Flinksql--订单宽表

参考： https://chbxw.blog.csdn.net/article/details/115078261 (datastream 实现)

一、ODS

模拟订单表及订单明细表

sql 复制代码

CREATE TABLE orders (
    order_id STRING,
    user_id STRING,
    order_time TIMESTAMP(3),
    -- 定义事件时间及 Watermark（允许5秒乱序）
    WATERMARK FOR order_time AS order_time - INTERVAL '5' SECOND
) WITH (
    'connector' = 'kafka',
    'topic' = 'orders',
    'properties.bootstrap.servers' = 'chb1:9092',
	'properties.group.id' = 'flink-sql-group-orders',  -- 消费者组 ID
    'scan.startup.mode' = 'earliest-offset',
    'format' = 'json'
);



CREATE TABLE order_details (
    detail_id STRING,
    order_id STRING,
    product_id STRING,
    price DECIMAL(10,2),
    quantity INT,
    detail_time TIMESTAMP(3),
    -- 定义事件时间及 Watermark（允许5秒乱序）
    WATERMARK FOR detail_time AS detail_time - INTERVAL '5' SECOND
) WITH (
    'connector' = 'kafka',
    'topic' = 'order_details',
    'properties.bootstrap.servers' = 'chb1:9092',
	'properties.group.id' = 'flink-sql-group-order_details',  -- 消费者组 ID
    'scan.startup.mode' = 'earliest-offset',
    'format' = 'json'
);

-- 造数据
insert into order_details values ('d001', 'o001', 'car', 5000, 1, now());
insert into orders values('o001', 'u001', now());


insert into orders values('o003', 'u003', now());

insert into order_details values ('d003', 'o003', 'water', 2, 12, now());
insert into order_details values ('d003', 'o003', 'food', 50, 3, now());

二、DWD 订单和订单明细关联

sql 复制代码

-- sink
CREATE TABLE dwd_trd_order (
    detail_id STRING,
    order_id STRING,
    product_id STRING,
    price DECIMAL(10,2),
    quantity INT,
    detail_time TIMESTAMP(3),
	user_id STRING,
	order_time TIMESTAMP(3),
    -- 定义事件时间及 Watermark（允许5秒乱序）
    WATERMARK FOR detail_time AS detail_time - INTERVAL '5' SECOND
) WITH (
    'connector' = 'kafka',
    'topic' = 'dwd_trd_order',
    'properties.bootstrap.servers' = 'chb1:9092',
    'scan.startup.mode' = 'earliest-offset',
    'format' = 'json'
);


insert into dwd_trd_order
SELECT 
	d.detail_id,
    o.order_id,
    d.product_id,
	d.price,
	d.quantity,
	d.detail_time,
	user_id,
	order_time
FROM orders o
JOIN order_details d 
ON o.order_id = d.order_id
AND d.detail_time BETWEEN o.order_time AND o.order_time + INTERVAL '10' MINUTE;

报错：

sql 复制代码

[ERROR] Could not execute SQL statement. Reason:
org.apache.flink.table.api.TableException: The query contains more than one rowtime attribute column [detail_time, order_time] for writing into table 'default_catalog.default_database.dwd_trd_order'.
Please select the column that should be used as the event-time timestamp for the table sink by casting all other columns to regular TIMESTAMP or TIMESTAMP_LTZ.

在 Flink SQL 中，每个表只能有一个 行时间属性（rowtime attribute） 用于定义事件时间（Event Time）。当写入目标表时，若查询结果包含多个行时间属性字段（如 order_time 和 detail_time），会导致冲突。以下是解决方案：

1. 问题定位

错误信息表明目标表 dwd_trd_order 在写入时检测到多个行时间属性字段（detail_time 和 order_time）。

根本原因是：JOIN 后的结果保留了双表的事件时间字段，且未被转换为普通时间戳。

2. 解决方案

方案一：仅保留一个行时间属性

在查询中选择一个时间字段作为事件时间，将其他时间字段转为普通 TIMESTAMP 类型。

假设目标表 dwd_trd_order 使用 order_time 作为事件时间：

sql 复制代码

INSERT INTO dwd_trd_order
SELECT 
    o.order_id,
    o.user_id,
    d.product_id,
    d.price,
    d.quantity,
    o.order_time,  -- 保留为行时间属性（需与目标表定义一致）
    CAST(d.detail_time AS TIMESTAMP(3)) AS detail_time  -- 转为普通时间戳
FROM orders o
JOIN order_details d ON o.order_id = d.order_id;

方案二：调整目标表定义

若业务需要同时保留两个时间字段，需在目标表 DDL 中 仅定义一个行时间属性，其他字段转为普通时间戳：

sql 复制代码

CREATE TABLE dwd_trd_order (
    order_id STRING,
    user_id STRING,
    product_id STRING,
    price DECIMAL(10,2),
    quantity INT,
    order_time TIMESTAMP(3),  -- 行时间属性
    detail_time TIMESTAMP(3),  -- 普通时间戳
    WATERMARK FOR order_time AS order_time - INTERVAL '5' SECOND  -- 仅一个事件时间
) WITH (...);

3. 关键步骤说明

检查目标表 DDL ：

确保目标表仅有一个 WATERMARK 定义，且对应字段为行时间属性。
转换多余的行时间属性 ：

在查询中使用 CAST 将非主时间字段转为普通 TIMESTAMP 或 TIMESTAMP_LTZ：
sql 复制代码
```
CAST(detail_time AS TIMESTAMP(3))  -- 转为非行时间属性
```
验证查询结果 ：

使用 DESCRIBE 确认查询结果的字段类型：
sql 复制代码
```
DESCRIBE (SELECT ... FROM ...);
```

目标表 DDL（仅一个行时间属性）

sql 复制代码

CREATE TABLE dwd_trd_order (
    order_id STRING,
    user_id STRING,
    product_id STRING,
    price DECIMAL(10,2),
    quantity INT,
    order_time TIMESTAMP(3),  -- 行时间属性
    detail_time TIMESTAMP(3),  -- 普通时间戳
    WATERMARK FOR order_time AS order_time - INTERVAL '5' SECOND
) WITH (...);

写入数据的 SQL（转换多余时间字段）

sql 复制代码

INSERT INTO dwd_trd_order
SELECT 
    o.order_id,
    o.user_id,
    d.product_id,
    d.price,
    d.quantity,
    o.order_time,  -- 保留为行时间属性
    CAST(d.detail_time AS TIMESTAMP(3)) AS detail_time  -- 转为普通时间戳
FROM orders o
JOIN order_details d ON o.order_id = d.order_id;

三、DWS

sql 复制代码

CREATE TABLE dws_trd_order (
    window_start TIMESTAMP(3),
    window_end TIMESTAMP(3),
    product_num bigint,
	uv bigint,
    total_amount DECIMAL(10,2)
) WITH (
    'connector' = 'kafka',
    'topic' = 'dws_trd_order',
    'properties.bootstrap.servers' = 'chb1:9092',
    'scan.startup.mode' = 'earliest-offset',
    'format' = 'json'
);

-- dws 
insert into dws_trd_order
SELECT
    window_start, window_end,
    COUNT(1) AS product_num,
    COUNT(DISTINCT user_id) AS uv,
    SUM(price * quantity) AS total_amount
FROM TABLE(
    CUMULATE(TABLE dwd_trd_order, DESCRIPTOR(detail_time), INTERVAL '5' SECOND, INTERVAL '1' DAY)
)
GROUP BY window_start, window_end;

有个问题：为什么窗口结束时间从 2025-04-02 20:48:50.000 开始？？？

sql 复制代码

dwd_trd_order 表的时间如下
 order_time              detail_time
 2025-04-02 20:06:01.281 2025-04-02 20:07:35.494
 2025-04-02 20:50:27.975 2025-04-02 20:50:33.233
 2025-04-02 20:50:27.975 2025-04-02 20:50:34.405
 
 累计窗口运算如下
 select
	window_start, window_end,
    count(1) product_num,
	count(distinct user_id) uv,
	sum(price*quantity) as total_amount
	
from TABLE(
	CUMULATE(TABLE dwd_trd_order, DESCRIPTOR(detail_time ), INTERVAL '5' SECOND, INTERVAL '1' DAY)
)
group by window_start,window_end;
为什么窗口结束时间从 2025-04-02 20:48:50.000 开始？？？
 window_start              window_end                    product_num                   uv                             total_amount
 2025-04-02 00:00:00.000 2025-04-02 20:48:50.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:48:55.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:49:00.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:49:05.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:49:10.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:49:15.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:49:20.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:49:25.000                    1                    1                                  5000.00
 2025-04-02 00:00:00.000 2025-04-02 20:49:30.000