五分钟上手Flink MySQL连接器
MySQL Connector可以将本地或远程的MySQL数据库连接到Flink中，并方便地使用Flink Table API与之交互、捕获数据变更、并将处理结果写回数据库。
本场景将以阿里云实时计算Flink版为基础，使用Flink自带的MySQL Connector连接RDS云数据库实例，并以一个实时商品销售数据统计的例子尝试上手Connector的数据捕获、数据写入等功能。
一、创建数据库表
进入RDS mysql的管理平台DMS。（注意登录的账号需要有读写权限。）
首先创建数据库test。
CREATE DATABASE IF NOT EXISTS test;
点击执行，创建成功后，打开此数据库的SQL Console。
建表语句如下
-- 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`)
);
选中后点击执行
执行成功后，返回如下结果
可以在左侧表列表看到对应的表
然后向数据库中插入多条数据，插入代码在下面：
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
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(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'),
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(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');
选中插入代码，点击执行
插入数据成功后，可以看到source_table和dimension_table中已有数据。
查询数据条数
select count(1) from source_table;
select count(1) from dimension_table;
二、创建session集群
使用之前的flink-sql-test-session集群即可。如若没有，安装下面步骤创建。
- 登录实时计算控制台。
- 在Flink全托管页签，单击目标工作空间名称对应应操作列下的控制台。
- 在左侧导航栏，单击Session集群。
- 单击创建Session集群。
表格中未提及的参数保持默认值即可，需要配置的参数说明请参见下表。
配置项
说明
配置示例
名称
Session集群名称。
flink-sql-test-session
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
引擎版本
Session集群引擎版本号。
vvr-6.0.7-flink-1.15
Task Managers数量
默认与并行度保持一致。
4
- 单击创建Session集群。
当Session集群状态（页面上方集群名称旁边）从启动中变为运行中后，可以进入后续步骤。
三、源表查询
进入Flink开发平台，点击左侧作业开发，在demo文件夹下创建mysqlcdc-test流作业草稿，版本选择vvr-6.0.7-flink-1.15。创建源表，代码如下
CREATE TABLE mysql_source_table(
id INT NOT NULL PRIMARY KEY NOT ENFORCED,
record_time TIMESTAMP(3),
good_id INT,
amount INT,
WATERMARK FOR record_time AS record_time-INTERVAL '5' SECOND
)WITH(
'connector' = 'mysql',
'hostname' = 'rm-cn-g48798978t000ca.rwlb.rds.aliyuncs.com',
'port' = '3306',
'username' = 'itlanson',
'password' = 'It123',
'database-name' = 'test',
'table-name' = 'source_table'
);
其中hostname后是mysql的内网ip，可以在mysql实例详情的数据库连接处查看。
username和password是mysql的账号和密码。
选中代码后，点击运行，完成源表创建。
查询表数据，验证源表数据可以正常捕获。
SELECT * FROM mysqlcdc_source_table;
选中查询代码，点击调试，选择flink-sql-test-session集群，结果如下。
查询数据条数
SELECT count(1) FROM mysql_source_table;
选择代码后，点击调试。
可以看到控制台的结果在不断增大，达到500会暂停。这是因为默认查询500条，此时需要点击左侧的绿色箭头，恢复查询。
最后结果如下，可以看到，与mysql中的对应表数据条数相同。
此时，在mysql中插入一条数据。
INSERT INTO `source_table` (`id`, `good_id`, `amount`, `record_time`)
VALUE (1001, 1, 19, '2023-06-09 11:59:34');
插入成功后，观察flink控制台，发现数据已经发生了变化。即已经捕获了新增的数据。
四、窗口计算
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。
在Flink作业编辑页签中，输入如下代码。选中代码，点击调试，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
mysql_source_table
GROUP BY
tumble(
record_time, interval '15' seconds
),
good_id;
结果如下
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。
首先建表
CREATE TABLE dimension_table (
good_id INT NOT NULL PRIMARY KEY NOT ENFORCED,
good_name VARCHAR(256),
good_price INT
)WITH(
'connector' = 'mysql',
'hostname' = 'rm-cn-g48798978t000ca.rwlb.rds.aliyuncs.com',
'port' = '3306',
'username' = 'itlanson',
'password' = 'It123',
'database-name' = 'test',
'table-name' = 'dimension_table'
);
查询数据条数
select count(1) from dimension_table;
查询结果，与mysql中对应表的数据条数一致。
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
mysql_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;
选中代码，点击调试，结果如下
五、结果数据写回数据库
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 表，代码如下所示：
CREATE TABLE mysql_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://rm-cn-g48798978t000ca.rwlb.rds.aliyuncs.com:3306/test',
'table-name' = 'sink_table',
'username' = 'itlanson',
'password' = 'It123',
'scan.auto-commit' = 'true'
);
注意：url中//与:之间的为mysql的内网ip，根据实际进行更改。
新建mysqlcdc-insert流作业草稿，版本选择vvr-6.0.7-flink-1.15。
INSERT INTO mysql_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
mysql_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;
点击右上角部署，提交上线。
进入左侧作业运维，启动新提交的作业。
等待一段时间，作业状态变成运行中。点击状态总览可以看到作业执行图。可以看到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。
等待数据写入后，可以在mysql对应表查看写入结果。
返回DMS的SQL Console，查询数据。
SELECT * FROM `sink_table`;
结果如下
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本文转载自: https://blog.csdn.net/xiaoweite1/article/details/142422554
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版权归原作者 Lansonli 所有， 如有侵权，请联系我们删除。