PolarDB-X 教程

演示: PolarDB-X CDC 导入 Elasticsearch #

本示例我们通过演示 PolarDB-X 借助 Flink-CDC 将数据导入至 Elasticsearch 来介绍 PolarDB-X 的增量订阅能力，你可以前往:PolarDB-X 了解更多细节。

准备教程所需要的组件 #

我们假设你运行在一台 MacOS 或者 Linux 机器上，并且已经安装 docker.

配置并启动容器 #

配置 docker-compose.yml。

version: '2.1'
services:
  polardbx:
    polardbx:
    image: polardbx/polardb-x:2.0.1
    container_name: polardbx
    ports:
      - "8527:8527"
  elasticsearch:
    image: 'elastic/elasticsearch:7.6.0'
    container_name: elasticsearch
    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'
    container_name: kibana
    ports:
      - '5601:5601'
    volumes:
      - '/var/run/docker.sock:/var/run/docker.sock'

该 Docker Compose 中包含的容器有：

  • PolarDB-X: 商品表 products 和 订单表 orders 将存储在该数据库中， 这两张表将进行关联，得到一张包含更多信息的订单表 enriched_orders
  • Elasticsearch: 最终的订单表 enriched_orders 将写到 Elasticsearch
  • Kibana: 用来可视化 ElasticSearch 的数据

在 docker-compose.yml 所在目录下执行下面的命令来启动本教程需要的组件：

docker-compose up -d

该命令将以 detached 模式自动启动 Docker Compose 配置中定义的所有容器。你可以通过 docker ps 来观察上述的容器是否正常启动了，也可以通过访问 http://localhost:5601/ 来查看 Kibana 是否运行正常

准备数据： #

使用已创建的用户名和密码进行登陆PolarDB-X。

mysql -h127.0.0.1 -P8527 -upolardbx_root -p"123456"

CREATE DATABASE mydb;
USE mydb;

-- 创建一张产品表，并写入一些数据
CREATE TABLE products (
                          id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,
                          name VARCHAR(255) NOT NULL,
                          description VARCHAR(512)
) 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");


-- 创建一张订单表，并写入一些数据
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);
  1. 下载 Flink 1.17.0 并将其解压至目录 flink-1.17.0
  2. 下载下面列出的依赖包，并将它们放到目录 flink-1.17.0/lib/ 下

下载链接只对已发布的版本有效, SNAPSHOT 版本需要本地编译

  1. 启动flink服务:
./bin/start-cluster.sh

我们可以访问 http://localhost:8081/ 看到Flink正常运行:

Flink UI
  1. 启动Flink SQL CLI:
./bin/sql-client.sh

-- 设置间隔时间为3秒                       
Flink SQL> SET execution.checkpointing.interval = 3s;

-- 创建source1 -订单表
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' = '127.0.0.1',
	'port' = '8527',
	'username' = 'polardbx_root',
	'password' = '123456',
	'database-name' = 'mydb',
	'table-name' = 'orders'
 );

-- 创建source2 -产品表
CREATE TABLE products (
    id INT,
    name STRING,
    description STRING,
    PRIMARY KEY (id) NOT ENFORCED
  ) WITH (
	'connector' = 'mysql-cdc',
	'hostname' = '127.0.0.1',
	'port' = '8527',
	'username' = 'polardbx_root',
	'password' = '123456',
	'database-name' = 'mydb',
	'table-name' = 'products'
);

-- 创建sink - 关联后的结果表
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,
   PRIMARY KEY (order_id) NOT ENFORCED
 ) WITH (
     'connector' = 'elasticsearch-7',
     'hosts' = 'http://localhost:9200',
     'index' = 'enriched_orders'
 );

-- 执行读取和写入   
Flink SQL> INSERT INTO enriched_orders
  SELECT o.order_id,
    o.order_date,
    o.customer_name,
    o.price,
    o.product_id,
    o.order_status,
    p.name,
    p.description
 FROM orders AS o
 LEFT JOIN products AS p ON o.product_id = p.id;

在 Kibana 中查看数据 #

访问 http://localhost:5601/app/kibana#/management/kibana/index_pattern

创建 index pattern enriched_orders，之后可以在 http://localhost:5601/app/kibana#/discover 看到写入的数据了。

修改监听表数据，查看增量数据变动 #

在PolarDB-X中依次执行如下修改操作，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。

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

UPDATE orders SET order_status = true WHERE order_id = 10004;

DELETE FROM orders WHERE order_id = 10004;

环境清理 #

在 docker-compose.yml 文件所在的目录下执行如下命令停止所有容器：

docker-compose down

进入Flink的部署目录，停止 Flink 集群：

./bin/stop-cluster.sh

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