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# 一、硬部署
无条件可直接硬部署MYSQL与REDIS即可使用项目。
## 01、安装MYSQL
**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
```
![](images/10.png)
**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
## 02、安装REDIS
**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
---
# 二、DOCKER-COMPOSE方式部署
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**。
## 01、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
#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)
## 02、安装MySql
`docker-compose.yaml`文件如下
```yaml
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**
## 03、安装REDIS
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:
```yaml
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**
## 04、安装KAFKA(可选)
新建搭建kafka环境的`docker-compose.yml`文件,内容如下:
```yaml
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**
## 05、安装APOLLO(可选)
```yaml
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:
![](images/11.png)
注:我的配置里更改过端口,所以我的程序`AustinApplication`写的端口为7000
![](images/12.png)
**<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。
![](images/13.png)
PS我的namespace是`boss.austin`
![](images/14.png)
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**
## 06、安装PROMETHEUS和GRAFANA(可选)
存放`docker-compose.yml`的信息:
```yaml
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`
```yaml
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
![](images/15.png)
进到配置页面写下对应的URL然后保存就好了。
![](images/16.png)
相关监控的模板可以在 <https://grafana.com/grafana/dashboards/> 这里查到。
![](images/17.png)
服务器的监控直接选用**8919**的就好了
![](images/18.png)
![](images/19.png)
import后就能直接看到高大上的监控页面了
![](images/20.png)
使用模板**893**来配置监控docker的信息
![](images/21.png)
![](images/22.png)
选用了`4701`模板的JVM监控和`12900`SpringBoot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
```
![](images/23.png)
![](images/24.png)
**安装文件详见./doc/docker/prometheus目录**
## 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`
![](images/25.png)
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)。
![](images/26.png)
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**
## 08、XXL-JOB(可选)
`docker-compose.yaml`文件如下
```yaml
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
![](images/27.png)
**安装文件详见./doc/docker/xxljob目录**
## 09、Flink(可选)
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
```yaml
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**
## 10、HIVE(可选)
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
```shell
git clone git@github.com:big-data-europe/docker-hive.git
```
2、进入到项目的文件夹里
```shell
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
```yml
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。
```shell
# 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**
## 11、FLINK和HIVE融合(可选)
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
```shell
wget https://dlcdn.apache.org/flink/flink-1.16.0/flink-1.16.0-bin-scala_2.12.tgz
```
2、解压`flink`
```shell
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`
```shell
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`flink`的`lib`目录下(一共4个)
```shell
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.jar`和`flink-table-planner-loader-1.16.0.jar` 这俩个`jar`包移动其目录;
```shell
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 $FLINK_HOME/opt/flink-table-planner-loader-1.16.0.jar
```
6、把后续`kafka`所需要的依赖也下载到`lib`目录下
```shell
wget https://repo1.maven.org/maven2/org/apache/flink/flink-connector-kafka/1.16.0/flink-connector-kafka-1.16.0.jar
wget https://repo1.maven.org/maven2/org/apache/kafka/kafka-clients/3.3.1/kafka-clients-3.3.1.jar
```
7、把工程下的`hive-site.xml`文件拷贝到`$FLINK_HOME/conf`下,内容如下(**hive_ip**自己变动)
```xml
<?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
```shell
wget https://archive.apache.org/dist/hadoop/common/hadoop-2.7.4/hadoop-2.7.4.tar.gz
```
2、解压`hadoop`
```shell
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`
```xml
<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`
```shell
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`jdk`和`hadoop`的环境。
1、编辑`/etc/profile`文件
```shell
vim /etc/profile
```
2、文件内容最底下增加以下配置
```shell
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:$PATH
export HADOOP_CLASSPATH=`hadoop classpath`
```
3、让配置文件生效
```shell
source /etc/profile
```
### 11.5 增加hosts进行通信flink和namenode/datanode之间
在部署`flink`服务器上增加`hosts`,有以下(`ip`为部署`hive`的地址):
```shell
127.0.0.1 namenode
127.0.0.1 datanode
127.0.0.1 b2a0f0310722
```
其中 `b2a0f0310722`是`datanode`的主机名,该主机名会随着`hive`的`docker`而变更,我们可以登录`namenode`的后台地址找到其主机名。而方法则是在部署`hive`的地址输入:
```
http://localhost:50070/dfshealth.html#tab-datanode
```
![](images/28.png)
### 11.6 启动flink调试kafka数据到hive
启动`flink-sql`的客户端:
```shell
./sql-client.sh
```
在`sql`客户端下执行以下脚本命令,注:`hive-conf-dir`要放在`$FLINK_HOME/conf`下
```shell
CREATE CATALOG my_hive WITH (
'type' = 'hive',
'hive-conf-dir' = '/root/flink-1.16.0/conf'
);
```
```shell
use catalog my_hive;
```
```shell
create database austin;
```
重启`flink`集群
```shell
./stop-cluster.sh
```
```shell
./start-cluster.sh
```
重新提交执行`flink`任务
```shell
./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
```shell
$KAFKA_HOME/bin/kafka-console-producer.sh --topic austinTraceLog --broker-list ip:port
```
输入测试数据:
```json
{"state":"1","businessId":"2","ids":[1,2,3],"logTimestamp":"123123"}
```
## 12、安装METABASE(可选)
```yaml
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**
## 13、安装单机nacos(可选)
`docker-compose.yaml`文件如下
```yaml
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**
## 14、安装单机rabbitmq(可选)
`docker-compose.yaml`文件如下
```yaml
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**
## 15、安装单机rocketmq(可选)
`docker-compose.yaml`文件如下
```yaml
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**