CentOS7下Apache kylin2.5.0安装与配置（一篇就够）

系统：CentOS7

所需环境版本三台CentOS7虚拟机，并确认能互相ping通且支持ssh免密登录，确保防火墙关闭且开机不启动状态，请提前配置。

三台都需要设置 
我的三台ip分别为 
master 192.168.0.180 
slave1 192.168.0.181
slave2 192.168.0.182

以下软件均为64位版本

1：JDK1.8.0_161

2：Hadoop2.7.6

3：zookeeper3.4.10

4：kafka_2.11-0.11.0.2（不需要可跳过，不影响后续正常运行）

5：Hbase1.2.6

6：hive1.2.2

7：kylin2.5.0

以上软件下载地址------->>>>>>>

https://blog.csdn.net/kuaikuai_945/article/details/97923878

准备好以上安装包，开始安装和配置

一、安装配置jdk

根目录下创建 public 文件夹

将jdk-8u161-linux-x64.tar.gz拷贝到public目录下，实行解压命令

tar -vzxf jdk-8u161-linux-x64.tar.gz

 根目录下 /usr/下创建java目录，然后将解压后的文件拷贝到java目录下并改名

mv /public/jdk1.8.0_161 /usr/java

设置Java 环境

vim /etc/profile

在文件的最后一行添加以下

export JAVA_HOME=/usr/java/jdk1.8.0_161
export PATH=$JAVA_HOME/bin:$PATH
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar

输入以下命令，确保环境变量生效

source  /etc/profile

输入 java -version查看java版本配置是否正确

以上操作，在三台虚拟机中都需要配置。

二、Hadoop安装

将hadoop-2.7.6.tar.gz拷贝到public目录下，实行解压命令

tar -vzxf hadoop-2.7.6.tar.gz

将解压后的文件拷贝到opt目录下并改名

mv public/hadoop-2.7.6 /opt/hadoop

设置 环境

vim /etc/profile

在文件的最后一行添加以下

export HADOOP_HOME=/opt/hadoop
export PATH=$PATH:$HADOOP_HOME/bin

输入以下命令，确保环境变量生效

source  /etc/profile

输入 hadoop version查看hadoop版本配置是否正确

在/opt/hadoop的路径下创建这四个文件夹

•     hadoop/hdfs
•     hadoop/hdfs/tmp
•     hadoop/hdfs/name
•     hadoop/hdfs/data

进入到/opt/hadoop/etc/hadoop/路径下 
最后一行添加JAVA_HOME环境到hadoop-env.sh和yarn-env.sh 

export JAVA_HOME=/usr/java/jdk1.8.0_161

配置以下文件

core-site.xml

        
        fs.default.name  
        hdfs://master:9000  
        
        
        hadoop.tmp.dir  
        file:/opt/hadoop/hdfs/tmp  
      
	
		io.file.buffer.size
		131702
	

hdfs-site.xml

        
        dfs.replication  
        2  
        
        
        dfs.namenode.name.dir  
        file:/opt/hadoop/hdfs/name
        true
        
        
        dfs.datanode.data.dir  
        file:/opt/hadoop/hdfs/data
        true 
      
	
		dfs.namenode.secondary.http-address
		192.168.0.180:9001
	
	
		dfs.webhdfs.enabled
		true
	
	
		dfs.namenode.http.address
		192.168.0.180:50070
	

mapred-site.xml

        
        mapreduce.framework.name  
        yarn  
        
        
        mapreduce.jobhistory.address  
        192.168.0.180:10020  
        
        
        mapreduce.jobhistory.webapp.address  
        192.168.0.180:19888  
      

yarn-site.xml

        
        yarn.nodemanager.aux-services  
        mapreduce_shuffle  
        
        
        yarn.nodemanager.aux-services.mapreduce.shuffle.class  
        org.apache.hadoop.mapred.ShuffleHandler  
        
        
        yarn.resourcemanager.address  
        master:8032  
        
        
    yarn.resourcemanager.scheduler.address  
        master:8030  
        
        
        yarn.resourcemanager.resource-tracker.address  
        master:8031  
        
        
        yarn.resourcemanager.admin.address  
        master:8033  
        
        
        yarn.resourcemanager.webapp.address  
        master:8088  
      

编辑slaves文件: 
清空slaves，再加入从节点的名字 

master

slave1

slave2

root用户下，将hadoop分发到各个节点

scp -r /opt/hadoop slave1:/opt/hadoop 
scp -r /opt/hadoop slave2:/opt/hadoop

只需在master服务器启动hadoop，从节点会自动启动，进入/opt/hadoop目录 
(1)初始化，输入命令

bin/hdfs namenode -format 

(2)全部启动

sbin/start-all.sh

(3)启动jobhistoryserver

sbin/mr-jobhistory-daemon.sh start historyserver

(4)终止服务器

sbin/stop-all.sh 

(5)查看hadoop启动状态

http://192.168.0.180:8088/

http://192.168.0.180:19888/

http://192.168.0.180:50070/

三、zookeeper安装

将zookeeper-3.4.10.tar.gz拷贝到public目录下，实行解压命令

tar -vzxf zookeeper-3.4.10.tar.gz

将解压后的文件拷贝到opt/hadoop/目录下并改名

mv public/zookeeper-3.4.10 /opt/hadoop/zookeeper

建立数据目录

mkdir /opt/hadoop/zookeeper/data
chown -R hadoop:hadoop /opt/hadoop/zookeeper/data

zookeeper/conf/目录下复制zoo文件并配置

cp zoo_sample.cfg zoo.cfg

zoo.cfg

# 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=/opt/hadoop/zookeeper/data
# 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=master:2888:3888
server.2=slave1:2888:3888
server.3=slave2:2888:3888

在zookeeper/data目录中新建myid文件,文件内容为server.id中的id号

echo 1 > myid

root用户下，将zookeeper分发到各个节点

scp -r /opt/hadoop/zookeeper slave1:/opt/hadoop/zookeeper
scp -r /opt/hadoop/zookeeper slave2:/opt/hadoop/zookeeper

slave1虚拟机中将myid的值改为2，slave2虚拟机中将myid的值改为3

三台虚拟机均进行以下环境变量配置

vim /etc/profile

 

export ZOOKEEPER_HOME=/opt/hadoop/zookeeper
export PATH=$PATH:$ZOOKEEPER_HOME/bin

 

source  /etc/profile

启动zookeeper
分别在master/slave1/slave2服务器的/opt/hadoop/zookeeper/ 目录下输入：bin/zkServer.sh start
三台虚拟机启动完成后检查启动状态，输入：bin/zkServer.sh status
三个虚拟机中，一个是leader,其它两个是follower，证明zookeeper正常启动

​

四、Kafka安装（不需要可直接跳过）

将kafka_2.11-1.0.0.tgz拷贝到public目录下，实行解压命令

tar -vzxf kafka_2.11-1.0.0.tgz

将解压后的文件拷贝到opt/目录下并改名

mv public/kafka_2.11-0.11.0.2 /opt/kafka

三台虚拟机中，在CentOS根目录下的tmp/目录下，创建kafka-logs目录。

编辑config/目录下的server.properties，并保存修改

server.properties

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# see kafka.server.KafkaConfig for additional details and defaults

############################# Server Basics #############################

# The id of the broker. This must be set to a unique integer for each broker.
# broker.id=0

# Switch to enable topic deletion or not, default value is false
#delete.topic.enable=true

############################# Socket Server Settings #############################

# The address the socket server listens on. It will get the value returned from 
# java.net.InetAddress.getCanonicalHostName() if not configured.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
#listeners=PLAINTEXT://:9092

# Hostname and port the broker will advertise to producers and consumers. If not set, 
# it uses the value for "listeners" if configured.  Otherwise, it will use the value
# returned from java.net.InetAddress.getCanonicalHostName().
#advertised.listeners=PLAINTEXT://your.host.name:9092

# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL

# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3

# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8

# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400

# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400

# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600


############################# Log Basics #############################

# A comma seperated list of directories under which to store log files
# log.dirs=/tmp/kafka-logs

# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1

# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1

############################# Internal Topic Settings  #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended for to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1

############################# Log Flush Policy #############################

# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to exceessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.

# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000

# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000

############################# Log Retention Policy #############################

# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.

# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168

# A size-based retention policy for logs. Segments are pruned from the log as long as the remaining
# segments don't drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824

# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824

# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000

############################# Zookeeper #############################

# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
# zookeeper.connect=localhost:2181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000


############################# Group Coordinator Settings #############################

# The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0
broker.id=1
zookeeper.connect=192.168.0.180:2181,192.168.0.181:2181,192.168.0.182:2181
listeners=PLAINTEXT://192.168.0.180:9092

将kafka分发到各个节点

scp -r /opt/kafka slave1:/opt/kafka
scp -r /opt/kafka slave2:/opt/kafka

slave1的config/server.properties做以下修改

broker.id=2
listeners=PLAINTEXT://192.168.0.181:9092

slave2的config/server.properties做以下修改

broker.id=3
listeners=PLAINTEXT://192.168.0.182:9092

启动Kafka
分别在master/slave1/slave2服务器的/opt/kafka/bin/ 目录下输入：

./kafka-server-start.sh -daemon /opt/kafka/config/server.properties 

三台虚拟机启动完成后检查启动状态，输入jps查看是否有KaFKa进程，以此来验证启动成功
kafka使用测试

在三台虚拟机的kafka都成功启动之后

master创建topic
kafka目录下输入
bin/kafka-topics.sh --create --zookeeper 192.168.0.180:2181 --replication-factor 1 --partitions 1 --topic test

如果成功的话，会输出：Created topic "test".

查看topic
在master、slave1或者slave2,kafka目录下输入
bin/kafka-topics.sh --list --zookeeper 192.168.0.180:2181

master下创建生产消息
kafka目录下
bin/kafka-console-producer.sh --broker-list 192.168.0.180:9092 --topic test
然后输入信息后按Ctrl+C退出。

创建消费
slave1或者slave2，kafka目录下
bin/kafka-console-consumer.sh --bootstrap-server 192.168.0.180:9092 --topic test --from-beginning
然后就能看到生产消息时保存的信息，按Ctrl+C退出。

到此kafka的安装与测试就完成了。

五、Hbase安装

将hbase-1.2.6-bin.tar.gz拷贝到public目录下，实行解压命令

tar -vzxf hbase-1.2.6-bin.tar.gz

将解压后的文件拷贝到opt/目录下并改名

mv public/hbase-1.2.6 /opt/hbase

三台虚拟机均进行以下环境变量配置

vim /etc/profile

输入以下环境变量

export HBASE_HOME=/opt/hbase
export PATH=$PATH:$HBASE_HOME/bin

保存环境变量生效

source  /etc/profile

/opt/hbase/conf目录下编辑配置以下文件

hbase-site.xml

	hbase.master
	master:60000


	hbase.rootdir
	hdfs://master:9000/hbase


	hbase.cluster.distributed
	true


	hbase.zookeeper.quorum
	master,slave1,slave2


	dfs.replication
	1

regionservers

master
slave1
slave2

hbase-env.sh

最后一行追加

export JAVA_HOME=/usr/java/jdk1.8.0_161
export HBASE_MANAGES_ZK=false

将hbase分发到各个节点

scp -r /opt/hbase slave1:/opt/hbase
scp -r /opt/hbase slave2:/opt/hbase

启动hbase
在master服务器的/opt/hbase/bin 目录下输入：start-hbase.sh
浏览器打开http://192.168.0.180:16010

六、hive安装

预先安装mysql

wget http://repo.mysql.com/yum/mysql-5.7-community/el/7/x86_64/mysql57-community-release-el7-10.noarch.rpm

将下载的文件引入repo库中

rpm -ivh mysql57-community-release-el7-10.noarch.rpm

安装完成之后，启动mysql服务，然后查看端口信息

service mysqld start
netstat -a|grep mysql

查询root密码

grep "password" /var/log/mysqld.log

localhost：密码（注：结尾；也属于密码的一部分）

添加mysql开机自启动

systemctl enable mysqld

systemctl daemon-reload

登录root账户

mysql -u root -p

输入密码，登录成功之后创建新用户用于hive 的登录，名为abc123,密码是123456

create user abc123 identified by '123456';

授权为远程用户并刷新权限

GRANT ALL PRIVILEGES ON *.* TO 'abc123'@'%' IDENTIFIED BY '123456' WITH GRANT OPTION;
flush privileges;

创建 hive 数据库

create database hive;

查询编码格式

show variables like 'character%';

修改编码方式，编辑etc/my.cnf，追加以下变量

character-set-server=utf8
init_connect='set names utf8'

修改完成后重启mysql即可

service mysqld restart

通过Navicat测试远程连接访问是否成功。

-------------------------------------------------------------------------------分割线-----------------------------------------------------------------------

将apache-hive-1.2.2-bin.tar.gz拷贝到public目录下并解压

tar -zxvf apache-hive-1.2.2-bin.tar.gz
mv public/apache-hive-1.2.2-bin /opt/hive

vi /etc/profile 在最后添加：

export HIVE_HOME=/opt/hive
export PATH=$PATH:$HIVE_HOME/bin

使配置文件生效

source /etc/profile

到hive/conf目录下，复制以下文件并改名

cp hive-env.sh.template hive-env.sh
cp hive-default.xml.template hive-site.xml
cp hive-log4j2.properties.template hive-log4j2.properties
cp hive-exec-log4j2.properties.template hive-exec-log4j2.properties

hive-site.xml

javax.jdo.option.ConnectionURL
jdbc:mysql://192.168.0.180:3306/hive?createDatabaseIfNotExist=true
JDBC connect string for a JDBC metastore



javax.jdo.option.ConnectionDriverName
com.mysql.jdbc.Driver
Driver class name for a JDBC metastore



javax.jdo.option.ConnectionUserName
abc123
username to use against metastore database



javax.jdo.option.ConnectionPassword
123456
password to use against metastore database

hive-env.sh

export JAVA_HOME=/usr/java/jdk1.8.0_161
export HADOOP_HOME=/opt/hadoop
export HIVE_HOME=/opt/hive
export HIVE_CONF_DIR=/opt/hive/conf

下载mysql-connector-java-5.1.32.jar，放入hive/lib目录下

下载地址：https://www.kumapai.com/open/467-mysql-connector-java/5-1-32

在hive 的lib目录下找到jline-2.12.jar，将其拷贝到hadoop/share/hadoop/yarn/lib/目录下

在以上软件都正常启动下，/opt/hive/bin目录下输入hive启动程序

hive

出现下图表示正常启动

接着输入show databases;查看数据库

到这里就是hive暗转的全部内容了，剩下你可以开心的通过hive操作数据库~

七、Apache Kylin安装

将apache-kylin-2.5.0-bin-hbase1x.tar.gz拷贝到public目录下，实行解压命令

tar -vzxf apache-kylin-2.5.0-bin-hbase1x.tar.gz

 将解压后的文件拷贝到opt目录下并改名

mv apache-kylin-2.5.0-bin-hbase1x /opt/kylin

kylin目录下新建文件夹kylin_meta

mkdir kylin_meta

配置conf目录下的kylin.properties，追加以下内容,myDatabaseNeme是在hive里创建的数据库名

kylin.metadata.url=/opt/kylin//kylin_meta
kylin.rest.servers=192.168.0.180:7070
kylin.server.mode=all
kylin.job.hive.database.for.intermediatetable=myDatabaseName

检查kylin环境，bin目录下输入

check-env.sh

启动kylin，bin目录下输入

kylin.sh start

打开浏览器输入http://192.168.0.1801:7070/kylin

用默认账号密码登录即可

账号：ADMIN

密码：KYLIN

到此就代表kylin环境正常配置完了！！感谢浏览观看~