flink 的安装以及fink-cdc 基于多数据源导入的es 的简单使用

此文档是参照flink-cdc 文档( https://ververica.github.io/flink-cdc-connectors/master/content/快速上手/mysql-postgres-tutorial-zh.html) 案例

 的最佳实践

1.下载flink release 最新版本1.18.0 并解压,

 https://repo.maven.apache.org/maven2/org/apache/flink/flink-sql-connector-elasticsearch7/3.0.1-1.17/flink-sql-connector-elasticsearch7-3.0.1-1.17.jar 下载es flink-cdc 驱动包

git clone github上面  flink-cdc master 分支并编译

mvn clean install -DskipTests 执行命令进行编译会生成jar 包如下

 复制jar 包到flink lib 目录

启动flink 主程序  bin/start-cluster.sh 

访问 http://localhost:8081/#/job-manager/logs  flink webui 界面 

0

需要注意的一点是 确保加入jar包到lib 或者修改配置时,确保stop-cluster 停止成功, 之前碰到执行了stop-cluster.sh  web ui 8080 还能访问的情况 

按照文档编写docker-compose 文件

version: '2.1'
services:
  postgres:
    image: debezium/example-postgres:1.1
    ports:
      - "5432:5432"
    environment:
      - POSTGRES_DB=postgres
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
  mysql:
    image: debezium/example-mysql:1.1
    ports:
      - "3306:3306"
    environment:
      - MYSQL_ROOT_PASSWORD=123456
      - MYSQL_USER=mysqluser
      - MYSQL_PASSWORD=mysqlpw
  elasticsearch:
    image: elastic/elasticsearch:7.6.0
    environment:
      - cluster.name=docker-cluster
      - bootstrap.memory_lock=true
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
      - discovery.type=single-node
    ports:
      - "9200:9200"
      - "9300:9300"
    ulimits:
      memlock:
        soft: -1
        hard: -1
      nofile:
        soft: 65536
        hard: 65536
  kibana:
    image: elastic/kibana:7.6.0
    ports:
      - "5601:5601"

　　启动 docker 容器 

    docker-compose  up  -d

   查看启动情况

 说明启动成功

准备mysql 数据

进入MySQL 容器

docker-compose exec mysql mysql -uroot -p123456

1. 创建数据库和表 products，orders，并插入数据(参照原文档案例)

   -- MySQL
   CREATE DATABASE mydb;
   USE mydb;
   CREATE TABLE products (
     id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,
     name VARCHAR(255) NOT NULL,
     description VARCHAR(512)
   );
   ALTER TABLE products AUTO_INCREMENT = 101;
   
   INSERT INTO products
   VALUES (default,"scooter","Small 2-wheel scooter"),
          (default,"car battery","12V car battery"),
          (default,"12-pack drill bits","12-pack of drill bits with sizes ranging from #40 to #3"),
          (default,"hammer","12oz carpenter's hammer"),
          (default,"hammer","14oz carpenter's hammer"),
          (default,"hammer","16oz carpenter's hammer"),
          (default,"rocks","box of assorted rocks"),
          (default,"jacket","water resistent black wind breaker"),
          (default,"spare tire","24 inch spare tire");
   
   CREATE TABLE orders (
     order_id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,
     order_date DATETIME NOT NULL,
     customer_name VARCHAR(255) NOT NULL,
     price DECIMAL(10, 5) NOT NULL,
     product_id INTEGER NOT NULL,
     order_status BOOLEAN NOT NULL -- Whether order has been placed
   ) AUTO_INCREMENT = 10001;
   
   INSERT INTO orders
   VALUES (default, '2020-07-30 10:08:22', 'Jark', 50.50, 102, false),
          (default, '2020-07-30 10:11:09', 'Sally', 15.00, 105, false),
          (default, '2020-07-30 12:00:30', 'Edward', 25.25, 106, false);
   
   

   在 Postgres 数据库中准备数据

   1. 进入 Postgres 容器

      docker-compose exec postgres psql -h localhost -U postgres
      

      

       

      1. 创建表 shipments，并插入数据

         -- PG
         CREATE TABLE shipments (
           shipment_id SERIAL NOT NULL PRIMARY KEY,
           order_id SERIAL NOT NULL,
           origin VARCHAR(255) NOT NULL,
           destination VARCHAR(255) NOT NULL,
           is_arrived BOOLEAN NOT NULL
         );
         ALTER SEQUENCE public.shipments_shipment_id_seq RESTART WITH 1001;
         ALTER TABLE public.shipments REPLICA IDENTITY FULL;
         INSERT INTO shipments
         VALUES (default,10001,'Beijing','Shanghai',false),
                (default,10002,'Hangzhou','Shanghai',false),
                (default,10003,'Shanghai','Hangzhou',false);

   
   
   cd  到flink 主目录 
   

   

    启动flink-sql-client

   

    

   

    

   在 Flink SQL CLI 中使用 Flink DDL 创建表

   首先，开启 checkpoint，每隔3秒做一次 checkpoint

   -- Flink SQL                   
   Flink SQL> SET execution.checkpointing.interval = 3s;
   

   然后, 对于数据库中的表 products, orders, shipments， 使用 Flink SQL CLI 创建对应的表，用于同步这些底层数据库表的数据

   -- Flink SQL
   Flink SQL> CREATE TABLE products (
       id INT,
       name STRING,
       description STRING,
       PRIMARY KEY (id) NOT ENFORCED
     ) WITH (
       'connector' = 'mysql-cdc',
       'hostname' = 'localhost',
       'port' = '3306',
       'username' = 'root',
       'password' = '123456',
       'database-name' = 'mydb',
       'table-name' = 'products'
     );
   
   Flink SQL> CREATE TABLE orders (
      order_id INT,
      order_date TIMESTAMP(0),
      customer_name STRING,
      price DECIMAL(10, 5),
      product_id INT,
      order_status BOOLEAN,
      PRIMARY KEY (order_id) NOT ENFORCED
    ) WITH (
      'connector' = 'mysql-cdc',
      'hostname' = 'localhost',
      'port' = '3306',
      'username' = 'root',
      'password' = '123456',
      'database-name' = 'mydb',
      'table-name' = 'orders'
    );
   
   Flink SQL> CREATE TABLE shipments (
      shipment_id INT,
      order_id INT,
      origin STRING,
      destination STRING,
      is_arrived BOOLEAN,
      PRIMARY KEY (shipment_id) NOT ENFORCED
    ) WITH (
      'connector' = 'postgres-cdc',
      'hostname' = 'localhost',
      'port' = '5432',
      'username' = 'postgres',
      'password' = 'postgres',
      'database-name' = 'postgres',
      'schema-name' = 'public',
      'table-name' = 'shipments',
      'slot.name' = 'flink'
    );
   

   最后，创建 enriched_orders 表， 用来将关联后的订单数据写入 Elasticsearch 中

   -- Flink SQL
   Flink SQL> CREATE TABLE enriched_orders (
      order_id INT,
      order_date TIMESTAMP(0),
      customer_name STRING,
      price DECIMAL(10, 5),
      product_id INT,
      order_status BOOLEAN,
      product_name STRING,
      product_description STRING,
      shipment_id INT,
      origin STRING,
      destination STRING,
      is_arrived BOOLEAN,
      PRIMARY KEY (order_id) NOT ENFORCED
    ) WITH (
        'connector' = 'elasticsearch-7',
        'hosts' = 'http://localhost:9200',
        'index' = 'enriched_orders'
    );
   

   关联订单数据并且将其写入 Elasticsearch 中

   使用 Flink SQL 将订单表 order 与 商品表 products，物流信息表 shipments 关联，并将关联后的订单信息写入 Elasticsearch 中

   -- Flink SQL
   Flink SQL> INSERT INTO enriched_orders
    SELECT o.*, p.name, p.description, s.shipment_id, s.origin, s.destination, s.is_arrived
    FROM orders AS o
    LEFT JOIN products AS p ON o.product_id = p.id
    LEFT JOIN shipments AS s ON o.order_id = s.order_id;
   
   
   
   这里执行

   INSERT INTO enriched_orders
    SELECT o.*, p.name, p.description, s.shipment_id, s.origin, s.destination, s.is_arrived
    FROM orders AS o
    LEFT JOIN products AS p ON o.product_id = p.id
    LEFT JOIN shipments AS s ON o.order_id = s.order_id; 时碰到的问题,
   

   

    

   任务提交成功, web ui 查询报错信息, 一直报时区不正确, 进入到mysql SET GLOBAL time_zone ='Asia/Shanghai';设置时区
   

   

    再次执行 上面insert sql 报什么资源不可用

   https://www.cnblogs.com/javasl/p/16861356.html

    

   

    重启flink  bin/start-cluster.sh

   启动 bin/sql-client.sh

   再次执行 

   INSERT INTO enriched_orders
    SELECT o.*, p.name, p.description, s.shipment_id, s.origin, s.destination, s.is_arrived
    FROM orders AS o
    LEFT JOIN products AS p ON o.product_id = p.id
    LEFT JOIN shipments AS s ON o.order_id = s.order_id;
   
   
   
   

   

    

   再次看web ui  界面 没有报错信息了,说明job run 成功  此job 会处于一直run 状态

   

    

   

    

   在mysql 和 psql 客户端 分别执行sql 看到数据同时同步到了es(实现原理是基于监听数据库binglog日志改动,执行日志重放 )