爱威尔-最新Hadoop-2.7.2+hbase-1.2.0+zookeeper-3.4.8 HA高可用集群配置安装

<table class="MsoTableGrid" border="1" cellspacing="0" cellpadding="0" width="532" style="border:none;">
		<tbody>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						Ip 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						 主机名 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						程序 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						进程 
					
				</td>
			</tr>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						192.168.128.11 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						h1 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						Jdk 
					
					
						Hadoop 
					
					
						hbase 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						Namenode 
					
					
						DFSZKFailoverController 
					
					
						Hamster 
					
				</td>
			</tr>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						192.168.128.12 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						h2 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						Jdk 
					
					
						Hadoop 
					
					
						hbase 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						Namenode 
					
					
						DFSZKFailoverController 
					
					
						Hamster 
					
				</td>
			</tr>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						192.168.128.13 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						h3 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						Jdk 
					
					
						Hadoop 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						resourceManager 
					
				</td>
			</tr>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						192.168.128.14 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						h4 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						Jdk 
					
					
						Hadoop 
					
					
						 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						resourceManager 
					
				</td>
			</tr>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						192.168.128.15 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						h5 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						Jdk 
					
					
						Hadoop 
					
					
						Zookeeper 
					
					
						Hbase 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						Datanode 
					
					
						nodeManager 
					
					
						JournalNode 
					
					
						QuorumPeerMain 
					
					
						HRegionServer 
					
				</td>
			</tr>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						192.168.128.16 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						h6 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						Jdk 
					
					
						Hadoop 
					
					
						Zookeeper 
					
					
						Hbase 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						Datanode 
					
					
						nodeManager 
					
					
						JournalNode 
					
					
						QuorumPeerMain 
					
					
						HRegionServer 
					
				</td>
			</tr>
			<tr>
				<td width="103" valign="top" style="border:solid windowtext 1.0pt;">
					
						192.168.128.17 
					
				</td>
				<td width="66" valign="top" style="border:solid windowtext 1.0pt;">
					
						h7 
					
				</td>
				<td width="111" valign="top" style="border:solid windowtext 1.0pt;">
					
						Jdk 
					
					
						Hadoop 
					
					
						Zookeeper 
					
					
						hbase 
					
				</td>
				<td width="253" valign="top" style="border:solid windowtext 1.0pt;">
					
						Datanode 
					
					
						nodeManager 
					
					
						JournalNode 
					
					
						QuorumPeerMain 
					
					
						HRegionServer 
					
				</td>
			</tr>
		</tbody>
	</table>
 
 


	 


	关于准备工作 我这里就不一一写出来了，总结一下有主机名，ip，主机名和ip的映射关系，防火墙，ssh免密码，jdk的安装及环境变量的设置。 


	链接：https://pan.baidu.com/s/1dFlPw8T 密码：sr8d （vm 下的虚拟系统，直接放在vm下可以运行的）

<h2>
	安装zookeeper到 h5、h6、h7上面 
</h2>

	修改 /home/zookeeper-3.4.8/conf的zoo_sample.cfg 


	cp zoo_sample.cfg 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/zookeeper-3.4.8/data 


	# the port at
which the clients will connect


	clientPort=2181


	# the maximum
number of client connections.


	# increase this
if you need to handle more clients


	#maxClientCnxns=60


	#


	# 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


	server.1=h5:2888:3888 


	server.2=h6:2888:3888 


	server.3=h7:2888:3888 

 

	 


	 
 


	 


	创建 data文件夹 和在里面 创建文件myid 并写入数字1 


	touch data/myid 


	 


	echo 1 > data/myid 


	 


	拷贝整个zookeeper到另外两个节点上 


	 


	scp -r /home/zookeeper-3.4.8  h6:/home/ 


	scp -r /home/zookeeper-3.4.8  h7:/home/ 


	其他两个节点的myid 修改为 2  3 

<h2>
	安装hadoop 
</h2>

	 
 


	/home/hadoop-2.7.2/etc/Hadoop 


	 


	hadoop-env.sh： 


	export JAVA_HOME=/home/jdk 


	 


	core-site.xml： 


	 


	 


	 


	


	 


	fs.defaultFS


	hdfs://masters 


	 


	


	 


	hadoop.tmp.dir


	/home/hadoop-2.7.2/tmp


	 


	


	 


	ha.zookeeper.quorum


	h5:2181,h6:2181,h7:2181 


	 


	 


	 


	 


	hdfs-site.xml： 


	 


	 


	 


	 


	


	 


	dfs.nameservices


	masters


	 


	


	 


	dfs.ha.namenodes.masters


	h1,h2


	 


	


	 


	dfs.namenode.rpc-address.masters.h1


	h1:9000


	 


	


	 


	dfs.namenode.http-address.masters.h1


	h1:50070


	 


	


	 


	dfs.namenode.rpc-address.masters.h2


	h2:9000


	 


	


	 


	dfs.namenode.http-address.masters.h2


	h2:50070


	 


	


	 


	dfs.namenode.shared.edits.dir


	qjournal://h5:8485;h6:8485;h7:8485/masters


	 


	


	 


	dfs.journalnode.edits.dir


	/home/hadoop-2.7.2/journal


	 


	


	 


	dfs.ha.automatic-failover.enabled


	true


	 


	


	 


	dfs.client.failover.proxy.provider.masters


	org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider


	 


	


	 


	dfs.ha.fencing.methods


	 


	sshfence


	shell(/bin/true)


	 


	 


	


	 


	dfs.ha.fencing.ssh.private-key-files


	/root/.ssh/id_rsa


	 


	


	 


	dfs.ha.fencing.ssh.connect-timeout


	30000


	 


	 


	 


	 


	 


	mapred-site.xml： 


	 


	 


	 


	


	 


	mapreduce.framework.name


	yarn


	 


	 


	 


	yarn-site.xml： 


	 


	 


	 


	


	 


	yarn.resourcemanager.ha.enabled


	true


	 


	


	 


	yarn.resourcemanager.cluster-id


	RM_HA_ID


	 


	


	 


	yarn.resourcemanager.ha.rm-ids


	rm1,rm2


	 


	


	 


	yarn.resourcemanager.hostname.rm1


	h3


	 


	 


	yarn.resourcemanager.hostname.rm2


	h4


	 


	 


	yarn.resourcemanager.recovery.enabled


	true


	 


	 


	 


	yarn.resourcemanager.store.class


	org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore


	 


	


	 


	yarn.resourcemanager.zk-address


	h5:2181,h6:2181,h7:2181


	 


	 


	yarn.nodemanager.aux-services


	mapreduce_shuffle


	 


	 


	 


	 


	Slaves： 


	 


	h5


	h6


	h7


	 


	然后
拷贝到其他节点 


	 


	 


	scp -r hadoop-2.7.2 h2:/home/ 等等 

 

	 


	这个地方说明一下  yarn 的HA  是在  h3和h4  上面 


	 
 


	 

<h2>
	启动顺序 
</h2>

	###注意：严格按照下面的步骤 


	 


	1. 启动zookeeper集群 


	 


	[root@h6 ~]# cd /home/zookeeper-3.4.8/bin/ 


	[root@h6 bin]# ./zkServer.sh start 


	 


	H5 
h6  h7 都一样 


	[root@h6 bin]# ./zkServer.sh status 


	查看状态 


	 


	2. 启动journalnode 


	[root@h5 bin]# cd /home/hadoop-2.7.2/sbin/ 


	[root@h5 sbin]# ./hadoop-daemons.sh start
journalnode 


	h5: starting journalnode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-journalnode-h5.out 


	h7: starting journalnode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-journalnode-h7.out 


	h6: starting journalnode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-journalnode-h6.out 


	[root@h5 sbin]# jps 


	2420 JournalNode 


	2309 QuorumPeerMain 


	2461 Jps 


	[root@h5 sbin]# ^C 


	 


	 


	3. 格式化HDFS 


	 


	在h1上执行命令:


	hdfs namenode -format 


	格式化后会在根据core-site.xml中的hadoop.tmp.dir配置生成个文件


	拷贝tmp 到 h2 


	[root@h1 hadoop-2.7.2]# scp -r tmp/
h2:/home/hadoop-2.7.2/ 


	 


	4. 格式化ZK(在h1上执行即可) 


	 


	[root@h1 hadoop-2.7.2]# hdfs zkfc -formatZK 


	 


	5. 启动HDFS(在h1上执行) 


	 


	[root@h1 hadoop-2.7.2]#
sbin/start-dfs.sh


	16/02/25 05:01:14 WARN hdfs.DFSUtil:
Namenode for ns1 remains unresolved for ID null.  Check your hdfs-site.xml file to ensure
namenodes are configured properly. 


	16/02/25 05:01:14 WARN hdfs.DFSUtil:
Namenode for ns2 remains unresolved for ID null.  Check your hdfs-site.xml file to ensure
namenodes are configured properly. 


	16/02/25 05:01:14 WARN hdfs.DFSUtil:
Namenode for ns3 remains unresolved for ID null.  Check your hdfs-site.xml file to ensure
namenodes are configured properly. 


	Starting namenodes on [h1 h2 masters
masters masters] 


	masters: ssh: Could not resolve hostname
masters: Name or service not known 


	masters: ssh: Could not resolve hostname
masters: Name or service not known 


	masters: ssh: Could not resolve hostname
masters: Name or service not known 


	h2: starting namenode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-namenode-h2.out 


	h1: starting namenode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-namenode-h1.out 


	h5: starting datanode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-datanode-h5.out 


	h7: starting datanode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-datanode-h7.out 


	h6: starting datanode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-datanode-h6.out 


	Starting journal nodes [h5 h6 h7] 


	h5: journalnode running as process 2420.
Stop it first. 


	h6: journalnode running as process 2885.
Stop it first. 


	h7: journalnode running as process 2896.
Stop it first. 


	Starting ZK Failover Controllers on NN
hosts [h1 h2 masters masters masters] 


	masters: ssh: Could not resolve hostname
masters: Name or service not known 


	masters: ssh: Could not resolve hostname
masters: Name or service not known 


	masters: ssh: Could not resolve hostname
masters: Name or service not known 


	h2: starting zkfc, logging to
/home/hadoop-2.7.2/logs/hadoop-root-zkfc-h2.out 


	h1: starting zkfc, logging to
/home/hadoop-2.7.2/logs/hadoop-root-zkfc-h1.out 


	[root@h1 hadoop-2.7.2]# 


	 


	6. 启动YARN(是在h3上执行start-yarn.sh，把namenode和resourcemanager分开是因为性能问题，因为他们都要占用大量资源，所以把他们分开了，他们分开了就要分别在不同的机器上启动) 


	 


	[root@h3 sbin]# ./start-yarn.sh 


	 


	[root@h4 sbin]# ./yarn-daemons.sh start
resourcemanager 


	 


	 


	验证： 


	 


	http://192.168.128.11:50070 


	 


	Overview 'h1:9000' (active) 


	 


	 


	http://192.168.128.12:50070 


	 


	 


	Overview 'h2:9000' (standby) 


	 


	上传文件 


	[root@h4 bin]# hadoop fs -put /etc/profile
/profile 


	[root@h4 bin]# hadoop fs -ls 


	ls: `.': No such file or directory 


	[root@h4 bin]# hadoop fs -ls / 


	Found 1 items 


	-rw-r--r--  
3 root supergroup       1814
2016-02-26 19:08 /profile 


	[root@h4 bin]# 


	 


	杀死h1 


	[root@h1 sbin]# jps 


	2480 NameNode 


	2868 Jps 


	2775 DFSZKFailoverController 


	[root@h1 sbin]# kill -9 2480 


	[root@h1 sbin]# jps 


	2880 Jps 


	2775 DFSZKFailoverController 


	[root@h1 sbin]# hadoop fs -ls / 


	Found 1 items 


	-rw-r--r--  
3 root supergroup       1814
2016-02-26 19:08 /profile 


	 


	此时 h2 变为active 


	 


	手动启动 h1的 namenode 


	 


	[root@h1 sbin]# ./hadoop-daemon.sh start
namenode 


	starting namenode, logging to
/home/hadoop-2.7.2/logs/hadoop-root-namenode-h1.out 


	[root@h1 sbin]# hadoop jar
/home/hadoop-2.7.2/s 


	 


	观察 h1 状态为standby 


	 


	验证yarn 


	 


	[root@h1 sbin]# hadoop jar
/home/hadoop-2.7.2/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar
wordcount /profile /out 


	16/02/26 19:14:23 INFO
input.FileInputFormat: Total input paths to process : 1 


	16/02/26 19:14:23 INFO
mapreduce.JobSubmitter: number of splits:1 


	16/02/26 19:14:23 INFO
mapreduce.JobSubmitter: Submitting tokens for job: job_1456484773347_0001 


	16/02/26 19:14:24 INFO impl.YarnClientImpl:
Submitted application application_1456484773347_0001 


	16/02/26 19:14:24 INFO mapreduce.Job: The
url to track the job: http://h3:8088/proxy/application_1456484773347_0001/ 


	16/02/26 19:14:24 INFO mapreduce.Job:
Running job: job_1456484773347_0001 


	16/02/26 19:14:49 INFO mapreduce.Job: Job
job_1456484773347_0001 running in uber mode : false 


	16/02/26 19:14:49 INFO mapreduce.Job:  map 0% reduce 0% 


	16/02/26 19:15:05 INFO mapreduce.Job:  map 100% reduce 0% 


	16/02/26 19:15:22 INFO mapreduce.Job:  map 100% reduce 100% 


	16/02/26 19:15:23 INFO mapreduce.Job: Job
job_1456484773347_0001 completed successfully 


	16/02/26 19:15:23 INFO mapreduce.Job:
Counters: 49 


	 File System Counters 


	 FILE: Number of bytes read=2099 


	 FILE: Number of bytes
written=243781 


	 FILE: Number of read operations=0 


	 FILE: Number of large read
operations=0 


	 FILE: Number of write
operations=0 


	 HDFS: Number of bytes read=1901 


	 HDFS: Number of bytes
written=1470 


	 HDFS: Number of read operations=6 


	 HDFS: Number of large read
operations=0 


	 HDFS: Number of write
operations=2 


	 Job Counters 


	 Launched map tasks=1 


	 Launched reduce tasks=1 


	 Data-local map tasks=1 


	 Total time spent by all maps in
occupied slots (ms)=13014 


	 Total time spent by all reduces
in occupied slots (ms)=13470 


	 Total time spent by all map
tasks (ms)=13014 


	 Total time spent by all reduce
tasks (ms)=13470 


	 Total vcore-milliseconds taken
by all map tasks=13014 


	 Total vcore-milliseconds taken
by all reduce tasks=13470 


	 Total megabyte-milliseconds
taken by all map tasks=13326336 


	 Total megabyte-milliseconds
taken by all reduce tasks=13793280 


	 Map-Reduce Framework 


	 Map input records=80 


	 Map output records=256 


	 Map output bytes=2588 


	 Map output materialized
bytes=2099 


	 Input split bytes=87 


	 Combine input records=256 


	 Combine output records=156 


	 Reduce input groups=156 


	 Reduce shuffle bytes=2099 


	 Reduce input records=156 


	 Reduce output records=156 


	 Spilled Records=312 


	 Shuffled Maps =1 


	 Failed Shuffles=0 


	 Merged Map outputs=1 


	 GC time elapsed (ms)=395 


	 CPU time spent (ms)=4100 


	 Physical memory (bytes)
snapshot=298807296 


	 Virtual memory (bytes)
snapshot=4201771008 


	 Total committed heap usage
(bytes)=138964992 


	 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=1814 


	 File Output Format Counters 


	 Bytes Written=1470 


	[root@h1 sbin]# hadoop fs -ls / 


	Found 3 items 


	drwxr-xr-x  
- root supergroup          0
2016-02-26 19:15 /out 


	-rw-r--r--  
3 root supergroup       1814
2016-02-26 19:08 /profile 


	drwx------  
- root supergroup          0
2016-02-26 19:14 /tmp 


	[root@h1 sbin]# 


	 


	Hadoop ha 集群搭建完成 


	 
 

<h2>
	安装hbase 
</h2>

	hbase-env.sh： 


	 


	export JAVA_HOME=/home/jdk 


	export HBASE_MANAGES_ZK=false


	 


	 


	 


	hbase-site.xml：


	 


	 


	 


	 


	hbase.rootdir


	hdfs://h1:9000/hbase


	 


	 


	hbase.cluster.distributed


	true


	 


	 


	 


	hbase.master


	h1:60000


	 


	 


	hbase.master.port


	60000


	The port master should bind
to.


	 


	 


	 


	 


	hbase.zookeeper.quorum


	h5,h6,h7


	 


	 


	dfs.replication


	3


	 


	 


	 


	注意：$HBASE_HOME/conf/hbase-site.xml的hbase.rootdir的主机和端口号与$HADOOP_HOME/conf/core-site.xml的fs.default.name的主机和端口号一致 


	 


	 


	Regionservers：内容为: 


	h5


	h6


	h7 


	 


	复制到h2  h5,h6,h7上面 


	 
 

<h2>
	整个启动顺序 
</h2>

	 


	按照上面启动hadoop  ha 的顺序 先启动好 


	 


	然后在h1，h2上启动hbase 


	 


	./start-hbase.sh 


	 


	 


	测试进入 hbase 


	 


	[root@h1 bin]# hbase shell 


	SLF4J: Class path contains multiple SLF4J
bindings. 


	SLF4J: Found binding in [jar:file:/home/hbase-1.2.0/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class] 


	SLF4J: Found binding in
[jar:file:/home/hadoop-2.7.2/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class] 


	SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings
for an explanation. 


	SLF4J: Actual binding is of type
[org.slf4j.impl.Log4jLoggerFactory] 


	HBase Shell; enter 'help' for
list of supported commands. 


	Type "exit" to
leave the HBase Shell 


	Version 1.2.0, r25b281972df2f5b15c426c8963cbf77dd853a5ad,
Thu Feb 18 23:01:49 CST 2016 


	 


	hbase(main):001:0> esit 


	NameError: undefined local variable or
method `esit' for # 


	 


	hbase(main):002:0> exit 


	 


	至此全部结束。