本篇内容介绍了“Hadoop2 namenode HA+联邦+Resource Manager HA实验分析”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
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实验的Hadoop版本为2.5.2，硬件环境是5台虚拟机，使用的均是CentOS6.6操作系统，虚拟机IP和hostname分别为：
192.168.63.171 node1.zhch
192.168.63.172 node2.zhch
192.168.63.173 node3.zhch
192.168.63.174 node4.zhch
192.168.63.175 node5.zhch
ssh免密码、防火墙、JDK这里就不在赘述了。虚拟机的角色分配是：
node1为主namenode1、主resource manager、zookeeper、journalnode
node2为备namendoe1、zookeeper、journalnode
node3为主namenode2、备resource manager、zookeeper、journalnode、datanode
node4为备namenode2、datanode
node5为datanode
步骤和
Namenode HA的安装配置基本相同，需要先
安装zookeeper集群，主要的不同在于core-site.xml、hdfs-site.xml、yarn-site.xml配置文件，其余文件的配置和Namenode HA安装配置基本一致。
一、配置Hadoop
## 解压
[yyl@node1 program]$ tar -zxf hadoop-2.5.2.tar.gz
## 创建文件夹
[yyl@node1 program]$ mkdir hadoop-2.5.2/name
[yyl@node1 program]$ mkdir hadoop-2.5.2/data
[yyl@node1 program]$ mkdir hadoop-2.5.2/journal
[yyl@node1 program]$ mkdir hadoop-2.5.2/tmp
## 配置hadoop-env.sh
[yyl@node1 program]$ cd hadoop-2.5.2/etc/hadoop/
[yyl@node1 hadoop]$ vim hadoop-env.sh
export JAVA_HOME=/usr/lib/java/jdk1.7.0_80
## 配置yarn-env.sh
[yyl@node1 hadoop]$ vim yarn-env.sh
export JAVA_HOME=/usr/lib/java/jdk1.7.0_80
## 配置slaves
[yyl@node1 hadoop]$ vim slaves
node3.zhch
node4.zhch
node5.zhch
## 配置mapred-site.xml
[yyl@node1 hadoop]$ cp mapred-site.xml.template mapred-site.xml
[yyl@node1 hadoop]$ vim mapred-site.xml
mapreduce.framework.name
yarn
mapreduce.jobhistory.address
node2.zhch:10020
mapreduce.jobhistory.webapp.address
node2.zhch:19888
## 配置core-site.xml
[yyl@node1 hadoop]$ vim core-site.xml
fs.defaultFS
hdfs://mycluster
io.file.buffer.size
131072
hadoop.tmp.dir
file:/home/yyl/program/hadoop-2.5.2/tmp
hadoop.proxyuser.hduser.hosts
*
hadoop.proxyuser.hduser.groups
*
ha.zookeeper.quorum
node1.zhch:2181,node2.zhch:2181,node3.zhch:2181
ha.zookeeper.session-timeout.ms
1000
## 配置hdfs-site.xml
[yyl@node1 hadoop]$ vim hdfs-site.xml
dfs.namenode.name.dir
file:/home/yyl/program/hadoop-2.5.2/name
dfs.datanode.data.dir
file:/home/yyl/program/hadoop-2.5.2/data
dfs.replication
1
dfs.webhdfs.enabled
true
dfs.permissions
false
dfs.permissions.enabled
false
dfs.nameservices
mycluster,yourcluster
dfs.ha.namenodes.mycluster
nn1,nn2
dfs.namenode.rpc-address.mycluster.nn1
node1.zhch:9000
dfs.namenode.rpc-address.mycluster.nn2
node2.zhch:9000
dfs.namenode.servicerpc-address.mycluster.nn1
node1.zhch:53310
dfs.namenode.servicerpc-address.mycluster.nn2
node2.zhch:53310
dfs.namenode.http-address.mycluster.nn1
node1.zhch:50070
dfs.namenode.http-address.mycluster.nn2
node2.zhch:50070
dfs.ha.namenodes.yourcluster
nn1,nn2
dfs.namenode.rpc-address.yourcluster.nn1
node3.zhch:9000
dfs.namenode.rpc-address.yourcluster.nn2
node4.zhch:9000
dfs.namenode.servicerpc-address.yourcluster.nn1
node3.zhch:53310
dfs.namenode.servicerpc-address.yourcluster.nn2
node4.zhch:53310
dfs.namenode.http-address.yourcluster.nn1
node3.zhch:50070
dfs.namenode.http-address.yourcluster.nn2
node4.zhch:50070
dfs.namenode.shared.edits.dir
qjournal://node1.zhch:8485;node2.zhch:8485;node3.zhch:8485/mycluster
dfs.client.failover.proxy.provider.mycluster
org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider
dfs.client.failover.proxy.provider.yourcluster
org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider
dfs.ha.fencing.methods
sshfence
dfs.ha.fencing.ssh.private-key-files
/home/yyl/.ssh/id_rsa
dfs.ha.fencing.ssh.connect-timeout
30000
dfs.journalnode.edits.dir
/home/yyl/program/hadoop-2.5.2/journal
dfs.ha.automatic-failover.enabled.mycluster
true
dfs.ha.automatic-failover.enabled.yourcluster
true
ha.failover-controller.cli-check.rpc-timeout.ms
60000
ipc.client.connect.timeout
60000
dfs.image.transfer.bandwidthPerSec
4194304
## 配置yarn-site.xml
[yyl@node1 hadoop]$ vim yarn-site.xml
yarn.nodemanager.aux-services
mapreduce_shuffle
yarn.nodemanager.aux-services.mapreduce.shuffle.class
org.apache.hadoop.mapred.ShuffleHandler
yarn.resourcemanager.connect.retry-interval.ms
2000
yarn.resourcemanager.ha.enabled
true
yarn.resourcemanager.ha.automatic-failover.enabled
true
yarn.resourcemanager.ha.automatic-failover.embedded
true
yarn.resourcemanager.cluster-id
yarn-cluster
yarn.resourcemanager.ha.rm-ids
rm1,rm2
yarn.resourcemanager.ha.id
rm1
yarn.resourcemanager.scheduler.class
org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler
yarn.resourcemanager.recovery.enabled
true
yarn.app.mapreduce.am.scheduler.connection.wait.interval-ms
5000
yarn.resourcemanager.store.class
org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore
yarn.resourcemanager.zk-address
node1.zhch:2181,node2.zhch:2181,node3.zhch:2181
yarn.resourcemanager.zk.state-store.address
node1.zhch:2181,node2.zhch:2181,node3.zhch:2181
yarn.resourcemanager.address.rm1
node1.zhch:23140
yarn.resourcemanager.address.rm2
node3.zhch:23140
yarn.resourcemanager.scheduler.address.rm1
node1.zhch:23130
yarn.resourcemanager.scheduler.address.rm2
node3.zhch:23130
yarn.resourcemanager.admin.address.rm1
node1.zhch:23141
yarn.resourcemanager.admin.address.rm2
node3.zhch:23141
yarn.resourcemanager.resource-tracker.address.rm1
node1.zhch:23125
yarn.resourcemanager.resource-tracker.address.rm2
node3.zhch:23125
yarn.resourcemanager.webapp.address.rm1
node1.zhch:23188
yarn.resourcemanager.webapp.address.rm2
node3.zhch:23188
yarn.resourcemanager.webapp.https.address.rm1
node1.zhch:23189
yarn.resourcemanager.webapp.https.address.rm2
node3.zhch:23189
## 分发到各个节点
[yyl@node1 hadoop]$ cd /home/yyl/program/
[yyl@node1 program]$ scp -rp hadoop-2.5.2 yyl@node2.zhch:/home/yyl/program/
[yyl@node1 program]$ scp -rp hadoop-2.5.2 yyl@node3.zhch:/home/yyl/program/
[yyl@node1 program]$ scp -rp hadoop-2.5.2 yyl@node4.zhch:/home/yyl/program/
[yyl@node1 program]$ scp -rp hadoop-2.5.2 yyl@node5.zhch:/home/yyl/program/
## 修改主namenode2(node3.zhch)和备namenode2(node4.zhch)的 hdfs-site.xml 配置文件中 dfs.namenode.shared.edits.dir 的值为 qjournal://node1.zhch:8485;node2.zhch:8485;node3.zhch:8485/yourcluster ，其余属性值不变。
## 修改备resource manager(node3.zhch)的 yarn-site.xml 配置文件中 yarn.resourcemanager.ha.id 的值为 rm2 ，其余属性值不变。
## 在各个节点上设置hadoop环境变量
[yyl@node1 ~]$ vim .bash_profile
export HADOOP_PREFIX=/home/yyl/program/hadoop-2.5.2
export HADOOP_COMMON_HOME=$HADOOP_PREFIX
export HADOOP_HDFS_HOME=$HADOOP_PREFIX
export HADOOP_MAPRED_HOME=$HADOOP_PREFIX
export HADOOP_YARN_HOME=$HADOOP_PREFIX
export HADOOP_CONF_DIR=$HADOOP_PREFIX/etc/hadoop
export PATH=$PATH:$HADOOP_PREFIX/bin:$HADOOP_PREFIX/sbin
二、格式化与启动
## 启动Zookeeper集群
## 在主namenode1(node1.zhch)、主namenode2(node3.zhch)上执行命令： $HADOOP_HOME/bin/hdfs zkfc -formatZK
[yyl@node1 ~]$ hdfs zkfc -formatZK
[yyl@node3 ~]$ hdfs zkfc -formatZK
[yyl@node2 ~]$ zkCli.sh
[zk: localhost:2181(CONNECTED) 0] ls /
[hadoop-ha, zookeeper]
[zk: localhost:2181(CONNECTED) 1] ls /hadoop-ha
[mycluster, yourcluster]
## 在node1.zhch node2.zhch node3.zhch上启动journalnode：
[yyl@node1 ~]$ hadoop-daemon.sh start journalnode
starting journalnode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-journalnode-node1.zhch.out
[yyl@node1 ~]$ jps
1985 QuorumPeerMain
2222 Jps
2176 JournalNode
[yyl@node2 ~]$ hadoop-daemon.sh start journalnode
starting journalnode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-journalnode-node2.zhch.out
[yyl@node2 ~]$ jps
1783 Jps
1737 JournalNode
1638 QuorumPeerMain
[yyl@node3 ~]$ hadoop-daemon.sh start journalnode
starting journalnode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-journalnode-node3.zhch.out
[yyl@node3 ~]$ jps
1658 JournalNode
1495 QuorumPeerMain
1704 Jps
## 在主namenode1(node1.zhch)上格式化namenode
[yyl@node1 ~]$ hdfs namenode -format -clusterId c1
## 在主namenode1(node1.zhch)上启动namenode进程
[yyl@node1 ~]$ hadoop-daemon.sh start namenode
starting namenode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-namenode-node1.zhch.out
[yyl@node1 ~]$ jps
2286 NameNode
1985 QuorumPeerMain
2369 Jps
2176 JournalNode
## 在备namenode1(node2.zhch)上同步元数据
[yyl@node2 ~]$ hdfs namenode -bootstrapStandby
## 在备namenode1(node2.zhch)上启动namenode进程
[yyl@node2 ~]$ hadoop-daemon.sh start namenode
starting namenode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-namenode-node2.zhch.out
[yyl@node2 ~]$ jps
1923 Jps
1737 JournalNode
1638 QuorumPeerMain
1840 NameNode
## 在主namenode2(node3.zhch)上格式化namenode
[yyl@node3 ~]$ hdfs namenode -format -clusterId c1
## 在主namenode2(node3.zhch)上启动namenode进程
[yyl@node3 ~]$ hadoop-daemon.sh start namenode
starting namenode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-namenode-node3.zhch.out
[yyl@node3 ~]$ jps
1658 JournalNode
1495 QuorumPeerMain
1767 NameNode
1850 Jps
## 在备namenode2(node4.zhch)上同步元数据
[yyl@node4 ~]$ hdfs namenode -bootstrapStandby
## 在备namenode2(node4.zhch)上启动namenode进程
[yyl@node4 ~]$ hadoop-daemon.sh start namenode
starting namenode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-namenode-node4.zhch.out
[yyl@node4 ~]$ jps
1602 Jps
1519 NameNode
## 在所有的namenode上启动ZooKeeperFailoverController
[yyl@node1 ~]$ hadoop-daemon.sh start zkfc
starting zkfc, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-zkfc-node1.zhch.out
[yyl@node2 ~]$ hadoop-daemon.sh start zkfc
starting zkfc, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-zkfc-node2.zhch.out
[yyl@node3 ~]$ hadoop-daemon.sh start zkfc
starting zkfc, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-zkfc-node3.zhch.out
[yyl@node4 ~]$ hadoop-daemon.sh start zkfc
starting zkfc, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-zkfc-node4.zhch.out
## 启动DataNode
[yyl@node1 ~]$ hadoop-daemons.sh start datanode
node4.zhch: starting datanode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-datanode-node4.zhch.out
node5.zhch: starting datanode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-datanode-node5.zhch.out
node3.zhch: starting datanode, logging to /home/yyl/program/hadoop-2.5.2/logs/hadoop-yyl-datanode-node3.zhch.out
## 启动Yarn
[yyl@node1 ~]$ start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /home/yyl/program/hadoop-2.5.2/logs/yarn-yyl-resourcemanager-node1.zhch.out
node3.zhch: starting nodemanager, logging to /home/yyl/program/hadoop-2.5.2/logs/yarn-yyl-nodemanager-node3.zhch.out
node4.zhch: starting nodemanager, logging to /home/yyl/program/hadoop-2.5.2/logs/yarn-yyl-nodemanager-node4.zhch.out
node5.zhch: starting nodemanager, logging to /home/yyl/program/hadoop-2.5.2/logs/yarn-yyl-nodemanager-node5.zhch.out
## 在备resource manager(node3.zhch)上启动resource manager
[yyl@node3 ~]$ yarn-daemon.sh start resourcemanager
starting resourcemanager, logging to /home/yyl/program/hadoop-2.5.2/logs/yarn-yyl-resourcemanager-node3.zhch.out
## 查看resource manager状态
[yyl@node1 ~]$ yarn rmadmin -getServiceState rm1
active
[yyl@node1 ~]$ yarn rmadmin -getServiceState rm2
standby
三、验证
开两个终端，都连接到主resource manager，在终端A中运行jps命令查看resource manager进程ID，在终端B中运行MapReduce程序；然后再到终端A中kill掉resource manager进程；最后观察在主resource manager进程挂掉后，MapReduce任务是否还能正常执行完毕。
“Hadoop2 namenode HA+联邦+Resource Manager HA实验分析”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注创新互联网站，小编将为大家输出更多高质量的实用文章！
标题名称：Hadoop2namenodeHA+联邦+ResourceManagerHA实验分析
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