$ hdfs namenode -format
f. 启动Hadoop
@hdp-server-01 #在不同vm上启动的服务不同，根据划分的角色
$cd $YARN_HOME
$sbin/hadoop-daemon.sh --script hdfs start namenode # 启动namenode
$sbin/hadoop-daemon.sh --script hdfs start datanode # 启动datanode
$sbin/yarndaemon.shstart nodemanager #启动nodemanager
$sbin/yarn-daemon.sh start resourcemanager # 启动resourcemanager
$sbin/yarn-daemon.shstart proxyserver #启动web App proxy
$sbin/mr-jobhistory-daemon.sh start historyserver
jps查看
$ jps
8770 ResourceManager
11609 Jps
8644 NodeManager
9071 JobHistoryServer
8479 NameNode
9000 WebAppProxyServer
8552 DataNode
@hdp-server-02
@hdp-server-03
$cd $YARN_HOME
$sbin/yarndaemon.shstart nodemanager # 启动nodemanager
$sbin/hadoop-daemon.sh --script hdfs start datanode # 启动datanode
jps查看
$ jps
6691 NodeManager
9089 Jps
6787 DataNode
至此，集群搭建完毕，跑一个测试用例试试：
cd $YARN_HOME
$ bin/yarn jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 10 1000
这是mongodb蒙特卡洛算法计算圆周率的测试用例，pi后跟的两个数字分别表示使用多少个map以及计算的精度。结果如下：
Number of Maps = 10
Samples per Map = 1000
Wrote input for Map #0
Wrote input for Map #1
Wrote input for Map #2
Wrote input for Map #3
Wrote input for Map #4
Wrote input for Map #5
Wrote input for Map #6
Wrote input for Map #7
Wrote input for Map #8
Wrote input for Map #9
Starting Job
13/12/22 17:50:42 INFO client.RMProxy: Connecting to ResourceManager at hdp-server-01/10.0.0.225:8032
13/12/22 17:50:43 INFO input.FileInputFormat: Total input paths to process : 10
13/12/22 17:50:43 INFO mapreduce.JobSubmitter: number of splits:10
13/12/22 17:50:43 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative
13/12/22 17:50:43 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name
13/12/22 17:50:43 INFO Configuration.deprecation: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class
13/12/22 17:50:43 INFO Configuration.deprecation: mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
13/12/22 17:50:43 INFO Configuration.deprecation: mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
13/12/22 17:50:43 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
13/12/22 17:50:44 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1387700249346_0004
13/12/22 17:50:44 INFO impl.YarnClientImpl: Submitted application application_1387700249346_0004 to ResourceManager at hdp-server-01/10.0.0.225:8032
13/12/22 17:50:44 INFO mapreduce.Job: The url to track the job: :8888/proxy/application_1387700249346_0004/
13/12/22 17:50:44 INFO mapreduce.Job: Running job: job_1387700249346_0004
13/12/22 17:50:53 INFO mapreduce.Job: Job job_1387700249346_0004 running in uber mode : false
13/12/22 17:50:53 INFO mapreduce.Job: map 0% reduce 0%
13/12/22 17:51:03 INFO mapreduce.Job: map 40% reduce 0%
13/12/22 17:51:13 INFO mapreduce.Job: map 90% reduce 0%
13/12/22 17:51:14 INFO mapreduce.Job: map 100% reduce 0%
13/12/22 17:51:15 INFO mapreduce.Job: map 100% reduce 100%
13/12/22 17:51:16 INFO mapreduce.Job: Job job_1387700249346_0004 completed successfully
13/12/22 17:51:16 INFO mapreduce.Job: Counters: 43
File System Counters
FILE: Number of bytes read=226
FILE: Number of bytes written=878638
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=2680
HDFS: Number of bytes written=215
HDFS: Number of read operations=43
HDFS: Number of large read operations=0
HDFS: Number of write operations=3
Job Counters
Launched map tasks=10
Launched reduce tasks=1
Data-local map tasks=10
Total time spent by all maps in occupied slots (ms)=142127
Total time spent by all reduces in occupied slots (ms)=8333
Map-Reduce Framework
Map input records=10
Map output records=20
Map output bytes=180
Map output materialized bytes=280
Input split bytes=1500
Combine input records=0
Combine output records=0
Reduce input groups=2
Reduce shuffle bytes=280
Reduce input records=20
Reduce output records=0
Spilled Records=40
Shuffled Maps =10
Failed Shuffles=0
Merged Map outputs=10
GC time elapsed (ms)=2606
CPU time spent (ms)=11090
Physical memory (bytes) snapshot=2605563904
Virtual memory (bytes) snapshot=11336945664
Total committed heap usage (bytes)=2184183808
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=1180
File Output Format Counters
Bytes Written=97
Job Finished in 34.098 seconds
Estimated value of Pi is 3.14080000000000000000
总结：
1. 在OpenStack环境启动的虚拟机中搭建Hadoop与物理机搭建并无太大不同，但需要注意虚拟机获取到的IP地址，用openstack分配的浮动ip（Floating ip）往往不能使用。因为浮动ip是由nova-network设置，用于nat转发的，虚拟机自身并不知道这个地址。
2. 集群中使用的虚拟机最好是同样的操作系统，这样可以使用编译好的文件，因为在Hadoop2.2.0框架中 hdfs不存在Master节点，因此每个节点的配置文件都是相同的，故可以先发起一台虚拟机，安装配置完之后将其做成镜像，后续可以起多个节点，区别在于启动的服务不同。