文章目录

• 一、DISTINCT Aggregation
• 二、GROUPING SETS
• • TTL
  • 2.1、ROLLUP
  • 2.2、CUBE
• 三、HAVING

一、DISTINCT Aggregation

二、GROUPING SETS

在一个GROUP BY 查询中，根据不同的维度组合进行聚合。GROUPING SETS就是一种将多个GROUP BY逻辑 UNION 在一起。GROUPING SETS会把在单个GROUP BY逻辑中没有参与GROUP BY的那一列置为NULL值。空分组集意味着所有行都聚合到一个组中

SELECT supplier_id, rating, COUNT(*) AS total
FROM (VALUES('supplier1', 'product1', 4),('supplier1', 'product2', 3),('supplier2', 'product3', 3),('supplier2', 'product4', 4))
AS Products(supplier_id, product_id, rating)
GROUP BY GROUPING SETS ((supplier_id, rating), (supplier_id), ())
Results:+-------------+--------+-------+
| supplier_id | rating | total |
+-------------+--------+-------+
|   supplier1 |      4 |     1 |
|   supplier1 |      3 |     1 |
|   supplier2 |      3 |     1 |
|   supplier2 |      4 |     1 ||   supplier1 | (NULL) |     2 |
|   supplier2 | (NULL) |     2 ||      (NULL) | (NULL) |     4 |
+-------------+--------+-------+

TTL

在持续查询的过程中，由于用于分组的 key 可能会不断增加，因此计算结果所需要维护的状态也会持续增长。为了防止状态无限增长耗尽资源，Flink Table API 和 SQL 可以在表环境中配置状态的生存时间（TTL）table.exec.state.ttl
 
配置 TTL 有可能会导致统计结果不准确，这其实是以牺牲正确性为代价换取了资源的释放

2.1、ROLLUP

ROLLUP 相当于 GROUPING SETS 的简化

SELECT supplier_id, rating, COUNT(*)
FROM (VALUES('supplier1', 'product1', 4),('supplier1', 'product2', 3),('supplier2', 'product3', 3),('supplier2', 'product4', 4))
AS Products(supplier_id, product_id, rating)
GROUP BY ROLLUP (supplier_id, rating)等价于
SELECT supplier_id, rating, COUNT(*)
FROM (VALUES('supplier1', 'product1', 4),('supplier1', 'product2', 3),('supplier2', 'product3', 3),('supplier2', 'product4', 4))
AS Products(supplier_id, product_id, rating)
GROUP BY GROUPING SETS (( supplier_id, rating ),( supplier_id         ),(                     )
)--- 三个字段分组
SELECT supplier_id, rating,product_id, COUNT(*)
FROM (VALUES('supplier1', 'product1', 4),('supplier1', 'product2', 3),('supplier2', 'product3', 3),('supplier2', 'product4', 4))
AS Products(supplier_id, product_id, rating)
GROUP BY ROLLUP(supplier_id, rating,product_id)
等价于
SELECT supplier_id, rating, product_id, COUNT(*)
FROM (VALUES('supplier1', 'product1', 4),('supplier1', 'product2', 3),('supplier2', 'product3', 3),('supplier2', 'product4', 4))
AS Products(supplier_id, product_id, rating)
GROUP BY GROUPING SETS (( supplier_id, product_id, rating ),( supplier_id, product_id         ),( supplier_id                     ),(                                 )
)

2.2、CUBE

SELECT supplier_id, rating,product_id, COUNT(*)
FROM (VALUES('supplier1', 'product1', 4),('supplier1', 'product2', 3),('supplier2', 'product3', 3),('supplier2', 'product4', 4))
AS Products(supplier_id, product_id, rating)
GROUP BY CUBE(supplier_id, rating,product_id)等价于
SELECT supplier_id, rating, product_id, COUNT(*)
FROM (VALUES('supplier1', 'product1', 4),('supplier1', 'product2', 3),('supplier2', 'product3', 3),('supplier2', 'product4', 4))
AS Products(supplier_id, product_id, rating)
GROUP BY GROUPING SETS (( supplier_id, product_id, rating ),( supplier_id, product_id         ),( supplier_id,             rating ),( supplier_id                     ),(              product_id, rating ),(              product_id         ),(                          rating ),(                                 )
)

下面是 CUBE 和 ROLLUP 之间的具体区别：

• CUBE 生成的结果集显示了所选列中值的所有组合的聚合。
• ROLLUP 生成的结果集显示了所选列中值的某一层次结构的聚合。

三、HAVING

用于分组后的过滤条件