## 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
```

[![img](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/98b36a9b01de4cc79f3a53245296a19c~tplv-k3u1fbpfcp-zoom-1.image)](https://tva1.sinaimg.cn/large/008i3skNgy1gwg6eiwyqfj313402mgm8.jpg)

**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 passrod`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 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
```

**七**、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：

![](https://p9-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/a532e3221834466a85b6739871694957~tplv-k3u1fbpfcp-watermark.image?)

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://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/1b3944f3a9e849219c60e673baa5b7ff~tplv-k3u1fbpfcp-watermark.image?)

**<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。

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/41b096b379244288a8ab25c67b484c62~tplv-k3u1fbpfcp-zoom-1.image)

PS：我的namespace是`boss.austin`

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/4c4636a5620a454b931aea8b248e2890~tplv-k3u1fbpfcp-watermark.image?)

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

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/76474c290b594d72b8c26f32e6c93753~tplv-k3u1fbpfcp-zoom-1.image)

进到配置页面，写下对应的URL，然后保存就好了。

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/8a78755f4f30431882878ab08e6855bc~tplv-k3u1fbpfcp-zoom-1.image)

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

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/5836357acce442b480628e06b2e7420a~tplv-k3u1fbpfcp-zoom-1.image)

服务器的监控直接选用**8919**的就好了

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/01a19e6370f54c10b096e1c9bd743b59~tplv-k3u1fbpfcp-zoom-1.image)

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/b97faddc55324c2bac2bf13a6e47355e~tplv-k3u1fbpfcp-zoom-1.image)

import后就能直接看到高大上的监控页面了：

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/4505d818d2474d8f83d033ad3ad60a64~tplv-k3u1fbpfcp-zoom-1.image)

使用模板**893**来配置监控docker的信息：

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/da69a42ffb984caa99c0beea410dde07~tplv-k3u1fbpfcp-zoom-1.image)

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/14a5c8b4fb5441598ddda816d42d56fd~tplv-k3u1fbpfcp-zoom-1.image)

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
```

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/dbd1b8e2b15242a194da0ce8a7c61a80~tplv-k3u1fbpfcp-zoom-1.image)

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/26f4d6d83f4a441d85cb0a396cd0543c~tplv-k3u1fbpfcp-zoom-1.image)

## 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`

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/c7d068f7bb7445238688a695eab49c8c~tplv-k3u1fbpfcp-zoom-1.image)

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）。

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/88878e8e4bb8428c9c6930cb09d5e249~tplv-k3u1fbpfcp-zoom-1.image)

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](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-admin`的`application.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：

```shell
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**ip**和**password**需要更改为自己的，并且，我开的是**6767**端口

![](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/180eabb4945e475494f3803c69318755~tplv-k3u1fbpfcp-zoom-1.image)

## 09、Flink

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: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

```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
```

## 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

```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
```

![](https://p9-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/384425d102464c059d462add377b4582~tplv-k3u1fbpfcp-watermark.image?)

### 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

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）。

```shell
docker pull metabase/metabase:latest
```

```shell
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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