在上篇介绍了3节点hadoop集群的安装配置，装完hadoop集群后，验证hadoop集群是否安装成功的方法。本人集群是三台虚拟机，一台是master，另外两台分别为slave1和slave2。在你用start-all.sh启动集群后，可以用jps命令和实例进行验证集群是否安装配置成功。
1、用jps命令
（1）master节点
启动集群：
cy@master:~$ start-all.sh
starting namenode, logging to /home/cy/Hadoop/hadoop-1.2.1/libexec/../logs/hadoop-cy-namenode-master.out
slave2: starting datanode, logging to /home/cy/Hadoop/hadoop-1.2.1/libexec/../logs/hadoop-cy-datanode-slave2.out
slave1: starting datanode, logging to /home/cy/Hadoop/hadoop-1.2.1/libexec/../logs/hadoop-cy-datanode-slave1.out
master: starting secondarynamenode, logging to /home/cy/Hadoop/hadoop-1.2.1/libexec/../logs/hadoop-cy-secondarynamenode-master.out
starting jobtracker, logging to /home/cy/Hadoop/hadoop-1.2.1/libexec/../logs/hadoop-cy-jobtracker-master.out
slave1: starting tasktracker, logging to /home/cy/Hadoop/hadoop-1.2.1/libexec/../logs/hadoop-cy-tasktracker-slave1.out
slave2: starting tasktracker, logging to /home/cy/Hadoop/hadoop-1.2.1/libexec/../logs/hadoop-cy-tasktracker-slave2.out
用jps命令查看Java进程：
cy@master:~$ jps
6670 NameNode
7141 Jps
7057 JobTracker
（2）slave1节点
用jps命令查看Java进程：
cy@slave1:~$ jps
3218 Jps
2805 DataNode
2995 TaskTracker
（3）slave2节点
用jps命令查看Java进程：
cy@slave2:~$ jps
2913 TaskTracker
2731 DataNode
3147 Jps
如果三台虚拟机用jps命令查询时如上面显示的那样子，就说明hadoop安装和配置成功了。
2、hadoop集群的测试，用hadoop-examples-1.2.1.jar中自带的wordcount程序进行测试，该程序的作用是统计单词的个数。
（1）我们现在桌面上创建一个新的文件test.txt，里面总共有10行，每行都是hello world
（2）在HDFS系统里创建一个input文件夹，使用命令如下：
hadoop fs -mkdir input
或 hadoop fs -mkdir /user/你的用户名/input
（3）把创建好的test.txt上传到HDFS系统的input文件夹下，使用命令如下所示。
hadoop fs -put /home/你的用户名/桌面/test.txt input
或 hadoop fs -put /home/你的用户名/桌面/test.txt /user/你的用户名/input
（4）我们可以查看test.txt是否在HDFS的input文件夹下，如下所示：
hadoop fs -ls input
如果显示如下就说明上传成功：
Found 1 items
-rw-r--r-- 3 cy supergroup 120 2015-05-08 20:26 /user/cy/input/test.txt
（5）执行hadoop-examples-1.2.1.jar中自带的wordcount程序，如下：（提示：在执行下面的命令之前，你要在终端用cd命令进入到/home/cy/Hadoop/hadoop-1.2.1目录）
hadoop jar hadoop-examples-1.2.1.jar wordcount /user/你的用户名/input/test.txt /user/你的用户名/output
如果显示如下结果就说明运行成功：
15/05/08 20:31:29 INFO input.FileInputFormat: Total input paths to process : 1
15/05/08 20:31:29 INFO util.NativeCodeLoader: Loaded the native-hadoop library
15/05/08 20:31:29 WARN snappy.LoadSnappy: Snappy native library not loaded
15/05/08 20:31:30 INFO mapred.JobClient: Running job: job_201505082010_0001
15/05/08 20:31:31 INFO mapred.JobClient: map 0% reduce 0%
15/05/08 20:31:35 INFO mapred.JobClient: map 100% reduce 0%
15/05/08 20:31:42 INFO mapred.JobClient: map 100% reduce 33%
15/05/08 20:31:43 INFO mapred.JobClient: map 100% reduce 100%
15/05/08 20:31:43 INFO mapred.JobClient: Job complete: job_201505082010_0001
15/05/08 20:31:43 INFO mapred.JobClient: Counters: 29
15/05/08 20:31:43 INFO mapred.JobClient: Job Counters
15/05/08 20:31:43 INFO mapred.JobClient: Launched reduce tasks=1
15/05/08 20:31:43 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=3117
15/05/08 20:31:43 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
15/05/08 20:31:43 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
15/05/08 20:31:43 INFO mapred.JobClient: Launched map tasks=1
15/05/08 20:31:43 INFO mapred.JobClient: Data-local map tasks=1
15/05/08 20:31:43 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=8014
15/05/08 20:31:43 INFO mapred.JobClient: File Output Format Counters
15/05/08 20:31:43 INFO mapred.JobClient: Bytes Written=18
15/05/08 20:31:43 INFO mapred.JobClient: FileSystemCounters
15/05/08 20:31:43 INFO mapred.JobClient: FILE_BYTES_READ=30
15/05/08 20:31:43 INFO mapred.JobClient: HDFS_BYTES_READ=226
15/05/08 20:31:43 INFO mapred.JobClient: FILE_BYTES_WRITTEN=116774
15/05/08 20:31:43 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=18
15/05/08 20:31:43 INFO mapred.JobClient: File Input Format Counters
15/05/08 20:31:43 INFO mapred.JobClient: Bytes Read=120
15/05/08 20:31:43 INFO mapred.JobClient: Map-Reduce Framework
15/05/08 20:31:43 INFO mapred.JobClient: Map output materialized bytes=30
15/05/08 20:31:43 INFO mapred.JobClient: Map input records=10
15/05/08 20:31:43 INFO mapred.JobClient: Reduce shuffle bytes=30
15/05/08 20:31:43 INFO mapred.JobClient: Spilled Records=4
15/05/08 20:31:43 INFO mapred.JobClient: Map output bytes=200
15/05/08 20:31:43 INFO mapred.JobClient: CPU time spent (ms)=610
15/05/08 20:31:43 INFO mapred.JobClient: Total committed heap usage (bytes)=176427008
15/05/08 20:31:43 INFO mapred.JobClient: Combine input records=20
15/05/08 20:31:43 INFO mapred.JobClient: SPLIT_RAW_BYTES=106
15/05/08 20:31:43 INFO mapred.JobClient: Reduce input records=2
15/05/08 20:31:43 INFO mapred.JobClient: Reduce input groups=2
15/05/08 20:31:43 INFO mapred.JobClient: Combine output records=2
15/05/08 20:31:43 INFO mapred.JobClient: Physical memory (bytes) snapshot=182902784
15/05/08 20:31:43 INFO mapred.JobClient: Reduce output records=2
15/05/08 20:31:43 INFO mapred.JobClient: Virtual memory (bytes) snapshot=756301824
15/05/08 20:31:43 INFO mapred.JobClient: Map output records=20
（6）我们可以使用下面的命令还查看运行后的结果：
hadoop fs -ls output
hadoop fs -text /user/你的用户名/output/part-r-00000
如果显示如下就说明hadoop三个节点安装和配置成功，测试也成功了，就可以继续更深入地使用和研究hadoop了
hello 10
world 10
以上都是本人在安装和配置hadoop的亲身体验，大家可以借鉴借鉴，请多多支持，我还会继续写关于我自己学习hadoop的一些经历。