## 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 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 ## 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 #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) ## 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?) **** **** 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 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 javax.jdo.option.ConnectionURL jdbc:postgresql://hive_ip:5432/metastore?createDatabaseIfNotExist=true JDBC connect string for a JDBC metastore javax.jdo.option.ConnectionDriverName org.postgresql.Driver Driver class name for a JDBC metastore javax.jdo.option.ConnectionUserName hive username to use against metastore database javax.jdo.option.ConnectionPassword hive password to use against metastore database hive.metastore.uris thrift://hive_ip:9083 Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore. datanucleus.schema.autoCreateAll true ``` ### 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 dfs.client.use.datanode.hostname true only cofig in clients ``` ### 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 安装更详细的过程以及整个文章系列的更新思路都在公众号**Java3y**连载哟! 如果你需要用这个项目写在简历上,**强烈建议关注公众号看实现细节的思路**。如果⽂档中有任何的不懂的问题,都可以直接来找我询问,我乐意帮助你们!公众号下有我的联系方式