基于zookeeper的高可用Hadoop HA集群安装

(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

aperise

5670人浏览 · 2016-06-18 00:50:33

aperise · 2016-06-18 00:50:33 发布

(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 hadoop@192.168.185.32 cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys
ssh hadoop@192.168.185.33 cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys
ssh hadoop@192.168.185.34 cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys
ssh hadoop@192.168.185.35 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 hadoop@192.168.185.32:/home/hadoop/.ssh/
scp -r /home/hadoop/.ssh/authorized_keys hadoop@192.168.185.33:/home/hadoop/.ssh/
scp -r /home/hadoop/.ssh/authorized_keys hadoop@192.168.185.34:/home/hadoop/.ssh/
scp -r /home/hadoop/.ssh/authorized_keys hadoop@192.168.185.35:/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

<?xml version="1.0" encoding="UTF-8"?>  
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>  
<configuration>  
	<!-- 开启垃圾回收站功能,HDFS文件删除后先进入垃圾回收站，垃圾回收站最长保留数据时间为1天，超过一天后就删除 --> 
	<property>
		<name>fs.trash.interval</name>
		<value>1440</value>
	</property>
	<!-- Hadoop HA部署方式下namenode访问地址，bigdatacluster-ha是名字可自定义，后面hdfs-site.xml会用到 --> 
	<property>
		<name>fs.defaultFS</name>  
		<value>hdfs:// bigdatacluster-ha</value>
	</property>
	<!--hadoop访问文件的IO操作都需要通过代码库。因此，在很多情况下，io.file.buffer.size都被用来设置SequenceFile中用到的读/写缓存大小。不论是对硬盘或者是网络操作来讲，较大的缓存都可以提供更高的数据传输，但这也就意味着更大的内存消耗和延迟。这个参数要设置为系统页面大小的倍数，以byte为单位，默认值是4KB，一般情况下，可以设置为64KB（65536byte）,这里设置128K-->  
	<property>  
		<name>io.file.buffer.size</name>  
		<value>131072</value>  
	</property> 
	<!-- 指定hadoop临时目录 --> 
	<property> 
		<name>hadoop.tmp.dir</name> 
		<value>/opt/hadoop/tmp</value> 
	</property> 
	<!-- 指定zookeeper地址 --> 
	<property> 
		<name>ha.zookeeper.quorum</name> 
		<value>hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181</value> 
	</property> 
	<property> 
		<name>ha.zookeeper.session-timeout.ms</name> 
		<value>300000</value> 
	</property>
	<!-- 指定Hadoop压缩格式，Apache官网下载的安装包不支持snappy，需要自己编译安装，如何编译安装包我在博客http://aperise.iteye.com/blog/2254487有讲解,不适用snappy的话可以不配置 --> 
	<property>  
		<name>io.compression.codecs</name>  
		<value>org.apache.hadoop.io.compress.SnappyCodec</value>  
	</property>  
</configuration>

3）hdfs-site.xml

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

<?xml version="1.0" encoding="UTF-8"?> 
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?> 
<configuration> 
	<!--指定hdfs的nameservice为bigdatacluster-ha，需要和core-site.xml中的保持一致 --> 
	<property> 
		<name>dfs.nameservices</name> 
		<value>bigdatacluster-ha</value> 
	</property> 
	<!—指定磁盘预留多少空间，防止磁盘被撑满用完，单位为bytes --> 
	<property>
		<name>dfs.datanode.du.reserved</name>
		<value>107374182400</value>
	</property>
	<!-- bigdatacluster-ha下面有两个NameNode，分别是namenode1，namenode2 --> 
	<property> 
		<name>dfs.ha.namenodes.bigdatacluster-ha</name> 
		<value>namenode1,namenode2</value> 
	</property> 
	<!-- namenode1的RPC通信地址,这里端口要和core-site.xml中fs.defaultFS保持一致 --> 
	<property> 
		<name>dfs.namenode.rpc-address.bigdatacluster-ha.namenode1</name> 
		<value>hadoop31:9000</value> 
	</property> 
	<!-- namenode1的http通信地址 --> 
	<property> 
		<name>dfs.namenode.http-address.bigdatacluster-ha.namenode1</name> 
		<value>hadoop31:50070</value> 
	</property> 
	<!-- namenode2的RPC通信地址,这里端口要和core-site.xml中fs.defaultFS保持一致 --> 
	<property> 
		<name>dfs.namenode.rpc-address.bigdatacluster-ha.namenode2</name> 
		<value>hadoop32:9000</value> 
	</property> 
	<!-- namenode2的http通信地址 --> 
	<property> 
		<name>dfs.namenode.http-address.bigdatacluster-ha.namenode2</name> 
		<value>hadoop32:50070</value> 
	</property> 

	<!-- 指定NameNode的元数据在JournalNode上的存放位置 --> 
	<property> 
		<name>dfs.namenode.shared.edits.dir</name> 
		<value>qjournal://hadoop31:8485;hadoop32:8485;hadoop33:8485;hadoop34:8485;hadoop35:8485/bigdatacluster-ha</value> 
	</property> 

	<!-- 配置失败自动切换实现方式 --> 
	<property> 
		<name>dfs.client.failover.proxy.provider.bigdatacluster-ha</name>
		<value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
	</property> 

	<!-- 配置隔离机制,主要用户远程管理监听其他机器相关服务 --> 
	<property> 
		<name>dfs.ha.fencing.methods</name> 
		<value>sshfence</value> 
	</property> 
	<!-- 使用隔离机制时需要ssh免密码登陆 --> 
	<property> 
		<name>dfs.ha.fencing.ssh.private-key-files</name> 
		<value>/home/hadoop/.ssh/id_rsa</value> 
	</property> 

	<!-- 指定NameNode的元数据在JournalNode上的存放位置 --> 
	<property> 
		<name>dfs.journalnode.edits.dir</name> 
		<value>/opt/hadoop/journal</value> 
	</property> 

	<!--指定支持高可用自动切换机制--> 
	<property> 
		<name>dfs.ha.automatic-failover.enabled</name> 
		<value>true</value> 
	</property> 

	<!--指定namenode名称空间的存储地址--> 
	<property> 
		<name>dfs.namenode.name.dir</name>    
		<value>file:/opt/hadoop/hdfs/name</value> 
	</property> 

	<!--指定datanode数据存储地址--> 
	<property> 
		<name>dfs.datanode.data.dir</name> 
		<value>file:/opt/hadoop/hdfs/data</value> 
	</property> 

	<!--指定数据冗余份数--> 
	<property> 
		<name>dfs.replication</name> 
		<value>3</value> 
	</property> 

	<!--指定可以通过web访问hdfs目录--> 
	<property> 
		<name>dfs.webhdfs.enabled</name> 
		<value>true</value> 
	</property> 

	<property> 
		<name>ha.zookeeper.quorum</name> 
		<value>hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181</value> 
	</property> 

	
	<property>
		<name>dfs.namenode.handler.count</name>
		<value>600</value>
		<description>The number of server threads for the namenode.</description>
	</property>
	<property>
		<name>dfs.datanode.handler.count</name>
		<value>600</value>
		<description>The number of server threads for the datanode.</description>
	</property>
	<property>
		<name>dfs.client.socket-timeout</name>
		<value>600000</value>
	</property>
	<property>  
		<!--这里设置Hadoop允许打开最大文件数，默认4096，不设置的话会提示xcievers exceeded错误-->  
		<name>dfs.datanode.max.transfer.threads</name>  
		<value>409600</value>  
	</property>   
</configuration>

4）mapred-site.xml

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

<?xml version="1.0"?>   
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>   
<configuration>   
    <!-- 配置MapReduce运行于yarn中 -->   
    <property>   
        <name>mapreduce.framework.name</name>   
        <value>yarn</value>   
    </property>    
    <property>  
        <name>mapreduce.job.maps</name>  
        <value>12</value>  
    </property>  
    <property>  
        <name>mapreduce.job.reduces</name>  
        <value>12</value>  
    </property>  
  
    <!-- 指定Hadoop压缩格式，Apache官网下载的安装包不支持snappy，需要自己编译安装，如何编译安装包我在博客http://aperise.iteye.com/blog/2254487有讲解,不适用snappy的话可以不配置 -->   
    <property>  
        <name>mapreduce.output.fileoutputformat.compress</name>  
        <value>true</value>  
        <description>Should the job outputs be compressed?  
        </description>  
    </property>  
    <property>  
        <name>mapreduce.output.fileoutputformat.compress.type</name>  
        <value>RECORD</value>  
        <description>If the job outputs are to compressed as SequenceFiles, how should  
               they be compressed? Should be one of NONE, RECORD or BLOCK.  
        </description>  
    </property>  
    <property>  
        <name>mapreduce.output.fileoutputformat.compress.codec</name>  
        <value>org.apache.hadoop.io.compress.SnappyCodec</value>  
        <description>If the job outputs are compressed, how should they be compressed?  
        </description>  
    </property>  
    <property>  
        <name>mapreduce.map.output.compress</name>  
        <value>true</value>  
        <description>Should the outputs of the maps be compressed before being  
               sent across the network. Uses SequenceFile compression.  
        </description>  
    </property>  
    <property>  
        <name>mapreduce.map.output.compress.codec</name>  
        <value>org.apache.hadoop.io.compress.SnappyCodec</value>  
        <description>If the map outputs are compressed, how should they be   
               compressed?  
        </description>  
    </property>    
</configuration>

5）yarn-site.xml

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

<?xml version="1.0"?> 
<configuration> 
	<!--日志聚合功能yarn.log start------------------------------------------------------------------------>  
	<property> 
		<name>yarn.log-aggregation-enable</name> 
		<value>true</value> 
	</property> 
	<!--在HDFS上聚合的日志最长保留多少秒。3天-->  
	<property> 
		<name>yarn.log-aggregation.retain-seconds</name> 
		<value>259200</value> 
	</property> 
	<!--日志聚合功能yarn.log end-------------------------------------------------------------------------->  

	<!--resourcemanager失联后重新链接的时间-->  
	<property>  
		<name>yarn.resourcemanager.connect.retry-interval.ms</name>
		<value>2000</value>  
	</property> 

	<!--配置resourcemanager start------------------------------------------------------------------------->
	<property> 
		<name>yarn.resourcemanager.zk-address</name> 
		<value>hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181</value>  
	</property> 
	<property>  
		<name>yarn.resourcemanager.cluster-id</name>  
		<value>besttonecluster-yarn</value>  
	</property>  
	<!--开启resourcemanager HA,默认为false-->  
	<property>  
		<name>yarn.resourcemanager.ha.enabled</name>  
		<value>true</value>  
	</property>  
	<property> 
		<name>yarn.resourcemanager.ha.rm-ids</name> 
		<value>rm1,rm2</value> 
	</property> 
	<property> 
		<name>yarn.resourcemanager.hostname.rm1</name> 
		<value>hadoop31</value> 
	</property>     
	<property> 
		<name>yarn.resourcemanager.hostname.rm2</name> 
		<value>hadoop32</value> 
	</property> 
	<!--配置rm1--> 
	<property>
		<name>yarn.resourcemanager.webapp.address.rm1</name>
		<value>hadoop31:8088</value>
	</property>
	<!--配置rm2-->  
	<property>
		<name>yarn.resourcemanager.webapp.address.rm2</name>
		<value>hadoop32:8088</value>
	</property>
	<!--开启故障自动切换-->  
	<property>
		<name>yarn.resourcemanager.ha.automatic-failover.enabled</name>
		<value>true</value>
	</property>
	<property>
		<name>yarn.resourcemanager.ha.automatic-failover.embedded</name>
		<value>true</value>
	</property>
	<property>
		<name>yarn.resourcemanager.ha.automatic-failover.zk-base-path</name>
		<value>/yarn-leader-election</value>
	</property>

	<!--开启自动恢复功能-->  
	<property> 
		<name>yarn.resourcemanager.recovery.enabled</name>  
		<value>true</value>  
	</property> 
	<property>
		<name>yarn.resourcemanager.store.class</name>
		<value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
	</property>
	<!--配置resourcemanager end--------------------------------------------------------------------------->

	<!--配置nodemanager start----------------------------------------------------------------------------->
	<property>  
		<name>yarn.nodemanager.aux-services</name>  
		<value>mapreduce_shuffle</value>  
	</property>  
	<property>  
		<name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>  
		<value>org.apache.hadoop.mapred.ShuffleHandler</value>  
	</property>  
	<!--配置nodemanager end------------------------------------------------------------------------------->
</configuration>

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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