Hadoop + Hbase + Zookeeper安装及配置完整版(Hadoop1系列)
第一步：安装Hadoop集群1、搭建环境所需介质准备
Enterprise-R5-U4-Server-x86_64-dvd.iso
hadoop-1.1.1.tar.gz
jdk-6u26-linux-x64-rpm.bin
2、创建5个节点的虚拟机
192.168.0.202hd202#NameNode
192.168.0.203hd203#SecondaryNameNode
192.168.0.204hd204#DataNode
192.168.0.205hd205#DataNode
192.168.0.206hd206#DataNode
虚拟机安装过程中，需要将sshd服务安装上。如果磁盘空间允许的话，尽可能的将系统包安装齐全了。
3、在五个节点的虚拟机中都安装Jdk(以root用户安装)
# mkdir /usr/java
# mv jdk-6u26-linux-x64-rpm.bin /usr/java
# cd /usr/java
# chmod 744 jdk-6u26-linux-x64-rpm.bin
# ./jdk-6u26-linux-x64-rpm.bin
# ln -s jdk1.6.0_26 default
4、创建hadoop管理用户(5台虚拟机中都要创建用户)
# useradd cbcloud #在没有先创建用户组的情况下，直接新增用户，用户默认所属的组和用户名相同。即cbcloud.cbcloud
# passwd cbcloud #修改用户cbcloud的密码，测试环境可设置为111111
5、编辑/etc/hosts文件(使用root用户分别在五台虚拟机上都编辑)
# Do not remove the following line, or various programs
# that require network functionality will fail.
127.0.0.1 localhost.localdomain localhost
::1 localhost6.localdomain6 localhost6
192.168.0.202 hd202
192.168.0.203 hd203
192.168.0.204 hd204
192.168.0.205 hd205
192.168.0.206 hd206
6、编辑/etc/sysconfig/network文件(使用root用户分别在五台虚拟机上都编辑)
NETWORKING=yes
NETWORKING_IPV6=no
HOSTNAME=hd202 #主机名(192.168.0.203上应该改为hd203，以此类推，五台机器都要修改为相应的名称)
GATEWAY=192.168.0.1
7、在五台机器之间配置用户等价性(以前面创建的用户cbcloud登陆进行操作)
$ mkdir .ssh
$ chmod 700 .ssh
$ ssh-keygen -t rsa
$ ssh-keygen -t dsa
$ cat ~/.ssh/id_rsa.pub > ~/.ssh/authorized_keys
$ cat ~/.ssh/id_dsa.pub > ~/.ssh/authorized_keys
$ mkdir .ssh
$ chmod 700 .ssh
$ ssh-keygen -t rsa
$ ssh-keygen -t dsa
$ cat ~/.ssh/id_rsa.pub > ~/.ssh/authorized_keys
$ cat ~/.ssh/id_dsa.pub > ~/.ssh/authorized_keys
$ mkdir .ssh
$ chmod 700 .ssh
$ ssh-keygen -t rsa
$ ssh-keygen -t dsa
$ cat ~/.ssh/id_rsa.pub > ~/.ssh/authorized_keys
$ cat ~/.ssh/id_dsa.pub > ~/.ssh/authorized_keys
$ mkdir .ssh
$ chmod 700 .ssh
$ ssh-keygen -t rsa
$ ssh-keygen -t dsa
$ cat ~/.ssh/id_rsa.pub > ~/.ssh/authorized_keys
$ cat ~/.ssh/id_dsa.pub > ~/.ssh/authorized_keys
$ mkdir .ssh
$ chmod 700 .ssh
$ ssh-keygen -t rsa
$ ssh-keygen -t dsa
$ cat ~/.ssh/id_rsa.pub > ~/.ssh/authorized_keys
$ cat ~/.ssh/id_dsa.pub > ~/.ssh/authorized_keys
$ cd .ssh
$ scp authorized_keyscbcloud@hd203:/home/cbcloud/.ssh/authorized_keys2#将hd202机器上的authorized_keys文件远程复制到hd203上的/home/cbcloud/.ssh/目录下，并重命名为authorized_keys2
$ cd .ssh
$ cat authorized_keys2 > authorized_keys#也就是将hd202上的authorized_keys中的内容合并到hd203机器上的authorized_keys文件中。
然后再将合并后的authorized_keys文件复制到hd204上，与204上的authorized_keys文件合并，依次类推，最后将5个节点的authorized_keys文件的内容都合并在一起以后，再将包含有五个节点密钥内容的authorized_keys文件，覆盖到其余4个节点上。
注意：authorized_keys文件的权限必须为644，否则用户等价性会失效。
在五个节点上都执行以下命令：
$ cd .ssh
$ chmod 644 authorized_keys
8、开始安装hadoop集群
8.1 建立目录 (在五台虚拟机上都执行以下命令_使用root用户)
# mkdir /home/cbcloud/hdtmp
# mkdir /home/cbcloud/hddata
# mkdir /home/cbcloud/hdconf
# chown -R cbcloud:cbcloud /home/cbcloud/hdtmp
# chown -R cbcloud:cbcloud /home/cbcloud/hddata
# chown -R cbcloud:cbcloud /home/cbcloud/hdconf
# chmod -R 755 /home/cbcloud/hddata#切记，hddata是用于DataNode节点存放数据用的，hadoop严格归定，这个目录的权限必须为755。如果不是这个权限值，则在后面启动DataNode时，将会因为权限不对，而不能成功启动DataNode节点。
8.2 解压hadoop-1.1.1.tar.gz到/home/cbcloud目录下(只需要在hd202一台机器上执行即可)
# mv hadoop-1.1.1.tar.gz /home/cbcloud
# cd /home/cbcloud
# tar -xzvf hadoop-1.1.1.tar.gz
# mv hadoop-1.1.1 hadoop
# chown -R cbcloud.cbcloud hadoop/
8.3 配置系统环境变量/etc/profile(在五台虚拟机上都执行_使用root用户)
# vi /etc/profile
在文件尾部加入以下内容
export JAVA_HOME=/usr/java/default
export> export PATH=$JAVA_HOME/bin:$JAVA_HOME/lib:$JAVA_HOME/jre/bin:$PATH:$HOME/bin
export HADOOP_HOME=/home/cbcloud/hadoop
export HADOOP_DEV_HOME=/home/cbcloud/hadoop
export HADOOP_COMMON_HOME=/home/cbcloud/hadoop
export HADOOP_HDFS_HOME=/home/cbcloud/hadoop
export HADOOP_CONF_DIR=/home/cbcloud/hdconf
export HADOOP_HOME_WARN_SUPPRESS=1
export PATH=$PATH:$HADOOP_HOME/bin
export> 8.4 配置用户环境变量
$ vi .bash_profile
在文件尾部加入以下内容
export JAVA_HOME=/usr/java/default
export> export PATH=$JAVA_HOME/bin:$JAVA_HOME/lib:$JAVA_HOME/jre/bin:$PATH:$HOME/bin
export HADOOP_HOME=/home/cbcloud/hadoop
export HADOOP_DEV_HOME=/home/cbcloud/hadoop
export HADOOP_COMMON_HOME=/home/cbcloud/hadoop
export HADOOP_HDFS_HOME=/home/cbcloud/hadoop
export HADOOP_CONF_DIR=/home/cbcloud/hdconf
export HADOOP_HOME_WARN_SUPPRESS=1
export PATH=$PATH:$HADOOP_HOME/bin
export> 8.5 修改hadoop配置文件(使用cbcloud用户操作，并且只需要在hd202一台机器上操作)
$ cp $HADOOP_HOME/conf/* $HADOOP_CONF_DIR/*
#从上一步的环境变量红色那一行可以看到，目前hadoop使用的配置文件应该位于/home/cbcloud/hdconf目录中，所以需要将/home/cbcloud/hadoop/conf目录下的所有配置文件都复制一份到/home/cbcloud/hdconf目录下。
8.5.1编辑core-site.xml配置文件
$ cd /home/cbcloud/hdconf
$ vi core-site.xml
fs.default.name
hdfs://hd202:9000
hadoop.tmp.dir
/home/cbcloud/hdtmp
8.5.2 编辑hdfs-site.xml
$ vi hdfs-site.xml
dfs.data.dir
/home/cbcloud/hddata
dfs.replication
3
8.5.3 编辑mapred-site.xml
$ vi mapred-site.xml
mapred.job.tracker
hd202:9001
8.5.4 编辑masters
$ vi masters
加入以下内容
hd203 # 因为hd203为SecondaryNameNode，所以在此只需要配置hd203即可，不需要配置hd202
8.5.5 编辑slaves
$ vi slaves
加入以下内容
hd204
hd205
hd206
8.6 复制/home/cbcloud/hadoop目录和/home/cbcloud/hdconf目录到其他四台虚拟机上
$ scp -r /home/cbcloud/hadoop hd203:/home/cbcloud#由于前面配置了用户等价性，因此这条命令执行时不再需要密码
$ scp -r /home/cbcloud/hadoop hd204:/home/cbcloud
$ scp -r /home/cbcloud/hadoop hd205:/home/cbcloud
$ scp -r /home/cbcloud/hadoop hd206:/home/cbcloud
$ scp -r /home/cbcloud/hdconf hd203:/home/cbcloud
$ scp -r /home/cbcloud/hdconf hd204:/home/cbcloud
$ scp -r /home/cbcloud/hdconf hd205:/home/cbcloud
$ scp -r /home/cbcloud/hdconf hd206:/home/cbcloud
8.7 在NameNode(hd202)上执行命令格式化命令空间
$ cd $HADOOP_HOME/bin
$ hadoop namenode -format
如果控制台打印的信息中没有ERROR之灰的信息，表示格式化命名空间命令就执行成功了。
8.8 启动hadoop
$ cd $HADOOP_HOME/bin
$ ./start-dfs.sh
starting namenode, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-namenode-hd202.out
hd204: starting datanode, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-datanode-hd204.out
hd205: starting datanode, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-datanode-hd205.out
hd206: starting datanode, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-datanode-hd206.out
hd203: starting secondarynamenode, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-secondarynamenode-hd203.out
8.9 启动mapred
$ ./start-mapred.sh
starting jobtracker, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-jobtracker-hd202.out
hd204: starting tasktracker, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-tasktracker-hd204.out
hd205: starting tasktracker, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-tasktracker-hd205.out
hd206: starting tasktracker, logging to /home/cbcloud/hadoop/libexec/../logs/hadoop-cbcloud-tasktracker-hd206.out
8.10 查看进程
$ jps
4335 JobTracker
4460 Jps
4153 NameNode
$ jps
1142 Jps
1078 SecondaryNameNode
$ jps
1783 Jps
1575 DataNode
1706 TaskTracker
$ jps
1669 Jps
1461 DataNode
1590 TaskTracker
$ jps
1494 DataNode
1614 TaskTracker
1694 Jps
8.11 查看集群状态
$ hadoop dfsadmin -report
Configured Capacity: 27702829056 (25.8 GB)
Present Capacity: 13044953088 (12.15 GB)
DFS Remaining: 13044830208 (12.15 GB)
DFS Used: 122880 (120 KB)
DFS Used%: 0%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0
-------------------------------------------------
Datanodes available: 3 (3 total, 0 dead)
Name: 192.168.0.205:50010
Decommission Status : Normal
Configured Capacity: 9234276352 (8.6 GB)
DFS Used: 40960 (40 KB)
Non DFS Used: 4885942272 (4.55 GB)
DFS Remaining: 4348293120(4.05 GB)
DFS Used%: 0%
DFS Remaining%: 47.09%
Last contact: Wed Jan 30 18:02:17 CST 2013
Name: 192.168.0.206:50010
Decommission Status : Normal
Configured Capacity: 9234276352 (8.6 GB)
DFS Used: 40960 (40 KB)
Non DFS Used: 4885946368 (4.55 GB)
DFS Remaining: 4348289024(4.05 GB)
DFS Used%: 0%
DFS Remaining%: 47.09%
Last contact: Wed Jan 30 18:02:17 CST 2013
Name: 192.168.0.204:50010
Decommission Status : Normal
Configured Capacity: 9234276352 (8.6 GB)
DFS Used: 40960 (40 KB)
Non DFS Used: 4885987328 (4.55 GB)
DFS Remaining: 4348248064(4.05 GB)
DFS Used%: 0%
DFS Remaining%: 47.09%
Last contact: Wed Jan 30 18:02:17 CST 2013
注意：如果报错“INFO ipc.Client: Retrying connect to server”,是因为core-site.xml失效的原因。停止，重启hadoop后，格式化namenode即可。
另外，每次启动VM都要关闭防火墙。
8.12 通过WEB浏览器查看Hadoop运行情况
http://192.168.1.202:50070 查看Hadoop运行情况
8.13 通过WEB浏览器查看Job运行情况
http://192.168.0.202:50030 查看Job执行情况
9、列出HDFS文件系统中存在的目录情况
$ hadoop dfs -ls
ls: Cannot access .: No such file or directory.
上面的错误是因为被访问目录为空所致。
可以改为执行hadoop fs -ls /
$ hadoop fs -ls /
Found 1 items
drwxr-xr-x - cbcloud supergroup 0 2013-01-30 15:52 /home
可以看到有一条空结果
执行hadoop fs -mkdir hello#hello为文件夹的名字
$ hadoop fs -mkdir hello
$ hadoop fs -ls
Found 1 items
drwxr-xr-x - cbcloud supergroup 0 2013-01-30 21:16 /user/cbcloud/hello
10、HDFS使用测试
$ hadoop dfs -rmr hello
Deleted hdfs://hd202:9000/user/cbcloud/hello #删除前面创建的文件夹
$ hadoop dfs -mkdir input
$ hadoop dfs -ls
Found 1 items
drwxr-xr-x - cbcloud supergroup 0 2013-01-30 21:18 /user/cbcloud/input
11、运行Hadoop自带框架的wordcount示例
11.1、建立数据文件
在主机192.168.0.202虚拟机中建立两个文件input1和input2
$ echo "Hello Hadoop in input1" > input1
$ echo "Hello Hadoop in input2" > input2
11.2、发布数据文件至Hadoop集群上
1、在HDFS中建立一个input目录
$ hadoop dfs -mkdir input
2、将文件input1和input2拷贝到HDFS的input目录下
$ hadoop dfs -copyFromLocal /home/cbcloud/hadoop/input* input
3、查看input目录下有没有复制成功
$ hadoop dfs -ls input
Found 2 items
-rw-r--r-- 3 cbcloud supergroup 23 2013-01-30 21:28 /user/cbcloud/input/input1
-rw-r--r-- 3 cbcloud supergroup 23 2013-01-30 21:28 /user/cbcloud/input/input2
11.3、执行wordcount程序#确保HDFS上没有output目录，查看结果
$ hadoop jar hadoop-examples-1.1.1.jar wordcount input output
13/01/30 21:33:05 INFO input.FileInputFormat: Total input paths to process : 2
13/01/30 21:33:05 INFO util.NativeCodeLoader: Loaded the native-hadoop library
13/01/30 21:33:05 WARN snappy.LoadSnappy: Snappy native library not loaded
13/01/30 21:33:07 INFO mapred.JobClient: Running job: job_201301302110_0001
13/01/30 21:33:08 INFO mapred.JobClient:map 0% reduce 0%
13/01/30 21:33:32 INFO mapred.JobClient:map 50% reduce 0%
13/01/30 21:33:33 INFO mapred.JobClient:map 100% reduce 0%
13/01/30 21:33:46 INFO mapred.JobClient:map 100% reduce 100%
13/01/30 21:33:53 INFO mapred.JobClient: Job complete: job_201301302110_0001
13/01/30 21:33:53 INFO mapred.JobClient: Counters: 29
13/01/30 21:33:53 INFO mapred.JobClient: Job Counters
13/01/30 21:33:53 INFO mapred.JobClient: Launched reduce tasks=1
13/01/30 21:33:53 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=29766
13/01/30 21:33:53 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
13/01/30 21:33:53 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
13/01/30 21:33:53 INFO mapred.JobClient: Launched map tasks=2
13/01/30 21:33:53 INFO mapred.JobClient: Data-local map tasks=2
13/01/30 21:33:53 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=13784
13/01/30 21:33:53 INFO mapred.JobClient: File Output Format Counters
13/01/30 21:33:53 INFO mapred.JobClient: Bytes Written=40
13/01/30 21:33:53 INFO mapred.JobClient: FileSystemCounters
13/01/30 21:33:53 INFO mapred.JobClient: FILE_BYTES_READ=100
13/01/30 21:33:53 INFO mapred.JobClient: HDFS_BYTES_READ=262
13/01/30 21:33:53 INFO mapred.JobClient: FILE_BYTES_WRITTEN=71911
13/01/30 21:33:53 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=40
13/01/30 21:33:53 INFO mapred.JobClient: File Input Format Counters
13/01/30 21:33:53 INFO mapred.JobClient: Bytes Read=46
13/01/30 21:33:53 INFO mapred.JobClient: Map-Reduce Framework
13/01/30 21:33:53 INFO mapred.JobClient: Map output materialized bytes=106
13/01/30 21:33:53 INFO mapred.JobClient: Map input records=2
13/01/30 21:33:53 INFO mapred.JobClient: Reduce shuffle bytes=106
13/01/30 21:33:53 INFO mapred.JobClient: Spilled Records=16
13/01/30 21:33:53 INFO mapred.JobClient: Map output bytes=78
13/01/30 21:33:53 INFO mapred.JobClient: CPU time spent (ms)=5500
13/01/30 21:33:53 INFO mapred.JobClient: Total committed heap usage (bytes)=336928768
13/01/30 21:33:53 INFO mapred.JobClient: Combine input records=8
13/01/30 21:33:53 INFO mapred.JobClient: SPLIT_RAW_BYTES=216
13/01/30 21:33:53 INFO mapred.JobClient: Reduce input records=8
13/01/30 21:33:53 INFO mapred.JobClient: Reduce input groups=5
13/01/30 21:33:53 INFO mapred.JobClient: Combine output records=8
13/01/30 21:33:53 INFO mapred.JobClient: Physical memory (bytes) snapshot=417046528
13/01/30 21:33:53 INFO mapred.JobClient: Reduce output records=5
13/01/30 21:33:53 INFO mapred.JobClient: Virtual memory (bytes) snapshot=1612316672
13/01/30 21:33:53 INFO mapred.JobClient: Map output records=8
$ hadoop dfs -ls output
Found 2 items
-rw-r--r-- 3 cbcloud supergroup 0 2013-01-30 21:33 /user/cbcloud/output/_SUCCESS
-rw-r--r-- 3 cbcloud supergroup 40 2013-01-30 21:33 /user/cbcloud/output/part-r-00000
$ hadoop dfs -cat output/part-r-00000
Hadoop2
Hello 2
in 2
input11
input21
第二步：搭建Zookeeper集群环境
上一篇关于Hadoop1.1.1集群安装记录中已经详细记录了在Oracle Linux 5.4 64bit上搭建Hadoop集群的方法。现在接着上一篇的内容，进一步安装Zookeeper和HBASE
1、安装zookeeper (在hd202上安装)
1.1、准备安装介质zookeeper-3.4.5.tar.gz
1.2、使用cbcloud用户将介质上传到hd202虚拟机上的/home/cbcloud/目录下面
1.3、解压缩zookeeper-3.4.5.tar.gz
$ tar zxvf zookeeper-3.4.5.tar.gz
1.4、在hd204、hd205、hd206三台机器上创建目录
$ mkdir /home/cbcloud/zookeeperdata
$ mkdir /home/cbcloud/zookeeperdata
$ mkdir /home/cbcloud/zookeeperdata
1.5、在hd202上执行以下内容
$ mv zookeeper-3.4.5 zookeeper
$ cd zookeeper/conf
$ mv zoo_sample.cfg zoo.cfg
$ vi zoo.cfg
# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just
# example sakes.
dataDir=/home/cbcloud/zookeeperdata
# the port at which the clients will connect
clientPort=2181
server.1=hd204:2888:3888
server.2=hd205:2888:3888
server.3=hd206:2888:3888
#
# Be sure to read the maintenance section of the
# administrator guide before turning on autopurge.
#
# http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1
1.6、将zookeeper文件夹复制到hd204、hd205、hd206三台虚拟机上
$ scp -r zookeeper hd204:/home/cbcloud/
$ scp -r zookeeper hd205:/home/cbcloud/
$ scp -r zookeeper hd206:/home/cbcloud/
1.7、在hd204、hd205、hd206三台虚拟机的/home/cbcloud/zookeeperdata目录下新建一个myid文件，并依次插入数字1、2、3
$ cd zookeeperdata
$ touch myid
$ vi myid
加入以下内容
1 #与前面配置文件中的server.1=hd204:2888:3888的编号相对应
$ cd zookeeperdata
$ touch myid
$ vi myid
加入以下内容
2 #与前面配置文件中的server.2=hd205:2888:3888的编号相对应
$ cd zookeeperdata
$ touch myid
$ vi myid
加入以下内容
3 #与前面配置文件中的server.3=hd206:2888:3888的编号相对应
1.8、启动zookeeper，在hd204、hd205、hd206机器上的/home/cbcloud/zookeeper/bin目录下执行zkServer.sh start
$ cd zookeeper
$ cd bin
$ ./zkServer.sh start
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
$ cd zookeeper
$ cd bin
$ ./zkServer.sh start
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
$ cd zookeeper
$ cd bin
$ ./zkServer.sh start
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
1.9 查看zookeeper的进程状态
$ ./zkServer.sh status
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Mode: follower #从此模式可以看出，hd204当前为跟随者模式
$ ./zkServer.sh status
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Mode: leader#从此模式可以看出，hd204当前为领导模式
$ ./zkServer.sh status
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Mode: follower #从此模式可以看出，hd206当前为跟随者模式
2、查看 zookeeper的进程详细状态
$ echo stat |nc localhost 2181
Zookeeper version: 3.4.5-1392090, built on 09/30/2012 17:52 GMT
Clients:
/127.0.0.1:41205(queued=0,recved=1,sent=0)
Latency min/avg/max: 0/0/0
Received: 2
Sent: 1
Connections: 1
Outstanding: 0
Zxid: 0x0
Mode: follower
Node count: 4
$ echo stat |nc localhost 2181
Zookeeper version: 3.4.5-1392090, built on 09/30/2012 17:52 GMT
Clients:
/127.0.0.1:38712(queued=0,recved=1,sent=0)
Latency min/avg/max: 0/0/0
Received: 2
Sent: 1
Connections: 1
Outstanding: 0
Zxid: 0x100000000
Mode: leader
Node count: 4
$ echo stat |nc localhost 2181
Zookeeper version: 3.4.5-1392090, built on 09/30/2012 17:52 GMT
Clients:
/127.0.0.1:39268(queued=0,recved=1,sent=0)
Latency min/avg/max: 0/0/0
Received: 2
Sent: 1
Connections: 1
Outstanding: 0
Zxid: 0x100000000
Mode: follower
Node count: 4
第三步：搭建HBase集群
1、准备安装介质hbase-0.94.4.tar.gz
2、使用用户cbcloud将安装介质上传到hd202虚拟机上的/home/cbcloud/目录下
3、使用cbcloud用户登陆到hd202虚拟机上，解压缩hbase-0.94.4.tar.gz
$ tar zxvf hbase-0.94.4.tar.gz
$ mv hbase-0.94.4 hbas
4、在五台虚拟机上都创建hbase的配置文件目录hbconf (使用cbcloud用户操作)
$ mkdir /home/cbcloud/hbconf
$ mkdir /home/cbcloud/hbconf
$ mkdir /home/cbcloud/hbconf
$ mkdir /home/cbcloud/hbconf
$ mkdir /home/cbcloud/hbconf
5、配置系统环境变量(以root用户操作)
# vi /etc/profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
# vi /etc/profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
# vi /etc/profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
# vi /etc/profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
# vi /etc/profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
6、配置用户环境变量(以cbcloud用户操作)
$ vi .bash_profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
$ vi .bash_profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
$ vi .bash_profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
$ vi .bash_profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
$ vi .bash_profile
在文件尾部加入以下内容
export HBASE_CONF_DIR=/home/cbcloud/hbconf
export HBASE_HOME=/home/cbcloud/hbase
7、复制$HBASE_HOME目录下的conf子目录下的所有文件到$HBASE_CONF_DIR目录下(只在hd202上操作)
$ cp /home/cbcloud/hbase/conf/*/home/cbcloud/hbconf/
8、编辑$HBASE_CONF_DIR目录下的hbase_env.sh(只在hd202上操作)
找到export HBASE_OPTS="-XX:+UseConcMarkSweepGC" 这一行，将其注释掉，然后添加以下内容
export HBASE_OPTS="$HBASE_OPTS -XX:+HeapDumpOnOutOfMemoryError -XX:+UseConcMarkSweepGC"
export JAVA_HOME=/usr/java/default
export HBASE_HOME=/home/cbcloud/hbase
export HADOOP_HOME=/home/cbcloud/hadoop
export HBASE_MANAGES_ZK=true //由HBASE自动管理zookeeper进程
9、编辑$HBASE_CONF_DIR目录下的hbase_site.xml(只在hd202上操作)
加入以下内容
hbase.rootdir
hdfs://hd202:9000/hbase
hbase.cluster.distributed
true
hbase.master
hd202:60000
hbase.master.port
60000
The port master should bind to.
hbase.zookeeper.quorum
hd204,hd205,hd206
hbase.zookeeper.property.dataDir
/home/cbcloud/zookeeperdata
10、编辑regionservers文件
删除localhost，然后加入以下内容
hd204
hd205
hd206
11、复制$HBASE_HOME目录及$HBASE_CONF_DIR目录到其他四台虚拟机上
$ scp -r hbase hd203:/home/cbcloud/
$ scp -r hbase hd204:/home/cbcloud/
$ scp -r hbase hd205:/home/cbcloud/
$ scp -r hbase hd206:/home/cbcloud/
$ scp -r hbconf hd203:/home/cbcloud/
$ scp -r hbconf hd204:/home/cbcloud/
$ scp -r hbconf hd205:/home/cbcloud/
$ scp -r hbconf hd206:/home/cbcloud/
12、启动HBASE
$ cd hbase
$ cd bin
$ ./start-hbase.sh#在主节点上启动hbase
starting master, logging to /home/cbcloud/hbase/logs/hbase-cbcloud-master-hd202.out
hd204: starting regionserver, logging to /home/cbcloud/hbase/logs/hbase-cbcloud-regionserver-hd204.out
hd205: starting regionserver, logging to /home/cbcloud/hbase/logs/hbase-cbcloud-regionserver-hd205.out
hd206: starting regionserver, logging to /home/cbcloud/hbase/logs/hbase-cbcloud-regionserver-hd206.out
$ jps
3779 JobTracker
4529 HMaster
4736 Jps
3633 NameNode
$ cd hbase
$ cd bin
$ ./hbase-daemon.sh start master#在SecondaryNameNode上启动HMaster
starting master, logging to /home/cbcloud/hbase/logs/hbase-cbcloud-master-hd203.out
$ jps
3815 Jps
3618 SecondaryNameNode
3722 HMaster
$ jps
3690 TaskTracker
3614 DataNode
4252 Jps
3845 QuorumPeerMain
4124 HRegionServer
$ jps
3826 QuorumPeerMain
3612 DataNode
3688 TaskTracker
4085 HRegionServer
4256 Jps
$ jps
3825 QuorumPeerMain
3693 TaskTracker
4091 HRegionServer
4279 Jps
3617 DataNode
13、使用WEB界面查看HMaster的情况http://192.168.0.202:60010
14、关闭HBbase的方法
第一步：关闭SecondaryNameNode上的HMaster服务
$ cd hbase
$ cd bin
$ ./hbase-daemon.sh stop master
stopping master.
$ jps
4437 Jps
3618 SecondaryNameNode
第二步：关闭NameNode上的HMaster服务
$ cd hbase
$ cd bin
$ ./stop-hbase.sh
stopping hbase...................
$ jps
5620 Jps
3779 JobTracker
3633 NameNode
第三步:关闭zookeeper服务
$ cd zookeeper/bin
$ ./zkServer.sh stop
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Stopping zookeeper ... STOPPED
$ jps
3690 TaskTracker
3614 DataNode
4988 Jps
$ cd ..
$ cd zookeeper/bin
$ ./zkServer.sh stop
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Stopping zookeeper ... STOPPED
$ jps
3612 DataNode
3688 TaskTracker
4920 Jps
$ cd zookeeper
$ cd bin
$ ./zkServer.sh stop
JMX enabled by default
Using config: /home/cbcloud/zookeeper/bin/../conf/zoo.cfg
Stopping zookeeper ... STOPPED
$ jps
4931 Jps
3693 TaskTracker
3617 DataNode
第四步：关闭hadoop
$ ./stop-all.sh
stopping jobtracker
hd205: stopping tasktracker
hd204: stopping tasktracker
hd206: stopping tasktracker
stopping namenode
hd205: stopping datanode
hd206: stopping datanode
hd204: stopping datanode
hd203: stopping secondarynamenode
15、启动HBase的顺序与上面的顺序严格相反
第一步：启动hadoop
第二步：启动各个DataNode节点上的zookeeper
第三步：启动NameNode上的HMaster
第四步：启动SecondaryNameNode上的HMaster
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