5分钟上手Flink MySQL连接器-阿里云开发者社区

5分钟上手Flink MySQL连接器

1. 实验资源方式简介及开始实验

云起实验室实验资源方式介绍

云起实验室支持实验资源体验、领取免费试用额度、个人账户资源三种实验资源方式。

实验资源体验

资源归属于客户，仅供本次实验使用

实验结束后，实验资源及实验记录将被释放。

资源创建过程需要3～5分钟（视资源不同开通时间有所差异，ACK等资源开通时间较长）。完成实验资源的创建后，在实验室页面左侧导航栏中，单击云产品资源列表，可查看本次实验资源相关信息（例如子用户名称、子用户密码、AK ID、AK Secret、资源中的项目名称等）。

说明：实验环境一旦开始创建则进入计时阶段，建议学员先基本了解实验具体的步骤、目的，真正开始做实验时再进行创建。

领取免费试用额度

使用个人账号开通试用，平台仅提供手册参考。

所有实验操作将保留至您的账号，请谨慎操作。

在实验页面下方卡片会展示本实验支持的试用规格，可以选择你要试用的云产品资源进行开通。您在实验过程中，可以随时用右下角icon唤起试用卡片。阿里云支持试用的产品列表、权益及具体规则说明请参考开发者试用中心。

说明：试用云产品开通在您的个人账号下，并占用您的试用权益。如试用超出免费试用额度，可能会产生一定费用。

个人账户资源

使用您个人的云资源进行操作，资源归属于个人。

所有实验操作将保留至您的账号，请谨慎操作。

平台仅提供手册参考，不会对资源做任何操作。

说明：使用个人账户资源，在创建资源时，可能会产生一定的费用，请您及时关注相关云产品资源的计费概述。

准备开始实验

在实验开始前，请您选择其中一种实验资源，单击确认开启实验。

说明：每个实验所支持的实验资源方式都不相同，实验不一定能满足有三种实验资源方式，请根据实验的实际情况，进行选择。

2. 领取免费试用资源

实验前必看！

在创建实时计算Flink版时，需要使用到对象存储OSS和负载均衡SLB，您需要领取负载均衡SLB和对象存储OSS资源抵扣包进行抵扣（单击对应产品卡片上的立即试用，在确认并了解相关信息后，根据页面提示申请试用），否则将会产生额外费用。如果您的对象存储OSS或SLB资源抵扣包已使用完毕或无领取资格，开通Flink试用后，将正常收取费用，计费详情请参见SLB按量付费和OSS按量付费。

如果您的阿里云主账号符合开通免费试用实时计算Flink版的资格，建议您开通免费试用实时计算Flink版，实时计算Flink版提供的按量付费（3个月5000CUH计算资源）试用，具有时长和容量限制，使用完请及时清理相关资源。实时计算Flink版如果未及时清理资源，相关Flink工作空间将按照按量付费模式正常进行计费，涉及的其他产品服务也会正常收取费用。具体计费详情，请参见按量付费。

如果您的阿里云主账号符合开通免费试用云数据库RDS MySQL Serverless的资格，建议您开通免费试用云数据库RDS MySQL Serverless，云数据库RDS MySQL Serverless只能免费试用3个月。实例到期后，您可以手动释放。具体操作，请参见释放实例。如果一直未释放该实例，实例将在试用时间结束后，按照正常价格计费，计费标准请参见Serverless费用。

实验所开通的云产品因数据连通性要求，需使用同一Region可用区，建议都选取杭州Region的同一可用区。涉及的云产品包括阿里云实时计算Flink版、阿里云数据库 RDS。

如果您的阿里云账号只能领取部分免费试用产品，请您领取符合免费试用资格的产品，不满足免费试用资格的产品将会使用个人账户资源进行创建，并会产生一定的费用，请您及时关注账户扣费。

为了避免资源浪费并造成账号扣费的情况，请严格按照本文提供的参数进行配置。在实验完成之后，请您及时删除或禁用压测任务。

在实验开始前，请您选择开通免费试用。

开通对象存储OSS免费试用。

2.1 在实验室页面下方，选择对象存储OSS，单击立即试用。

说明：如果您的对象存储OSS资源抵扣包已使用完毕或无领取资格，请您跳过2.1-2.2步骤，请根据2.3-2.4步骤创建对象存储OSS，将正常收取费用，计费详情请参见OSS按量付费。

2.2 在对象存储OSS试用开通页面，在确认并了解相关信息后，根据页面提示申请试用。

2.3 前往对象存储OSS控制台。如果您没有开通过OSS服务，系统会提示您开通OSS服务，请按照页面提示开通OSS服务。

2.4 在左侧导航栏中，单击Bucket列表。

2.5 在Bucket列表页面，单击创建Bucket。

2.6 在创建Bucket页面，根据页面引导进行创建Bucket，其中地域选择华东1（杭州），其他配置保持默认即可，单击确定。

开通负载均衡SLB免费试用。

3.1 在实验室页面下方，选择传统型负载均衡CLB，单击立即试用。

说明：如果您的负载均衡SLB资源抵扣包已使用完毕或无领取资格，请您跳过此步骤，开通Flink试用后，将正常收取费用，计费详情请参见SLB按量付费。

3.2 在传统型负载均衡CLB开通页面，地域选择华东1（杭州），可用区选择华东1可用区J，选中服务协议，单击立即试用。

开通实时计算Flink版免费试用。

4.1 在实验室页面下方，选择实时计算Flink版，单击立即试用。

4.2 如果您第一次访问该服务，可能需要进行角色授权。请在弹出的授权请求页面，单击前往RAM进行授权后，单击同意授权，完成自动化角色授权。授权成功后，需返回实验室页面。

4.3 Flink开通页面，单击上方提示框中的领取免费的资源抵扣包，领取Flink资源抵扣包。

4.4 在Flink资源抵扣包页面，确认相关信息后，单击确认订单，根据页面提示完成领取。

4.5 领取完毕后，在Flink开通页面，刷新此页面后完成参数配置。

参数	说明	教程配置
付费模式	阿里云实时计算Flink版免费试用仅支持按量付费。	按量付费
地域	本教程不涉及上下游存储，所以可不与上下游存储保持一致。	华东1（杭州）
可用区	本教程不涉及上下游存储，所以可不与上下游存储保持一致。	可用区J
资源抵扣包	可用于抵扣中国内地公有云地域（目前仅支持上海，杭州，北京，深圳，张家口区域）按量付费模式用量，自购买资源包日起有效期三个月。领取资源包后，单击刷新按钮，即可更新此参数。其中，本教程示例不适用于张家口地域。	5000CUH
资源抵扣包	您需要领取负载均衡SLB和对象存储OSS资源抵扣包进行抵扣（即单击所给链接，单击对应产品卡片上的立即试用，在确认并了解相关信息后，根据页面提示申请试用），否则将会产生额外费用。如果您的对象存储OSS或SLB资源抵扣包已使用完毕或无领取资格，开通Flink试用后，将正常收取费用，计费详情请参见SLB按量付费和OSS按量付费。	确认领取后，选中
SLB服务	SLB是通过浏览器访问Flink产品控制台的必要网络连接组件。在您开通阿里云实时计算Flink版服务后，会自动开通SLB服务，该SLB专用于阿里云实时计算Flink版服务。	选中
专有网络	选择您已创建的专有网络。如果您没有创建，请参见创建和管理专有网络进行创建。	选中您需要使用的VPC名称。
虚拟交换机	每个Flink作业的Task Manager和Job Manager实例都会占用一个IP，您需要至少选择1个虚拟交换机。如果当前无可用虚拟交换机，请在当前地域当前可用区下进行创建，详情请参见创建和管理专有网络。	选中您需要使用的交换机名称。
工作空间名称	每个工作空间的计算资源隔离，开发控制台相互独立。创建成功后不可修改。	flink-test
OSS存储	OSS用于存储作业系统检查点、作业快照、日志和JAR包等信息。新建OSS存储的具体操作，请参见开通OSS服务。不推荐对选择的Bucket开启非默认策略，例如多版本、合规保留等，可能会导致Flink作业异常。	选中您需要使用的OSS Bucket名称。
监控服务	如果您选中了监控服务（为您提供商业版Prometheus监控能力），则会正常进行收费，计费详情请参见ARMS按量计费。	不选中

4.6 请再次确认已了解并领取实时计算Flink版资源抵扣包。

实时计算Flink版资源抵扣包领取已在上述步骤4.3-4.4中完成。若未领取请查看对应步骤完成领取。

如果您的实时计算Flink版资源抵扣包已使用完毕或无领取资格，开通Flink试用后，将正常收取费用。

4.7 单击立即试用，并根据页面提示，完成阿里云实时计算Flink版工作空间创建。

说明：工作空间创建需要约5~10分钟，请耐心等待。5~10分钟后，请在实时计算控制台Flink全托管页签，刷新页面查看工作空间状态，当工作空间状态为运行中时，即可进入下一步。

开通云数据库RDS MySQL Serverless免费试用。

5.1 在实验室页面下方，选择云数据库RDS MySQL Serverless，单击立即试用。

5.2 在云数据库RDS MySQL Serverless开通页面，根据页面引导进行开通云数据库RDS MySQL Serverless，其中地域选择华东1（杭州），可用区选择杭州可用区J，数据库类型选择MySQL，数据库版本号选择8.0，其他配置保持默认即可，选中服务协议，单击立即试用。

说明：如果您的云数据库RDS MySQL Serverless资源抵扣包已使用完毕或无领取资格，请您跳过5.1-5.2步骤，使用个人账户创建云数据库RDS MySQL Serverless，将正常收取费用，计费详情请参见Serverless费用。前往云数据库RDS控制台，在实例列表页面，单击创建实例。在购买页面，根据页面引导进行开通云数据库RDS MySQL Serverless，计费方式选择为Serverless，地域选择华东1（杭州），数据库类型选择MySQL，数据库版本号选择8.0，其他配置保持默认即可，单击立即购买。

领取完免费试用后，返回资源领取界面，单击我已开通，进入实验。

说明：如果您的阿里云账号只能领取部分免费试用产品，请您领取符合免费试用资格的产品，然后进入实验，不满足免费试用资格的产品将会使用个人账户资源进行创建，并会产生一定的费用，请您及时关注账户扣费。

3. 创建资源

创建对象存储OSS。

说明：

如果您已有对象存储OSS资源，您可跳过此步骤。

请您根据如下操作，开通对象存储OSS，会产生一定的费用，具体计费详情，请参见OSS按量付费。

1.1 前往对象存储OSS控制台。如果您没有开通过OSS服务，系统会提示您开通OSS服务，请按照页面提示开通OSS服务。

1.2 在左侧导航栏中，单击Bucket列表。

2.5 在Bucket列表页面，单击创建Bucket。

2.6 在创建Bucket页面，根据页面引导进行创建Bucket，其中地域选择华东1（杭州），其他配置保持默认即可，单击确定。

创建实时计算Flink版。

说明：

如果您已开通实时计算Flink，请您跳过本步骤。

请您根据如下操作，开通实时计算Flink，并会产生一定的费用，具体计费详情，请参见计费概述。在开通实时计算Flink时，需要使用到对存储OSS和负载均衡SLB，计费详情请参见SLB按量付费和OSS按量付费。

2.1 前往实时计算Flink版控制台。

2.2 在实时计算控制台页面，单击立即购买。

2.3 在Flink开通页面，按照如下提示完成参数配置。计费详情请参见实时计算 Flink版按量计费。

说明：如果您第一次访问该服务，可能需要进行角色授权。请在弹出的授权请求页面，单击前往RAM进行授权后，单击同意授权，完成自动化角色授权。

参数	说明	教程配置
付费模式	阿里云实时计算Flink版免费试用仅支持按量付费。	按量付费
地域	本教程不涉及上下游存储，所以可不与上下游存储保持一致。	华东1（杭州）
可用区	本教程不涉及上下游存储，所以可不与上下游存储保持一致。	可用区J
SLB服务	SLB是通过浏览器访问Flink产品控制台的必要网络连接组件。在您开通阿里云实时计算Flink版服务后，会自动开通SLB服务，该SLB专用于阿里云实时计算Flink版服务。SLB会产生额外付费，单个收费约3元/天，实际收费详情请参见SLB按量付费。	选中
专有网络	选择您已创建的专有网络。如果您没有创建，请参见创建和管理专有网络进行创建。	选中您需要使用的VPC名称。
虚拟交换机	每个Flink作业的Task Manager和Job Manager实例都会占用一个IP，您需要至少选择1个虚拟交换机。如果当前无可用虚拟交换机，请在当前地域当前可用区下进行创建，详情请参见创建和管理专有网络。	选中您需要使用的交换机名称。
工作空间名称	每个工作空间的计算资源隔离，开发控制台相互独立。创建成功后不可修改。	例如flink-test
OSS存储	OSS用于存储作业系统检查点、作业快照、日志和JAR包等信息。新建OSS存储的具体操作，请参见开通OSS服务。不推荐对选择的Bucket开启非默认策略，例如多版本、合规保留等，可能会导致Flink作业异常。	选中您需要使用的OSS Bucket名称。
监控服务	如果您选中了监控服务（为您提供商业版Prometheus监控能力），则会正常进行收费，计费详情请参见ARMS按量计费。	不选中

2.4 单击确认订单，并根据页面提示，完成阿里云实时计算Flink版工作空间创建。

创建云数据库RDS MySQL Serverless。

说明：

如果您已开通云数据库RDS MySQL Serverless，请您跳过本步骤。

请您根据如下操作，开通云数据库RDS MySQL Serverless，并会产生一定的费用，具体计费详情，请参见Serverless费用。

3.1 前往云数据库RDS控制台。

3.2 在实例列表页面，单击创建实例。

3.3 在购买页面，根据页面引导进行开通云数据库RDS MySQL Serverless，计费方式选择为Serverless，地域选择华东1（杭州），可用区选择杭州可用区J，数据库类型选择MySQL，数据库版本号选择8.0，其他配置保持默认即可，单击立即购买。

3.4 在实例列表页面，请耐心等待大约5分钟，当工作空间状态为运行中时，即可进入下一步。

4. 创建数据库、数据库账号及获取数据库访问地址

步骤将指导您在RDS MySQL Serverless版实例上创建数据库和账号，及获取数据库访问地址。

前往前往云数据库RDS控制台。

在左侧导航栏中，单击实例列表。

返回如下页面，您可看到刚刚创建的RDS MySQL Serverless版实例资源。

创建数据库。

3.1 在实例列表页面，单击实例ID，进入实例基本信息页面，在左侧导航栏单击数据库管理，可以创建数据库。

3.2 在创建数据库对话框中，根据如下说明配置数据库，单击创建。

参数说明：

数据库（DB）名称：输入数据库名称，例如serverless。

支持字符集：默认设为utf8。

备注说明：非必填。用于备注该数据库的相关信息，便于后续数据库管理，最多支持256个字符。

创建账号及授权。

4.1 在左侧导航栏单击账号管理，可以创建数据库帐号，并完成授权。

4.2 在创建账号对话框中，根据如下说明配置账号，单击确定。

数据库账号：输入数据库账号名称，例如test_user。

账号类型：选择普通账号。

授权数据库：将上一步中创建的数据库添加至已授权数据库列表中，并设置权限为读写（DDL+DML）。

密码：输入账号密码，例如Password123。

确认密码：再次输入账号密码。

获取数据库访问地址。

后续步骤需要使用数据库的内网地址。

5. 创建数据库表

在本实验中，我们将在RDS MySQL Serverless版实例上的数据库中创建三张数据表，分别作为源表、维表、汇表，演示MySQL Connector的不同功能。

在账号管理页面，单击登录数据库。

在登录实例对话框中，输入数据库账号和数据库密码，单击测试连接，测试连接通过后，单击登录。

在首页左侧，单击数据库实例，在已登录实例中找到您创建的数据库，并双击数据库。

在SQLConsole页签中，输入如下SQL建表语句，然后单击执行，创建三张数据表，分别作为源表、维表、汇表。

-- Source Table;
CREATE TABLE `source_table` (
  `id` int unsigned NOT NULL AUTO_INCREMENT,
  `good_id` int DEFAULT NULL,
  `amount` int DEFAULT NULL,
  `record_time` timestamp NULL DEFAULT NULL,
  PRIMARY KEY (`id`)
);
-- Dimension Table;
CREATE TABLE `dimension_table` (
  `good_id` int unsigned NOT NULL,
  `good_name` varchar(256) DEFAULT NULL,
  `good_price` int DEFAULT NULL,
  PRIMARY KEY (`good_id`)
);
-- Sink Table;
CREATE TABLE `sink_table` (
  `record_timestamp` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
  `good_name` varchar(128) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL,
  `sell_amount` int DEFAULT NULL,
  PRIMARY KEY (`record_timestamp`)
);

返回如下结果，表示三张表创建成功。

插入数据。

我们需要向数据库中插入多条数据。输入如下语句，单击执行。

说明：您可以在RDS中执行附件中的示例数据.sql来插入数据，或者使用示例数据生成.py脚本实时地插入数据。

INSERT INTO `dimension_table` (`good_id`, `good_name`, `good_price`)
VALUES
  (1, 'Cheetah', 53),
  (2, 'Puma', 46),
  (3, 'Jaguar', 22),
  (4, 'Panther', 18),
  (5, 'Tiger', 99),
  (6, 'Leopard', 84),
  (7, 'Snow Leopard', 81),
  (8, 'Lion', 17),
  (9, 'Mountain Lion', 19),
  (10, 'Mavericks', 77),
  (11, 'Yosemite', 16),
  (12, 'El Capitan', 16),
  (13, 'Sierra', 85),
  (14, 'High Sierra', 87),
  (15, 'Mojave', 51),
  (16, 'Catalina', 92),
  (17, 'Big Sur', 14),
  (18, 'Monterey', 99),
  (19, 'Ventura', 15),
  (20, 'Sonoma', 68);
INSERT INTO `source_table` (`id`, `good_id`, `amount`, `record_time`)
VALUES
  (1, 16, 19, '2023-06-09 11:06:34'),
  (2, 8, 20, '2023-06-09 11:06:35'),
  (3, 19, 24, '2023-06-09 11:06:36'),
  (4, 17, 23, '2023-06-09 11:06:37'),
  (5, 2, 25, '2023-06-09 11:06:40'),
  (6, 10, 28, '2023-06-09 11:06:44'),
  (7, 20, 26, '2023-06-09 11:06:48'),
  (8, 18, 19, '2023-06-09 11:06:49'),
  (9, 16, 21, '2023-06-09 11:06:53'),
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  (11, 7, 20, '2023-06-09 11:06:59'),
  (12, 17, 21, '2023-06-09 11:07:03'),
  (13, 2, 19, '2023-06-09 11:07:05'),
  (14, 5, 22, '2023-06-09 11:07:08'),
  (15, 10, 21, '2023-06-09 11:07:12'),
  (16, 2, 29, '2023-06-09 11:07:16'),
  (17, 6, 27, '2023-06-09 11:07:19'),
  (18, 16, 23, '2023-06-09 11:07:23'),
  (19, 8, 20, '2023-06-09 11:07:24'),
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6. 创建Session集群

在开始VVP作业编写前，需要先创建Session集群，只有创建了Session集群，才能执行任务。

前往实时计算控制台，找到您创建的Flink，单击右侧操作列下的控制台。

在左侧导航栏，单击Session集群。

在Session集群页面，单击创建Session集群。

在创建Session集群页面，根据如下说明配置参数，未提及的参数保持默认值即可，然后单击创建Session集群。

参数说明：

配置项	说明	教程配置
名称	Session集群名称。	test
状态	设置当前集群的期望运行状态： STOPPED：当集群配置完成后保持停止状态，同样会停止所有在运行中的作业。 RUNNING：当集群配置完成后保持运行状态。	RUNNING
设置为SQL Previews集群	将此Session集群设置为SQL Preview查询的资源集群。	开启

在Session集群页面，等待您创建的Session集群的状态从启动中变为运行中后，您可以进入后续步骤。

7. 创建Flink作业

在左侧导航栏，单击作业开发。

在作业开发页签，单击新建。

在新建文件对话框，文件名称输入为test，文件类型选择流作业/SQL，其他保持默认即可，单击确认。

在右侧编辑页签中，输入以下语句，创建一张临时表，并使用MySQL CDC连接器实时捕获source_table的变化，然后单击验证，确认作业Flink SQL语句中是否存在语法错误。

说明：您需要将语句中hostname参数替换为RDS MySQL Serverless数据库的内网地址、将 username和 password参数替换为您创建的数据库账号及密码、将 database-name参数替换为您创建的数据库名称。其中，'connector' = 'mysql-cdc'指定了使用MySQL CDC连接器来捕获变化数据。

任何时候您都可以单击顶部工具栏中的验证，来确认作业Flink SQL语句中是否存在语法错误。

CREATE TEMPORARY TABLE source_table(
    id INT NOT NULL PRIMARY KEY NOT ENFORCED,
    record_time TIMESTAMP_LTZ(3),
    good_id INT,
    amount INT,
    WATERMARK FOR record_time AS record_time-INTERVAL '5' SECOND
)WITH(
    'connector' = 'mysql-cdc',
    'hostname' = '******************.mysql.rds.aliyuncs.com',
    'port' = '3306',
    'username' = '***********',
    'password' = '***********',
    'database-name' = '***********',
    'table-name' = 'source_table'
);

为了测试是否成功地捕获了源表数据，紧接着在下面写一行SELECT * FROM source_table;语句，然后单击工具栏中的执行。接着，向source_table表中插入一些数据。如果控制台中打印了相应的数据行，则说明捕获成功，如下图所示。

接下来，我们希望对原始数据按照时间窗口进行分组计算。我们使用TUMBLE相关窗口函数结合GROUP BY，将长度 15 秒内的订单数据按照商品 ID 进行归类，并使用SUM计算其销售总额

我们在Flink作业编辑页签中，输入如下代码。在保证源表中有数据的情况下，再次执行Flink作业，观察控制台的输出结果。

SELECT
  good_id,
  tumble_start(
    record_time, interval '15' seconds
 ) AS record_timestamp,
  sum(amount) AS total_amount
FROM
  source_table
GROUP BY
  tumble(
    record_time, interval '15' seconds
  ),
 good_id;

在这个业务场景中，购买商品信息使用good_id记录，而商品ID到可读商品名字的映射表、每件商品的价格等信息则存储在另一张维度表（Dimension Table）中。我们同样可以使用Flink SQL连接维度表，只需在Flink作业中编写下面的语句。

CREATE TEMPORARY TABLE dimension_table (
    good_id INT NOT NULL PRIMARY KEY NOT ENFORCED,
    good_name VARCHAR(256),
    good_price INT
)WITH(
    'connector' = 'mysql',
    'hostname' = '******************.mysql.rds.aliyuncs.com',
    'port' = '3306',
    'username' = '******************',
    'password' = '*********',
    'database-name' = '*********',
    'table-name' = 'dimension_table'
);

这里，我们希望根据上一步中统计出的「每 15 秒商品销售量」信息，计算出每件商品的销售额。由于商品名称及商品价格数据存储在另一张维度表 dimension_table中，我们需要将结果视图和 dimension_table进行JOIN操作，并将「商品销售量」、「商品价格」相乘计算出「商品销售额」，并提取结果中的商品可读名称信息作为结果表。

需要确保 dimension_table中存在对应商品 ID 的条目。

作业代码如下。

SELECT
  record_timestamp,
  good_name,
  total_amount * good_price AS revenue
FROM
  (
    SELECT
     good_id,
     tumble_start(
        record_time, interval '15' seconds
     ) AS record_timestamp,
      sum(amount) AS total_amount
    FROM
      source_table
    GROUP BY
      tumble(
        record_time,interval '15'seconds
        ),
      good_id
  )AS tumbled_table
  LEFT JOIN dimension_table ON tumbled_table.good_id = dimension_table.good_id;

其中第7到20行和上一步骤中第7到20行的SQL语句一致。

执行上面的语句，并观察控制台中的统计数据。

最后，我们将这些实时的统计数据写回数据库，Flink SQL也可以简单地实现这一点。首先我们需要创建一张用于连接汇表的 Flink 临时表，如下所示：

CREATE TEMPORARY TABLE sink_table (
    record_timestamp TIMESTAMP(3) NOT NULL PRIMARY KEY NOT ENFORCED,
    good_name VARCHAR(128),
    sell_amount INT
) WITH (
  'connector' = 'jdbc',
  'url' = 'jdbc:mysql://******************.mysql.rds.aliyuncs.com:3306/您创建的数据库名称',
  'table-name' = 'sink_table',
  'username' = '***********',
  'password' = '***********',
  'scan.auto-commit' = 'true'
);

然后，只需要将上面的 SELECT 语句的输出结果 INSERT 到该表就可以了：

INSERT INTO sink_table 
SELECT 
  record_timestamp, 
  good_name, 
  total_amount * good_price AS revenue 
FROM 
  (
    SELECT 
      good_id, 
      tumble_start(
        record_time, interval '15' seconds
      ) AS record_timestamp, 
      sum(amount) AS total_amount 
    FROM 
      source_table 
    GROUP BY 
      tumble (
        record_time, interval '15' seconds
      ), 
      good_id
  ) AS tumbled_table 
  LEFT JOIN dimension_table ON tumbled_table.good_id = dimension_table.good_id;

现在，单击上线，即可将我们编写的 Flink SQL 作业部署上线执行。您可以使用数据库客户端等软件观察汇表中是否写入了正确的数据。

阿里云实时计算控制台在使用执行功能调试时，不会写入任何数据到下游中。因此为了测试使用SQL Connector写入汇表，您必须使用上线功能。

您也可以进入Flink UI控制台观察流数据处理图。在这个简单的示例中，首先进行的是源表数据的捕获与窗口聚合；接着和维度表进行JOIN操作得到运算结果；最后将处理数据存入汇表。

8. 实验附件

以上就是本实验的全部步骤。完整的Flink SQL语句如下：

-- Source Table;
CREATE TEMPORARY TABLE source_table (
    id INT NOT NULL PRIMARY KEY NOT ENFORCED,
    record_time TIMESTAMP_LTZ(3),
    good_id INT,
    amount INT,
    WATERMARK FOR record_time AS record_time - INTERVAL '5' SECOND
) WITH (
    'connector' = 'mysql-cdc',
    'hostname' = '******************.mysql.rds.aliyuncs.com',
    'port' = '3306',
    'username' = '***********',
    'password' = '***********',
    'database-name' = '***********',
    'table-name' = 'source_table'
);
-- Dimension Table;
CREATE TEMPORARY TABLE dimension_table (
    good_id INT NOT NULL PRIMARY KEY NOT ENFORCED,
    good_name VARCHAR(256),
    good_price INT
) WITH (
    'connector' = 'mysql',
    'hostname' = '******************.mysql.rds.aliyuncs.com',
    'port' = '3306',
    'username' = '***********',
    'password' = '***********',
    'database-name' = '***********',
    'table-name' = 'dimension_table'
);
-- Sink Table;
CREATE TEMPORARY TABLE sink_table (
    record_timestamp TIMESTAMP(3) NOT NULL PRIMARY KEY NOT ENFORCED,
    good_name VARCHAR(128),
    sell_amount INT
) WITH (
  'connector' = 'jdbc',
  'url' = 'jdbc:mysql://******************.mysql.rds.aliyuncs.com:3306/***********',
  'table-name' = 'sink_table',
  'username' = '***********',
  'password' = '***********',
  'scan.auto-commit' = 'true'
);
-- Job script;
INSERT INTO sink_table 
SELECT 
  record_timestamp, 
  good_name, 
  total_amount * good_price AS revenue 
FROM 
  (
    SELECT 
      good_id, 
      tumble_start(
        record_time, interval '15' seconds
      ) AS record_timestamp, 
      sum(amount) AS total_amount 
    FROM 
      source_table 
    GROUP BY 
      tumble (
        record_time, interval '15' seconds
      ), 
      good_id
  ) AS tumbled_table 
  LEFT JOIN dimension_table ON tumbled_table.good_id = dimension_table.good_id;

为了向云数据库 RDS 中填充示例数据，您可以在数据库后台执行下面的 SQL 语句：

INSERT INTO `dimension_table` (`good_id`, `good_name`, `good_price`)
VALUES
  (1, 'Cheetah', 53),
  (2, 'Puma', 46),
  (3, 'Jaguar', 22),
  (4, 'Panther', 18),
  (5, 'Tiger', 99),
  (6, 'Leopard', 84),
  (7, 'Snow Leopard', 81),
  (8, 'Lion', 17),
  (9, 'Mountain Lion', 19),
  (10, 'Mavericks', 77),
  (11, 'Yosemite', 16),
  (12, 'El Capitan', 16),
  (13, 'Sierra', 85),
  (14, 'High Sierra', 87),
  (15, 'Mojave', 51),
  (16, 'Catalina', 92),
  (17, 'Big Sur', 14),
  (18, 'Monterey', 99),
  (19, 'Ventura', 15),
  (20, 'Sonoma', 68);
INSERT INTO `source_table` (`id`, `good_id`, `amount`, `record_time`)
VALUES
  (1, 16, 19, '2023-06-09 11:06:34'),
  (2, 8, 20, '2023-06-09 11:06:35'),
  (3, 19, 24, '2023-06-09 11:06:36'),
  (4, 17, 23, '2023-06-09 11:06:37'),
  (5, 2, 25, '2023-06-09 11:06:40'),
  (6, 10, 28, '2023-06-09 11:06:44'),
  (7, 20, 26, '2023-06-09 11:06:48'),
  (8, 18, 19, '2023-06-09 11:06:49'),
  (9, 16, 21, '2023-06-09 11:06:53'),
  (10, 7, 19, '2023-06-09 11:06:55'),
  (11, 7, 20, '2023-06-09 11:06:59'),
  (12, 17, 21, '2023-06-09 11:07:03'),
  (13, 2, 19, '2023-06-09 11:07:05'),
  (14, 5, 22, '2023-06-09 11:07:08'),
  (15, 10, 21, '2023-06-09 11:07:12'),
  (16, 2, 29, '2023-06-09 11:07:16'),
  (17, 6, 27, '2023-06-09 11:07:19'),
  (18, 16, 23, '2023-06-09 11:07:23'),
  (19, 8, 20, '2023-06-09 11:07:24'),
  (20, 8, 31, '2023-06-09 11:07:26'),
  (21, 6, 30, '2023-06-09 11:07:29'),
  (22, 7, 21, '2023-06-09 11:07:32'),
  (23, 2, 22, '2023-06-09 11:07:33'),
  (24, 9, 23, '2023-06-09 11:07:37'),
  (25, 19, 29, '2023-06-09 11:07:41'),
  (26, 16, 31, '2023-06-09 11:07:45'),
  (27, 13, 19, '2023-06-09 11:07:48'),
  (28, 6, 20, '2023-06-09 11:07:50'),
  (29, 4, 20, '2023-06-09 11:07:52'),
  (30, 4, 25, '2023-06-09 11:07:54'),
  (31, 4, 20, '2023-06-09 11:07:55'),
  (32, 4, 21, '2023-06-09 11:07:58'),
  (33, 11, 24, '2023-06-09 11:07:59'),
  (34, 1, 24, '2023-06-09 11:08:03'),
  (35, 7, 31, '2023-06-09 11:08:06'),
  (36, 2, 27, '2023-06-09 11:08:10'),
  (37, 19, 26, '2023-06-09 11:08:13'),
  (38, 3, 23, '2023-06-09 11:08:15'),
  (39, 11, 20, '2023-06-09 11:08:19'),
  (40, 20, 21, '2023-06-09 11:08:23'),
  (41, 7, 22, '2023-06-09 11:08:25'),
  (42, 5, 21, '2023-06-09 11:08:29'),
  (43, 7, 22, '2023-06-09 11:08:30'),
  (44, 11, 28, '2023-06-09 11:08:31'),
  (45, 2, 19, '2023-06-09 11:08:32'),
  (46, 1, 26, '2023-06-09 11:08:36'),
  (47, 3, 31, '2023-06-09 11:08:39'),
  (48, 20, 20, '2023-06-09 11:08:41'),
  (49, 6, 24, '2023-06-09 11:08:43'),
  (50, 10, 28, '2023-06-09 11:08:44'),
  (51, 11, 21, '2023-06-09 11:08:48'),
  (52, 9, 20, '2023-06-09 11:08:52'),
  (53, 5, 28, '2023-06-09 11:08:53'),
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  (60, 19, 25, '2023-06-09 11:09:08'),
  (61, 17, 28, '2023-06-09 11:09:11'),
  (62, 5, 25, '2023-06-09 11:09:12'),
  (63, 16, 30, '2023-06-09 11:09:16'),
  (64, 1, 23, '2023-06-09 11:09:17'),
  (65, 1, 22, '2023-06-09 11:09:21'),
  (66, 3, 19, '2023-06-09 11:09:22'),
  (67, 10, 19, '2023-06-09 11:09:24'),
  (68, 12, 20, '2023-06-09 11:09:26'),
  (69, 18, 20, '2023-06-09 11:09:29'),
  (70, 16, 21, '2023-06-09 11:09:32'),
  (71, 4, 27, '2023-06-09 11:09:33'),
  (72, 14, 31, '2023-06-09 11:09:37'),
  (73, 15, 30, '2023-06-09 11:09:40'),
  (74, 8, 24, '2023-06-09 11:09:43'),
  (75, 2, 24, '2023-06-09 11:09:44'),
  (76, 4, 31, '2023-06-09 11:09:45'),
  (77, 15, 25, '2023-06-09 11:09:49'),
  (78, 1, 28, '2023-06-09 11:09:53'),
  (79, 9, 29, '2023-06-09 11:09:56'),
  (80, 3, 30, '2023-06-09 11:09:57'),
  (81, 9, 20, '2023-06-09 11:09:58'),
  (82, 9, 23, '2023-06-09 11:10:01'),
  (83, 2, 30, '2023-06-09 11:10:03'),
  (84, 16, 26, '2023-06-09 11:10:04'),
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  (86, 14, 25, '2023-06-09 11:10:08'),
  (87, 3, 22, '2023-06-09 11:10:10'),
  (88, 14, 24, '2023-06-09 11:10:11'),
  (89, 9, 21, '2023-06-09 11:10:12'),
  (90, 8, 19, '2023-06-09 11:10:16'),
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  (92, 15, 30, '2023-06-09 11:10:19'),
  (93, 6, 21, '2023-06-09 11:10:21'),
  (94, 10, 26, '2023-06-09 11:10:22'),
  (95, 10, 30, '2023-06-09 11:10:25'),
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  (97, 7, 19, '2023-06-09 11:10:33'),
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  (109, 7, 30, '2023-06-09 11:11:00'),
  (110, 13, 28, '2023-06-09 11:11:01'),
  (111, 8, 29, '2023-06-09 11:11:03'),
  (112, 7, 26, '2023-06-09 11:11:04'),
  (113, 2, 23, '2023-06-09 11:11:07'),
  (114, 5, 25, '2023-06-09 11:11:08'),
  (115, 4, 27, '2023-06-09 11:11:10'),
  (116, 7, 25, '2023-06-09 11:11:12'),
  (117, 17, 26, '2023-06-09 11:11:14'),
  (118, 6, 31, '2023-06-09 11:11:17'),
  (119, 6, 27, '2023-06-09 11:11:20'),
  (120, 20, 30, '2023-06-09 11:11:23'),
  (121, 1, 24, '2023-06-09 11:11:26'),
  (122, 1, 27, '2023-06-09 11:11:30'),
  (123, 3, 28, '2023-06-09 11:11:31'),
  (124, 20, 30, '2023-06-09 11:11:32'),
  (125, 1, 31, '2023-06-09 11:11:33'),
  (126, 9, 21, '2023-06-09 11:11:37'),
  (127, 12, 24, '2023-06-09 11:11:39'),
  (128, 17, 30, '2023-06-09 11:11:40'),
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  (130, 5, 23, '2023-06-09 11:11:44'),
  (131, 1, 28, '2023-06-09 11:11:45'),
  (132, 16, 22, '2023-06-09 11:11:46'),
  (133, 8, 21, '2023-06-09 11:11:47'),
  (134, 15, 20, '2023-06-09 11:11:50'),
  (135, 8, 28, '2023-06-09 11:11:53'),
  (136, 16, 25, '2023-06-09 11:11:57'),
  (137, 14, 31, '2023-06-09 11:11:58'),
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  (142, 8, 24, '2023-06-09 11:12:12'),
  (143, 11, 24, '2023-06-09 11:12:14'),
  (144, 9, 24, '2023-06-09 11:12:18'),
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  (929, 2, 19, '2023-06-09 11:44:34'),
  (930, 5, 26, '2023-06-09 11:44:38'),
  (931, 20, 25, '2023-06-09 11:44:40'),
  (932, 3, 19, '2023-06-09 11:44:43'),
  (933, 14, 28, '2023-06-09 11:44:45'),
  (934, 14, 29, '2023-06-09 11:44:46'),
  (935, 1, 20, '2023-06-09 11:44:49'),
  (936, 1, 27, '2023-06-09 11:44:51'),
  (937, 10, 27, '2023-06-09 11:44:55'),
  (938, 18, 29, '2023-06-09 11:44:56'),
  (939, 1, 28, '2023-06-09 11:44:57'),
  (940, 16, 22, '2023-06-09 11:44:59'),
  (941, 7, 22, '2023-06-09 11:45:02'),
  (942, 11, 30, '2023-06-09 11:45:06'),
  (943, 18, 31, '2023-06-09 11:45:07'),
  (944, 12, 22, '2023-06-09 11:45:11'),
  (945, 13, 29, '2023-06-09 11:45:15'),
  (946, 17, 26, '2023-06-09 11:45:19'),
  (947, 7, 26, '2023-06-09 11:45:22'),
  (948, 1, 19, '2023-06-09 11:45:24'),
  (949, 4, 31, '2023-06-09 11:45:27'),
  (950, 5, 26, '2023-06-09 11:45:29'),
  (951, 14, 26, '2023-06-09 11:45:33'),
  (952, 17, 24, '2023-06-09 11:45:34'),
  (953, 6, 21, '2023-06-09 11:45:38'),
  (954, 5, 21, '2023-06-09 11:45:40'),
  (955, 9, 19, '2023-06-09 11:45:44'),
  (956, 12, 26, '2023-06-09 11:45:47'),
  (957, 5, 30, '2023-06-09 11:45:48'),
  (958, 3, 26, '2023-06-09 11:45:52'),
  (959, 12, 21, '2023-06-09 11:45:55'),
  (960, 13, 23, '2023-06-09 11:45:57'),
  (961, 2, 22, '2023-06-09 11:46:01'),
  (962, 10, 31, '2023-06-09 11:46:05'),
  (963, 14, 24, '2023-06-09 11:46:06'),
  (964, 15, 24, '2023-06-09 11:46:08'),
  (965, 18, 19, '2023-06-09 11:46:09'),
  (966, 16, 24, '2023-06-09 11:46:12'),
  (967, 4, 22, '2023-06-09 11:46:16'),
  (968, 11, 25, '2023-06-09 11:46:20'),
  (969, 3, 24, '2023-06-09 11:46:22'),
  (970, 20, 30, '2023-06-09 11:46:24'),
  (971, 7, 24, '2023-06-09 11:46:27'),
  (972, 14, 26, '2023-06-09 11:46:31'),
  (973, 10, 22, '2023-06-09 11:46:32'),
  (974, 8, 30, '2023-06-09 11:46:34'),
  (975, 5, 30, '2023-06-09 11:46:36'),
  (976, 4, 26, '2023-06-09 11:46:39'),
  (977, 12, 19, '2023-06-09 11:46:40'),
  (978, 9, 23, '2023-06-09 11:46:41'),
  (979, 13, 22, '2023-06-09 11:46:43'),
  (980, 12, 29, '2023-06-09 11:46:45'),
  (981, 12, 22, '2023-06-09 11:46:47'),
  (982, 4, 30, '2023-06-09 11:46:51'),
  (983, 11, 19, '2023-06-09 11:46:54'),
  (984, 9, 24, '2023-06-09 11:46:56'),
  (985, 10, 20, '2023-06-09 11:46:59'),
  (986, 10, 19, '2023-06-09 11:47:01'),
  (987, 16, 25, '2023-06-09 11:47:03'),
  (988, 17, 25, '2023-06-09 11:47:05'),
  (989, 10, 26, '2023-06-09 11:47:09'),
  (990, 15, 25, '2023-06-09 11:47:12'),
  (991, 20, 24, '2023-06-09 11:47:16'),
  (992, 10, 25, '2023-06-09 11:47:20'),
  (993, 10, 31, '2023-06-09 11:47:24'),
  (994, 19, 19, '2023-06-09 11:47:25'),
  (995, 8, 29, '2023-06-09 11:47:28'),
  (996, 19, 27, '2023-06-09 11:47:32'),
  (997, 15, 31, '2023-06-09 11:47:35'),
  (998, 10, 24, '2023-06-09 11:47:39'),
  (999, 19, 20, '2023-06-09 11:47:43'),
  (1000, 4, 25, '2023-06-09 11:47:45');

或者，您也可以执行下面的 Python 脚本实时向数据库填充数据（需要安装 mysql-connector依赖）：

import mysql.connector
from time import sleep
from random import randint
from datetime import datetime
mydb = mysql.connector.connect(
    host='******************.mysql.rds.aliyuncs.com',
    port=3306,
    user='***********',
    passwd='***********',
    database='***********'
)
while True:    
    cur = mydb.cursor()
    cur.execute("INSERT INTO source_table (good_id, amount, record_time) VALUES (%d, %d, '%s')" % ( randint(1, 20), randint(19, 31), datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
    mydb.commit()
    cur.close()
    sleep(randint(1, 4))

9. 清理及后续

通过简单的数据实时分析示例，您已完成了阿里云实时计算Flink版的开通、SQL查询语句的执行，以及可视化数据分析，快速体验了实时计算Flink版产品。

清理

实时计算Flink版提供的按量付费（3个月5000CUH计算资源）试用，具有时长和容量限制，使用完请及时清理相关资源。如果未及时清理资源，相关Flink工作空间将按照按量付费模式正常进行计费，涉及的其他产品服务也会正常收取费用。完成教程后，您可以按照如下场景进行处理：

如果您需要继续使用，此时5000CUH资源已使用完或者已经超过3个月，请随时查看欠费金额，并在规定时间内充值结清欠费账单，否则无法继续正常使用工作空间。计费详情请参见按量付费和欠费说明。

如果您不需要继续使用，请及时清理测试数据和试用资源：

实时计算Flink版资源清理

登录实时计算控制台，单击目标工作空间操作列下的更多>释放资源，单击确定。应用实时监控服务ARMS（如果您开通时选中）和负载均衡SLB会随实时计算Flink版一起被释放。

对象存储OSS资源清理

删除对象存储空间，详情请参见删除存储空间。

负载均衡SLB资源清理

删除传统型负载均衡CLB，详情请参见释放负载均衡实例。

专有网络VPC资源清理

删除专有网络，详情请参见删除专有网络。

RDS MySQL Serverless资源清理

本教程使用的RDS MySQL Serverless实例只能免费试用3个月。实例到期后，您可以手动释放。具体操作，请参见释放实例。如果一直未释放该实例，实例将在试用时间结束后，按照正常价格计费，计费标准请参见Serverless费用。

后续

如果您需要将某个作业提交至生产环境运行（请勿将Session集群用于正式生产环境），需要在作业开发页面，单击上线，在弹出的确认对话框中，单击确定，然后在作业运维页面，单击启动，具体操作请参见作业开发和作业启动。

10. 清理及后续

通过简单的数据实时分析示例，您已完成了阿里云实时计算Flink版的开通、SQL查询语句的执行，以及可视化数据分析，快速体验了实时计算Flink版产品

清理

如果您需要继续使用，请随时查看欠费金额，并在规定时间内充值结清欠费账单，否则无法继续正常使用工作空间。计费详情请参见按量付费和欠费说明。

如果您不需要继续使用，请及时清理测试数据和试用资源：

实时计算Flink版资源清理

对象存储OSS资源清理

删除对象存储空间，详情请参见删除存储空间。

负载均衡SLB资源清理

删除传统型负载均衡CLB，详情请参见释放负载均衡实例。

专有网络VPC资源清理

删除专有网络，详情请参见删除专有网络。

RDS MySQL Serverless资源清理

删除RDS MySQL Serverless，详情请参见释放实例。如果一直未释放该实例，实例将在试用时间结束后，按照正常价格计费，计费标准请参见Serverless费用。

后续

实验链接：https://developer.aliyun.com/adc/scenario/bc060d656dca42498992fee2bb47ed6b

5分钟上手Flink MySQL连接器

5分钟上手Flink MySQL连接器

实时计算 Flink

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