Flink CDC系列之：基于 Flink CDC 构建 MySQL 和 Postgres 的 Streaming ETL

一、技术路线

二、MySQL数据库建表

mysql数据库创建数据库和表 products，orders

创建products表

-- 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;

创建orders表

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;

products表插入数据

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");

orders表插入数据

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);

三、PostgreSQL数据库建表

创建表 shipments

-- 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);

插入数据

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);

四、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');

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'
);

五、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;

六、Kibana查看商品和物流信息的订单数据

创建 index pattern enriched_orders

查看写入的数据

七、修改数据库中表的数据，Kibana查看更新

修改 MySQL 和 Postgres 数据库中表的数据，Kibana中显示的订单数据也将实时更新：

在 MySQL 的 orders 表中插入一条数据

--MySQL

INSERT INTO orders
VALUES (default, '2020-07-30 15:22:00', 'Jark', 29.71, 104, false);

在 Postgres 的 shipment 表中插入一条数据

--PG
INSERT INTO shipmentsVALUES (default,10004,'Shanghai','Beijing',false);

在 MySQL 的 orders 表中更新订单的状态

--MySQL
UPDATE orders SET order_status = true WHERE order_id = 10004;

在 Postgres 的 shipment 表中更新物流的状态

--PG
UPDATE shipments SET is_arrived = true WHERE shipment_id = 1004;

在 MYSQL 的 orders 表中删除一条数据

--MySQL
DELETE FROM orders WHERE order_id = 10004;

每执行一步就刷新一次 Kibana，可以看到 Kibana 中显示的订单数据将实时更新，如下所示：