hadoop 2.7.2 + zookeeper 高可用叢集部署
一．環境說明
虛擬機器：vmware 11
作業系統：Ubuntu 16.04
Hadoop版本：2.7.2
Zookeeper版本：3.4.9
二．節點部署說明
三．Hosts增加配置
sudo gedit /etc/hosts
wxzz-pc、wxzz-pc0、wxzz-pc1、wxzz-pc2均配置如下：
127.0.0.1 localhost 192.168.72.132 wxzz-pc 192.168.72.138 wxzz-pc0 192.168.72.135 wxzz-pc1 192.168.72.136 wxzz-pc2
四.zookeeper
上配置
Zoo.cfg配置檔案內容如下：
tickTime=2000 initLimit=10 syncLimit=5 dataDir=/opt/zookeeper-3.4.9/tmp/dataDir dataLogDir=/opt/zookeeper-3.4.9/tmp/logs/ clientPort=2181 server.1=wxzz-pc:2182:2183 server.2=wxzz-pc0:2182:2183 server.3=wxzz-pc1:2182:2183
在/opt/zookeeper-3.4.9/tmp/dataDir下新建”myid”檔案，wxzz-pc、wxzz-pc0、wxzz-pc1三臺虛擬機器中myid檔案分別對應的內容為：1、2、3，也就是server.X=wxzz-pc:2182:2183，對應X的數值。
五．Hadoop配置
1．core-site.xml 配置
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://myhadoop:8020</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/opt/hadoop-2.7.2/tmp/hadoop-${user.name}</value>
</property>
<property>
<name>ha.zookeeper.quorum</name>
<value>wxzz-pc:2181,wxzz-pc0:2181,wxzz-pc1:2181</value>
</property>
</configuration>
2. hdfs-site.xml 配置
<configuration>
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
<property>
<name>dfs.block.size</name>
<value>10485760</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/opt/hadoop-2.7.2/tmp/hadoop-${user.name}</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>${hadoop.tmp.dir}/dfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>${hadoop.tmp.dir}/dfs/data</value>
</property>
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
<property>
<name>dfs.permissions.enabled</name>
<value>false</value>
</property>
<property>
<name>dfs.webhdfs.enabled</name>
<value>true</value>
</property>
<property>
<name>dfs.nameservices</name>
<value>myhadoop</value>
</property>
<property>
<name>dfs.ha.namenodes.myhadoop</name>
<value>nn1,nn2</value>
</property>
<property>
<name>dfs.namenode.rpc-address.myhadoop.nn1</name>
<value>wxzz-pc:8020</value>
</property>
<property>
<name>dfs.namenode.http-address.myhadoop.nn1</name>
<value>wxzz-pc:50070</value>
</property>
<property>
<name>dfs.namenode.rpc-address.myhadoop.nn2</name>
<value>wxzz-pc0:8020</value>
</property>
<property>
<name>dfs.namenode.http-address.myhadoop.nn2</name>
<value>wxzz-pc0:50070</value>
</property>
<property>
<name>dfs.namenode.servicerpc-address.myhadoop.nn1</name>
<value>wxzz-pc:53310</value>
</property>
<property>
<name>dfs.namenode.servicerpc-address.cluster1.nn2</name>
<value>wxzz-pc0:53310</value>
</property>
<property>
<name>dfs.ha.automatic-failover.enabled.cluster1</name>
<value>true</value>
</property>
<property>
<name>dfs.namenode.shared.edits.dir</name>
<value>qjournal://wxzz-pc:8485;wxzz-pc0:8485;wxzz-pc1:8485/myhadoop</value>
</property>
<property>
<name>dfs.client.failover.proxy.provider.myhadoop</name> <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
</property>
<property>
<name>dfs.journalnode.edits.dir</name>
<value>/opt/hadoop-2.7.2/journal</value>
</property>
<property>
<name>dfs.ha.fencing.methods</name>
<value>sshfence</value>
</property>
<property>
<name>dfs.ha.fencing.ssh.private-key-files</name>
<value>/opt/hadoop-2.7.2/.ssh/id_rsa</value>
</property>
<property>
<name>dfs.ha.fencing.ssh.connect-timeout</name>
<value>1000</value>
</property>
<property>
<name>dfs.namenode.handler.count</name>
<value>10</value>
</property>
<property>
<name>dfs.ha.automatic-failover.enabled.myhadoop</name>
<value>true</value>
</property>
</configuration>
3. mapred-site.xml 配置
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.jobhistory.address</name>
<value>0.0.0.0:10020</value>
</property>
<property>
<name>mapreduce.jobhistory.webapp.address</name>
<value>0.0.0.0:19888</value>
</property>
</configuration>
4．yarn-site.xml 配置
<configuration>
<property>
<name>yarn.resourcemanager.ha.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.cluster-id</name>
<value>rm-id</value>
</property>
<property>
<name>yarn.resourcemanager.ha.rm-ids</name>
<value>rm1,rm2</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm1</name>
<value>wxzz-pc</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm2</name>
<value>wxzz-pc0</value>
</property>
<property>
<name>yarn.resourcemanager.zk-address</name>
<value>wxzz-pc:2181,wxzz-pc0:2181,wxzz-pc1:2181</value>
</property>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>
六．服務啟動
1.在各個Journal Node節點上，輸入以下命令啟動Journal Node
sbin/hadoop-daemon.sh start journalnode
2.在[nn1]上，進行格式化，並啟動
bin/hdfs namenode -format
sbin/hadoop-daemon.sh start namenode
3.在[nn2]上，同步[nn1]的元資料資訊，並啟動
bin/hdfs namenode -bootstrapStandby
sbin/hadoop-daemon.sh start namenode
經過以上3步，[nn1]和[nn2]均處在standby狀態
4.[nn1]節點上，將其轉換為active狀態
bin/hdfs haadmin –transitionToActive --forcemanual nn1
5.在[nn1]上，啟動所有datanode
sbin/hadoop-daemons.sh start datanode
6.在[nn1]上，啟動yarn
sbin/start-yarn.sh
如果要關閉叢集，在[nn1]上輸入sbin/stop-all.sh即可。以後每次啟動的時候，需要按照上面的步驟啟動，不過不需要執行2 的格式化操作。
七．執行效果
管理介面：
命令列效果：
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