Zookeeper3.4.12、Hadoop2.8.3、Hbase2.0搭建HA高可用集群

一、环境准备

1、版本选择

Hbase2.0不能跟Hadoop3.0官方不推荐目前，还是NT状态，2.8.3是S支持状态。

2、机器配置

配置固定ip（根据主机设置）
/etc/network/interfaces.d

#auto eth0
#iface eth0 inet dhcp
auto eth0  
iface eth0 inet static  
address 192.168.1.141  
gateway 192.168.1.1  
netmask 255.255.255.0  

配置dns
/etc/resolvconf/resolv.conf.d

nameserver 119.29.29.29
nameserver 182.254.116.116

配置Host
/etc/hosts

#主机信息
192.168.1.141     hadoop01
#添加节点的信息
192.168.1.142     hadoop02
192.168.1.143     hadoop03

配置Hostname
/etc/hostname（根据主机设置）

hadoop01

修改最大线程数（略）

设置时区

sudo tzselect 

sudo cp /usr/share/zoneinfo/Asia/Shanghai  /etc/localtime

3、jdk和ntp（各主机都需要）

jdk1.8

sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer

root@hadoop01:~# java -version
java version "1.8.0_171"
Java(TM) SE Runtime Environment (build 1.8.0_171-b11)
Java HotSpot(TM) 64-Bit Server VM (build 25.171-b11, mixed mode)

写入path到/etc/profile

export JAVA_HOME=/usr/lib/jvm/java-8-oracle 
export JRE_HOME=$JAVA_HOME/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export PATH=${JAVA_HOMR}/bin:$PATH

即使生效
source /etc/profile

ntp

sudo apt-get install ntp
service ntp start

二、配置各主机ssh免登陆

1、生成密钥（主机全部执行一遍)

ssh-keygen -t rsa 

root@hadoop01:~# ssh-keygen -t rsa 
Generating public/private rsa key pair.
Enter file in which to save the key (/root/.ssh/id_rsa): 
Created directory '/root/.ssh'.
Enter passphrase (empty for no passphrase): 
Enter same passphrase again: 
Your identification has been saved in /root/.ssh/id_rsa.
Your public key has been saved in /root/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:ZZhKxPH5mFe1jglHPW24sSu/Ovb2yaTYWPqZGezPgps root@hadoop01
The key's randomart image is:
+---[RSA 2048]----+
|     .o.    ...o |
|     ... + . .=.o|
|      . = + o .* |
|     . . * + +o  |
|      . S o o .. |
|         .  o .  |
|            .* . |
|           .@.% .|
|           E+&=B.|
+----[SHA256]-----+

2、指向互信主机

cd /root/.ssh  
cat id_rsa.pub >>authorized_keys

scp ~/.ssh/authorized_keys hadoop01:/root/.ssh/authorized_keys
scp ~/.ssh/authorized_keys hadoop02:/root/.ssh/authorized_keys
scp ~/.ssh/authorized_keys hadoop03:/root/.ssh/authorized_keys

3、ssh测试

root@hadoop01:~/.ssh# ssh hadoop01
Welcome to Ubuntu 16.04.1 LTS (GNU/Linux 3.10.65 aarch64)

 * Documentation:  https://help.ubuntu.com
 * Management:     https://landscape.canonical.com
 * Support:        https://ubuntu.com/advantage
Last login: Sun May 13 10:12:08 2018 from 192.168.1.107
root@hadoop01:~# exit
logout
Connection to hadoop01 closed.
root@hadoop01:~/.ssh# ssh hadoop02
Welcome to Ubuntu 16.04.1 LTS (GNU/Linux 3.10.65 aarch64)

 * Documentation:  https://help.ubuntu.com
 * Management:     https://landscape.canonical.com
 * Support:        https://ubuntu.com/advantage
Last login: Sun May 13 10:26:55 2018 from 192.168.1.107
root@hadoop02:~# exit
logout
Connection to hadoop02 closed.
root@hadoop01:~/.ssh# ssh hadoop03
Welcome to Ubuntu 16.04.1 LTS (GNU/Linux 3.10.65 aarch64)

 * Documentation:  https://help.ubuntu.com
 * Management:     https://landscape.canonical.com
 * Support:        https://ubuntu.com/advantage
Last login: Sun May 13 10:24:10 2018 from 192.168.1.107
root@hadoop03:~# exit
logout
Connection to hadoop03 closed.
root@hadoop01:~/.ssh# 

三、搭建Zookeeper

http://zookeeper.apache.org/

1、下载

wget http://mirrors.tuna.tsinghua.edu.cn/apache/zookeeper/zookeeper-3.4.12/zookeeper-3.4.12.tar.gz
 tar zxvf zookeeper-3.4.12.tar.gz 

2、部署

复制配置文件

mkdir /home/zookeeper-3.4.12/data
mkdir -p  /home/zookeeper-3.4.12/datalog
cd /home/zookeeper-3.4.12/conf
cp zoo_sample.cfg zoo.cfg

zoo.cfg内容

# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial 
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between 
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just 
# example sakes.
dataDir=/home/zookeeper-3.4.12/data
dataLogDir=/home/zookeeper-3.4.12/datalog
# 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=hadoop01:2888:3888
server.2=hadoop02:2888:3888
server.3=hadoop03:2888:3888

在zookeeper的data目录下创建myid文件，master机内容1，其他主机2和3；（复制后记得修改)
复制到slave主机

scp -r  zookeeper-3.4.12  hadoop02:/home/
scp -r  zookeeper-3.4.12  hadoop03:/home/

各主机etc/profile

export ZOOKEEPER_HOME=/home/zookeeper-3.4.12
export PATH=$PATH:$ZOOKEEPER_HOME/bin:$ZOOKEEPER_HOME/conf


记得
source /etc/profile

3、启动

各主机启动

zkServer.sh start


root@hadoop01:/home# zkServer.sh start
ZooKeeper JMX enabled by default
Using config: /home/zookeeper-3.4.12/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED

4、常用命令

启动
zkServer.sh start

停止
zkServer.sh stop

状态

zkServer.sh status

5、验证

leader主机由zookeeper推荐，自动标03为leader。

root@hadoop03:/#  zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /home/zookeeper-3.4.12/bin/../conf/zoo.cfg
Mode: leader

root@hadoop02:/#  zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /home/zookeeper-3.4.12/bin/../conf/zoo.cfg
Mode: follower


root@hadoop01:~# zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /home/zookeeper-3.4.12/bin/../conf/zoo.cfg
Mode: follower

正常运行。

四、搭建Hadoop

http://hadoop.apache.org/

1、下载

下载bin链接版本，src版本还需要自己编译。

wget http://mirrors.hust.edu.cn/apache/hadoop/common/hadoop-2.8.3/hadoop-2.8.3.tar.gz
tar zxvf hadoop-2.8.3.tar.gz

2、配置

在各主机上建立相关目录

mkdir  /home/data
mkdir  /home/data/journal
mkdir  /home/data/tmp
mkdir  /home/data/hdfs
mkdir  /home/data/hdfs/data
mkdir  /home/data/hdfs/name

配置core-site.xml

     
     <property>
          <name>fs.defaultFSname>
          <value>hdfs://nsvalue>
     property>
     
     <property>
          <name>hadoop.tmp.dirname>
          <value>/home/data/tmpvalue>
     property>
     <property>
          <name>io.file.buffer.sizename>
          <value>4096value>
     property>
     
     <property>
          <name>ha.zookeeper.quorumname>
          <value>hadoop01:2181,hadoop02:2181,hadoop03:2181value>
     property>

配置hdfs-site.xml

    <property>
        <name>dfs.nameservicesname>
        <value>nsvalue>
    property>
    
    <property>
       <name>dfs.ha.namenodes.nsname>
       <value>nn1,nn2value>
    property>
    
    <property>
       <name>dfs.namenode.rpc-address.ns.nn1name>
       <value>hadoop01:9000value>
    property>
    
    <property>
        <name>dfs.namenode.http-address.ns.nn1name>
        <value>hadoop01:50070value>
    property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns.nn2name>
        <value>hadoop02:9000value>
    property>
    
    <property>
        <name>dfs.namenode.http-address.ns.nn2name>
        <value>hadoop02:50070value>
    property>
    
    <property>
         <name>dfs.namenode.shared.edits.dirname>
         <value>qjournal://hadoop01;hadoop02;hadoop03/nsvalue>
    property>
    
    <property>
          <name>dfs.journalnode.edits.dirname>
          <value>/home/data/journalvalue>
    property>
    
    <property>
          <name>dfs.ha.automatic-failover.enabledname>
          <value>truevalue>
    property>
    
    <property>
            <name>dfs.client.failover.proxy.provider.nsname>
            <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvidervalue>
    property>
    
    <property>
             <name>dfs.ha.fencing.methodsname>
             
                 <value>
                    sshfence
                    shell(/bin/true)
                value>
    property>
    
    <property>
            <name>dfs.ha.fencing.ssh.private-key-filesname>
            <value>/root/.ssh/id_rsavalue>
    property>

    <property>
        <name>dfs.namenode.name.dirname>
        <value>file:/home/data/hdfs/namevalue>
    property>

    <property>
        <name>dfs.datanode.data.dirname>
        <value>file:/home/data/hdfs/datavalue>
    property>

    <property>
       <name>dfs.replicationname>
       <value>2value>
    property>
    
    <property>
       <name>dfs.webhdfs.enabledname>
       <value>truevalue>
    property>
configuration>

配置mapred-site.xml
内存配置问题，是主机只有1G内存。内存大可不用配置。

<configuration>
    <property>
        <name>mapreduce.framework.namename>
        <value>yarnvalue>
    property>
    <property>
        <name>mapreduce.application.classpathname>
        <value>
            /home/hadoop-2.8.3/etc/hadoop,
            /home/hadoop-2.8.3/share/hadoop/common/*,
           /home/hadoop-2.8.3/share/hadoop/common/lib/*,
            /home/hadoop-2.8.3/share/hadoop/hdfs/*,
           /home/hadoop-2.8.3/share/hadoop/hdfs/lib/*,
            /home/hadoop-2.8.3/share/hadoop/mapreduce/*,
           /home/hadoop-2.8.3/share/hadoop/mapreduce/lib/*,
           /home/hadoop-2.8.3/share/hadoop/yarn/*,
           /home/hadoop-2.8.3/share/hadoop/yarn/lib/*
        value>
    property>
    <property>
　　<name>mapreduce.map.memory.mbname>
　　  <value>512value>
    property>
    <property>
    　　<name>mapreduce.map.java.optsname>
    　　<value>-Xmx512Mvalue>
    property>
    <property>
    　　<name>mapreduce.reduce.memory.mbname>
    　　<value>512value>
    property>
    <property>
    　　<name>mapreduce.reduce.java.optsname>
    　　<value>-Xmx256Mvalue>
    property>


configuration>

配置yarn-site.xml

<configuration>

      <property>
        <name>yarn.resourcemanager.hostnamename>
        <value>hadoop01value>
    property>
    <property>
        <name>yarn.nodemanager.aux-servicesname>
        <value>mapreduce_shufflevalue>
    property>
    <property> 
        <description>The address of the RM web application.description> 
        <name>yarn.resourcemanager.webapp.addressname> 
        <value>hadoop01:18008value> 
    property>
configuration>

配置slaves

hadoop01
hadoop02
hadoop03

配置hadoop-env.sh

export HADOOP_OPTS="$HADOOP_OPTS -Duser.timezone=GMT+08"

配置yarn-env.sh

YARN_OPTS="$YARN_OPTS -Duser.timezone=GMT+08"

配置path
etc/profile

export HADOOP_HOME=/home/hadoop-2.8.3
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

source /etc/profile

3、配置slave

复制到slave

cd /home
scp -r  hadoop-2.8.3  hadoop02:/home/
scp -r  hadoop-2.8.3  hadoop03:/home/

4、首次启动

1、首先启动各个节点的Zookeeper，在各个节点上执行以下命令：
zkServer.sh start
2、在某一个namenode节点执行如下命令，创建命名空间
hdfs zkfc -formatZK
3、在每个journalnode节点用如下命令启动journalnode
hadoop-daemon.sh start journalnode
4、在主namenode节点格式化namenode和journalnode目录
hdfs namenode -format ns
5、在主namenode节点启动namenode进程
hadoop-daemon.sh start namenode
6、在备namenode节点执行第一行命令，这个是把备namenode节点的目录格式化并把元数据从主namenode节点copy过来，并且这个命令不会把journalnode目录再格式化了！然后用第二个命令启动备namenode进程！
hdfs namenode -bootstrapStandby
hadoop-daemon.sh start namenode
7、在两个namenode节点都执行以下命令
hadoop-daemon.sh start zkfc
8、在所有datanode节点都执行以下命令启动datanode
hadoop-daemon.sh start datanode

5、常用命令

启动和停止

start-dfs.sh
start-yarn.sh

stop-yarn.sh
stop-dfs.sh

6、验证

看图示意

http://192.168.1.141:18008/cluster/nodes

http://192.168.1.142:50070/dfshealth.html#tab-overview

http://192.168.1.141:50070/dfshealth.html#tab-overview

访问正常

五、搭建Hbase

http://hbase.apache.org/

1、下载

带bin的不用编译。

wget http://mirrors.hust.edu.cn/apache/hbase/2.0.0/hbase-2.0.0-bin.tar.gz
tar -zvxf hbase-2.0.0-bin.tar.gz

2、配置

配置hbase-env.sh

export JAVA_HOME=/usr/lib/jvm/java-8-oracle

export HBASE_CLASSPATH=/home/hadoop-2.8.3/etc/hadoop

export HBASE_MANAGES_ZK=false

export TZ="Asia/Shanghai"

关闭hbase自带的zookeeper，这个只能测试，不能生产环境。
classpath一定要改成hadoop的目录，不然不认识集群名称。
网上大部分教程都不是真正的分布式。

配置hbase-site.xml

<configuration>
   <property>  
       <name>hbase.rootdirname>  
       <value>hdfs://ns/hbasevalue>  
   property>  
         
   <property>  
       <name>hbase.cluster.distributedname>  
       <value>truevalue>  
   property>  
         
   <property>  
       <name>hbase.master.info.portname>  
       <value>16010value>  
    property>  
         
    <property>  
       <name>hbase.regionserver.info.portname>  
       <value>16030value>  
    property>  
   <property>  
       <name>hbase.zookeeper.quorumname>  
       <value>hadoop01:2181,hadoop02:2181,hadoop03:2181value> 
   property> 
 <property>
    <name>hbase.coprocessor.abortonerrorname>
    <value>falsevalue>
    property>
configuration>

ns是前面配置的namenode集群名称

配置regionservers

hadoop02
hadoop03

配置profile

export HBASE_HOME=/home/hbase-2.0.0

export PATH=$HBASE_HOME/bin:$PATH

source /etc/profile

3、启动

复制到slave

cd /home/
scp -r  /home/hbase-2.0.0  hadoop02:/home/
scp -r  /home/hbase-2.0.0  hadoop03:/home/

start-hbase.sh

root@hadoop01:/home# start-hbase.sh
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hbase-2.0.0/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop-2.8.3/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
running master, logging to /home/hbase-2.0.0/logs/hbase-root-master-hadoop01.out
hadoop03: running regionserver, logging to /home/hbase-2.0.0/bin/../logs/hbase-root-regionserver-hadoop03.out
hadoop02: running regionserver, logging to /home/hbase-2.0.0/bin/../logs/hbase-root-regionserver-hadoop02.out



stop-hbase.sh

4、常用命令

启动（master机器)
start-hbase.sh
关闭
stop-hbase.sh
启动节点
hbase-daemon.sh start regionserver  

5、验证

http://192.168.1.141:16010/master-status

http://192.168.1.142:16030/rs-status

http://192.168.1.143:16030/rs-status

六、测试系统

1、测试Namenode自动切换

在02上

root@Hadoop02:~# jps
3410 QuorumPeerMain
5636 DFSZKFailoverController
5765 NodeManager
5367 DataNode
5287 NameNode
5498 JournalNode
5979 Jps

kill namenode

root@Hadoop02:~# kill -9 5287

回去看standby的是否变成active自动切换成功图片

2、扩容量增加Datanode

3、Hadoop Wordcount Sample

4、Hbase Shell

# hbase shell
（1）创建表
create 'test','address'

（2）添加记录
put'test','row1','address:province','zhejiang'
put 'test','row2','address:city','hangzhou'

（3）查看记录
get 'test','row1'

（4）查看表中的记录总数
count 'test'

（5）删除记录
delete 'test','row1','address'
（6）删除一张表
disable 'test'
drop 'test'
（7）查看所有记录
scan 'test'

hbase(main):001:0> create 'test','address'
Created table test
Took 7.7403 seconds                                                                                                                                                                                                                                                           
=> Hbase::Table - test
hbase(main):002:0> put'test','row1','address:province','zhejiang'
Took 1.0868 seconds                                                                                                                                                                                                                                                           
hbase(main):003:0> put 'test','row2','address:city','hangzhou'
Took 0.0293 seconds                                                                                                                                                                                                                                                           
hbase(main):004:0> get 'test','row1'
COLUMN                                                               CELL                                                                                                                                                                                                     
 address:province                                                    timestamp=1526199666251, value=zhejiang                                                                                                                                                                  
1 row(s)
Took 0.2447 seconds                                                                                                                                                                                                                                                           
hbase(main):005:0> count 'test'
2 row(s)
Took 0.2910 seconds                                                                                                                                                                                                                                                           
=> 2
hbase(main):006:0> scan 'test'
ROW                                                                  COLUMN+CELL                                                                                                                                                                                              
 row1                                                                column=address:province, timestamp=1526199666251, value=zhejiang                                                                                                                                         
 row2                                                                column=address:city, timestamp=1526199674131, value=hangzhou                                                                                                                                             
2 row(s)
Took 0.0520 seconds                                                                                                                                                                                                                                                           
hbase(main):007:0> 

从0开始耗时10小时可用。

5、Java访问Hbase

6、Python访问Hbase

6、Php访问Hbase

作者：大道至简非简
链接：https://www.jianshu.com/p/b2d9194ad6f5
來源：简书
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