Flink SQL Over 聚合详解
Over 聚合定义（⽀持 Batch\Streaming）：**特殊的滑动窗⼝聚合函数，拿 Over 聚合 与 窗⼝聚合 做对⽐。
窗⼝聚合：不在 group by 中的字段，不能直接在 select 中拿到
Over 聚合：能够保留原始字段
注意： ⽣产环境中，Over 聚合的使⽤场景较少。
**应⽤场景：**计算最近⼀段滑动窗⼝的聚合结果数据。
**实际案例：**查询每个产品最近⼀⼩时订单的⾦额总和：
SELECT order_id,
order_time,
amount,
SUM(amount) OVER (
PARTITION BY product
ORDER BY order_time
RANGE BETWEEN INTERVAL '1' HOUR PRECEDING AND CURRENT ROW
) AS one_hour_prod_amount_sum
FROM Orders
Over 聚合语法如下：
SELECT
agg_func(agg_col) OVER (
[PARTITION BY col1[, col2, ...]]
ORDER BY time_col
range_definition),
...
FROM ...
ORDER BY：必须是时间戳列（事件时间、处理时间）；
PARTITION BY：标识了聚合窗⼝的聚合粒度，如上述案例是按照 product 进⾏聚合；
range_definition：标识聚合窗⼝的聚合数据范围，在 Flink 中有两种指定数据范围的⽅式。第⼀种为 按照⾏数聚合 ，第⼆种为 按照时间区间聚合 。
1.时间区间聚合
**案例：**输出一个产品最近⼀⼩时数据的 amount 之和。
结果就是最近⼀⼩时数据的 amount 之和。
CREATE TABLE source_table (
order_id BIGINT,
product BIGINT,
amount BIGINT,
order_time as cast(CURRENT_TIMESTAMP as TIMESTAMP(3)),
WATERMARK FOR order_time AS order_time - INTERVAL '0.001' SECOND
) WITH (
'connector' = 'datagen',
'rows-per-second' = '1',
'fields.order_id.min' = '1',
'fields.order_id.max' = '2',
'fields.amount.min' = '1',
'fields.amount.max' = '10',
'fields.product.min' = '1',
'fields.product.max' = '2'
);
CREATE TABLE sink_table (
product BIGINT,
order_time TIMESTAMP(3),
amount BIGINT,
one_hour_prod_amount_sum BIGINT
) WITH (
'connector' = 'print'
);
INSERT INTO sink_table
SELECT product,
order_time,
amount,
SUM(amount) OVER (
PARTITION BY product
ORDER BY order_time
-- 标识统计范围是⼀个 product 的最近 1 ⼩时的数据
RANGE BETWEEN INTERVAL '1' HOUR PRECEDING AND CURRENT ROW
) AS one_hour_prod_amount_sum
FROM source_table
结果如下：
+I[2, 2021-12-24T22:08:26.583, 7, 73]
+I[2, 2021-12-24T22:08:27.583, 7, 80]
+I[2, 2021-12-24T22:08:28.583, 4, 84]
+I[2, 2021-12-24T22:08:29.584, 7, 91]
+I[2, 2021-12-24T22:08:30.583, 8, 99]
+I[1, 2021-12-24T22:08:31.583, 9, 138]
+I[2, 2021-12-24T22:08:32.584, 6, 105]
+I[1, 2021-12-24T22:08:33.584, 7, 145]
2.⾏数聚合
**案例：**输出一个产品最近 5 ⾏数据的 amount 之和。
CREATE TABLE source_table (
order_id BIGINT,
product BIGINT,
amount BIGINT,
order_time as cast(CURRENT_TIMESTAMP as TIMESTAMP(3)),
WATERMARK FOR order_time AS order_time - INTERVAL '0.001' SECOND
) WITH (
'connector' = 'datagen',
'rows-per-second' = '1',
'fields.order_id.min' = '1',
'fields.order_id.max' = '2',
'fields.amount.min' = '1',
'fields.amount.max' = '2',
'fields.product.min' = '1',
'fields.product.max' = '2'
);
CREATE TABLE sink_table (
product BIGINT,
order_time TIMESTAMP(3),
amount BIGINT,
one_hour_prod_amount_sum BIGINT
) WITH (
'connector' = 'print'
);
INSERT INTO sink_table
SELECT product,
order_time,
amount,
SUM(amount) OVER (
PARTITION BY product
ORDER BY order_time
-- 标识统计范围是⼀个 product 的最近 5 ⾏数据
ROWS BETWEEN 5 PRECEDING AND CURRENT ROW
) AS one_hour_prod_amount_sum
FROM source_table
结果如下：
+I[2, 2021-12-24T22:18:19.147, 1, 9]
+I[1, 2021-12-24T22:18:20.147, 2, 11]
+I[1, 2021-12-24T22:18:21.147, 2, 12]
+I[1, 2021-12-24T22:18:22.147, 2, 12]
+I[1, 2021-12-24T22:18:23.148, 2, 12]
+I[1, 2021-12-24T22:18:24.147, 1, 11]
+I[1, 2021-12-24T22:18:25.146, 1, 10]
+I[1, 2021-12-24T22:18:26.147, 1, 9]
+I[2, 2021-12-24T22:18:27.145, 2, 11]
+I[2, 2021-12-24T22:18:28.148, 1, 10]
+I[2, 2021-12-24T22:18:29.145, 2, 10]
在⼀个 SELECT 中有多个聚合窗⼝，简化写法如下：
SELECT order_id,
order_time,
amount,
SUM(amount) OVER w AS sum_amount,
AVG(amount) OVER w AS avg_amount
FROM Orders
-- 使⽤下⾯⼦句，定义 Over Window
WINDOW w AS (
PARTITION BY product
ORDER BY order_time
RANGE BETWEEN INTERVAL '1' HOUR PRECEDING AND CURRENT ROW)