无条件，可直接硬部署MYSQL与REDIS，即可使用项目。
一、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
二、启动并查看状态MySQL：
systemctl start mysqld.service
systemctl status mysqld.service
三、查看MySQL的默认密码：
grep "password" /var/log/mysqld.log
四、登录进MySQL
mysql -uroot -p
五、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 password替换成自己的密码
ALTER USER 'root'@'localhost' IDENTIFIED BY 'my password';
六、开启远程访问 (把下面的my password替换成自己的密码)
grant all privileges on *.* to 'root'@'%' identified by 'my password' with grant option;
flush privileges;
exit
七、在云服务上增加MySQL的端口（打开防火墙对应端口）
一、安装redis：
yum -y update
yum -y install redis
二、修改配置文件
vi /etc/redis.conf
protected-mode no
port 6379
timeout 0
save 900 1
save 300 10
save 60 10000
rdbcompression yes
dbfilename dump.rdb
appendonly yes
appendfsync everysec
requirepass austin
三、启动redis
systemctl start redis
service redis start
四、检查redis状态
sudo systemctl status redis
五、连接redis
# 默认端口号6379
redis-cli
# 验证密码
AUTH austin
六、在云服务上增加Redis的端口（打开防火墙对应端口）
为方便管理与部署，可以选择DOCKER-COMPOSE方式部署组件，同理除了MYSQL与REDIS，其余组件都是可选。
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
#慢的话可以用这个
sudo curl -L https://get.daocloud.io/docker/compose/releases/download/1.25.1/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)
docker-compose.yaml文件如下
version: '3'
services:
mysql:
image: mysql:5.7
container_name: mysql
restart: always
ports:
- 3306:3306
volumes:
- mysql-data:/var/lib/mysql
environment:
MYSQL_ROOT_PASSWORD: root123_A
TZ: Asia/Shanghai
command: --character-set-server=utf8mb4 --collation-server=utf8mb4_unicode_ci
volumes:
mysql-data:docker-compose up -d
docker ps
部署后，初始化SQL为./doc/sql/austin.sql，其余SQL安装对应组件才需要
安装文件详见./doc/docker/mysql目录
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:3.2
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
docker-compose up -d
docker ps
docker exec -it redis redis-cli
auth austin
安装文件详见./doc/docker/redis目录
新建搭建kafka环境的docker-compose.yml文件，内容如下：
version: '3'
services:
zookeeper:
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
- zookeeper
kafka-manager:
image: kafkamanager/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
安装文件详见./doc/docker/kafka目录
version: '2.1'
services:
apollo-quick-start:
image: nobodyiam/apollo-quick-start
container_name: apollo-quick-start
depends_on:
apollo-db:
condition: service_healthy
ports:
- "8080:8080"
- "8090:8090"
- "8070:8070"
links:
- apollo-db
apollo-db:
image: mysql:5.7
container_name: apollo-db
environment:
TZ: Asia/Shanghai
MYSQL_ALLOW_EMPTY_PASSWORD: 'yes'
healthcheck:
test: ["CMD", "mysqladmin" ,"ping", "-h", "localhost"]
interval: 5s
timeout: 1s
retries: 10
depends_on:
- apollo-dbdata
ports:
- "13306:3306"
volumes:
- ./sql:/docker-entrypoint-initdb.d
volumes_from:
- apollo-dbdata
apollo-dbdata:
image: alpine:latest
container_name: apollo-dbdata
volumes:
- /var/lib/mysql
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
安装文件详见./doc/docker/apollo目录
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: 1s
evaluation_interval: 1s
scrape_configs:
- job_name: 'prometheus'
static_configs: # TODO ip地址自己填我有相同的端口，因为是有两台机器，你们可以干掉相同的端口
- targets: ['ip:9090']
- job_name: 'cadvisor'
static_configs:
- targets: ['ip:8899']
- job_name: 'node'
static_configs:
- targets: ['ip:9100']
- job_name: 'cadvisor2'
static_configs:
- targets: ['ip:8899']
- job_name: 'node2'
static_configs:
- targets: ['ip:9100']
- job_name: 'austin'
metrics_path: '/actuator/prometheus'
static_configs:
- targets: ['ip:8888']（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
安装文件详见./doc/docker/prometheus目录
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
安装文件详见./doc/docker/graylog目录
docker-compose.yaml文件如下
version: '3'
services:
austin-xxl-job:
image: xuxueli/xxl-job-admin:2.3.0
container_name: xxl-job-admin
restart: always
ports:
- "6767:8080"
environment:
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=root123_A'
# TODO 添加MySql网络，并更改ip注意：ip和password需要更改为自己的，并且，我开的是6767端口
安装文件详见./doc/docker/xxljob目录
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:1.16.1
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:1.16.1
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安装文件详见./doc/docker/flink目录
部署Hive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 [email protected]:big-data-europe/docker-hive.git2、进入到项目的文件夹里
cd docker-hive3、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安装文件详见./doc/docker/hive目录
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）
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.tgz2、解压flink
tar -zxf flink-1.16.0-bin-scala_2.12.tgz3、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: 0.0.0.04、将flink官网提到连接hive所需要的jar包下载到flink的lib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.jar5、按照官网指示把flink-table-planner_2.12-1.16.0.jar和flink-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 $FLINK_HOME/opt/flink-table-planner-loader-1.16.0.jar6、把后续kafka所需要的依赖也下载到lib目录下
wget https://repo1.maven.org/maven2/org/apache/flink/flink-connector-kafka/1.16.0/flink-connector-kafka-1.16.0.jar
wget https://repo1.maven.org/maven2/org/apache/kafka/kafka-clients/3.3.1/kafka-clients-3.3.1.jar7、把工程下的hive-site.xml文件拷贝到$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>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.gz2、解压hadoop
tar -zxf hadoop-2.7.4.tar.gz3、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>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这一步为了能让flink在启动的时候，加载到jdk和hadoop的环境。
1、编辑/etc/profile文件
vim /etc/profile2、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:$PATH
export HADOOP_CLASSPATH=`hadoop classpath`3、让配置文件生效
source /etc/profile在部署flink服务器上增加hosts，有以下(ip为部署hive的地址）：
127.0.0.1 namenode
127.0.0.1 datanode
127.0.0.1 b2a0f0310722其中 b2a0f0310722是datanode的主机名，该主机名会随着hive的docker而变更，我们可以登录namenode的后台地址找到其主机名。而方法则是在部署hive的地址输入：
http://localhost:50070/dfshealth.html#tab-datanode
启动flink-sql的客户端：
./sql-client.sh在sql客户端下执行以下脚本命令，注：hive-conf-dir要放在$FLINK_HOME/conf下
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ip和port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"}version: '3'
services:
metabase:
image: metabase/metabase
container_name: metabase
ports:
- "5001:3000"
restart: always安装文件详见./doc/docker/metabase目录
docker-compose.yaml文件如下
version: "3"
services:
nacos1:
container_name: nacos-server
hostname: nacos-server
image: nacos/nacos-server:v2.1.0
environment:
- MODE=standalone
- PREFER_HOST_MODE=hostname
- SPRING_DATASOURCE_PLATFORM=mysql
- MYSQL_SERVICE_HOST=ip # TODO ip需设置
- MYSQL_SERVICE_PORT=3306
- MYSQL_SERVICE_USER=root
- MYSQL_SERVICE_PASSWORD=root123_A
- MYSQL_SERVICE_DB_NAME=nacos_config
- JVM_XMS=128m
- JVM_XMX=128m
- JVM_XMN=128m
volumes:
- /home/nacos/single-logs/nacos-server:/home/nacos/logs
- /home/nacos/init.d:/home/nacos/init.d
ports:
- 8848:8848
- 9848:9848
- 9849:9849
restart: on-failure安装文件详见./doc/docker/nacos目录
docker-compose.yaml文件如下
version: '3'
services:
rabbitmq:
image: registry.cn-hangzhou.aliyuncs.com/zhengqing/rabbitmq:3.7.8-management # 原镜像`rabbitmq:3.7.8-management` 【 注：该版本包含了web控制页面 】
container_name: rabbitmq # 容器名为'rabbitmq'
hostname: my-rabbit
restart: unless-stopped # 指定容器退出后的重启策略为始终重启，但是不考虑在Docker守护进程启动时就已经停止了的容器
environment: # 设置环境变量,相当于docker run命令中的-e
TZ: Asia/Shanghai
LANG: en_US.UTF-8
RABBITMQ_DEFAULT_VHOST: my_vhost # 主机名
RABBITMQ_DEFAULT_USER: admin # 登录账号
RABBITMQ_DEFAULT_PASS: admin # 登录密码
volumes: # 数据卷挂载路径设置,将本机目录映射到容器目录
- "./rabbitmq/data:/var/lib/rabbitmq"
ports: # 映射端口
- "5672:5672"
- "15672:15672"
安装文件详见./doc/docker/rabbitmq目录
docker-compose.yaml文件如下
version: '3.5'
services:
# mq服务
rocketmq_server:
image: foxiswho/rocketmq:server
container_name: rocketmq_server
ports:
- 9876:9876
volumes:
- ./rocketmq/rocketmq_server/logs:/opt/logs
- ./rocketmq/rocketmq_server/store:/opt/store
networks:
rocketmq:
aliases:
- rocketmq_server
# mq中间件
rocketmq_broker:
image: foxiswho/rocketmq:broker
container_name: rocketmq_broker
ports:
- 10909:10909
- 10911:10911
volumes:
- ./rocketmq/rocketmq_broker/logs:/opt/logs
- ./rocketmq/rocketmq_broker/store:/opt/store
- ./rocketmq/rocketmq_broker/conf/broker.conf:/etc/rocketmq/broker.conf
environment:
NAMESRV_ADDR: "rocketmq_server:9876"
JAVA_OPTS: " -Duser.home=/opt"
JAVA_OPT_EXT: "-server -Xms128m -Xmx128m -Xmn128m"
command: mqbroker -c /etc/rocketmq/broker.conf
depends_on:
- rocketmq_server
networks:
rocketmq:
aliases:
- rocketmq_broker
# mq可视化控制台
rocketmq_console_ng:
image: styletang/rocketmq-console-ng
container_name: rocketmq_console_ng
ports:
- 9002:8080
environment:
JAVA_OPTS: "-Drocketmq.namesrv.addr=rocketmq_server:9876 -Dcom.rocketmq.sendMessageWithVIPChannel=false"
depends_on:
- rocketmq_server
networks:
rocketmq:
aliases:
- rocketmq_console_ng
#容器通信network
networks:
rocketmq:
name: rocketmq
driver: bridge安装文件详见./doc/docker/rocketmq目录