3、运行wordcount实例
wordcount例子是Hadoop发行包中自带的实例，通过运行实例可以感受并尝试理解hadoop在执行MapReduce任务时的执行过程。按照官方的“Hadoop Quick Start”教程基本可以容易地实现，下面简单说一下我的练习过程。
导航到hadoop目录下面，我的是/root/hadoop-0.19.0。
（1）格式化HDFS
执行格式化HDFS的命令行：
[root@localhost hadoop-0.19.0]# bin/hadoop namenode -format
格式化执行信息如下所示：
10/08/01 19:04:02 INFO namenode.NameNode: STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: host = localhost/127.0.0.1
STARTUP_MSG: args = [-format]
STARTUP_MSG: version = 0.19.0
STARTUP_MSG: build = https://svn.apache.org/repos/asf/hadoop/core/branches/branch-0.19 -r 713890; compiled by 'ndaley' on Fri Nov 14 03:12:29 UTC 2008
************************************************************/
Re-format filesystem in /tmp/hadoop-root/dfs/name ? (Y or N) y
Format aborted in /tmp/hadoop-root/dfs/name
10/08/01 19:04:05 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at localhost/127.0.0.1
************************************************************/
（2）启动Hadoop相关后台进程
执行命令行：
[root@localhost hadoop-0.19.0]# bin/start-all.sh
启动执行信息如下所示：
starting namenode, logging to /root/hadoop-0.19.0/bin/../logs/hadoop-root-namenode-localhost.out
localhost: starting datanode, logging to /root/hadoop-0.19.0/bin/../logs/hadoop-root-datanode-localhost.out
localhost: starting secondarynamenode, logging to /root/hadoop-0.19.0/bin/../logs/hadoop-root-secondarynamenode-localhost.out
starting jobtracker, logging to /root/hadoop-0.19.0/bin/../logs/hadoop-root-jobtracker-localhost.out
localhost: starting tasktracker, logging to /root/hadoop-0.19.0/bin/../logs/hadoop-root-tasktracker-localhost.out
（3）准备执行wordcount任务的数据
首先，这里在本地创建了一个数据目录input，并拷贝一些文件到该目录下面，如下所示：
[root@localhost hadoop-0.19.0]# mkdir input
[root@localhost hadoop-0.19.0]# cp CHANGES.txt LICENSE.txt NOTICE.txt README.txt input/
然后，将本地目录input上传到HDFS文件系统上，执行如下命令：
[root@localhost hadoop-0.19.0]# bin/hadoop fs -put input/ input
（4）启动wordcount任务
执行如下命令行：
[root@localhost hadoop-0.19.0]# bin/hadoop jar hadoop-0.19.0-examples.jar wordcount input output
元数据目录为input，输出数据目录为output。
任务执行信息如下所示：
10/08/01 19:06:15 INFO mapred.FileInputFormat: Total input paths to process : 4
10/08/01 19:06:15 INFO mapred.JobClient: Running job: job_201008011904_0002
10/08/01 19:06:16 INFO mapred.JobClient: map 0% reduce 0%
10/08/01 19:06:22 INFO mapred.JobClient: map 20% reduce 0%
10/08/01 19:06:24 INFO mapred.JobClient: map 40% reduce 0%
10/08/01 19:06:25 INFO mapred.JobClient: map 60% reduce 0%
10/08/01 19:06:27 INFO mapred.JobClient: map 80% reduce 0%
10/08/01 19:06:28 INFO mapred.JobClient: map 100% reduce 0%
10/08/01 19:06:38 INFO mapred.JobClient: map 100% reduce 26%
10/08/01 19:06:40 INFO mapred.JobClient: map 100% reduce 100%
10/08/01 19:06:41 INFO mapred.JobClient: Job complete: job_201008011904_0002
10/08/01 19:06:41 INFO mapred.JobClient: Counters: 16
10/08/01 19:06:41 INFO mapred.JobClient: File Systems
10/08/01 19:06:41 INFO mapred.JobClient: HDFS bytes read=301489
10/08/01 19:06:41 INFO mapred.JobClient: HDFS bytes written=113098
10/08/01 19:06:41 INFO mapred.JobClient: Local bytes read=174004
10/08/01 19:06:41 INFO mapred.JobClient: Local bytes written=348172
10/08/01 19:06:41 INFO mapred.JobClient: Job Counters
10/08/01 19:06:41 INFO mapred.JobClient: Launched reduce tasks=1
10/08/01 19:06:41 INFO mapred.JobClient: Launched map tasks=5
10/08/01 19:06:41 INFO mapred.JobClient: Data-local map tasks=5
10/08/01 19:06:41 INFO mapred.JobClient: Map-Reduce Framework
10/08/01 19:06:41 INFO mapred.JobClient: Reduce input groups=8997
10/08/01 19:06:41 INFO mapred.JobClient: Combine output records=10860
10/08/01 19:06:41 INFO mapred.JobClient: Map input records=7363
10/08/01 19:06:41 INFO mapred.JobClient: Reduce output records=8997
10/08/01 19:06:41 INFO mapred.JobClient: Map output bytes=434077
10/08/01 19:06:41 INFO mapred.JobClient: Map input bytes=299871
10/08/01 19:06:41 INFO mapred.JobClient: Combine input records=39193
10/08/01 19:06:41 INFO mapred.JobClient: Map output records=39193
10/08/01 19:06:41 INFO mapred.JobClient: Reduce input records=10860
（5）查看任务执行结果
可以通过如下命令行：
bin/hadoop fs -cat output/*
执行结果，截取部分显示如下所示：
vijayarenu 20
violations. 1
virtual 3
vis-a-vis 1
visible 1
visit 1
volume 1
volume, 1
volumes 2
volumes. 1
w.r.t 2
wait 9
waiting 6
waiting. 1
waits 3
want 1
warning 7
warning, 1
warnings 12
warnings. 3
warranties 1
warranty 1
warranty, 1
（6）终止Hadoop相关后台进程
执行如下命令行：
[root@localhost hadoop-0.19.0]# bin/stop-all.sh
执行信息如下所示：
stopping jobtracker
localhost: stopping tasktracker
stopping namenode
localhost: stopping datanode
localhost: stopping secondarynamenode
已经将上面列出的5个进程jobtracker、tasktracker、namenode、datanode、secondarynamenode终止。