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01、安装MYSQL

、下载并安装mysql

wget -i -c http://dev.mysql.com/get/mysql57-community-release-el7-10.noarch.rpm
yum -y install mysql57-community-release-el7-10.noarch.rpm
yum -y install mysql-community-server --nogpgcheck

、启动并查看状态MySQL

systemctl start  mysqld.service
systemctl status mysqld.service

、查看MySQL的默认密码

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

img

、登录进MySQL

mysql -uroot -p

、修改默认密码(设置密码需要有大小写符号组合—安全性),把下面的my passrod替换成自己的密码

ALTER USER 'root'@'localhost' IDENTIFIED BY 'my password';

、开启远程访问 (把下面的my passrod替换成自己的密码)

grant all privileges on *.* to 'root'@'%' identified by 'my password' with grant option;

flush privileges;

exit

、在云服务上增加MySQL的端口

02、安装DOCKER和DOCKER-COMPOSE

首先我们需要安装GCC相关的环境

yum -y install gcc

yum -y install gcc-c++

安装Docker需要的依赖软件包

yum install -y yum-utils device-mapper-persistent-data lvm2

设置国内的镜像(提高速度)

yum-config-manager --add-repo http://mirrors.aliyun.com/docker-ce/linux/centos/docker-ce.repo

更新yum软件包索引

yum makecache fast

安装DOCKER CE(注意Docker分为CE版和EE版一般我们用CE版就够用了.)

yum -y install docker-ce

启动Docker

systemctl start docker

下载回来的Docker版本:

docker version

运行以下命令以下载 Docker Compose 的当前稳定版本:

sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

将可执行权限应用于二进制文件:

sudo chmod +x /usr/local/bin/docker-compose

创建软链:

sudo ln -s /usr/local/bin/docker-compose /usr/bin/docker-compose

测试是否安装成功:

docker-compose --version

Austin项目的中间件使用docker进行部署文件内容可以参考项目中docker文件夹)

03、安装KAFKA

新建搭建kafka环境的docker-compose.yml文件,内容如下:

version: '3'
services:
  zookepper:
    image: wurstmeister/zookeeper                    # 原镜像`wurstmeister/zookeeper`
    container_name: zookeeper                        # 容器名为'zookeeper'
    volumes:                                         # 数据卷挂载路径设置,将本机目录映射到容器目录
      - "/etc/localtime:/etc/localtime"
    ports:                                           # 映射端口
      - "2181:2181"

  kafka:
    image: wurstmeister/kafka                                # 原镜像`wurstmeister/kafka`
    container_name: kafka                                    # 容器名为'kafka'
    volumes:                                                 # 数据卷挂载路径设置,将本机目录映射到容器目录
      - "/etc/localtime:/etc/localtime"
    environment:                                                       # 设置环境变量,相当于docker run命令中的-e
      KAFKA_BROKER_ID: 0                                               # 在kafka集群中每个kafka都有一个BROKER_ID来区分自己
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://ip:9092 # TODO 将kafka的地址端口注册给zookeeper
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092                        # 配置kafka的监听端口
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181                
      KAFKA_CREATE_TOPICS: "hello_world"
      KAFKA_HEAP_OPTS: -Xmx1G -Xms256M
    ports:                              # 映射端口
      - "9092:9092"
    depends_on:                         # 解决容器依赖启动先后问题
      - zookepper

  kafka-manager:
    image: sheepkiller/kafka-manager                         # 原镜像`sheepkiller/kafka-manager`
    container_name: kafka-manager                            # 容器名为'kafka-manager'
    environment:                        # 设置环境变量,相当于docker run命令中的-e
      ZK_HOSTS: zookeeper:2181 
      APPLICATION_SECRET: xxxxx
      KAFKA_MANAGER_AUTH_ENABLED: "true"  # 开启kafka-manager权限校验
      KAFKA_MANAGER_USERNAME: admin       # 登陆账户
      KAFKA_MANAGER_PASSWORD: 123456      # 登陆密码
    ports:                              # 映射端口
      - "9000:9000"
    depends_on:                         # 解决容器依赖启动先后问题
      - kafka

文件内 // TODO 中的ip需要改成自己的,并且如果你用的是云服务器,那需要把端口给打开。

在存放docker-compose.yml的目录下执行启动命令:

docker-compose up -d

可以查看下docker镜像运行的情况

docker ps 

进入kafka 的容器:

docker exec -it kafka sh

创建两个topic(这里我的topicName就叫austinBusiness、austinTraceLog、austinRecall你们可以改成自己的)

$KAFKA_HOME/bin/kafka-topics.sh --create --topic austinBusiness --partitions 1 --zookeeper zookeeper:2181 --replication-factor 1

$KAFKA_HOME/bin/kafka-topics.sh --create --topic austinTraceLog --partitions 1 --zookeeper zookeeper:2181 --replication-factor 1

$KAFKA_HOME/bin/kafka-topics.sh --create --topic austinRecall --partitions 1 --zookeeper zookeeper:2181 --replication-factor 1
 

查看刚创建的topic信息

$KAFKA_HOME/bin/kafka-topics.sh --zookeeper zookeeper:2181 --describe --topic austinBusiness

04、安装REDIS

首先,我们新建一个文件夹redis,然后在该目录下创建出data文件夹、redis.conf文件和docker-compose.yaml文件

redis.conf文件的内容如下(后面的配置可在这更改比如requirepass 我指定的密码为austin)

protected-mode no
port 6379
timeout 0
save 900 1 
save 300 10
save 60 10000
rdbcompression yes
dbfilename dump.rdb
dir /data
appendonly yes
appendfsync everysec
requirepass austin

docker-compose.yaml的文件内容如下:

version: '3'
services:
  redis:
    image: redis:latest
    container_name: redis
    restart: always
    ports:
      - 6379:6379
    volumes:
      - ./redis.conf:/usr/local/etc/redis/redis.conf:rw
      - ./data:/data:rw
    command:
      /bin/bash -c "redis-server /usr/local/etc/redis/redis.conf "

配置的工作就完了如果是云服务器记得开redis端口6379

启动Redis跟之前安装Kafka的时候就差不多啦

docker-compose up -d

docker ps

docker exec -it redis redis-cli

auth austin

05、安装APOLLO

部署Apollo跟之前一样直接用docker-compose就完事了在GitHub已经给出了对应的教程和docker-compose.yml以及相关的文件,直接复制粘贴就完事咯。

PS: Apollo 的docker配置文件可以参考:docker/apollo/文件夹, 简单来说,在 docker/apollo/docker-quick-start/文件夹下执行docker-compose up -d 执行即可.

目录结构最好保持一致:

注:我的配置里更改过端口,所以我的程序AustinApplication写的端口为7000

https://www.apolloconfig.com/#/zh/deployment/quick-start-docker

https://github.com/apolloconfig/apollo/tree/master/scripts/docker-quick-start

部门的创建其实也是一份"配置",输入organizations就能把现有的部门给改掉,我新增了boss股东部门,大家都是我的股东。

PS我的namespace是boss.austin

apollo配置样例可看example/apollo.properties文件的内容

dynamic-tp-apollo-dtp它是一个apollo的namespace存放着动态线程池的配置

动态线程池样例配置可看 dynamic-tp-apollo-dtp.yml 文件的内容

06、安装PROMETHEUS和GRAFANA(可选)

存放docker-compose.yml的信息:

version: '2'

networks:
    monitor:
        driver: bridge

services:
    prometheus:
        image: prom/prometheus
        container_name: prometheus
        hostname: prometheus
        restart: always
        volumes:
            - ./prometheus.yml:/etc/prometheus/prometheus.yml
        ports:
            - "9090:9090"
        networks:
            - monitor

    alertmanager:
        image: prom/alertmanager
        container_name: alertmanager
        hostname: alertmanager
        restart: always
        ports:
            - "9093:9093"
        networks:
            - monitor

    grafana:
        image: grafana/grafana
        container_name: grafana
        hostname: grafana
        restart: always
        ports:
            - "3000:3000"
        networks:
            - monitor

    node-exporter:
        image: quay.io/prometheus/node-exporter
        container_name: node-exporter
        hostname: node-exporter
        restart: always
        ports:
            - "9100:9100"
        networks:
            - monitor

    cadvisor:
        image: google/cadvisor:latest
        container_name: cadvisor
        hostname: cadvisor
        restart: always
        volumes:
            - /:/rootfs:ro
            - /var/run:/var/run:rw
            - /sys:/sys:ro
            - /var/lib/docker/:/var/lib/docker:ro
        ports:
            - "8899:8080"
        networks:
            - monitor

新建prometheus的配置文件prometheus.yml

global:
  scrape_interval:     15s
  evaluation_interval: 15s
scrape_configs:
  - job_name: 'prometheus'
    static_configs:
    - targets: ['ip:9090']  
  - job_name: 'cadvisor'
    static_configs:
    - targets: ['ip:8899']  
  - job_name: 'node'
    static_configs:
    - targets: ['ip:9100']  

这里要注意端口,按自己配置的来,ip也要填写为自己的

把这份prometheus.yml的配置往/etc/prometheus/prometheus.yml 路径下复制一份。随后在目录下docker-compose up -d启动,于是我们就可以分别访问:

  • http://ip:9100/metrics( 查看服务器的指标)
  • http://ip:8899/metrics查看docker容器的指标
  • http://ip:9090/(prometheus的原生web-ui)
  • http://ip:3000/(Grafana开源的监控可视化组件页面)

进到Grafana首页配置prometheus作为数据源

进到配置页面写下对应的URL然后保存就好了。

相关监控的模板可以在 https://grafana.com/grafana/dashboards/ 这里查到。

服务器的监控直接选用8919的就好了

import后就能直接看到高大上的监控页面了

使用模板893来配置监控docker的信息

选用了4701模板的JVM监控和12900SpringBoot监控程序代码已经接入了actuator和prometheus)。需要在prometheus.yml配置下新增暴露的服务地址:

  - job_name: 'austin'
    metrics_path: '/actuator/prometheus' # 采集的路径
    static_configs:
    - targets: ['ip:port'] # todo 这里的ip和端口写自己的应用下的

07、安装GRAYLOG可选-分布式日志收集框架

docker-compose.yml文件内容:

version: '3'
services:
    mongo:
      image: mongo:4.2
      networks:
        - graylog
    elasticsearch:
      image: docker.elastic.co/elasticsearch/elasticsearch-oss:7.10.2
      environment:
        - http.host=0.0.0.0
        - transport.host=localhost
        - network.host=0.0.0.0
        - "ES_JAVA_OPTS=-Dlog4j2.formatMsgNoLookups=true -Xms512m -Xmx512m"
        - GRAYLOG_ROOT_TIMEZONE=Asia/Shanghai
      ulimits:
        memlock:
          soft: -1
          hard: -1
      deploy:
        resources:
          limits:
            memory: 1g
      networks:
        - graylog
    graylog:
      image: graylog/graylog:4.2
      environment:
        - GRAYLOG_PASSWORD_SECRET=somepasswordpepper
        - GRAYLOG_ROOT_PASSWORD_SHA2=8c6976e5b5410415bde908bd4dee15dfb167a9c873fc4bb8a81f6f2ab448a918
        - GRAYLOG_HTTP_EXTERNAL_URI=http://ip:9009/ # 这里注意要改ip
        - GRAYLOG_ROOT_TIMEZONE=Asia/Shanghai
      entrypoint: /usr/bin/tini -- wait-for-it elasticsearch:9200 --  /docker-entrypoint.sh
      networks:
        - graylog
      restart: always
      depends_on:
        - mongo
        - elasticsearch
      ports:
        - 9009:9000
        - 1514:1514
        - 1514:1514/udp
        - 12201:12201
        - 12201:12201/udp
networks:
    graylog:
      driver: bridge

这个文件里唯一需要改动的就是ip(本来的端口是9000的,我由于已经占用了9000端口了,所以我这里把端口改成了9009,你们可以随意)

启动以后,我们就可以通过ip:port访问对应的Graylog后台地址了默认的账号和密码是admin/admin

配置下inputs的配置,找到GELF UDP,然后点击Launch new input,只需要填写Title字段,保存就完事了(其他不用动)。

最后配置austin.grayLogIp的ip即可实现分布式日志收集

08、XXL-JOB

文档:https://www.xuxueli.com/xxl-job/#2.1%20%E5%88%9D%E5%A7%8B%E5%8C%96%E2%80%9C%E8%B0%83%E5%BA%A6%E6%95%B0%E6%8D%AE%E5%BA%93%E2%80%9D

xxl-job的部署我这边其实是依赖官网的文档的步骤可以简单总结为

1、把xxl-job的仓库拉下来

2、执行/xxl-job/doc/db/tables_xxl_job.sql的脚本(创建对应的库、创建表以及插入测试数据记录)

3、如果是本地启动「调度中心」则在xxl-job-adminapplication.properties更改相应的数据库配置,改完启动即可

4、如果是云服务启动「调度中心」,则可以选择拉取docker镜像进行部署,我拉取的是2.30版本,随后执行以下命令即可:

docker pull xuxueli/xxl-job-admin:2.3.0

docker run -e PARAMS="--spring.datasource.url=jdbc:mysql://ip:3306/xxl_job?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&useSSL=false&zeroDateTimeBehavior=convertToNull --spring.datasource.username=root --spring.datasource.password=password " -p 6767:8080 --name xxl-job-admin  -d xuxueli/xxl-job-admin:2.3.0

注意:第二条命令的ippassword需要更改为自己的,并且,我开的是6767端口

部署Flink也是直接上docker-compose就完事了值得注意的是我们在部署的时候需要在配置文件里指定时区

docker-compose.yml配置内容如下

version: "2.2"
services:
  jobmanager:
    image: flink:latest
    ports:
      - "8081:8081"
    command: jobmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager        
      - SET_CONTAINER_TIMEZONE=true
      - CONTAINER_TIMEZONE=Asia/Shanghai
      - TZ=Asia/Shanghai
  taskmanager:
    image: flink:latest
    depends_on:
      - jobmanager
    command: taskmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
        taskmanager.numberOfTaskSlots: 2        
      - SET_CONTAINER_TIMEZONE=true
      - CONTAINER_TIMEZONE=Asia/Shanghai
      - TZ=Asia/Shanghai

10、HIVE

部署Flink也是直接上docker-compose就完事了

1、把仓库拉到自己的服务器上

git clone git@github.com:big-data-europe/docker-hive.git

2、进入到项目的文件夹里

cd docker-hive

3、微调下docker-compose文件内容如下主要是增加了几个通信的端口

version: "3"

services:
  namenode:
    image: bde2020/hadoop-namenode:2.0.0-hadoop2.7.4-java8
    volumes:
      - namenode:/hadoop/dfs/name
    environment:
      - CLUSTER_NAME=test
    env_file:
      - ./hadoop-hive.env
    ports:
      - "50070:50070"
      - "9000:9000"
      - "8020:8020"
  datanode:
    image: bde2020/hadoop-datanode:2.0.0-hadoop2.7.4-java8
    volumes:
      - datanode:/hadoop/dfs/data
    env_file:
      - ./hadoop-hive.env
    environment:
      SERVICE_PRECONDITION: "namenode:50070"
    ports:
      - "50075:50075"
      - "50010:50010"
      - "50020:50020"
  hive-server:
    image: bde2020/hive:2.3.2-postgresql-metastore
    env_file:
      - ./hadoop-hive.env
    environment:
      HIVE_CORE_CONF_javax_jdo_option_ConnectionURL: "jdbc:postgresql://hive-metastore/metastore"
      SERVICE_PRECONDITION: "hive-metastore:9083"
    ports:
      - "10000:10000"
  hive-metastore:
    image: bde2020/hive:2.3.2-postgresql-metastore
    env_file:
      - ./hadoop-hive.env
    command: /opt/hive/bin/hive --service metastore
    environment:
      SERVICE_PRECONDITION: "namenode:50070 datanode:50075 hive-metastore-postgresql:5432"
    ports:
      - "9083:9083"
  hive-metastore-postgresql:
    image: bde2020/hive-metastore-postgresql:2.3.0
    ports:
      - "5432:5432"
  presto-coordinator:
    image: shawnzhu/prestodb:0.181
    ports:
      - "8080:8080"
volumes:
  namenode:
  datanode:

4、最后我们可以连上hive的客户端,感受下快速安装好hive的成功感。

# 进入bash
docker-compose exec hive-server bash

# 使用beeline客户端连接
/opt/hive/bin/beeline -u jdbc:hive2://localhost:10000

11、FLINK和HIVE融合

实时流处理的flink用的是docker-compose进行部署而与hive融合的flink我这边是正常的姿势安装主要是涉及的环境很多用docker-compose就相对没那么方便了

11.1 安装flink环境

1、下载flink压缩包

wget https://dlcdn.apache.org/flink/flink-1.16.0/flink-1.16.0-bin-scala_2.12.tgz

2、解压flink

tar -zxf flink-1.16.0-bin-scala_2.12.tgz

3、修改该目录下的conf下的flink-conf.yaml文件中rest.bind-address配置,不然远程访问不到8081端口,将其改为0.0.0.0

rest.bind-address: 0.0.0.0

4、将flink官网提到连接hive所需要的jar包下载到flinklib目录下(一共4个)

wget https://repo.maven.apache.org/maven2/org/apache/flink/flink-sql-connector-hive-2.3.9_2.12/1.16.0/flink-sql-connector-hive-2.3.9_2.12-1.16.0.jar

wget https://repo.maven.apache.org/maven2/org/apache/hive/hive-exec/2.3.4/hive-exec-2.3.4.jar

wget https://repo.maven.apache.org/maven2/org/apache/flink/flink-connector-hive_2.12/1.16.0/flink-connector-hive_2.12-1.16.0.jar 

wget https://repo.maven.apache.org/maven2/org/antlr/antlr-runtime/3.5.2/antlr-runtime-3.5.2.jar

5、按照官网指示把flink-table-planner_2.12-1.16.0.jarflink-table-planner-loader-1.16.0.jar 这俩个jar包移动其目录;

mv $FLINK_HOME/opt/flink-table-planner_2.12-1.16.0.jar $FLINK_HOME/lib/flink-table-planner_2.12-1.16.0.jar
mv $FLINK_HOME/lib/flink-table-planner-loader-1.16.0.jar $FLINK_HOME/opt/flink-table-planner-loader-1.16.0.jar

6、把后续kafka所需要的依赖也下载到lib目录下

wget https://repo1.maven.org/maven2/org/apache/flink/flink-connector-kafka/1.16.0/flink-connector-kafka-1.16.0.jar

wget https://repo1.maven.org/maven2/org/apache/kafka/kafka-clients/3.3.1/kafka-clients-3.3.1.jar

7、把工程下的hive-site.xml文件拷贝到$FLINK_HOME/conf下,内容如下(hive_ip自己变动)

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>


<configuration>
    <property>
        <name>javax.jdo.option.ConnectionURL</name>
        <value>jdbc:postgresql://hive_ip:5432/metastore?createDatabaseIfNotExist=true</value>
        <description>JDBC connect string for a JDBC metastore</description>
    </property>

    <property>
        <name>javax.jdo.option.ConnectionDriverName</name>
        <value>org.postgresql.Driver</value>
        <description>Driver class name for a JDBC metastore</description>
    </property>

    <property>
        <name>javax.jdo.option.ConnectionUserName</name>
        <value>hive</value>
        <description>username to use against metastore database</description>
    </property>

    <property>
        <name>javax.jdo.option.ConnectionPassword</name>
        <value>hive</value>
        <description>password to use against metastore database</description>
    </property>

    <property>
        <name>hive.metastore.uris</name>
        <value>thrift://hive_ip:9083</value>
        <description>Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.
        </description>
    </property>

    <property>
        <name>datanucleus.schema.autoCreateAll</name>
        <value>true</value>
    </property>

</configuration>

11.2 安装hadoop环境

由于hive的镜像已经锁死了hadoop的版本为2.7.4,所以我这边flink所以来的hadoop也是下载2.7.4版本

1、下载hadoop压缩包

wget https://archive.apache.org/dist/hadoop/common/hadoop-2.7.4/hadoop-2.7.4.tar.gz

2、解压hadoop

tar -zxf hadoop-2.7.4.tar.gz

3、hadoop的配置文件hdfs-site.xml增加以下内容(我的目录在/root/hadoop-2.7.4/etc/hadoop

<property>
    <name>dfs.client.use.datanode.hostname</name>
    <value>true</value>
    <description>only cofig in clients</description>
</property>

11.3 安装jdk11

由于高版本的flink需要jdk 11,所以这边安装下该版本的jdk

yum install java-11-openjdk.x86_64
yum install java-11-openjdk-devel.x86_64

11.4 配置jdk、hadoop的环境变量

这一步为了能让flink在启动的时候,加载到jdkhadoop的环境。

1、编辑/etc/profile文件

vim /etc/profile

2、文件内容最底下增加以下配置

JAVA_HOME=/usr/lib/jvm/java-11-openjdk-11.0.17.0.8-2.el7_9.x86_64
JRE_HOME=$JAVA_HOME/jre
CLASS_PATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar:$JRE_HOME/lib
PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin
export JAVA_HOME JRE_HOME CLASS_PATH PATH
export HADOOP_HOME=/root/hadoop-2.7.4
export PATH=$HADOOP_HOME/bin:$PATH
export HADOOP_CLASSPATH=`hadoop classpath`

3、让配置文件生效

source /etc/profile

11.5 增加hosts进行通信flink和namenode/datanode之间

在部署flink服务器上增加hosts,有以下(ip为部署hive的地址):

127.0.0.1 namenode
127.0.0.1 datanode
127.0.0.1 b2a0f0310722

其中 b2a0f0310722datanode的主机名,该主机名会随着hivedocker而变更,我们可以登录namenode的后台地址找到其主机名。而方法则是在部署hive的地址输入:

http://localhost:50070/dfshealth.html#tab-datanode

11.6 启动flink调试kafka数据到hive

启动flink-sql的客户端:

./sql-client.sh

sql客户端下执行以下脚本命令,注:hive-conf-dir要放在$FLINK_HOME/conf

CREATE CATALOG my_hive WITH (
    'type' = 'hive',
    'hive-conf-dir' = '/root/flink-1.16.0/conf'
);
use catalog my_hive;
create database austin;

重启flink集群

./stop-cluster.sh
./start-cluster.sh

重新提交执行flink任务

./flink run austin-data-house-0.0.1-SNAPSHOT.jar

启动消费者的命令(将ipport改为自己服务器所部署的Kafka信息

$KAFKA_HOME/bin/kafka-console-producer.sh --topic austinTraceLog  --broker-list ip:port

输入测试数据:

{"state":"1","businessId":"2","ids":[1,2,3],"logTimestamp":"123123"}

12、安装METABASE

部署Metabase很简单,也是使用docker进行安装部署,就两行命令(后续我会将其加入到docker-compose里面)。

docker pull metabase/metabase:latest
docker run -d -p 5001:3000 --name metabase metabase/metabase

完了之后,我们就可以打开5001端口到Metabase的后台了。

13、未完待续

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