CentOS 搭建八节点高可用 Hadoop HBase Kafka Flink Spark Zookeeper 集群


版本选择

组件版本
JDK1.8.0_261
Zookeeper3.5.8
Hadoop3.2.1
HBase2.2.5
Spark3.0.1-hadoop_3.2
Kafka2.6.0-scala_2.13
Flink1.11.2-scala_2.12
flink-shaded-hadoop-3-uber3.1.1.7.1.1.0-565-9.0

基础环境部署

服务器规划

共计 8 台分布式存储服务器

ZK 集群高可用需要奇数个节点,这里用 5 节点部署 ZK 高可用集群

hadoop 集群高可用集群使用 8 个节点

NameNode 一主(hadoop001)三备(hadoop002、hadoop003、hadoop004)
ResourceManager 一主(hadoop002)三备(hadoop003、hadoop004、hadoop005)
JournalNode 使用 7 个节点(hadoop001-007)
hadoop001: zookeeper、hadoop、主NameNode、DFSZKFailoverController、DataNode、JournalNode、NodeManager
hadoop002: zookeeper、hadoop、备NameNode、DFSZKFailoverController、主ResourceManager、DataNode、JournalNode、NodeManager
hadoop003: zookeeper、hadoop、备NameNode、备ResourceManager、DataNode、JournalNode、NodeManager
hadoop004: zookeeper、hadoop、备NameNode、备ResourceManager、DataNode、JournalNode、NodeManager
hadoop005: zookeeper、hadoop、备ResourceManager、DataNode、JournalNode、NodeManager
hadoop006: hadoop、DataNode、JournalNode、NodeManager
hadoop007: hadoop、DataNode、JournalNode、NodeManager
hadoop008: hadoop、DataNode、NodeManager

防火墙配置(all nodes)

# sed -i -e "s/SELINUX=enforcing/SELINUX=disabled/g" /etc/selinux/config
# systemctl stop firewalld
# systemctl disable firewalld
# setenforce 0

通用工具安装(all nodes)

# yum -y install wget tree vim unzip zlib zip net-tools lsof telnet dos2unix ntfs-3g pcre gcc-c++ openssl pcre-devel yum-utils

hosts 准备(all nodes)

所有集群节点

# echo '
192.168.2.11   hadoop001
192.168.2.12   hadoop002
192.168.2.13   hadoop003
192.168.2.14   hadoop004
192.168.2.15   hadoop005
192.168.2.16   hadoop006
192.168.2.17   hadoop007
192.168.2.18   hadoop008' > /etc/hosts

免密登录配置

第一个节点即主节点生成密钥(hadoop001)

# ssh-keygen -t rsa

将 hadoop001 的公钥写到本机和远程机器的 ~/ .ssh/authorized_key 文件中(hadoop001)

# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop001
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop002
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop003
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop004
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop005
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop006
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop007
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop008

验证免密登录(hadoop001)

# ssh hadoop001
# ssh hadoop002
# ssh hadoop003
# ssh hadoop004
# ssh hadoop005
# ssh hadoop006
# ssh hadoop007
# ssh hadoop008

yum 优化(hadoop001)

# cd /etc/yum.repos.d
# mkdir bak && mv *.repo bak/
# curl -o /etc/yum.repos.d/CentOS-Base.repo https://mirrors.aliyun.com/repo/Centos-7.repo
# curl -o /etc/yum.repos.d/epel.repo http://mirrors.aliyun.com/repo/epel-7.repo

分发

# scp -rf /etc/yum.repos.d [email protected]:/etc/
# scp -r /etc/yum.repos.d [email protected]:/etc/
# scp -r /etc/yum.repos.d [email protected]:/etc/
# scp -r /etc/yum.repos.d [email protected]:/etc/
# scp -r /etc/yum.repos.d [email protected]:/etc/
# scp -r /etc/yum.repos.d [email protected]:/etc/
# scp -r /etc/yum.repos.d [email protected]:/etc/

时间同步

ntpdata(all nodes)

# yum -y install ntpdate
# ntpdate ntp1.aliyun.com

Java 运行环境部署(hadoop001)

解压安装(hadoop001)

# mkdir /usr/java
# tar zxvf jdk-8u261-linux-x64.tar.gz -C /usr/java
# echo '# Java Env
export JAVA_HOME=/usr/java/jdk1.8.0_261
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools/jar
export PATH=$PATH:$JAVA_HOME/bin' >> /etc/profile
# source /etc/profile

分发至其它节点

# scp -r /usr/java [email protected]:/usr/
# scp -r /usr/java [email protected]:/usr/
# scp -r /usr/java [email protected]:/usr/
# scp -r /usr/java [email protected]:/usr/
# scp -r /usr/java [email protected]:/usr/
# scp -r /usr/java [email protected]:/usr/
# scp -r /usr/java [email protected]:/usr/

# scp /etc/profile [email protected]:/etc/profile
# scp /etc/profile [email protected]:/etc/profile
# scp /etc/profile [email protected]:/etc/profile
# scp /etc/profile [email protected]:/etc/profile
# scp /etc/profile [email protected]:/etc/profile
# scp /etc/profile [email protected]:/etc/profile
# scp /etc/profile [email protected]:/etc/profile

Zookeeper 高可用集群搭建

下载安装 (/usr/local) (hadoop001)

# cd /usr/local
# wget https://mirrors.ustc.edu.cn/apache/zookeeper/stable/apache-zookeeper-3.5.8-bin.tar.gz
# tar zxvf apache-zookeeper-3.5.8-bin.tar.gz
# mv apache-zookeeper-3.5.8-bin zookeeper

主节点修改配置(hadoop001)

# cp /usr/local/zookeeper/conf/zoo_sample.cfg /usr/local/zookeeper/conf/zoo.cfg

/usr/local/zookeeper/conf/zoo.cfg

maxClientCnxns=0
tickTime=2000
initLimit=10
syncLimit=5
dataDir=/usr/local/zookeeper/data
dataLogDir=/usr/local/zookeeper/log
clientPort=2181

# server.1 这个1是服务器的标识,可以是任意有效数字,标识这是第几个服务器节点,这个标识要写到dataDir目录下面myid文件里
# 指名集群间通讯端口和选举端口
server.1=hadoop001:2888:3888
server.2=hadoop002:2888:3888
server.3=hadoop003:2888:3888
server.4=hadoop004:2888:3888
server.5=hadoop005:2888:3888

创建数据存储目录和日志目录

# mkdir -p /usr/local/zookeeper/{data,log}

分发至其它节点

# scp -r /usr/local/zookeeper [email protected]:/usr/local/
# scp -r /usr/local/zookeeper [email protected]:/usr/local/
# scp -r /usr/local/zookeeper [email protected]:/usr/local/
# scp -r /usr/local/zookeeper [email protected]:/usr/local/

写入节点标识(所有 ZK 节点执行)

## hadoop001 主机执行
# echo "1" > /usr/local/zookeeper/data/myid
## hadoop002 主机执行
# echo "2" > /usr/local/zookeeper/data/myid
## hadoop003 主机执行
# echo "3" > /usr/local/zookeeper/data/myid
## hadoop004 主机执行
# echo "4" > /usr/local/zookeeper/data/myid
## hadoop005 主机执行
# echo "5" > /usr/local/zookeeper/data/myid

配置环境变量

所有 ZK 节点均需配置

# echo '
export ZOOKEEPER_HOME=/usr/local/zookeeper
export PATH=$PATH:$ZOOKEEPER_HOME/bin' >> /etc/profile
source /etc/profile

启动集群

分别在 5 台主机上,执行如下命令启动服务:

# zkServer.sh start

各个 ZK 节点使用如下命令查看状态

[[email protected] ~]# zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/zookeeper/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost.
Mode: follower
[[email protected] ~]# 
[[email protected] ~]# zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/zookeeper/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost.
Mode: follower
[[email protected] ~]# 
[[email protected] ~]# zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/zookeeper/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost.
Mode: leader
[[email protected] ~]# 
[[email protected] ~]# zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/zookeeper/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost.
Mode: follower
[[email protected] ~]# 
[[email protected] ~]# zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/zookeeper/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost.
Mode: follower
[[email protected] ~]# 

Hadoop 高可用集群搭建

Hadoop 高可用 (High Availability) 分为 HDFS 高可用和 YARN 高可用,两者的实现基本类似,但 HDFS NameNode 对数据存储及其一致性的要求比 YARN ResourceManger 高得多,所以它的实现也更加复杂。

HDFS 高可用架构主要由以下组件所构成:

Active NameNodeStandby NameNode:两台 NameNode 形成互备,一台处于 Active 状态,为主 NameNode,另外一台处于 Standby 状态,为备 NameNode,只有主 NameNode 才能对外提供读写服务。

主备切换控制器 ZKFailoverController:ZKFailoverController 作为独立的进程运行,对 NameNode 的主备切换进行总体控制。ZKFailoverController 能及时检测到 NameNode 的健康状况,在主 NameNode 故障时借助 Zookeeper 实现自动的主备选举和切换,当然 NameNode 目前也支持不依赖于 Zookeeper 的手动主备切换。

Zookeeper 集群:为主备切换控制器提供主备选举支持。

共享存储系统:共享存储系统是实现 NameNode 的高可用最为关键的部分,共享存储系统保存了 NameNode 在运行过程中所产生的 HDFS 的元数据。主 NameNode 和 NameNode 通过共享存储系统实现元数据同步。在进行主备切换的时候,新的主 NameNode 在确认元数据完全同步之后才能继续对外提供服务。

DataNode 节点:除了通过共享存储系统共享 HDFS 的元数据信息之外,主 NameNode 和备 NameNode 还需要共享 HDFS 的数据块和 DataNode 之间的映射关系。DataNode 会同时向主 NameNode 和备 NameNode 上报数据块的位置信息。

Hadoop 主节点免密登录配置

NameNode 主节点生成密钥(hadoop002)

# ssh-keygen -t rsa

将 hadoop002 的公钥写到本机和远程机器的 ~/ .ssh/authorized_key 文件中(hadoop002)

# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop001
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop002
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop003
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop004
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop005
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop006
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop007
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop008

验证免密登录(hadoop002)

# ssh hadoop001
# ssh hadoop002
# ssh hadoop003
# ssh hadoop004
# ssh hadoop005
# ssh hadoop006
# ssh hadoop007
# ssh hadoop008

解压安装(hadoop001)

# tar zxvf hadoop-3.2.1.tar.gz -C /usr/local
# cd /usr/local && mv hadoop-3.2.1 hadoop

配置环境变量(所有 hadoop 节点)

# echo '
export HADOOP_HOME=/usr/local/hadoop
export PATH=$PATH:$HADOOP_HOME/sbin:$HADOOP_HOME/bin' >> /etc/profile

配置 Hadoop (hadoop001)

${HADOOP_HOME}/etc/hadoop/hadoop-env.sh

export JAVA_HOME=/usr/java/jdk1.8.0_261
export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_ZKFC_USER=root
export HDFS_JOURNALNODE_USER=root

${HADOOP_HOME}/etc/hadoop/yarn-env.sh

export YARN_RESOURCEMANAGER_USER=root
export HADOOP_SECURE_DN_USER=yarn
export YARN_NODEMANAGER_USER=root

${HADOOP_HOME}/etc/hadoop/core-site.xml

<configuration>
    <property>
        <!-- 指定 namenode 的 hdfs 协议文件系统的通信地址 -->
        <name>fs.defaultFS</name>
        <value>hdfs://hdfscluster:8020</value>
    </property>
    <property>
        <!-- 指定 hadoop 集群存储临时文件的目录 -->
        <name>hadoop.tmp.dir</name>
        <value>/home/hadoop/tmp</value>
    </property>
    <!-- 用户角色配置,不配置此项会导致 web 页面报错 -->
    <property>
        <name>hadoop.http.staticuser.user</name>
        <value>root</value>
    </property>
    <property>
        <!-- ZooKeeper 集群的地址 -->
        <name>ha.zookeeper.quorum</name>
        <value>hadoop001:2181,hadoop002:2181,hadoop003:2181,hadoop004:2181,hadoop005:2181</value>
    </property>
</configuration>

创建 hadoop 集群存储临时文件的目录

# mkdir -p /home/hadoop/tmp

${HADOOP_HOME}/etc/hadoop/hdfs-site.xml

<configuration>
    <property>
        <!-- 指定 HDFS 副本的数量 -->
        <name>dfs.replication</name>
        <value>3</value>
    </property>
    <property>
        <!-- namenode 节点数据(即元数据)的存放位置,可以指定多个目录实现容错,多个目录用逗号分隔 -->
        <name>dfs.namenode.name.dir</name>
        <value>/home/hadoop/namenode/data</value>
    </property>
    <property>
        <!-- datanode 节点数据(即数据块)的存放位置 -->
        <name>dfs.datanode.data.dir</name>
        <value>/home/hadoop/datanode/data</value>
    </property>
    <property>
        <!-- 集群服务的逻辑名称 -->
        <name>dfs.nameservices</name>
        <value>hdfscluster</value>
    </property>
    <property>
        <!-- NameNode ID 列表-->
        <name>dfs.ha.namenodes.hdfscluster</name>
        <value>nn1,nn2,nn3,nn4</value>
    </property>
    <property>
        <!-- nn1 的 RPC 通信地址 -->
        <name>dfs.namenode.rpc-address.hdfscluster.nn1</name>
        <value>hadoop001:8020</value>
    </property>
    <property>
        <!-- nn2 的 RPC 通信地址 -->
        <name>dfs.namenode.rpc-address.hdfscluster.nn2</name>
        <value>hadoop002:8020</value>
    </property>
    <property>
        <!-- nn3 的 RPC 通信地址 -->
        <name>dfs.namenode.rpc-address.hdfscluster.nn3</name>
        <value>hadoop003:8020</value>
    </property>
    <property>
        <!-- nn4 的 RPC 通信地址 -->
        <name>dfs.namenode.rpc-address.hdfscluster.nn4</name>
        <value>hadoop004:8020</value>
    </property>
    <property>
        <!-- nn1 的 http 通信地址 -->
        <name>dfs.namenode.http-address.hdfscluster.nn1</name>
        <value>hadoop001:9870</value>
    </property>
    <property>
        <!-- nn2 的 http 通信地址 -->
        <name>dfs.namenode.http-address.hdfscluster.nn2</name>
        <value>hadoop002:9870</value>
    </property>
    <property>
        <!-- nn3 的 http 通信地址 -->
        <name>dfs.namenode.http-address.hdfscluster.nn3</name>
        <value>hadoop003:9870</value>
    </property>
    <property>
        <!-- nn4 的 http 通信地址 -->
        <name>dfs.namenode.http-address.hdfscluster.nn4</name>
        <value>hadoop004:9870</value>
    </property>
    <property>
        <!-- NameNode 元数据在 JournalNode 上的共享存储目录 -->
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://hadoop001:8485;hadoop002:8485;hadoop003:8485;hadoop004:8485;hadoop005:8485;hadoop006:8485;hadoop007:8485/hdfscluster</value>
    </property>
    <property>
        <!-- Journal Edit Files 的存储目录 -->
        <name>dfs.journalnode.edits.dir</name>
        <value>/home/hadoop/journalnode/data</value>
    </property>
    <property>
        <!-- 配置隔离机制,确保在任何给定时间只有一个 NameNode 处于活动状态 -->
        <name>dfs.ha.fencing.methods</name>
        <value>sshfence</value>
    </property>
    <property>
        <!-- 使用 sshfence 机制时需要 ssh 免密登录 -->
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/root/.ssh/id_rsa</value>
    </property>
    <property>
        <!-- 访问代理类,用于确定当前处于 Active 状态的 NameNode -->
        <name>dfs.client.failover.proxy.provider.hdfscluster</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property>
    <property>
        <!-- 开启故障自动转移 -->
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
</configuration>

创建并分发 Journal Edit Files 的存储目录、namenode 节点数据(即元数据)的存放位置、datanode 节点数据(即数据块)的存放位置

# mkdir -p /home/hadoop/journalnode/data
# mkdir -p /home/hadoop/namenode/data
# mkdir -p /home/hadoop/datanode/data
#
# scp -r /home/hadoop [email protected]
# scp -r /home/hadoop [email protected]
# scp -r /home/hadoop [email protected]
# scp -r /home/hadoop [email protected]
# scp -r /home/hadoop [email protected]
# scp -r /home/hadoop [email protected]
# scp -r /home/hadoop [email protected]

${HADOOP_HOME}/etc/hadoop/yarn-site.xml

<configuration>
    <property>
        <!--配置 NodeManager 上运行的附属服务。需要配置成 mapreduce_shuffle 后才可以在 Yarn 上运行 MapReduce 程序。-->
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <!-- 是否启用日志聚合 (可选) -->
        <name>yarn.log-aggregation-enable</name>
        <value>true</value>
    </property>
    <property>
        <!-- 聚合日志的保存时间 (可选) -->
        <name>yarn.log-aggregation.retain-seconds</name>
        <value>86400</value>
    </property>
    <property>
        <!-- 启用 RM HA -->
        <name>yarn.resourcemanager.ha.enabled</name>
        <value>true</value>
    </property>
    <property>
        <!-- RM 集群标识 -->
        <name>yarn.resourcemanager.cluster-id</name>
        <value>yarncluster</value>
    </property>
    <property>
        <!-- RM 的逻辑 ID 列表 -->
        <name>yarn.resourcemanager.ha.rm-ids</name>
        <value>rm1,rm2,rm3,rm4</value>
    </property>
    <property>
        <!-- RM1 的服务地址 -->
        <name>yarn.resourcemanager.hostname.rm1</name>
        <value>hadoop002</value>
    </property>
    <property>
        <!-- RM2 的服务地址 -->
        <name>yarn.resourcemanager.hostname.rm2</name>
        <value>hadoop003</value>
    </property>
    <property>
        <!-- RM3 的服务地址 -->
        <name>yarn.resourcemanager.hostname.rm3</name>
        <value>hadoop004</value>
    </property>
    <property>
        <!-- RM4 的服务地址 -->
        <name>yarn.resourcemanager.hostname.rm4</name>
        <value>hadoop005</value>
    </property>
    <property>
        <!-- RM1 Web 应用程序的地址 -->
        <name>yarn.resourcemanager.webapp.address.rm1</name>
        <value>hadoop002:8088</value>
    </property>
    <property>
        <!-- RM2 Web 应用程序的地址 -->
        <name>yarn.resourcemanager.webapp.address.rm2</name>
        <value>hadoop003:8088</value>
    </property>
    <property>
        <!-- RM3 Web 应用程序的地址 -->
        <name>yarn.resourcemanager.webapp.address.rm3</name>
        <value>hadoop004:8088</value>
    </property>
    <property>
        <!-- RM4 Web 应用程序的地址 -->
        <name>yarn.resourcemanager.webapp.address.rm4</name>
        <value>hadoop005:8088</value>
    </property>
    <property>
        <!-- ZooKeeper 集群的地址 -->
        <name>yarn.resourcemanager.zk-address</name>
        <value>hadoop001:2181,hadoop002:2181,hadoop003:2181,hadoop004:2181,hadoop005:2181</value>
    </property>
    <property>
        <!-- 启用自动恢复 -->
        <name>yarn.resourcemanager.recovery.enabled</name>
        <value>true</value>
    </property>
    <property>
        <!-- 用于进行持久化存储的类 -->
        <name>yarn.resourcemanager.store.class</name>
        <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
    </property>
</configuration>

${HADOOP_HOME}/etc/hadoop/mapred-site.xml

<configuration>
    <property>
        <!--指定 mapreduce 作业运行在 yarn 上-->
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>

${HADOOP_HOME}/etc/hadoop/workers

hadoop001
hadoop002
hadoop003
hadoop004
hadoop005
hadoop006
hadoop007
hadoop008

分发程序

# scp -r /usr/local/hadoop hadoop002:/usr/local/
# scp -r /usr/local/hadoop hadoop003:/usr/local/
# scp -r /usr/local/hadoop hadoop004:/usr/local/
# scp -r /usr/local/hadoop hadoop005:/usr/local/
# scp -r /usr/local/hadoop hadoop006:/usr/local/
# scp -r /usr/local/hadoop hadoop007:/usr/local/
# scp -r /usr/local/hadoop hadoop008:/usr/local/

启动集群

启动 JournalNode (所有 JournalNode)

# hdfs --daemon start journalnode

初始化 NameNode (hadoop001,主 NameNode)

# hdfs namenode -format

启动初始化后的 NameNode (hadoop001,主 NameNode)

# hdfs --daemon start namenode

同步 NameNode 信息 (其它 NameNode 节点,hadoop002、hadoop003、hadoop004)

# hdfs namenode -bootstrapStandby

初始化 HA 状态(任意一台 NameNode,hadoop001)

# hdfs zkfc -formatZK

启动 HDFS(hadoop001,执行后所有节点的 NameNode 和 DataNode 都会启动)

# start-dfs.sh

如果 DataNode 服务没有启动,一般是 clusterID 和 NameNode 不匹配的原因,修复方法是在 $HADOOP_HOME/logs/hadoop-root-datanode-hadoop001.log 的错误提示中找到 NameNode 的 clusterID 替换 /home/hadoop/datanode/data/current/VERSION 中的 clusterID 字段,然后分发此文件重启 dfs 即可。

启动 YARN 集群(hadoop002,执行后所有节点的 ResourceManager 和 NodeManager 都会启动,若有的节点 ResourceManager 服务没有启动的话,需要使用 yarn-daemon.sh start resourcemanager 手动启动 )

# start-yarn.sh

HBase 高可用集群搭建

HBase 高可用集群规划

hadoop001: Region Server
hadoop002: Region Server
hadoop003: Region Server
hadoop004: Region Server
hadoop005: Region Server
hadoop006: Region Server、备Master
hadoop007: Region Server、备Master
hadoop008: Region Server、主Master

HBase 主节点免密登录配置

HMaster 主节点生成密钥(hadoop008)

# ssh-keygen -t rsa

将 hadoop008 的公钥写到本机和远程机器的 ~/ .ssh/authorized_key 文件中(hadoop008)

# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop001
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop002
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop003
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop004
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop005
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop006
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop007
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop008

验证免密登录(hadoop008)

# ssh hadoop001
# ssh hadoop002
# ssh hadoop003
# ssh hadoop004
# ssh hadoop005
# ssh hadoop006
# ssh hadoop007
# ssh hadoop008

解压安装(hadoop008)

# tar zxvf hbase-2.2.5-bin.tar.gz -C /usr/local/
# cd /usr/local
# mv hbase-2.2.5 hbase

配置环境变量(所有 HBase 节点)

# echo '
export HBASE_HOME=/usr/local/hbase
export PATH=$PATH:$HBASE_HOME/bin' >> /etc/profile
# source /etc/profile

配置 HBase (hadoop008)

$HBASE_HOME/conf/hbase-env.sh

export JAVA_HOME=/usr/java/jdk1.8.0_261
export HBASE_MANAGES_ZK=false
export HBASE_CLASSPATH=/usr/local/hadoop/etc/hadoop

$HBASE_HOME/conf/hbase-site.xml

<configuration>
  <!--<property>-->
    <!-- 配置hbase的主节点 -->
  <!--  <name>hbase.master</name>-->
  <!--  <value>hadoop008:60000</value>-->
  <!--</property>-->
  <property>
    <!-- 是否为集群模式:true -->
    <name>hbase.cluster.distributed</name>
    <value>true</value>
  </property>
  <property>
    <name>hbase.tmp.dir</name>
    <value>/usr/local/hbase/tmp</value>
  </property>
  <property>
    <name>hbase.unsafe.stream.capability.enforce</name>
    <value>false</value>
  </property>
  <property>
    <!-- 指定 zookeeper 的地址-->
    <name>hbase.zookeeper.quorum</name>
    <value>hadoop001:2181,hadoop002:2181,hadoop003:2181,hadoop004:2181,hadoop005:2181</value>
  </property>
  <property>
    <!-- 指定 hbase 在 HDFS 上的存储位置 -->
    <name>hbase.rootdir</name>
    <value>hdfs://hdfscluster:8020/hbase</value>
  </property>
  <property>
    <!-- 指定 hbase 主节点的端口 -->
    <name>hbase.master.port</name>
    <value>16000</value>
  </property>
  <property>
    <!-- 指定 hbase region server 的端口 -->
    <name>hbase.regionserver.port</name>
    <value>16020</value>
  </property>
  <property>
    <!-- 指定 hbase region server 的 web 端口 -->
    <name>hbase.regionserver.info.port</name>
    <value>16030</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.dataDir</name>
    <value>/usr/local/zookeeper/hbase/data</value>
  </property>
</configuration>

创建 HBase 临时文件存储目录及 ZK 数据存储目录

# mkdir -p /usr/local/hbase/tmp
# mkdir -p /usr/local/zookeeper/hbase/data

regionservers

hadoop001
hadoop002
hadoop003
hadoop004
hadoop005
hadoop006
hadoop007
hadoop008

backup-masters

# touch /usr/local/hbase/conf/backup-masters
# echo 'hadoop006
hadoop007' > /usr/local/hbase/conf/backup-masters

分发 HBase (hadoop008)

# scp -r /usr/local/hbase hadoop001:/usr/local/
# scp -r /usr/local/hbase hadoop002:/usr/local/
# scp -r /usr/local/hbase hadoop003:/usr/local/
# scp -r /usr/local/hbase hadoop004:/usr/local/
# scp -r /usr/local/hbase hadoop005:/usr/local/
# scp -r /usr/local/hbase hadoop006:/usr/local/
# scp -r /usr/local/hbase hadoop007:/usr/local/

# scp -r /usr/local/zookeeper/hbase hadoop001:/usr/local/zookeeper/
# scp -r /usr/local/zookeeper/hbase hadoop002:/usr/local/zookeeper/
# scp -r /usr/local/zookeeper/hbase hadoop003:/usr/local/zookeeper/
# scp -r /usr/local/zookeeper/hbase hadoop004:/usr/local/zookeeper/
# scp -r /usr/local/zookeeper/hbase hadoop005:/usr/local/zookeeper/
# scp -r /usr/local/zookeeper/hbase hadoop006:/usr/local/zookeeper/
# scp -r /usr/local/zookeeper/hbase hadoop007:/usr/local/zookeeper/

启动 HBase 集群 (主节点HMaster – hadoop008)

主节点执行后会自动启动所有 RegionServer 及 所有HMaster 服务

# start-hbase.sh

Kafka 高可用集群部署

Kafka 高可用集群规划

hadoop001: zookeeper、kafka、kafka-manager
hadoop002: zookeeper、kafka
hadoop003: zookeeper、kafka
hadoop004: zookeeper、kafka
hadoop005: zookeeper、kafka
hadoop006: 
hadoop007: 
hadoop008: 

Kafka 主节点免密登录配置

Kafka 主节点生成密钥(hadoop001)

# ssh-keygen -t rsa

将 hadoop001 的公钥写到本机和远程机器的 ~/ .ssh/authorized_key 文件中(hadoop001)

# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop001
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop002
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop003
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop004
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop005
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop006
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop007
# ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop008

验证免密登录(hadoop001)

# ssh hadoop001
# ssh hadoop002
# ssh hadoop003
# ssh hadoop004
# ssh hadoop005
# ssh hadoop006
# ssh hadoop007
# ssh hadoop008

解压安装(hadoop001)

# tar zxvf kafka_2.13-2.6.0.tgz -C /usr/local/
# cd /usr/local
# mv kafka_2.13-2.6.0 kafka

配置环境变量(所有 Kafka 节点)

# echo '
export KAFKA_HOME=/usr/local/kafka
export PATH=$KAFKA_HOME/bin:$PATH' >> /etc/profile
# source /etc/profile

配置 Kafka (hadoop001)

$KAFKA_HOME/conf/server.properties

# 对应 ZK 的 myid,集群中每个节点的唯一标识
broker.id=1
# 监听地址
listeners=PLAINTEXT://hadoop001:9092
advertised.listeners=PLAINTEXT://hadoop001:9092
# 数据的存储位置
log.dirs=/usr/local/kafka/logs
# Zookeeper连接地址
zookeeper.connect=hadoop001:2181,hadoop002:2181,hadoop003:2181,hadoop004:2181,hadoop005:2181

创建日志存储目录

# mkdir /usr/local/kafka/logs

分发 kafka 到各个节点(hadoop001)

# scp -r kafka hadoop002:/usr/local/
# scp -r kafka hadoop003:/usr/local/
# scp -r kafka hadoop004:/usr/local/
# scp -r kafka hadoop005:/usr/local/

修改分发节点的 Kafka 配置文件 – $KAFKA_HOME/conf/server.properties(hadoop002-005)

hadoop002

broker.id=2
listeners=PLAINTEXT://hadoop002:9092
advertised.listeners=PLAINTEXT://hadoop002:9092

hadoop003

broker.id=3
listeners=PLAINTEXT://hadoop003:9092
advertised.listeners=PLAINTEXT://hadoop003:9092

hadoop004

broker.id=4
listeners=PLAINTEXT://hadoop004:9092
advertised.listeners=PLAINTEXT://hadoop004:9092

hadoop005

broker.id=5
listeners=PLAINTEXT://hadoop005:9092
advertised.listeners=PLAINTEXT://hadoop005:9092

启动 Kafka 集群(所有 Kafka 节点)

# kafka-server-start.sh -daemon /usr/local/kafka/config/server.properties
hadoop001: zookeeper、主 JobManager
hadoop002: zookeeper、备 JobManager
hadoop003: zookeeper、备 JobManager
hadoop004: zookeeper、备 JobManager
hadoop005: zookeeper、TaskManager
hadoop006: TaskManager
hadoop007: TaskManager
hadoop008: TaskManager

解压安装(hadoop001)

# tar zxvf flink-1.11.2-bin-scala_2.12.tgz -C /usr/local/
# cd /usr/local
# mv flink-1.11.2 flink
# echo '
export FLINK_HOME=/usr/local/flink
export PATH=$FLINK_HOME/bin:$PATH' >> /etc/profile
# source /etc/profile

${FLINK_HOME}/conf/flink-conf.yaml

high-availability: zookeeper
high-availability.zookeeper.quorum: hadoop001:2181,hadoop002:2181,hadoop003:2181,hadoop004:2181,hadoop005:2181
high-availability.zookeeper.path.root: /flink
high-availability.cluster-id: /flinkcluster
high-availability.storageDir: hdfs:///flink/recovery
# high-availability.storageDir: hdfs://hdfscluster:8020/flink/recovery

${FLINK_HOME}/conf/masters

hadoop001:8081
hadoop002:8081
hadoop003:8081
hadoop004:8081

${FLINK_HOME}/conf/workers

hadoop005
hadoop006
hadoop007
hadoop008
# scp -r /usr/local/flink hadoop002:/usr/local/
# scp -r /usr/local/flink hadoop003:/usr/local/
# scp -r /usr/local/flink hadoop004:/usr/local/
# scp -r /usr/local/flink hadoop005:/usr/local/
# scp -r /usr/local/flink hadoop006:/usr/local/
# scp -r /usr/local/flink hadoop007:/usr/local/
# scp -r /usr/local/flink hadoop008:/usr/local/

启动 ZooKeeper 集群(所有 ZK 节点)

# zkServer.sh start

启动 Hadoop 集群(hadoop001)

## 启动 dfs 高可用
# start-dfs.sh
## 启动 yarn 高可用
# start-yarn.sh

启动 Flink 集群(hadoop001)

# /usr/local/flink/bin/start-cluster.sh

Flink 启动常见错误

Caused by: org.apache.flink.core.fs.UnsupportedFileSystemSchemeException: Hadoop is not in 
the classpath/dependencies.

是因为在 classpath 目录下找不到 Hadoop 的相关依赖,从下面地址下载对应 hadoop 版本的 flink-shaded-hadoop-3-uberjar 包,拷贝到所有 Flink${FLINK_HOME}/lib 目录中重启 Flink 即可,测试发现 3.2.1Hadoop 可以使用 3.1.1 的依赖包。

https://repository.cloudera.com/artifactory/libs-release-local/org/apache/flink/flink-shaded-hadoop-3-uber/

Spark 高可用集群部署

Spark 高可用集群规划

hadoop001: zookeeper、主spark master、spark worker
hadoop002: zookeeper、备spark master、spark worker
hadoop003: zookeeper、备spark master、spark worker
hadoop004: zookeeper、备spark master、spark worker
hadoop005: zookeeper、spark worker
hadoop006: spark worker
hadoop007: spark worker
hadoop008: spark worker

解压安装(hadoop001)

# tar zxvf spark-3.0.1-bin-hadoop3.2.tgz -C /usr/local/
# cd /usr/local
# mv spark-3.0.1-bin-hadoop3.2 spark

配置环境变量(所有 Spark 节点)

# echo '
export SPARK_HOME=/usr/local/spark
export PATH=$SPARK_HOME/bin:$SPARK_HOME/sbin:$PATH' >> /etc/profile
# source /etc/profile

配置 Spark (hadoop001)

${SPARK_HOME}/conf/spark-env.sh

JAVA_HOME=/usr/java/jdk1.8.0_261
HADOOP_CONF_DIR=/usr/local/hadoop/etc/hadoop
SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=hadoop001:2181,hadoop002:2181,hadoop003:2181,hadoop004:2181,hadoop005:2181 -Dspark.deploy.zookeeper.dir=/spark"

${SPARK_HOME}/conf/slaves – 配置所有 Worker 节点列表

# cp /usr/local/spark/conf/slaves.template /usr/local/spark/conf/slaves
# echo 'hadoop001
hadoop002
hadoop003
hadoop004
hadoop005
hadoop006
hadoop007
hadoop008' > /usr/local/spark/conf/slaves

分发 Spark

# scp -r /usr/local/spark hadoop002:/usr/local/
# scp -r /usr/local/spark hadoop003:/usr/local/
# scp -r /usr/local/spark hadoop004:/usr/local/
# scp -r /usr/local/spark hadoop005:/usr/local/
# scp -r /usr/local/spark hadoop006:/usr/local/
# scp -r /usr/local/spark hadoop007:/usr/local/
# scp -r /usr/local/spark hadoop008:/usr/local/

启动 Spark 集群

启动 ZooKeeper 集群(所有 ZK 节点)

# zkServer.sh start

启动 Hadoop 集群(hadoop001)

## 启动 dfs 高可用
# start-dfs.sh
## 启动 yarn 高可用
# start-yarn.sh

启动 Spark 集群

## 启动所有的 worker 和 主 master -- hadoop001
# /usr/local/spark/sbin/start-all.sh
## 分别到所有备 master 节点启动所有的备 master -- hadoop001-004
# /usr/local/spark/sbin/start-master.sh

验证集群高可用

使用 kill 命令杀死 hadoop001Master 进程,此时备用 Master 进程中会有一个成为 主 Master,再次启动 hadoop001Master 服务,其会作为 备 Master 存在。

集群启停顺序

启动

ZooKeeper -> Hadoop(DFS) -> Yarn -> HBase -> Spark Master -> Spark Worker -> Kafka -> Flink

## ZK 服务需要在每个 ZK 节点执行
# zkServer.sh start
## Hadoop 主节点(hadoop001)
# start-dfs.sh
## Yarn 主节点(hadoop002或hadoop001)
# start-yarn.sh
## HBase 主节点(hadoop008)
# start-hbase.sh
## Spark 主节点(hadoop001)
# /usr/local/spark/sbin/start-all.sh
## Spark Worker 节点(hadoop001-004)
# /usr/local/spark/sbin/start-master.sh
## Kafka 服务需要在每个 Kafka 节点执行
# kafka-server-start.sh -daemon /usr/local/kafka/config/server.properties
## Flink 主节点(hadoop001)
# /usr/local/flink/bin/start-cluster.sh

停止

Flink -> Kafka -> Spark Worker -> Spark Master -> HBase -> Yarn -> Hadoop(DFS) -> ZooKeeper

## Flink 主节点(hadoop001)
# /usr/local/flink/bin/stop-cluster.sh
## Kafka 服务需要在每个 Kafka 节点执行
# kafka-server-stop.sh
## Spark Worker 节点(hadoop001-004)
# /usr/local/spark/sbin/stop-master.sh
## Spark 主节点(hadoop001)
# /usr/local/spark/sbin/stop-all.sh
## HBase 主节点(hadoop008)
# stop-hbase.sh
## Yarn 主节点(hadoop002)
# stop-yarn.sh
## Hadoop 主节点(hadoop001)
# stop-dfs.sh
## ZK 服务需要在每个 ZK 节点执行
# zkServer.sh stop

Hadoop 生态圈常用组件 WebUI

服务端口
Hadoop8042
Hadoop HDFS NameNode9870
Hadoop HDFS DataNode9864
Yarn JournalNode8480
Yarn ResourceManager8088
HBase Master16010
HBase RegionServer16030
Spark Master8082
Spark Worker8083
Kafka(非 Web 端口)9092
Flink8081
ZooKeeper(非 Web 端口)2181

文章作者: 套陆
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