实时数仓及olap可视化构建(基于mysql，将maxwell改成seatunnel可以快速达成异构数据源实时同步)

惊世智慧2024-11-05 12:08

1. OLAP可视化实现（需要提前整合版本）

Linux121 Linux122 Linux123
jupyter ✔
spark ✔ ✔ ✔
python3+SuperSet3.0 ✔
hive ✔
ClinckHouse ✔
Kafka ✔ ✔ ✔
Phoenix ✔
DataX ✔
maxwell ✔
Hadoop ✔ ✔ ✔
MySQL ✔
ZK ✔ ✔ ✔
HBASE ✔ ✔ ✔

1.1 安装Vmware，安装虚拟机集群

1.1.1 安装 (VMware-workstation-full-15.5.5-16285975)

许可证:

UY758-0RXEQ-M81WP-8ZM7Z-Y3HDA

1.1.2 安装 centos7

text 复制代码 
123456

1.1.3 配置静态IP

shell 复制代码 
vi /etc/sysconfig/network-scripts/ifcfg-ens33
shell 复制代码 
:wq
systemctl restart network
ip addr
ping www.baidu.com
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快照
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安装jdk
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mkdir -p /opt/lagou/software    --软件安装包存放目录
mkdir -p /opt/lagou/servers     --软件安装目录
rpm -qa | grep java
清理上面显示的包名
sudo yum remove java-1.8.0-openjdk

上传文件jdk-8u421-linux-x64.tar.gz
chmod 755 jdk-8u421-linux-x64.tar.gz
解压文件到/opt/lagou/servers目录下

tar -zxvf jdk-8u421-linux-x64.tar.gz -C /opt/lagou/servers

cd /opt/lagou/servers
ll

配置环境

vi /etc/profile
text 复制代码 
export JAVA_HOME=/opt/lagou/servers/jdk1.8.0_421
export CLASSPATH=.:${JAVA_HOME}/jre/lib/rt.jar:${JAVA_HOME}/lib/dt.jar:${JAVA_HOME}/lib/tools.jar
export PATH=$PATH:${JAVA_HOME}/bin
source /etc/profile
java -version

1.1.4 安装Xmanager

text 复制代码 
连接192.168.49.121:22

密码：123456

1.1.5 克隆2台机器,并配置

shell 复制代码 
vi /etc/sysconfig/network-scripts/ifcfg-ens33
shell 复制代码 
systemctl restart network
ip addr
hostnamectl
hostnamectl set-hostname linux121
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关闭防火墙
shell 复制代码 
systemctl status firewalld
systemctl stop firewalld
systemctl disable firewalld
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关闭selinux
shell 复制代码 
vi /etc/selinux/config
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三台机器免密登录
text 复制代码 
vi /etc/hosts
text 复制代码 
192.168.49.121 linux121
192.168.49.122 linux122
192.168.49.123 linux123
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第一步: ssh-keygen -t rsa 在centos7-1和centos7-2和centos7-3上面都要执行，产生公钥
和私钥
ssh-keygen -t rsa

第二步：在centos7-1 ,centos7-2和centos7-3上执行:
ssh-copy-id linux121 将公钥拷贝到centos7-1上面去
ssh-copy-id linux122 将公钥拷贝到centos7-2上面去
ssh-copy-id linux123 将公钥拷贝到centos7-3上面去
ssh-copy-id linux121 
ssh-copy-id linux122 
ssh-copy-id linux123 
第三步：
centos7-1执行:
scp /root/.ssh/authorized_keys linux121:$PWD
scp /root/.ssh/authorized_keys linux122:$PWD
scp /root/.ssh/authorized_keys linux123:$PWD
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三台机器时钟同步
sudo cp -a /etc/yum.repos.d/CentOS-Base.repo /etc/yum.repos.d/CentOS-Base.repo.bak

sudo curl -o /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-7.repo

sudo yum clean all
sudo yum makecache



sudo yum install ntpdate

ntpdate us.pool.ntp.org

crontab -e

*/1 * * * * /usr/sbin/ntpdate us.pool.ntp.org;

快照

1.2 安装ZK,Hadoop,Hbase集群，安装mysql

1.2.1 安装hadoop集群(推荐2.7.3版本)

text 复制代码 
在/opt目录下创建文件夹
shell 复制代码 
mkdir -p /opt/lagou/software    --软件安装包存放目录
mkdir -p /opt/lagou/servers     --软件安装目录
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上传hadoop安装文件到/opt/lagou/software
https://archive.apache.org/dist/hadoop/common/hadoop-2.7.3/

hadoop-2.7.3.tar.gz
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linux121节点
tar -zxvf hadoop-2.7.3.tar.gz -C /opt/lagou/servers
ll /opt/lagou/servers/hadoop-2.7.3

yum install -y vim

添加环境变量
vim /etc/profile
text 复制代码 
##HADOOP_HOME
export HADOOP_HOME=/opt/lagou/servers/hadoop-2.7.3
export PATH=$PATH:$HADOOP_HOME/bin
export PATH=$PATH:$HADOOP_HOME/sbin
shell 复制代码 
source /etc/profile
hadoop version
text 复制代码 
HDFS集群配置
cd /opt/lagou/servers/hadoop-2.7.3/etc/hadoop
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vim hadoop-env.sh

export JAVA_HOME=/opt/lagou/servers/jdk1.8.0_421
xml 复制代码 
vim core-site.xml

 <!-- 指定HDFS中NameNode的地址 -->
<property>
 <name>fs.defaultFS</name>
 <value>hdfs://linux121:9000</value>
</property>
 <!-- 指定Hadoop运行时产生文件的存储目录 -->
<property>
 <name>hadoop.tmp.dir</name>
 <value>/opt/lagou/servers/hadoop-2.7.3/data/tmp</value>
</property>
text 复制代码 
 vim slaves
 
linux121
linux122
linux123
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vim mapred-env.sh

export JAVA_HOME=/opt/lagou/servers/jdk1.8.0_421
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mv mapred-site.xml.template mapred-site.xml
 vim mapred-site.xml
 <!-- 指定MR运行在Yarn上 -->
 <property>
 <name>mapreduce.framework.name</name>
 <value>yarn</value>
 </property>
vi mapred-site.xml
在该文件里面增加如下配置。
<!-- 历史服务器端地址 -->
 <property>
 <name>mapreduce.jobhistory.address</name>
 <value>linux121:10020</value>
 </property>
 <!-- 历史服务器web端地址 -->
 <property>
 <name>mapreduce.jobhistory.webapp.address</name>
 <value>linux121:19888</value>
 </property>
text 复制代码 
vim yarn-env.sh

export JAVA_HOME=/opt/lagou/servers/jdk1.8.0_421
text 复制代码 
 vim yarn-site.xml
 <!-- 指定YARN的ResourceManager的地址 -->
 <property>
 <name>yarn.resourcemanager.hostname</name>
 <value>linux123</value>
 </property>
 <!-- Reducer获取数据的方式 -->
 <property>
 <name>yarn.nodemanager.aux-services</name>
 <value>mapreduce_shuffle</value>
 </property>
vi yarn-site.xml
在该文件里面增加如下配置。
<!-- 日志聚集功能使能 -->
<property>
 <name>yarn.log-aggregation-enable</name>
 <value>true</value>
 </property>
 <!-- 日志保留时间设置7天 -->
 <property>
 <name>yarn.log-aggregation.retain-seconds</name>
     <value>604800</value>
 </property>
 <property>
     <name>yarn.log.server.url</name>
     <value>http://linux121:19888/jobhistory/logs</value>
 </property>

​

复制代码 
chown -R root:root /opt/lagou/servers/hadoop-2.7.3
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分发配置
三台都要
sudo yum install -y rsync

touch rsync-script
vim rsync-script

#!/bin/bash
 #1 获取命令输入参数的个数，如果个数为0，直接退出命令
paramnum=$#
 if((paramnum==0)); then
 echo no params;
 exit;
 fi
 #2 根据传入参数获取文件名称
p1=$1
 file_name=`basename $p1`
 echo fname=$file_name
 #3 获取输入参数的绝对路径
pdir=`cd -P $(dirname $p1); pwd`
 echo pdir=$pdir
 #4 获取用户名称
user=`whoami`
 #5 循环执行rsync
 for((host=121; host<124; host++)); do
 echo ------------------- linux$host -------------- 
rsync -rvl $pdir/$file_name $user@linux$host:$pdir
 done

chmod 777 rsync-script
./rsync-script /home/root/bin
./rsync-script /opt/lagou/servers/hadoop-2.7.3
./rsync-script /opt/lagou/servers/jdk1.8.0_421
./rsync-script /etc/profile

在namenode，linux121上格式化节点

hadoop namenode -format

ssh localhost

集群群起

cd $HADOOP_HOME/sbin
start-dfs.sh

datanode可能起不来

sudo rm -rf /opt/lagou/servers/hadoop-2.7.3/data/tmp/*

hadoop namenode -format

sbin/start-dfs.sh

注意：NameNode和ResourceManger不是在同一台机器，不能在NameNode上启动 YARN，应该
在ResouceManager所在的机器上启动YARN

sbin/start-yarn.sh

linux121：
cd /opt/lagou/servers/hadoop-2.7.3
sbin/mr-jobhistory-daemon.sh start historyserver

地址:

hdfs：

http://linux121:50070/dfshealth.html#tab-overview

日志:

http://linux121:19888/jobhistory

cd /opt/lagou/servers/hadoop-2.7.3

sbin/mr-jobhistory-daemon.sh stop historyserver

stop-yarn.sh

stop-dfs.sh

测试

hdfs dfs -mkdir /wcinput

cd /root/
touch wc.txt

vi wc.txt


hadoop mapreduce yarn
 hdfs hadoop mapreduce
 mapreduce yarn lagou
 lagou
 lagou
 
保存退出
: wq!

hdfs dfs -put wc.txt /wcinput


hadoop jar share/hadoop/mapreduce/hadoop mapreduce-examples-2.7.3.jar wordcount /wcinput /wcoutput

1.2.2 安装zk集群

上传并解压zookeeper-3.4.14.tar.gz 

tar -zxvf zookeeper-3.4.14.tar.gz -C ../servers/

修改配置⽂文件创建data与log⽬目录
text 复制代码 
#创建zk存储数据⽬目录
mkdir -p /opt/lagou/servers/zookeeper-3.4.14/data

 #创建zk⽇日志⽂文件⽬目录
mkdir -p /opt/lagou/servers/zookeeper-3.4.14/data/logs

 #修改zk配置⽂文件
cd /opt/lagou/servers/zookeeper-3.4.14/conf

 #⽂文件改名
mv zoo_sample.cfg zoo.cfg

mkdir -p /opt/lagou/servers/zookeeper-3.4.14/data
mkdir -p /opt/lagou/servers/zookeeper-3.4.14/data/logs
cd /opt/lagou/servers/zookeeper-3.4.14/conf
mv zoo_sample.cfg zoo.cfg


 vim zoo.cfg
 
 #更更新datadir
 dataDir=/opt/lagou/servers/zookeeper-3.4.14/data
 #增加logdir
 dataLogDir=/opt/lagou/servers/zookeeper-3.4.14/data/logs
 #增加集群配置
##server.服务器器ID=服务器器IP地址：服务器器之间通信端⼝口：服务器器之间投票选举端⼝口
server.1=linux121:2888:3888
server.2=linux122:2888:3888
server.3=linux123:2888:3888
 #打开注释
#ZK提供了了⾃自动清理理事务⽇日志和快照⽂文件的功能，这个参数指定了了清理理频率，单位是⼩小时
autopurge.purgeInterval=1
text 复制代码 
 cd /opt/lagou/servers/zookeeper-3.4.14/data
 echo 1 > myid
 
 安装包分发并修改myid的值
cd /opt/lagou/servers/hadoop-2.7.3/etc/hadoop


 ./rsync-script /opt/lagou/servers/zookeeper-3.4.14
 
 修改myid值 linux122
 echo 2 >/opt/lagou/servers/zookeeper-3.4.14/data/myid 
 
 修改myid值 linux123
 echo 3 >/opt/lagou/servers/zookeeper-3.4.14/data/myid 
 
 依次启动三个zk实例例
启动命令（三个节点都要执⾏行行）

/opt/lagou/servers/zookeeper-3.4.14/bin/zkServer.sh start

查看zk启动情况
/opt/lagou/servers/zookeeper-3.4.14/bin/zkServer.sh status

集群启动停⽌止脚本

vim zk.sh


 #!/bin/sh
 echo "start zookeeper server..."
 if(($#==0));then
 echo "no params";
 exit;
 fi
 hosts="linux121 linux122 linux123"
 for host in $hosts
 do
 ssh $host "source /etc/profile; /opt/lagou/servers/zookeeper-3.4.14/bin/zkServer.sh $1"
 done

chmod 777 zk.sh


cd /root
./zk.sh start
./zk.sh stop
./zk.sh status

1.2.3 安装Hbase集群(先启动Hadoop和zk才能启动Hbase)

解压安装包到指定的规划目录 hbase-2.4.15-bin.tar.gz

tar -zxvf hbase-2.4.15-bin.tar.gz -C /opt/lagou/servers

修改配置文件

把hadoop中的配置core-site.xml 、hdfs-site.xml拷贝到hbase安装目录下的conf文件夹中

shell 复制代码 
ln -s /opt/lagou/servers/hadoop-2.7.3/etc/hadoop/core-site.xml /opt/lagou/servers/hbase-2.4.15/conf/core-site.xml 
ln -s /opt/lagou/servers/hadoop-2.7.3/etc/hadoop/hdfs-site.xml /opt/lagou/servers/hbase-2.4.15/conf/hdfs-site.xml

修改conf目录下配置文件

cd /opt/lagou/servers/hbase-2.4.15/conf

vim hbase-env.sh

 #添加java环境变量
export JAVA_HOME=/opt/lagou/servers/jdk1.8.0_421
 #指定使用外部的zk集群
export HBASE_MANAGES_ZK=FALSE
 
vim hbase-site.xml


<configuration>
          <!-- 指定hbase在HDFS上存储的路径 -->
        <property>
                <name>hbase.rootdir</name>
                <value>hdfs://linux121:9000/hbase</value>
        </property>
                <!-- 指定hbase是分布式的 -->
        <property>
                <name>hbase.cluster.distributed</name>
                <value>true</value>
        </property>
                <!-- 指定zk的地址，多个用","分割 -->
        <property>
                <name>hbase.zookeeper.quorum</name>
                <value>linux121:2181,linux122:2181,linux123:2181</value>
        </property>
 </configuration>       

vim regionservers

linux121
linux122
linux123

vim backup-masters

linux122


vim /etc/profile

export HBASE_HOME=/opt/lagou/servers/hbase-2.4.15
export PATH=$PATH:$HBASE_HOME/bin

分发hbase目录和环境变量到其他节点
cd /opt/lagou/servers/hadoop-2.7.3/etc/hadoop
./rsync-script /opt/lagou/servers/hbase-2.4.15
./rsync-script /etc/profile

让所有节点的hbase环境变量生效
在所有节点执行  source /etc/profile

cd /opt/lagou/servers/hbase-2.4.15/bin

HBase集群的启动和停止
前提条件：先启动hadoop和zk集群
启动HBase：start-hbase.sh
停止HBase：stop-hbase.sh

HBase集群的web管理界面
启动好HBase集群之后，可以访问地址：HMaster的主机名:16010
hbase shell

linux121:16010

1.2.4 安装mysql

卸载系统自带的mysql

rpm -qa | grep mysql

rpm -e --nodeps mysql-libs-5.1.73-8.el6_8.x86_64


安装mysql-community-release-el6-5.noarch.rpm


rpm -ivh mysql-community-release-el6-5.noarch.rpm

安装mysql 服务器
yum -y install mysql-community-server

启动服务
service mysqld start

如果出现:serivce: command not found
安装service

yum install initscripts
text 复制代码 
配置数据库
text 复制代码 
设置密码
/usr/bin/mysqladmin -u root password '123'
# 进入mysql
mysql -uroot -p123

# 清空 mysql 配置文件内容
>/etc/my.cnf
修改
vi /etc/my.cnf

[client]
default-character-set=utf8
[mysql]
default-character-set=utf8
[mysqld]
character-set-server=utf8

重启查看，授权远程连接

service mysqld restart
mysql -uroot -p123
show variables like 'character_set_%';
# 给root授权:既可以本地访问, 也可以远程访问
grant all privileges on *.* to 'root'@'%' identified by '123' with grant
option;
# 刷新权限(可选)
flush privileges;
开启Mysql 的binlog日志

vim /etc/my.cnf

[mysqld]
log-bin=/var/lib/mysql/mysql-bin # 开启 binlog
binlog-format=ROW # 选择 ROW 模式
server_id=1 # 配置 slaveId 重复


systemctl restart mysqld

mysql -root -p123
show variables like '%log_bin%';

查看是否生产binlog
cd /var/lib/mysql/

快照

1.3 安装Phoenix，来创建hbase表，安装datax来导入数据到hbase

1.3.1 数据初始化

text 复制代码 
运行资料中的talents.sql文件

1.3.2 Phoenix安装(按对应hbase版本下载)

https://www.apache.org/dyn/closer.lua/phoenix/phoenix-4.16.1/phoenix-hbase-1.3-4.16.1-bin.tar.gz
下载解压phoenix-hbase-1.3-4.16.1-bin.tar.gz

tar -xvzf phoenix-hbase-1.3-4.16.1-bin.tar.gz -C ../servers/

拷贝Phoenix整合HBase所需JAR包
cd /opt/lagou/servers/phoenix-hbase-1.3-4.16.1-bin

cp phoenix-server-hbase-1.3-4.16.1.jar /opt/lagou/servers/hbase-2.4.15/lib

scp phoenix-server-hbase-1.3-4.16.1.jar linux122:/opt/lagou/servers/hbase-2.4.15/lib

scp phoenix-server-hbase-1.3-4.16.1.jar linux123:/opt/lagou/servers/hbase-2.4.15/lib

cd /opt/lagou/servers/phoenix-hbase-1.3-4.16.1-bin/bin

将hbase的配置文件hbase-site.xml、 hadoop/etc/hadoop下的core-site.xml 、hdfs-site.xml放到
phoenix/bin/下，替换phoenix原来的配置文件


# 备份原先的 hbase-site.xml文件
mv hbase-site.xml hbase-site.xml.bak
ln -s $HBASE_HOME/conf/hbase-site.xml .
ln -s $HADOOP_HOME/etc/hadoop/core-site.xml .
ln -s $HADOOP_HOME/etc/hadoop/hdfs-site.xml .

开启二级索引

登录到RegionSever节点，修改hbase-site.xml配置文件，加入如下配置

vi /opt/lagou/servers/hbase-2.4.15/conf/hbase-site.xml
xml 复制代码 
修改

<property>
  <name>hbase.zookeeper.quorum</name>
  <value>linux121,linux122,linux123:2181</value>
</property>

新增

xml 复制代码 
<property>
  <name>hbase.regionserver.wal.codec</name>
  <value>org.apache.hadoop.hbase.regionserver.wal.IndexedWALEditCodec</value>
</property>

<property>
    <name>hbase.table.sanity.checks</name>
    <value>false</value>
    <description>Disables sanity checks on HBase tables.</description>
</property>
stop-hbase.sh
start-hbase.sh

重启
stop-hbase.sh

./zk.sh stop
mr-jobhistory-daemon.sh stop historyserver

stop-yarn.sh 

stop-dfs.sh









start-dfs.sh
start-yarn.sh 
mr-jobhistory-daemon.sh start historyserver
./zk.sh start
start-hbase.sh


简单的:

hbase clean --cleanAll
stop-hbase.sh
start-hbase.sh

测试:

cd /opt/lagou/servers/phoenix-hbase-1.3-4.16.1-bin/bin

./sqlline.py linux121:2181

可能会内存不足
free -h

1.3.3 Phoenix创建业务表

sql 复制代码 
--用户表
DROP TABLE IF EXISTS "dim_account";
create table "dim_account" (
"id" varchar primary key,
"user"."sex" varchar,
"user"."age" varchar,
"user"."expectcity" varchar,
"user"."expectpositionname" varchar,
"user"."expectpositionnametype1" varchar,
"user"."expectpositionnametype2" varchar,
"user"."expectsalarys" varchar,
"user"."highesteducation" varchar,
"user"."latest_schoolname" varchar,
"user"."_c10" varchar,
"user"."latest_companyname" varchar,
"user"."is_famous_enterprise" varchar,
"user"."work_year" varchar,
"user"."status" varchar) column_encoded_bytes=0;

--公司表
DROP TABLE IF EXISTS "dim_company";
create table "dim_company" (
"cid" varchar primary key,
"cy"."companyname" varchar,
"cy"."is_famous_enterprise" varchar,
"cy"."financestage" varchar,
"cy"."city" varchar,
"cy"."companysize" varchar,
"cy"."industryfield" varchar) column_encoded_bytes=0;

-- 职位表
DROP TABLE IF EXISTS "dim_position";
create table "dim_position" (
"id" varchar primary key,
"position"."positionname" varchar,
"position"."positionfirstcategory" varchar,
"position"."positionsecondcategory" varchar,
"position"."positionthirdcategory" varchar,
"position"."workyear" varchar,
"position"."education" varchar,
"position"."salarymin" varchar,
"position"."salarymax" varchar,
"position"."city" varchar,
"position"."companyid" varchar,
"position"."createtime" varchar,
"position"."lastupdatetime" varchar) column_encoded_bytes=0;

测试

SELECT * FROM "dim_position";

1.3.4 DataX安装

上传并解压datax.tar.gz

tar -xvzf datax.tar.gz -C ../servers/

配置环境变量

vi /etc/profile

export DATAX_HOME="/opt/lagou/servers/datax"
export PATH=$PATH:${DATAX_HOME}/bin

source /etc/profile

1.3.5 DataX实现全量同步

方便直接查询相应的字段变成json内容

SELECT GROUP_CONCAT('"' , COLUMN_NAME , '"' ORDER BY ORDINAL_POSITION SEPARATOR ',\n')
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_SCHEMA = 'talents' AND TABLE_NAME = 'lg_account';

找Phoenix对应的写入包

json 复制代码 
{
  "job": {
    "content": [
      {
        "reader": {
          "name": "mysqlreader",
          "parameter": {
            "column": [
              "id",
              "sex",
              "age",
              "expectcity",
              "expectpositionname",
              "expectpositionnametype1",
              "expectpositionnametype2",
              "expectsalarys",
              "highesteducation",
              "latest_schoolname",
              "c10",
              "latest_companyname",
              "is_famous_enterprise",
              "work_year",
              "status"
            ],
            "connection": [
              {
                "jdbcUrl": [
                  "jdbc:mysql://hadoop1:3306/talents"
                ],
                "table": [
                  "lg_account"
                ]
              }
            ],
            "password": "123456",
            "username": "root"
          }
        },
        "writer": {
          "name": "hbase11xsqlwriter",
          "parameter": {
            "batchSize": "256",
            "column": [
              "id",
              "sex",
              "age",
              "expectcity",
              "expectpositionname",
              "expectpositionnametype1",
              "expectpositionnametype2",
              "expectsalarys",
              "highesteducation",
              "latest_schoolname",
              "_c10",
              "latest_companyname",
              "is_famous_enterprise",
              "work_year",
              "status"
            ],
            "hbaseConfig": {
              "hbase.zookeeper.quorum": "hadoop4",
              "zookeeper.znode.parent": "/hbase"
            },
            "nullMode": "skip",
            "table": "dim_account"
          }
        }
      }
    ],
    "setting": {
      "speed": {
        "channel": "5"
      }
    }
  }
}

--公司表

jps 复制代码 
{
  "job": {
    "content": [
      {
        "reader": {
          "name": "mysqlreader",
          "parameter": {
            "column": [
              "cid",
              "companyname",
              "is_famous_enterprise",
              "financestage",
              "city",
              "companysize",
              "industryfield"
            ],
            "connection": [
              {
                "jdbcUrl": [
                  "jdbc:mysql://linux123:3306/talents"
                ],
                "table": [
                  "lg_company"
                ]
              }
            ],
            "password": "123",
            "username": "root"
          }
        },
        "writer": {
          "name": "hbase11xsqlwriter",
          "parameter": {
            "batchSize": "256",
            "column": [
              "cid",
              "companyname",
              "is_famous_enterprise",
              "financestage",
              "city",
              "companysize",
              "industryfield"
            ],
            "hbaseConfig": {
              "hbase.zookeeper.quorum": "linux122",
              "zookeeper.znode.parent": "/hbase"
            },
            "nullMode": "skip",
            "table": "dim_company"
          }
        }
      }
    ],
    "setting": {
      "speed": {
        "channel": "5"
      }
    }
  }
}

--职位表

{
  "job": {
    "content": [
      {
        "reader": {
          "name": "mysqlreader",
          "parameter": {
            "column": [
              "id",
              "positionname",
              "positionfirstcategory",
              "positionsecondcategory",
              "positionthirdcategory",
              "workyear",
              "education",
              "salarymin",
              "salarymax",
              "city",
              "companyid",
              "createtime",
              "lastupdatetime"
            ],
            "connection": [
              {
                "jdbcUrl": [
                  "jdbc:mysql://linux123:3306/talents"
                ],
                "table": [
                  "lg_position"
                ]
              }
            ],
            "password": "123",
            "username": "root"
          }
        },
        "writer": {
          "name": "hbase11xsqlwriter",
          "parameter": {
            "batchSize": "256",
            "column": [
              "id",
              "positionname",
              "positionfirstcategory",
              "positionsecondcategory",
              "positionthirdcategory",
              "workyear",
              "education",
              "salarymin",
              "salarymax",
              "city",
              "companyid",
              "createtime",
              "lastupdatetime"
            ],
            "hbaseConfig": {
              "hbase.zookeeper.quorum": "linux122",
              "zookeeper.znode.parent": "/hbase"
            },
            "nullMode": "skip",
            "table": "dim_position"
          }
        }
      }
    ],
    "setting": {
      "speed": {
        "channel": "5"
      }
    }
  }
}

测试

cd $DATAX_HOME/bin
vim $DATAX_HOME/job/mysql2phoenix_account.json
vim $DATAX_HOME/job/mysql2phoenix_company.json
vim po$DATAX_HOME/job/mysql2phoenix_position.json
python $DATAX_HOME/bin/datax.py $DATAX_HOME/job/mysql2phoenix_account.json
python $DATAX_HOME/bin/datax.py $DATAX_HOME/job/mysql2phoenix_company.json
python $DATAX_HOME/bin/datax.py $DATAX_HOME/job/mysql2phoenix_position.json

1.3.6 Kafka安装

上传kafka_2.12-1.0.2.tgz到服务器并解压：

tar -xvzf kafka_2.12-1.0.2.tgz -C ../servers/


安装包分发

cd /opt/lagou/servers/hadoop-2.7.3/etc/hadoop
./rsync-script /opt/lagou/servers/kafka_2.12-1.0.2

配置环境变量
vim /etc/profile


export KAFKA_HOME=/opt/lagou/servers/kafka_2.12-1.0.2
export PATH=$PATH:$KAFKA_HOME/bin

配置分发
./rsync-script /etc/profile

配置生效
source /etc/profile


修改linux121的配置文件

vim $KAFKA_HOME/config/server.properties

broker.id=0
listeners=PLAINTEXT://:9092
advertised.listeners=PLAINTEXT://linux121:9092
log.dirs=/var/lagou/kafka/kafka-logs
zookeeper.connect=linux121:2181,linux122:2181,linux123:2181/myKafka


分发配置文件


./rsync-script $KAFKA_HOME/config/server.properties

修改linux122的配置文件

broker.id=1
listeners=PLAINTEXT://:9092
advertised.listeners=PLAINTEXT://linux122:9092
log.dirs=/var/lagou/kafka/kafka-logs
zookeeper.connect=linux121:2181,linux122:2181,linux123:2181/myKafka


修改linux123的配置文件

broker.id=2
listeners=PLAINTEXT://:9092
advertised.listeners=PLAINTEXT://linux123:9092
log.dirs=/var/lagou/kafka/kafka-logs
zookeeper.connect=linux121:2181,linux122:2181,linux123:2181/myKafka


启动kafka集群，每台机器都执行
kafka-server-start.sh $KAFKA_HOME/config/server.properties

测试
cd /opt/lagou/servers/zookeeper-3.4.14/bin
./zkCli.sh 
# 查看每个Broker的信息
get /myKafka/brokers/ids/0
get /myKafka/brokers/ids/1
get /myKafka/brokers/ids/2

1.3.7 Maxwell安装(Linux123)

上传解压maxwell-1.29.0.tar.gz

tar -xvzf maxwell-1.29.0.tar.gz -C ../servers/

cd ../servers/maxwell-1.29.0

编写任务配置文件
vim driver.properties

######### binlog ###############
log_level=INFO
producer=kafka
host = linux123
user = maxwell
password = 123456
producer_ack_timeout = 600000
######### binlog ###############
######### output format stuff ###############
output_binlog_position=true
output_server_id=true
output_thread_id=true
output_commit_info=true
output_row_query=true
output_ddl=false
output_nulls=true
output_xoffset=true
output_schema_id=true
######### output format stuff ###############
############ kafka stuff #############
kafka.bootstrap.servers=linux121:9092,linux122:9092,linux123:9092
kafka_topic=mysql_incre
kafka_partition_hash=murmur3
kafka_key_format=hash
kafka.retries=5
kafka.acks=all
producer_partition_by=primary_key
############ kafka stuff #############
############## misc stuff ###########
bootstrapper=async
############## filter ###############
filter=exclude:*.*, include:talents.*

新增maxwell用户

mysql -uroot -p123

INSTALL PLUGIN validate_password SONAME 'validate_password.so';
set global validate_password_policy=LOW;
set global validate_password_length=4;
CREATE USER 'maxwell'@'%' IDENTIFIED BY '123456';
CREATE USER 'maxwell'@'linux123' IDENTIFIED BY '123456';
GRANT ALL ON maxwell.* TO 'maxwell'@'%';
GRANT ALL ON maxwell.* TO 'maxwell'@'linux123';
GRANT SELECT, REPLICATION CLIENT, REPLICATION SLAVE ON *.* TO 'maxwell'@'%';
GRANT SELECT, REPLICATION CLIENT, REPLICATION SLAVE ON *.* TO 'maxwell'@'linux123';
flush privileges;




启动consumer

kafka-console-consumer.sh --bootstrap-server linux121:9092,linux122:9092,linux123:9092 --topic mysql_incre

启动maxwell
cd /opt/lagou/servers/maxwell-1.29.0


bin/maxwell --config driver.properties

nohup bin/maxwell --daemon --config driver.properties 2>&1 >> maxwell.log &

重启会出现日志不一致，直接删除maxwell数据库即可

1.3.8 Maxwell实现增量同步

Windows下Scala环境配置

下载scala-2.12.20.msi
windows上安装即可


idea配置scala

Flink程序，实现从kafak消费数据，写入Hbase

先看能不能从Kafka拿到数据

/** *
 * 1 使用flink消费kafka中mysql_incre主题的数据
 * 2 解析对应的操作，同步数据到hbase指定表中
 * kafka中消息格式如下：
 * {"database":"talents","table":"lg_account","type":"update","ts":1612503687,"xid":5254102,
 * "commit":true,"position":"mysql-bin.000001:125536870","server_id":1,"thread_id":1443,
 * "schema_id":221,"data":{"id":556,"sex":"男","age":23,"expectcity":"北京","expectpositionname":"广告协调","
 * expectpositionnametype1":"市场|商务类","expectpositionnametype2":"媒介|公关","expectsalarys":"20k-40k","
 * highesteducation":"本科","latest_schoolname":"北京工商大学","c10":"0","latest_companyname":"昌荣传媒股份有限公司","
 * is_famous_enterprise":"1","work_year":"10年","status":"离职找工作"},"old":{"age":33}}
 */

case class TableObject(database: String, tableName: String, typeInfo: String, dataInfo: String) extends Serializable

object SyncApp {
  def main(args: Array[String]): Unit = {
    // 获取flink运行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //创建kafka消费者
    val kafkaSource: FlinkKafkaConsumer[String] = new SourceKafka().getKafkaSource("mysql_incre")
    val Stream = env.addSource(kafkaSource)
    Stream.print()

    //启动
    env.execute("mysql_data_incre_sync")
  }
}

再解析拿到的数据

    //解析maxwell传递的数据
    val tableObjectStream: DataStream[TableObject] = Stream.map(msg => {
      val jsonObject = JSON.parseObject(msg)
      //获取数据库信息
      val databaseName = jsonObject.get("database")
      //获取表信息
      val tableName = jsonObject.get("table")
      //获取操作类型
      val typeInfo = jsonObject.get("type")
      //获取到最新数据
      val newData = jsonObject.get("data")
      TableObject(databaseName.toString, tableName.toString, typeInfo.toString, newData.toString);
    })
    tableObjectStream.print()

再写入数据到hbase

见lagou_deliver代码

验证

select * from "dim_account" where "id" = '30';

1.4 安装Hive，安装CH，对用户行为数据进行实时OLAP分析

1.4.1 Hive安装(Linux122)

下载mysql-connector-java-5.1.46.jar
https://downloads.mysql.com/archives/c-j/
下载解压apache-hive-2.3.7-bin.tar.gz
http://archive.apache.org/dist/hive/

cd /opt/lagou/software
tar zxvf apache-hive-2.3.7-bin.tar.gz -C ../servers/
cd ../servers
mv apache-hive-2.3.7-bin hive-2.3.7

vi /etc/profile
 
export HIVE_HOME=/opt/lagou/servers/hive-2.3.7
export PATH=$PATH:$HIVE_HOME/bin
# 执行并生效
source /etc/profile

cd $HIVE_HOME/conf 
cp hive-default.xml.template hive-site.xml
vi hive-site.xml

新增和修改

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<!-- hive元数据的存储位置 -->
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://linux123:3306/hivemetadata?createDatabaseIfNotExist=true&amp;useSSL=false</value>
<description>JDBC connect string for a JDBC
metastore</description>
</property>
<!-- 指定驱动程序 -->
<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
<description>Driver class name for a JDBC
metastore</description>
</property>
<!-- 连接数据库的用户名 -->
<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>hive</value>
<description>username to use against metastore
database</description>
</property>
<!-- 连接数据库的口令 -->
<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>12345678</value>
<description>password to use against metastore
database</description>
</property>
</configuration>


修改

记得去掉 配置文件中的system:，且不用在配置的value里面留空格

cd /opt/lagou/software
cp mysql-connector-java-5.1.46.jar $HIVE_HOME/lib/

在mysql中执行

-- 创建用户设置口令、授权、刷新
CREATE USER 'hive'@'%' IDENTIFIED BY '12345678';
GRANT ALL ON *.* TO 'hive'@'%';
FLUSH PRIVILEGES;

schematool -dbType mysql -initSchema

hive

show functions;

1.4.2 业务数据导入hive

create database ods;
use ods;

CREATE EXTERNAL TABLE ods.`ods_company`(
`cid` STRING,
`companyname` string,
`is_famous_enterprise` string,
`financestage` string,
`city` string,
`companysize` string,
`industryfield` string)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES
("hbase.columns.mapping"
=":key,cy:companyname,cy:is_famous_enterprise,cy:financestage,cy:city,cy:company
size,cy:industryfield")
TBLPROPERTIES("hbase.table.name" = "dim_company");

CREATE EXTERNAL TABLE ods.`ods_account1`(
`id` String,
`sex` string,
`age` String,
`expectcity` string,
`expectpositionname` string,
`expectpositionnametype1` string,
`expectpositionnametype2` string,
`expectsalarys` string,
`highesteducation` string,
`latest_schoolname` string,
`_c10` string,
`latest_companyname` string,
`is_famous_enterprise` string,
`work_year` string,
`status` string)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES
("hbase.columns.mapping"
=":key,user:sex,user:age,user:expectcity,user:expectpositionname,user:expectposi
tionnametype1,user:expectpositionnametype2,user:expectsalarys,user:highesteducat
ion,user:latest_schoolname,user:_c10,user:latest_companyname,user:is_famous_ente
rprise,user:work_year,user:status")
TBLPROPERTIES("hbase.table.name" = "dim_account");

CREATE EXTERNAL TABLE ods.`ods_position`(
`id` string,
`positionname` string,
`positionfirstcategory` string,
`positionsecondcategory` string,
`positionthirdcategory` string,
`workyear` string,
`education` string,
`salarymin` STRING,
`salarymax` STRING,
`city` string,
`companyid` STRING,
`createtime` string,
`lastupdatetime` string)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES
("hbase.columns.mapping"
=":key,position:positionname,position:positionfirstcategory,position:positionsec
ondcategory,position:positionthirdcategory,position:workyear,position:education,
position:salarymin,position:salarymax,position:city,position:companyid,position:
createtime,position:lastupdatetime")
TBLPROPERTIES("hbase.table.name" = "dim_position");

1.4.2 CH安装(Linux122)

下载v23.12.1.1368-stable

上传4个文件到/opt/lagou/software/clickhouse_rpm

安装
rpm -ivh ./*.rpm

vi /etc/clickhouse-server/config.xml
新增
<listent_host>0.0.0.0</listen_host>

启动
sudo -u clickhouse clickhouse-server --config-file=/etc/clickhouse-server/config.xml

连接
clickhouse-client -m

1.4.3 实时ETL

根据用户id,职位id,公司id到hbase中查询对应的信息，
插入数据到clickhouse中

见lagou_deliver代码

mvn install:install -file -DgroupId=com.clickhouse -DartifactId=clickhouse-jdbc  -Dversion=0.6.3 -Dpackaging=jar  -Dfile=E:\clickhouse-jdbc-0.6.3.jar

CREATE DATABASE IF NOT EXISTS lg_deliver_detail;

drop table lg_deliver_detail.deliver_detail;
CREATE TABLE lg_deliver_detail.deliver_detail(
    user_id UInt64,
    work_year String,
    expectpositionname String,
    positionid UInt64,
    positionname String,
    positionfirstcategory String,
    positionsecondcategory String,
    companyid UInt64,
    companyname String,
    highesteducation String,
    company_city String,
    is_famous_enterprise Int8,
    companysize String,
    expectsalarys String,
    expectcity String,
    education String,
    gender String,
    city String,
    workyear String,
    status String,
    dt String
) ENGINE = MergeTree()
PARTITION BY dt
ORDER BY user_id
SETTINGS index_granularity = 8192;

1.4.4 SuperSet安装

安装Python环境

mkdir /opt/soft
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

bash Miniconda3-latest-Linux-x86_64.sh

回车之后，一直按空格，提示Please answer 'yes' or 'no':' 输入yes。
指定安装路径/opt/soft/conda，回车默认

>>>/opt/soft/conda
PREFIX=/opt/soft/conda


初始化conda3，输入yes

Do you wish the installer to initialize Miniconda3
[no] >>> yes

3) 配置系统环境变量

vim /etc/profile
export CONDA_HOME=/opt/soft/conda
export PATH=$PATH:$CONDA_HOME/bin

source /etc/profile
source ~/.bashrc

可以发现前面多了（base）,python版本是3.11


取消激活base环境

conda config --set auto_activate_base false
bash

查看conda版本

复制成功
conda --version
conda 24.3.0

配置conda国内镜像

复制成功
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
 
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
 
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/

conda config --set show_channel_urls yes

conda config --show channels


7）创建python3.9环境

conda create --name superset python=3.9
y
8）激活superset环境

conda activate superset
若要退出环境使用以下命令：

conda deactivate

Superset部署

1）安装准备依赖

复制成功
sudo yum install -y gcc gcc-c++ libffi-devel python-devel python-pip python-wheel python-setuptools openssl-devel cyrus-sasl-devel openldap-devel


2）安装setuptools和pip

pip install --upgrade setuptools pip


3）安装supetest

pip install apache-superset --trusted-host https://repo.huaweicloud.com -i https://repo.huaweicloud.com/repository/pypi/simple


4）初始化数据库

superset db upgrade


遇到密匙安全性弱的报错

pip show apache-superset

进入superset安装路径

生成paste_your_generated_secret_key_her

openssl rand -base64 42

vi superset_config.py

SECRET_KEY = 'paste_your_generated_secret_key_here'

SECRET_KEY = 'ocuiR5/s93tYYrIjuGhMFkWrM00tt7Kd3lt2tJ07rAnxgp+cg4jKFmHF'

vi /etc/profile 

export SUPERSET_CONFIG_PATH=/opt/soft/conda/envs/superset/superset_config.py

source /etc/profile



5）创建管理员用户

export FLASK_APP=superset
superset fab create-admin


Username [admin]: 
User first name [admin]: 
User last name [user]: 
Email [admin@fab.org]: 
Password:
Repeat for confirmation：
Recognized Database Authentications.


root 12345678

6）Superset初始化

superset init


7）安装gunicorn

pip install gunicorn -i https://pypi.douban.com/simple/



8）启动superset
superset run -h linux122 -p 8080 --with-threads --reload --debugger


gunicorn --workers 5 --timeout 120 --bind [ip]:[port] "superset.app:create_app()" --daemon
若要停止superset使用以下命令：

ps -ef | awk '/superset/ && !/awk/{print $2}' | xargs kill -9




9）登录 Superset

linux122:8080

用户名:root 
密码:12345678

访问 ip:[port]，并使用前面创建的管理员账号进行登录。

连接数据库：
先安装环境:
conda activate superset

yum install python-devel -y
pip install gevent
sudo yum install groupinstall 'development tools'
yum install mysql-devel -y
yum install gcc -y
pip install mysqlclient


报错

pip install mysqlclient==1.4.4

测试

mysql://root:123@linux123/superset_demo?charset=utf8

点击 SQLLab > SQL Editor编写以下SQL语句

选择 数据库

select case when gender = 0 then '男' when gender = 1 then '女' else '保密' end as
gender, count(id) as total_count from t_user group by gender;

保存查询

点击 saved queries

运行查询，点击expolore浏览数据

配置图表类型为 Bar Chart 条形图

指定统计指标 sum(total_count)

指定序列为 gender（性别）

1.4.5 Superset展示ClickHouse数据

安装驱动

见官网文档:

Connecting to Databases | Superset

pip install clickhouse-connect

点击database，新增ch连接

clickhousedb://default:click@linux122:8123/default
clickhousedb://linux122:8123/default

新增图表

期望城市分组投递次数

select expectcity, count(1) total_cnt from lg_deliver_detail.deliver_detail group by
expectcity;

期望城市分组用户数

select expectcity, count(distinct(user_id)) total_user_cnt from
deliver_detail group by user_id,expectcity;

职位所在地分组统计职位数

select count(distinct(positionid)) total_jobs, city from deliver_detail
group by city

将之前设计好的图标整合到看板中

操作步骤：

1、点击 Dashboards > 添加看板

2、拖动之前开发好的 Charts 到看板中

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