中