Hadoop2.7.1+Hbase1.2.1集群环境搭建(10)基于ZK的Hadoop HA集群安装

阅读更多

(1)hadoop2.7.1源码编译	http://aperise.iteye.com/blog/2246856
(2)hadoop2.7.1安装准备	http://aperise.iteye.com/blog/2253544
(3)1.x和2.x都支持的集群安装	http://aperise.iteye.com/blog/2245547
(4)hbase安装准备	http://aperise.iteye.com/blog/2254451
(5)hbase安装	http://aperise.iteye.com/blog/2254460
(6)snappy安装	http://aperise.iteye.com/blog/2254487
(7)hbase性能优化	http://aperise.iteye.com/blog/2282670
(8)雅虎YCSBC测试hbase性能测试	http://aperise.iteye.com/blog/2248863
(9)spring-hadoop实战	http://aperise.iteye.com/blog/2254491
(10)基于ZK的Hadoop HA集群安装	http://aperise.iteye.com/blog/2305809

 

1.Hadoop集群方式介绍

    1.1 hadoop1.x和hadoop2.x都支持的namenode+secondarynamenode方式

         优点：搭建环境简单，适合开发者模式下调试程序

         缺点：namenode作为很重要的服务，存在单点故障，如果namenode出问题，会导致整个集群不可用

    

    1.2.仅hadoop2.x支持的active namenode+standby namenode方式

       优点：为解决1.x中namenode单节点故障而生，充分保障Hadoop集群的高可用

       缺点：需要zookeeper最少3台，需要journalnode最少三台，目前最多支持2台namenode，不过节点可以复用，但是不建议

 

    1.3 Hadoop官网关于集群方式介绍

        1）单机Hadoop环境搭建

        http://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-common/SingleCluster.html

        2）集群方式

            集群方式一(hadoop1.x和hadoop2.x都支持的namenode+secondarynamenode方式)

            http://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-common/ClusterSetup.html

 

            集群方式二(仅hadoop2.x支持的active namenode+standby namenode方式，也叫HADOOP HA方式)，这种方式又分为HDFS的HA和YARN的HA单独分开讲解。

                     HDFS HA(zookeeper+journalnode)http://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-hdfs/HDFSHighAvailabilityWithQJM.html

                     HDFS HA(zookeeper+NFS)http://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-hdfs/HDFSHighAvailability

                     YARN HA(zookeeper)http://hadoop.apache.org/docs/r2.7.2/hadoop-yarn/hadoop-yarn-site/ResourceManagerHA.html

 

        生产环境多采用HDFS(zookeeper+journalnode)(active NameNode+standby NameNode+JournalNode+DFSZKFailoverController+DataNode)+YARN(zookeeper)(active ResourceManager+standby ResourceManager+NodeManager)方式，这里我讲解的是仅hadoop2.x支持基于zookeeper的Hadoop HA集群方式，这种方式主要适用于生产环境。

 

2.基于zookeeper的Hadoop HA集群安装

    2.1 安装环境介绍

 

    2.2 安装前准备工作

        1）关闭防火墙

centos7防火墙操作介绍 

#centos7启动firewall
systemctl start firewalld.service
#centos7重启firewall
systemctl restart firewalld.service
#centos7停止firewall
systemctl stop firewalld.service 
#centos7禁止firewall开机启动
systemctl disable firewalld.service 
#centos7查看防火墙状态
firewall-cmd --state
#开放防火墙端口
vi /etc/sysconfig/iptables-config
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 6379 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 6380 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 6381 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 16379 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 16380 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 16381 -j ACCEPT

         这里我关闭防火墙，root下执行如下命令：

systemctl stop firewalld.service 
systemctl disable firewalld.service

 

        2）优化selinux

        作用：Hadoop主节点管理子节点是通过SSH实现的， SELinux不关闭的情况下无法实现，会限制ssh免密码登录。

        编辑/etc/selinux/config，修改前：

# This file controls the state of SELinux on the system.
# SELINUX= can take one of these three values:
# enforcing - SELinux security policy is enforced.
# permissive - SELinux prints warnings instead of enforcing.
# disabled - No SELinux policy is loaded.
SELINUX=enforcing
# SELINUXTYPE= can take one of these two values:
# targeted - Targeted processes are protected,
# minimum - Modification of targeted policy. Only selected processes are protected. 
# mls - Multi Level Security protection.
SELINUXTYPE=targeted

         修改后：

# This file controls the state of SELinux on the system.
# SELINUX= can take one of these three values:
# enforcing - SELinux security policy is enforced.
# permissive - SELinux prints warnings instead of enforcing.
# disabled - No SELinux policy is loaded.
#SELINUX=enforcing
SELINUX=disabled
# SELINUXTYPE= can take one of these two values:
# targeted - Targeted processes are protected,
# minimum - Modification of targeted policy. Only selected processes are protected. 
# mls - Multi Level Security protection.
#SELINUXTYPE=targeted

         执行以下命令使selinux 修改立即生效：

setenforce 0

 

    3）机器名配置

        作用：Hadoop集群中机器IP可能变化导致集群间服务中断，所以在Hadoop中最好以机器名进行配置。

        修改各机器上文件/etc/hostname，配置主机名称如下：

127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.185.31 hadoop31
192.168.185.32 hadoop32
192.168.185.33 hadoop33
192.168.185.34 hadoop34
192.168.185.35 hadoop35

         而centos7下各个机器的主机名设置文件为/etc/hostname，以hadoop31节点主机配置为例，配置如下：

#localdomain
hadoop31

 

    4)创建hadoop用户和组

        作用：后续单独以用户hadoop来管理Hadoop集群,防止其他用户误操作关闭Hadoop 集群

#以root用户创建hadoop用户和组创建hadoop用户和组 
groupadd hadoop 
useradd -g hadoop hadoop 
#修改用户密码
passwd hadoop

 

    5）用户hadoop免秘钥登录

        作用：Hadoop中主节点管理从节点是通过SSH协议登录到从节点实现的，而一般的SSH登录，都是需要输入密码验证的，为了Hadoop主节点方便管理成千上百的从节点，这里将主节点公钥拷贝到从节点，实现SSH协议免秘钥登录，我这里做的是所有主从节点之间机器免秘钥登录

#首先切换到上面的hadoop用户,这里我是在hadoop31机器上操作 
ssh hadoop31
su hadoop 
#生成非对称公钥和私钥，这个在集群中所有节点机器都必须执行,一直回车就行 
ssh-keygen -t rsa 
#通过ssh登录远程机器时，本机会默认将当前用户目录下的.ssh/authorized_keys带到远程机器进行验证，这里是/home/hadoop/.ssh/authorized_keys中公钥(来自其他机器上的/home/hadoop/.ssh/id_rsa.pub.pub),以下代码只在主节点执行就可以做到主从节点之间SSH免密码登录 
cd /home/hadoop/.ssh/ 
#首先将Master节点的公钥添加到authorized_keys 
cat id_rsa.pub>>authorized_keys 
#其次将Slaves节点的公钥添加到authorized_keys，这里我是在Hadoop31机器上操作的 
ssh [email protected] cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
ssh [email protected] cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
ssh [email protected] cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
ssh [email protected] cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
#必须设置修改/home/hadoop/.ssh/authorized_keys权限 
chmod 600 /home/hadoop/.ssh/authorized_keys 
#这里将Master节点的authorized_keys分发到其他slaves节点 
scp -r /home/hadoop/.ssh/authorized_keys [email protected]:/home/hadoop/.ssh/ 
scp -r /home/hadoop/.ssh/authorized_keys [email protected]:/home/hadoop/.ssh/ 
scp -r /home/hadoop/.ssh/authorized_keys [email protected]:/home/hadoop/.ssh/ 
scp -r /home/hadoop/.ssh/authorized_keys [email protected]:/home/hadoop/.ssh/

 

    6）JDK安装

        作用：Hadoop需要java环境支撑，而Hadoop2.7.1最少需要java版本1.7，安装如下：

#登录到到到hadoop用户下
su hadoop
#下载jdk-7u65-linux-x64.gz放置于/home/hadoop/java并解压
cd /home/hadoop/java
tar -zxvf jdk-7u65-linux-x64.gz
#编辑vi /home/hadoop/.bashrc,在文件末尾追加如下内容
export JAVA_HOME=/home/hadoop/java/jdk1.7.0_65 
export CLASSPATH=.:$JAVA_HOME/jre/lib/rt.jar:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar 
export PATH=$PATH:$JAVA_HOME/bin 
#使得/home/hadoop/.bashrc配置生效
source /home/hadoop/.bashrc

         很多人是配置linux全局/etc/profile，这里不建议这么做，一旦有人在里面降级了java环境或者删除了java环境，就会出问题，建议的是在管理Hadoop集群的用户下面修改其.bashrc单独配置该用户环境变量

 

    7）zookeeper安装

#1登录hadoop用户并下载并解压zookeeper3.4.6
su hadoop
cd /home/hadoop 
tar -zxvf zookeeper-3.4.6.tar.gz 

#2在集群中各个节点中配置/etc/hosts，内容如下：
127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.185.31 hadoop31 
192.168.185.32 hadoop32 
192.168.185.33 hadoop33 
192.168.185.34 hadoop34 
192.168.185.35 hadoop35

#3在集群中各个节点中创建zookeeper数据文件
ssh hadoop31
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop32
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop33
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop34
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop35
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 

#4配置zoo.cfg
ssh hadoop31
cd /home/hadoop/zookeeper-3.4.6/conf
cp zoo_sample.cfg zoo.cfg
vi zoo.cfg
#内容如下
initLimit=10 
syncLimit=5 
dataDir=/opt/hadoop/zookeeper 
clientPort=2181 

#数据文件保存最近的3个快照，默认是都保存，时间长的话会占用很大磁盘空间
autopurge.snapRetainCount=3
#单位为小时，每小时清理一次快照数据
autopurge.purgeInterval=1
server.1=hadoop31:2888:3888 
server.2=hadoop32:2888:3888 
server.3=hadoop33:2888:3888
server.4=hadoop34:2888:3888 
server.5=hadoop35:2888:3888 
#5在hadoop31上远程复制分发安装文件
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop32:/home/hadoop/ 
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop33:/home/hadoop/ 
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop34:/home/hadoop/ 
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop35:/home/hadoop/ 

#6在集群中各个节点设置myid必须为数字 
ssh hadoop31 
echo "1" > /opt/hadoop/zookeeper/myid 
ssh hadoop32 
echo "2" > /opt/hadoop/zookeeper/myid 
ssh hadoop33 
echo "3" > /opt/hadoop/zookeeper/myid 

#7.各个节点如何启动zookeeper
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start

#8.各个节点如何关闭zookeeper
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh stop 

#9.各个节点如何查看zookeeper状态
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh status 

#10.各个节点如何通过客户端访问zookeeper上目录数据
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkCli.sh -server hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181

 

    2.3 Hadoop HA安装

        1）hadoop-2.7.1.tar.gz

#下载hadoop-2.7.1.tar.gz放置于/home/hadoop下并解压,这里我在hadoop31操作
ssh hadoop31
su hadoop
cd /home/hadoop
tar –zxvf hadoop-2.7.1.tar.gz

      

        2）core-site.xml

        修改配置文件/home/hadoop/hadoop-2.7.1/etc/hadoop/core-site.xml

  
  
  
	 
	
		fs.trash.interval
		1440
	
	 
	
		fs.defaultFS  
		hdfs:// bigdatacluster-ha
	
	  
	  
		io.file.buffer.size  
		131072  
	 
	 
	 
		hadoop.tmp.dir 
		/opt/hadoop/tmp 
	 
	 
	 
		ha.zookeeper.quorum 
		hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181 
	 
	 
		ha.zookeeper.session-timeout.ms 
		300000 
	
	 
	  
		io.compression.codecs  
		org.apache.hadoop.io.compress.SnappyCodec  
	  

 

        3）hdfs-site.xml

        修改配置文件/home/hadoop/hadoop-2.7.1/etc/hadoop/hdfs-site.xml

 
 
 
	 
	 
		dfs.nameservices 
		bigdatacluster-ha 
	 
	 
	
		dfs.datanode.du.reserved
		107374182400
	
	 
	 
		dfs.ha.namenodes.bigdatacluster-ha 
		namenode1,namenode2 
	 
	 
	 
		dfs.namenode.rpc-address.bigdatacluster-ha.namenode1 
		hadoop31:9000 
	 
	 
	 
		dfs.namenode.http-address.bigdatacluster-ha.namenode1 
		hadoop31:50070 
	 
	 
	 
		dfs.namenode.rpc-address.bigdatacluster-ha.namenode2 
		hadoop32:9000 
	 
	 
	 
		dfs.namenode.http-address.bigdatacluster-ha.namenode2 
		hadoop32:50070 
	 

	 
	 
		dfs.namenode.shared.edits.dir 
		qjournal://hadoop31:8485;hadoop32:8485;hadoop33:8485;hadoop34:8485;hadoop35:8485/bigdatacluster-ha 
	 

	 
	 
		dfs.client.failover.proxy.provider.bigdatacluster-ha
		org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider
	 

	 
	 
		dfs.ha.fencing.methods 
		sshfence 
	 
	 
	 
		dfs.ha.fencing.ssh.private-key-files 
		/home/hadoop/.ssh/id_rsa 
	 

	 
	 
		dfs.journalnode.edits.dir 
		/opt/hadoop/journal 
	 

	 
	 
		dfs.ha.automatic-failover.enabled 
		true 
	 

	 
	 
		dfs.namenode.name.dir    
		file:/opt/hadoop/hdfs/name 
	 

	 
	 
		dfs.datanode.data.dir 
		file:/opt/hadoop/hdfs/data 
	 

	 
	 
		dfs.replication 
		3 
	 

	 
	 
		dfs.webhdfs.enabled 
		true 
	 

	 
		ha.zookeeper.quorum 
		hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181 
	 

	
	
		dfs.namenode.handler.count
		600
		The number of server threads for the namenode.
	
	
		dfs.datanode.handler.count
		600
		The number of server threads for the datanode.
	
	
		dfs.client.socket-timeout
		600000
	
	  
		  
		dfs.datanode.max.transfer.threads  
		409600  
	   

 

        4）mapred-site.xml

        修改配置文件/home/hadoop/hadoop-2.7.1/etc/hadoop/mapred-site.xml

   
   
   
       
       
        mapreduce.framework.name   
        yarn   
        
      
        mapreduce.job.maps  
        12  
      
      
        mapreduce.job.reduces  
        12  
      
  
       
      
        mapreduce.output.fileoutputformat.compress  
        true  
        Should the job outputs be compressed?  
          
      
      
        mapreduce.output.fileoutputformat.compress.type  
        RECORD  
        If the job outputs are to compressed as SequenceFiles, how should  
               they be compressed? Should be one of NONE, RECORD or BLOCK.  
          
      
      
        mapreduce.output.fileoutputformat.compress.codec  
        org.apache.hadoop.io.compress.SnappyCodec  
        If the job outputs are compressed, how should they be compressed?  
          
      
      
        mapreduce.map.output.compress  
        true  
        Should the outputs of the maps be compressed before being  
               sent across the network. Uses SequenceFile compression.  
          
      
      
        mapreduce.map.output.compress.codec  
        org.apache.hadoop.io.compress.SnappyCodec  
        If the map outputs are compressed, how should they be   
               compressed?  
          
        
 

 

 

        5）yarn-site.xml

        修改配置文件/home/hadoop/hadoop-2.7.1/etc/hadoop/yarn-site.xml

 
 
	  
	 
		yarn.log-aggregation-enable 
		true 
	 
	  
	 
		yarn.log-aggregation.retain-seconds 
		259200 
	 
	  

	  
	  
		yarn.resourcemanager.connect.retry-interval.ms
		2000  
	 

	
	 
		yarn.resourcemanager.zk-address 
		hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181  
	 
	  
		yarn.resourcemanager.cluster-id  
		besttonecluster-yarn  
	  
	  
	  
		yarn.resourcemanager.ha.enabled  
		true  
	  
	 
		yarn.resourcemanager.ha.rm-ids 
		rm1,rm2 
	 
	 
		yarn.resourcemanager.hostname.rm1 
		hadoop31 
	     
	 
		yarn.resourcemanager.hostname.rm2 
		hadoop32 
	 
	 
	
		yarn.resourcemanager.webapp.address.rm1
		hadoop31:8088
	
	  
	
		yarn.resourcemanager.webapp.address.rm2
		hadoop32:8088
	
	  
	
		yarn.resourcemanager.ha.automatic-failover.enabled
		true
	
	
		yarn.resourcemanager.ha.automatic-failover.embedded
		true
	
	
		yarn.resourcemanager.ha.automatic-failover.zk-base-path
		/yarn-leader-election
	

	  
	 
		yarn.resourcemanager.recovery.enabled  
		true  
	 
	
		yarn.resourcemanager.store.class
		org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore
	
	

	
	  
		yarn.nodemanager.aux-services  
		mapreduce_shuffle  
	  
	  
		yarn.nodemanager.aux-services.mapreduce.shuffle.class  
		org.apache.hadoop.mapred.ShuffleHandler  
	  
	

 

        6）slaves

        修改配置文件/home/hadoop/hadoop-2.7.1/etc/hadoop/slaves

Hadoop31
Hadoop32
Hadoop33
Hadoop34
Hadoop35

 

        7）hadoop-env.sh和yarn-env.sh

        在/home/hadoop/hadoop-2.7.1/etc/hadoop/hadoop-env.sh和/home/hadoop/hadoop-2.7.1/etc/hadoop/yarn-env.sh中配置JAVA_HOME

export JAVA_HOME=/home/hadoop/java/jdk1.7.0_65

 

        8）bashrc

        当前用户hadoop生效，在用户目录下/home/hadoop/.bashrc增加如下配置

export HADOOP_HOME=/home/hadoop/hadoop2.7.1
export PATH=${HADOOP_HOME}/bin:${PATH}

 

        9）分发安装文件到其他机器

#这里我是在hadoop31上操作
scp -r /home/hadoop/hadoop-2.7.1 hadoop@hadoop32:/home/hadoop/
scp -r /home/hadoop/hadoop-2.7.1 hadoop@ hadoop33:/home/hadoop/
scp -r /home/hadoop/hadoop-2.7.1 hadoop@ hadoop34:/home/hadoop/ 
scp -r /home/hadoop/hadoop-2.7.1 hadoop@ hadoop35:/home/hadoop/

 

    2.4 Hadoop HA初次启动

        1）启动zookeeper

ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop32
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop33
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop34
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop35
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start

         #jps查看是否有QuorumPeerMain 进程

        #/home/hadoop/zookeeper-3.4.6/ bin/zkServer.sh status查看zookeeper状态

        #/home/hadoop/zookeeper-3.4.6/ bin/zkServer.sh stop关闭zookeeper

 

        2）格式化zookeeper上hadoop-ha目录

/home/hadoop/hadoop-2.7.1/bin/hdfs zkfc –formatZK
#可以通过如下方法检查zookeeper上是否已经有Hadoop HA目录
# /home/hadoop/zookeeper-3.4.6/bin/zkCli.sh -server hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181 
#ls /

 

        3）启动namenode日志同步服务journalnode

ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop32
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop33
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop34
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop35
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode

 

        4）格式化namenode

#这步操作只能在namenode服务节点hadoop31或者hadoop32执行中一台上执行
ssh hadoop31
/home/hadoop/hadoop-2.7.1/bin/hdfs namenode -format

 

        5）启动namenode、同步备用namenode、启动备用namenode

#启动namenode
ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start namenode 
#同步备用namenode、启动备用namenode
ssh hadoop32
/home/hadoop/hadoop-2.7.1/bin/hdfs namenode -bootstrapStandby 
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start namenode

 

        6)启动DFSZKFailoverController

ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start zkfc 
ssh hadoop32
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start zkfc

 

        7）启动datanode

#注意hadoop-daemons.sh datanode是启动所有datanode，而hadoop-daemon.sh datanode是启动单个datanode
ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemons.sh start datanode

 

        8）启动yarn

#在hadoop31上启动resouremanager,在hadoop31，hadoop32，hadoop33，hadoop34，hadoop35上启动nodemanager
ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/start-yarn.sh 
#在hadoop31上启动备用resouremanager
ssh hadoop32
/home/hadoop/hadoop-2.7.1/sbin/yarn-daemon.sh start resourcemanager

         至此，Hadoop 基于zookeeper的高可用集群就安装成功，并且启动了。

 

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