Centos 下 Apache 原生 Hbase + Phoen

2019-07-18 本文已影响0人 etrols

前置条件

各软件版本：hadoop-2.7.7、hbase-2.1.5 、jdk1.8.0_211、zookeeper-3.4.10、apache-phoenix-5.0.0-HBase-2.0-bin.tar.gz
至少 3 台 Centos 服务器，主机名分别为：hadoop0001、hadoop0002、hadoop0003
这里所有的软件将安装在 hadoop 用户的 /home/hadoop/app 目录下
在每台服务器设置 hosts

[hadoop@hadoop0001 ~]$ vim /etc/hosts

host 内容如下：

# 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
# ::1         localhost localhost.localdomain localhost6 localhost6.localdomain6
10.2.1.102  hadoop0001
10.2.1.103  hadoop0002
10.2.1.104  hadoop0003

ssh 免密登录（此步骤可以忽略，但 Hadoop 每次启动都需要输入密码）

在 hadoop0001 终端执行以下命令：

[hadoop@hadoop0001 ~]$ ssh-keygen -t rsa -P "" //一直回车即可
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> hadoop@hadoop0002:~/.ssh/authorized_keys
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> hadoop@hadoop0003:~/.ssh/authorized_keys

在 hadoop0002 终端执行以下命令：

[hadoop@hadoop0001 ~]$ ssh-keygen -t rsa -P "" //一直回车即可
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> hadoop@hadoop0001:~/.ssh/authorized_keys
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> hadoop@hadoop0003:~/.ssh/authorized_keys

在 hadoop0003 终端执行以下命令：

[hadoop@hadoop0001 ~]$ ssh-keygen -t rsa -P "" //一直回车即可
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> hadoop@hadoop0001:~/.ssh/authorized_keys
[hadoop@hadoop0001 ~]$ cat ~/.ssh/id_rsa.pub >> hadoop@hadoop0002:~/.ssh/authorized_keys

验证免密登录

[hadoop@hadoop0001 ~]$ ssh localhost
Last login: Fri Jan  4 13:45:54 2019 //出现这个结果表示免密登录成功

JDK 安装
JDK 版本：
Linux：jdk-8u192-linux-x64.tar.gz

JDK 环境变量配置：

# 用户家目录下
[hadoop@hadoop0001 ~]$ vim .bashrc

添加以下内容：

JAVA_HOME=/home/hadoop/app/jdk1.8.0_192
CLASSPATH=.:$JAVA_HOME/lib/tools.jar:$JAVA_HOME/lib/dt.jar 
PATH=$JAVA_HOME/bin:$HOME/bin:$HOME/.local/bin:$PATH

最后使环境变量生效：

# 用户家目录下
[hadoop@hadoop0001 ~]$ . .bashrc

JDK 验证：

java -version
java version "1.8.0_192"
Java(TM) SE Runtime Environment (build 1.8.0_192-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.192-b12, mixed mode) java -version

将 hadoop0001 的 JDK 复制到其他服务器上

[hadoop@hadoop0001 app]$ scp -r jdk1.8.0_192/ hadoop@hadoop0002:~/app/jdk1.8.0_192/
[hadoop@hadoop0001 app]$ scp -r jdk1.8.0_192/ hadoop@hadoop0003:~/app/jdk1.8.0_192/
[hadoop@hadoop0001 ~]$ scp /etc/profile hadoop@hadoop0002:/etc/profile
[hadoop@hadoop0001 ~]$ scp /etc/profile hadoop@hadoop0003:/etc/profile

NTP 服务搭建
每台服务器上安装 ntp

[hadoop@hadoop0001 ~]$ yum install -y ntp

hadoop0001 配置 ntp

[hadoop@hadoop0001 ~]$ vim /etc/ntp.conf

添加以下配置：

restrict 10.2.1.0 mask 255.255.255.0 nomodify notrap
logfile /var/log/ntpd.log
server ntp1.aliyun.com
server ntp2.aliyun.com
server ntp3.aliyun.com
server 127.0.0.1
fudge 127.0.0.1 stratum 10

完整配置文件(ntp.conf)：

# For more information about this file, see the man pages
# ntp.conf(5), ntp_acc(5), ntp_auth(5), ntp_clock(5), ntp_misc(5), ntp_mon(5).

driftfile /var/lib/ntp/drift

logfile /var/log/ntpd.log

# Permit time synchronization with our time source, but do not
# permit the source to query or modify the service on this system.
restrict default nomodify notrap nopeer noquery

# Permit all access over the loopback interface.  This could
# be tightened as well, but to do so would effect some of
# the administrative functions.
restrict 127.0.0.1
restrict ::1

# Hosts on local network are less restricted.
#restrict 192.168.1.0 mask 255.255.255.0 nomodify notrap
restrict 10.2.1.0 mask 255.255.255.0 nomodify notrap

# Use public servers from the pool.ntp.org project.
# Please consider joining the pool (http://www.pool.ntp.org/join.html).
#server 0.centos.pool.ntp.org iburst
#server 1.centos.pool.ntp.org iburst
#server 2.centos.pool.ntp.org iburst
#server 3.centos.pool.ntp.org iburst
server ntp1.aliyun.com
server ntp2.aliyun.com
server ntp3.aliyun.com

server 127.0.0.1
fudge 127.0.0.1 stratum 10

#broadcast 192.168.1.255 autokey        # broadcast server
#broadcastclient                        # broadcast client
#broadcast 224.0.1.1 autokey            # multicast server
#multicastclient 224.0.1.1              # multicast client
#manycastserver 239.255.254.254         # manycast server
#manycastclient 239.255.254.254 autokey # manycast client

# Enable public key cryptography.
#crypto

includefile /etc/ntp/crypto/pw

# Key file containing the keys and key identifiers used when operating
# with symmetric key cryptography. 
keys /etc/ntp/keys

# Specify the key identifiers which are trusted.
#trustedkey 4 8 42

# Specify the key identifier to use with the ntpdc utility.
#requestkey 8

# Specify the key identifier to use with the ntpq utility.
#controlkey 8

# Enable writing of statistics records.
#statistics clockstats cryptostats loopstats peerstats

# Disable the monitoring facility to prevent amplification attacks using ntpdc
# monlist command when default restrict does not include the noquery flag. See
# CVE-2013-5211 for more details.
# Note: Monitoring will not be disabled with the limited restriction flag.
disable monitor

时间服务器可参考：https://www.pool.ntp.org/zone/asia

时间同步：

[hadoop@hadoop0001 ~]$ sudo ntpdate -u ntp1.aliyun.com
16 Jul 16:46:39 ntpdate[12700]: adjust time server 120.25.115.20 offset -0.002546 sec

启动时间服务：

[hadoop@hadoop0001 ~]$ sudo systemctl start ntpd

时间服务开机自启：

[hadoop@hadoop0001 ~]$ sudo systemctl enable ntpd

在 hadoop0002 和 hadoop0003 配置 ntp 客户端
在 /etc/ntp.conf 配置如下代码

server hadoop0001

查看 ntp 是否同步
如下表示未同步

[root@hadoop0002 ~]# ntpstat 
unsynchronised
  time server re-starting
   polling server every 8 s

如下表示已同步

[root@hadoop0001 ~]# ntpstat
synchronised to NTP server (120.25.115.20) at stratum 3 
   time correct to within 976 ms
   polling server every 64 s

注意：同步需要 10 分钟左右

Hadoop 安装

下载 Hadoop

wget http://mirror.bit.edu.cn/apache/hadoop/common/hadoop-2.7.7/hadoop-2.7.7.tar.gz

解压 Hadoop

tar -zxvf hadoop-2.7.7.tar.gz

配置 hadoop-env.sh

# 根据实际业务需要配置
export HADOOP_HEAPSIZE=1024

配置 mapred-env.sh

export JAVA_HOME=${JAVA_HOME}

配置 yarn-env.sh

# 根据实际业务需要配置
JAVA_HEAP_MAX=-Xmx512m
YARN_HEAPSIZE=1024

配置 core-site.xml

<!-- hdfs 端口 -->
  <property>
    <name>fs.defaultFS</name>
    <value>hdfs://hadoop0001:8020</value>
  </property>
  <!-- hadoop 临时数据目录 -->
  <property>
    <name>hadoop.tmp.dir</name>
    <value>/home/hadoop/application/hadoop-2.7.7/data</value>
  </property>
  <property>
    <name>fs.trash.interval</name>
    <value>14400</value>
  </property>

配置 yarn-site.xml

<property>
    <name>yarn.resourcemanager.hostname</name>
    <value>hadoop0001</value>
    <discription>指定 YARN 的 ResourceManager 的地址</discription>
  </property>

  <property>
    <name>yarn.log-aggregation-enable</name>
    <value>true</value>
    <discription>日志聚集功能</discription>
  </property>

  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
    <discription>Reducer 获取数据方式</discription>
  </property>

  <property>
    <name>yarn.log-aggregation-enable</name>
    <value>true</value>
  </property>

  <property>
    <name>yarn.log-aggregation.retain-seconds</name>
    <value>604800</value>
    <discription>日志保留时间设置 7 天</discription>
  </property>

  <property>
    <name>yarn.nodemanager.pmem-check-enabled</name>
    <value>false</value>
  </property>

  <property>
    <name>yarn.nodemanager.vmem-check-enabled</name>
    <value>false</value>
  </property>

  <property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>15000</value>
    <discription>每个节点可用内存,单位MB</discription>
  </property>

  <property>
    <name>yarn.scheduler.minimum-allocation-mb</name>
    <value>100</value>
    <discription>单个任务可申请最少内存，默认1024MB</discription>
  </property>

  <property>
    <name>yarn.scheduler.maximum-allocation-mb</name>
    <value>15000</value>
    <discription>单个任务可申请最大内存，默认8192MB</discription>
  </property>

  <property>
    <name>yarn.nodemanager.resource.cpu-vcores</name>
    <value>2</value>
    <discription>NodeManager总的可用虚拟CPU个数</discription>
  </property>

  <property>
    <name>yarn.scheduler.minimum-allocation-vcores</name>
    <value>1</value>
    <discription>单个可申请的最小。比如设置为1，则运行MapRedce作业时，每个Task最少可申请1个虚拟CPU</discription>
  </property>

  <property>
    <name>yarn.scheduler.maximum-allocation-vcores</name>
    <value>4</value>
    <discription>单个可申请的最大虚拟CPU个数。比如设置为4，则运行MapRedce作业时,最多可申请4个虚拟CPU</discription>
  </property>

  <property>
    <name>yarn.resourcemanager.scheduler.class</name>
    <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
  </property>

  <property>
    <name>yarn.scheduler.fair.preemption</name>
    <value>true</value>
  </property>

  <property>
    <name>yarn.scheduler.fair.preemption.cluster-utilization-threshold</name>
    <value>0.8</value>
  </property>

配置 hdfs-site.xml

<!-- hdfs 数据副本数目  -->
  <property>
    <name>dfs.replication</name>
    <value>3</value>
  </property>

  <!-- hdfs 存储 fsimage 的地方
         <property>
    <name>dfs.namenode.name.dir</name>
    <value>file:/home/hadoop/application/hadoop-2.8.5/data/hdfs/name</value>
  </property>
  -->

  <!-- hdfs 数据存放 block 的地方
         <property>
    <name>dfs.datanode.data.dir</name>
    <value>file:/home/hadoop/application/hadoop-2.8.5/data/hdfs/data</value>
  </property>
  -->

  <property>
    <name>dfs.namenode.secondary.http-address</name>
    <value>hadoop0001:50090</value>
  </property>

  <property>
    <name>dfs.namenode.http-address</name>
    <value>hadoop0001:50070</value>
  </property>

  <property>
    <name>dfs.permissions.enabled</name>
    <value>false</value>
  </property>

配置 mapred-site.xml

<!-- 历史服务器端地址 -->
  <property>
    <name>mapreduce.jobhistory.address</name>
    <value>hadoop0001:10020</value>
  </property>
  <!-- 历史服务器 web 端地址 -->
  <property>
    <name>mapreduce.jobhistory.webapp.address</name>
    <value>hadoop0001:19888</value>
  </property>
  <!-- 指定 MR 运行在 Yarn 上 -->
  <property>
    <name>mapreduce.framework.name</name>
    <value>yarn</value>
  </property>

配置 slaves (/home/hadoop/app/hadoop-2.7.7)

hadoop0001
hadoop0002
hadoop0003

配置 Hadoop 环境变量

在用户家目录下的 .bashrc

# added by Hadoop installer
export HADOOP_HOME=/home/hadoop/app/hadoop-2.7.7
export HADOOP_INSTALL=$HADOOP_HOME
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export YARN_HOME=$HADOOP_HOME
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib:$HADOOP_COMMON_LIB_NATIVE_DIR"
export PATH=$PATH:$HADOOP_HOME/sbin:$HADOOP_HOME/bin
export CLASSPATH=$CLASSPATH:$HADOOP_HOME/lib

使环境生效：

. .bashrc

将配置好的 hadoop 发送到其他服务器

[hadoop@hadoop0001 app]$ scp -r /hadoop-2.7.7 hadoop@hadoop0002:~/app/hadoop-2.7.7
[hadoop@hadoop0001 app]$ scp -r /hadoop-2.7.7 hadoop@hadoop0003:~/app/hadoop-2.7.7
[hadoop@hadoop0001 ~]$ scp .bashrc hadoop@hadoop0002:~/
[hadoop@hadoop0001 ~]$ scp .bashrc hadoop@hadoop0003:~/

在主 master 初始化 namenode

hadoop namenode -format

启动 hadoop 集群

# mater 节点 出现 NameNode、SecondaryNameNode，其他机器上出现 DataNode 说明集群搭建成功
start-all.sh

停止集群

stop-all.sh

Zookeeper 分布式集群搭建

下载 Zookeeper

wget https://archive.apache.org/dist/zookeeper/zookeeper-3.4.10/zookeeper-3.4.10.tar.gz

解压 Zookeeper

tar -zxvf zookeeper-3.4.10.tar.gz

配置 zoo.cfg

cp zoo_sample.cfg zoo.cfg
vim zoo.cfg

配置内容如下：

# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial 
# synchronization phase can take
initLimit=20
# The number of ticks that can pass between 
# sending a request and getting an acknowledgement
syncLimit=10
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just 
# example sakes.
dataDir=/root/app/zookeeper-3.4.10/data
dataLogDir=/root/app/zookeeper-3.4.10/logs
# the port at which the clients will connect
clientPort=2181
# the maximum number of client connections.
# increase this if you need to handle more clients
#maxClientCnxns=60
#
# Be sure to read the maintenance section of the 
# administrator guide before turning on autopurge.
#
# http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1
server.1=hadoop0001:2888:3888
server.2=hadoop0002:2888:3888
server.3=hadoop0003:2888:3888

在 zookeeper 根目录下创建 data 和 logs 文件夹

mkdir data
mkdir logs

在 data 目录下创建 myid

vim myid

内容为：

配置 zookeeper 环境变量

在用户家目录下的 .bashrc

# added by zookeeper installer
export ZOOKEEPER_HOME=/home/hadoop/app/zookeeper-3.4.10
export CLASSPATH=$CLASSPATH:$ZOOKEEPER_HOME/lib
export PATH=$PATH:$ZOOKEEPER_HOME/bin

将配置好的 zookeeper 发送到其他机器上

[hadoop@hadoop0001 app]$ scp -r /zookeeper-3.4.10 hadoop@hadoop0002:~/app/zookeeper-3.4.10
[hadoop@hadoop0001 app]$ scp -r /zookeeper-3.4.10 hadoop@hadoop0003:~/app/zookeeper-3.4.10
[hadoop@hadoop0001 ~]$ scp .bashrc hadoop@hadoop0002:~/
[hadoop@hadoop0001 ~]$ scp .bashrc hadoop@hadoop0003:~/

修改其他机器的 myid

将其他节点的 myid 修改为 2、3，保证每台机器的 myid 在集群内唯一

启动 zookeeper 服务

每台机器执行：

zkServer.sh start

查看 zookeeper 状态

zkServer.sh status

Hbase HA 分布式集群搭建

下载 hbase

wget http://mirror.bit.edu.cn/apache/hbase/2.1.5/hbase-2.1.5-bin.tar.gz

解压 hbase

tar -zxvf hbase-2.1.5-bin.tar.gz

配置 hbase-site.xml

  <property>
    <name>hbase.rootdir</name>
    <value>hdfs://hadoop0001:8020/hbase</value>
  </property>

  <property>
    <name>hbase.cluster.distributed</name>
    <value>true</value>
  </property>

  <!-- 0.98 后的新变动，之前版本没有.port,默认端口为 60000 -->
  <property>
    <name>hbase.master.port</name>
    <value>16000</value>
  </property>

  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>hadoop0001,hadoop0002,hadoop0003</value>
  </property>

  <property>
    <name>hbase.regionserver.restart.on.zk.expire</name>
    <value>true</value>
  </property>

  <property>
    <name>hbase.coprocessor.abortonerror</name>
    <value>false</value>
  </property>

  <property>
    <name>hbase.zookeeper.property.dataDir</name>
    <value>/root/app/zookeeper-3.4.10/data</value>
  </property>

  <property>
    <name>hbase.unsafe.stream.capability.enforce</name>
    <value>false</value>
    <description>
        Controls whether HBase will check for stream capabilities (hflush/hsyn    c).

        Disable this if you intend to run on LocalFileSystem, denoted by a roo    tdir
        with the 'file://' scheme, but be mindful of the NOTE below.

        WARNING: Setting this to false blinds you to potential data loss and
        inconsistent system state in the event of process and/or node failures    . If
        HBase is complaining of an inability to use hsync or hflush it's most
        likely not a false positive.
    </description>
  </property>

配置 regionservers

在 hbase 根目录下的 conf 目录下的 regionservers 文件加入如下配置：

# 主机名即 host
hadoop0001
hadoop0002
hadoop0003

配置 hbase 环境变量

在用户家目录下的 .bashrc

# added by hbase installer
export HBASE_HOME=/root/app/hbase-2.1.5/
export CLASSPATH=$CLASSPATH:$HBASE_HOME/lib
export PATH=$PATH:$HBASE_HOME/bin

将配置好的 hbase 发送到其他机器

[hadoop@hadoop0001 app]$ scp -r /hbase-2.1.5 hadoop@hadoop0002:~/app/hbase-2.1.5
[hadoop@hadoop0001 app]$ scp -r /hbase-2.1.5 hadoop@hadoop0003:~/app/hbase-2.1.5
[hadoop@hadoop0001 ~]$ scp .bashrc hadoop@hadoop0002:~/
[hadoop@hadoop0001 ~]$ scp .bashrc hadoop@hadoop0003:~/

配置 backup-masters（备用 master 节点）

在 hbase 根目录下的 conf 目录下的 backup-masters文件加入如下配置：

# master 节点配置，可配置多个
hadoop0002

启动 hbse 集群

start-hbase.sh

注意：在主节点出现 HMaster、HRegionServer（有可能没有，属于正常）及备用节点出现 HMaster、HRegionServer；其他节点出现 HRegionServer；说明Hbase集群搭建成功；

停止 hbase 集群

stop-hbase.sh

Phoenix 集群安装

下载 Phoenix

wget http://mirror.bit.edu.cn/apache/phoenix/apache-phoenix-5.0.0-HBase-2.0/bin/apache-phoenix-5.0.0-HBase-2.0-bin.tar.gz

解压 Phoenix

tar -zxvf apache-phoenix-5.0.0-HBase-2.0-bin.tar.gz

复制以下 jar 包到所有节点的 Habse 根目录下的 lib 目录下

[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ cp phoenix-5.0.0-HBase-2.0-queryserver.jar ~/app/hbase-2.1.5/lib/
[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ scp phoenix-5.0.0-HBase-2.0-queryserver.jar hadoop@hadoop0002:~/app/hbase-2.1.5/lib/
[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ scp phoenix-5.0.0-HBase-2.0-queryserver.jar hadoop@hadoop0003:~/app/hbase-2.1.5/lib/

[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ cp phoenix-5.0.0-HBase-2.0-server.jar ~/app/hbase-2.1.5/lib/
[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ scp phoenix-5.0.0-HBase-2.0-server.jar hadoop@hadoop0002:~/app/hbase-2.1.5/lib/
[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ scp phoenix-5.0.0-HBase-2.0-server.jar hadoop@hadoop0003:~/app/hbase-2.1.5/lib/

[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ cp phoenix-core-5.0.0-HBase-2.0.jar ~/app/hbase-2.1.5/lib/
[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ scp phoenix-core-5.0.0-HBase-2.0.jar hadoop@hadoop0002:~/app/hbase-2.1.5/lib/
[hadoop@hadoop0001 apache-phoenix-5.0.0-HBase-2.0-bin]$ scp phoenix-core-5.0.0-HBase-2.0.jar hadoop@hadoop0003:~/app/hbase-2.1.5/lib/

配置 Phoenix 环境变量(无需复制到其他节点)

# added by phoenix installer
export PHOENIX_HOME=/root/app/apache-phoenix-5.0.0-HBase-2.0-bin
export CLASSPATH=$CLASSPATH:$PHOENIX_HOME
export PATH=$PATH:$PHOENIX_HOME/bin

启动 Phoenix queryserver 模式

queryserver.py start

停止 Phoenix queryserver 模式

queryserver.py stop

连接 Phoenix queryserver

sqlline-thin.py hadoop0001:8765

客户端 jdbc 连接（jdbcUrl）

jdbc:phoenix:thin:url=http://10.2.1.102:8765?doAs=alice

Centos 下 Apache 原生 Hbase + Phoen

前置条件

Hadoop 安装

配置 hadoop-env.sh

配置 mapred-env.sh

配置 yarn-env.sh

配置 core-site.xml

配置 yarn-site.xml

配置 mapred-site.xml

配置 slaves (/home/hadoop/app/hadoop-2.7.7)

配置 Hadoop 环境变量

将配置好的 hadoop 发送到其他服务器

Zookeeper 分布式集群搭建

下载 Zookeeper

解压 Zookeeper

配置 zoo.cfg

配置 zookeeper 环境变量

将配置好的 zookeeper 发送到其他机器上

修改其他机器的 myid

启动 zookeeper 服务

查看 zookeeper 状态

Hbase HA 分布式集群搭建

下载 hbase

解压 hbase

配置 hbase-site.xml

配置 regionservers

配置 hbase 环境变量

将配置好的 hbase 发送到其他机器

配置 backup-masters（备用 master 节点）

启动 hbse 集群

停止 hbase 集群

Phoenix 集群安装

下载 Phoenix

解压 Phoenix

复制以下 jar 包到所有节点的 Habse 根目录下的 lib 目录下

配置 Phoenix 环境变量(无需复制到其他节点)

启动 Phoenix queryserver 模式

停止 Phoenix queryserver 模式

连接 Phoenix queryserver

客户端 jdbc 连接（jdbcUrl）

猜你喜欢

热点阅读