大数据集群安装系列2：Hadoop HA 模式安装

╰+哭是因爲堅強的太久メ 2022-05-24 12:57 185阅读 0赞

## 1,基本环境配置：依赖zookeeper， ##

请参看zookeeper 的安装：  
[大数据集群安装系列1：Zookeeper 的安装][1_Zookeeper]  
欢迎留言哦，欢迎提问。

## 2,hadoop HA 模式安装 ##

### 2.1，下载相应的hadoop 安装包，本文是2.7.2， 放到服务器目录下，解压，类似如下： ###

[root@master /opt/bigdata/component]# pwd
    /opt/bigdata/component
    [root@master /opt/bigdata/component]# ls
    azkaban.tar.gz  GsFaceLib.tar.gz  hadoop.tar.gz         hbase.tar.gz  kafka.tar.gz  spark.tgz            zookeeper
    elastic.tar.gz  hadoop            haproxy-1.7.9.tar.gz  hive.tar.gz   rocketmq.zip  zabbix-3.4.6.tar.gz  zookeeper.tar.gz
    [root@master /opt/bigdata/component]#

### 2.2 配置cores-site.xml：请根据具体提示修改 ###

<configuration>
    
        
        <property>
        <name>dfs.nameservices</name>
        <value>hacluster</value>
        </property>
    
        
        <property>
        <name>dfs.ha.namenodes.hacluster</name>
        <value>nn1,nn2</value>
        </property>
    
        
        <property>
        <name>dfs.namenode.rpc-address.hacluster.nn1</name>
        <value>master:9000</value>
        </property>
    
        
        <property>
        <name>dfs.namenode.http-address.hacluster.nn1</name>
            <value>master:50070</value>
        </property>
    
        
        <property>
               <name>dfs.namenode.rpc-address.hacluster.nn2</name>
        <value>workerI:9000</value>
        </property>
    
        
        <property>
        <name>dfs.namenode.http-address.hacluster.nn2</name>
        <value>workerI:50070</value>
        </property>
    
        
        <property>
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://workerII:8485;workerI:8485;master:8485;/hacluster</value>
        </property>
    
        
        <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/opt/bigdata/Hadoop/hadoop/dfs_journalnode_edits_dir</value>
        </property>
    
        
        <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
        </property>
    
        
        <property>
        <name>dfs.client.failover.proxy.provider.hacluster</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
        </property>
    
        
        <property>
        <name>dfs.ha.fencing.methods</name>
        <value>
            sshfence
            shell(/bin/true)
        </value>
        </property>
    
        
        <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/root/.ssh/id_rsa</value>
        </property>
    
        
        <property>
        <name>dfs.ha.fencing.ssh.connect-timeout</name>
        <value>30000</value>
        </property>
    
        
        
        
        <property>
            <name>dfs.replication</name>
            <value>3</value>
        </property>

### 2.4 配置yarn-site.xml ###

-->
    <configuration>
    
        
        
        <property>
            <name>yarn.resourcemanager.ha.enabled</name>
            <value>true</value>
        </property>
    
        
        <property>
            <name>yarn.resourcemanager.cluster-id</name>
            <value>yrc</value>
        </property>
    
        
        <property>
            <name>yarn.resourcemanager.ha.rm-ids</name>
            <value>rm1,rm2</value>
        </property>
    
        
        <property>
            <name>yarn.resourcemanager.hostname.rm1</name>
            <value>master</value>
        </property>
    
        <property>
            <name>yarn.resourcemanager.hostname.rm2</name>
            <value>workerI</value>
        </property>
    
        
        <property>
            <name>yarn.resourcemanager.zk-address</name>
            <value>workerII:2181,workerI:2181,master:2181,</value>
        </property>
    
        <property>
            <name>yarn.nodemanager.aux-services</name>
            <value>mapreduce_shuffle,spark_shuffle</value>
        </property>
    
        
    
          
        <property>
            <name>yarn.nodemanager.resource.memory-mb</name>
            <value>19000</value>
        </property>
    
    
        <property>
            <name>yarn.scheduler.minimum-allocation-mb</name>
            <value>8192</value>
        </property>
    
    
        <property>
            <name>yarn.scheduler.maximum-allocation-mb</name>
            <value>19000</value>
        </property>
    
    
        <property>
            <name>yarn.app.mapreduce.am.resource.mb</name>
            <value>8192</value>
        </property>
    
        <property>
            <name>yarn.app.mapreduce.am.command-opts</name>
            <value>-Xmx6553m</value>
        </property>
    
    
    
        <property>
            <name>yarn.resourcemanager.webapp.address.rm1</name>
            <value>master:8088</value>
        </property>
    
        <property>
            <name>yarn.resourcemanager.webapp.address.rm2</name>
            <value>workerI:8088</value>
        </property>
    
        
        <property>
            <name>yarn.nodemanager.aux-services.spark_shuffle.class</name>
        <value>org.apache.spark.network.yarn.YarnShuffleService</value>
        </property>
        <property>
        <name>spark.shuffle.service.port</name>
        <value>7337</value>
        </property>
    
    </configuration>

### 2.6 修改yarn-env.sh 和hadoop-env.sh 中java 的路径， 以及配置slaves 节点 ###

export JAVA\_HOME=/opt/bigdata/basic/jdk

[root@master /opt/bigdata/component/hadoop/etc/hadoop]# vim slaves 
    [root@master /opt/bigdata/component/hadoop/etc/hadoop]# cat slaves 
    master
    workerI
    workerII

### 2.7 分发文件： ###

scp -r hadoop workerI:`pwd`  
scp -r hadoop workerII:`pwd`

## 3，安装好后，接下来就是启动了 ##

### 3.1 在master 节点，首先格式化zk（此时请确保zookeeper 已经启动） ###

在$HADOOP\_INSTALL\_HOME/bin  
./hdfs zkfc -formatZK -force

### 3.2 启动zkfc(每个节点都需要这样做) ###

在$HADOOP\_INSTALL\_HOME/sbin  
./hadoop-daemon.sh start zkfc

### 3.3 启动journalnode（每个节点） ###

在$HADOOP\_INSTALL\_HOME/sbin  
./hadoop-daemon.sh start journalnode

### 3.4 格式化hdfs 文件系统 ###

在$HADOOP\_INSTALL\_HOME/bin  
./hadoop namenode -format -force

### 3.5 在master 节点，进行启动hdfs ###

在$HADOOP\_INSTALL\_HOME/sbin  
./start-dfs.sh

### 3.6 启动yarn ###

在$HADOOP\_INSTALL\_HOME/sbin  
./start-yarn.sh

### 3.7 在备用节点，启动备用namenode 和 备用resourcemanager ###

本文备用节点是workerI
     在HADOOP_INSTALL_HOME/sbin    
     yarn-daemon.sh start resourcemanager
     在$HADOOP_INSTALL_HOME/sbin  
     hdfs namenode -bootstrapStandby
     在$HADOOP_INSTALL_HOME/sbin   
     hadoop-daemon.sh start namenode

### 3.8 至此，所有hdfs 内容已经启动，具体进程如下： ###

[root@workerI ~]# jps
    1969 WrapperSimpleApp
    6961 Jps
    5682 JournalNode
    5956 DFSZKFailoverController
    5527 QuorumPeerMain
    5832 DataNode
    6057 NodeManager
    6203 ResourceManager
    [root@workerI ~]# 
    
    
    [root@master ~]# jps
    6560 DFSZKFailoverController
    6644 JournalNode
    7460 ResourceManager
    7112 DataNode
    7563 NodeManager
    8396 Jps
    2076 WrapperSimpleApp
    6957 NameNode
    6174 QuorumPeerMain
    
    
    [root@workerII ~]# jps
    4067 NodeManager
    3923 DataNode
    3848 JournalNode
    2200 WrapperSimpleApp
    4606 Jps
    3679 QuorumPeerMain
    (请不要在一Jps 和WrapperSimpleApp 这两个不需要关注 )

[1_Zookeeper]: https://blog.csdn.net/eases_stone/article/details/80472281