MariaDB · 新特性 · 窗口函数
最后更新于：2022-04-01 10:34:44
## 简介
窗口函数（Window Function）是 SQL:2013 标准中提出的，在后续标准版本的更新中也多次扩展，最新的版本是 SQL:2011 中的标准。
从某些方面来说，窗口函数与聚合函数（Aggregate Function）有些类似，他们都是对一系列的行进行聚合计算，然而不同于聚合函数的是，窗口函数的输出并不是被聚集到单独的一行。像 SUM, AVG, COUNT 这些聚合函数最终输出都是一行记录。
窗口函数可以通过计算每行周围窗口上的集合值来分析数据，例如：计算一定记录范围内、一定值域内、或者一段时间内的累计和以及移动平均值等等。之所以使用窗口这个术语，是因为对结果的处理使用了一个滑动的查询结果集范围。
大部分数据库，尤其是商业数据库都支持SQL标准中定义的部分窗口函数，但是MySQL一直没有支持这个特性。窗口函数在MySQL社区一直呼声很高，却一直没有被实现，直到 MairaDB 10.2 响应了客户的需求，实现了部分窗口函数，并且在持续完善中，我们就来简单介绍一下 MairaDB 的窗口函数。
## 语法
MariaDB的窗口函数查询指定使用 OVER 关键字，默认情况下，用于计算的行集合（Window，窗口）是整个数据集，并且可以用 ORDER BY 子句排序。PARTITION BY 子句可以用于将窗口缩小到特定的集合内。
使用语法：
~~~
function (expression) OVER (
[ PARTITION BY expression_list ]
[ ORDER BY order_list [ frame_clause ] ] )
function:
A valid window function
expression_list:
expression | column_name [, expr_list ]
order_list:
expression | column_name [ ASC | DESC ]
[, ... ]
frame_clause:
~~~
例如，给出下面的原始数据：
~~~
CREATE TABLE student (name CHAR(10), test CHAR(10), score TINYINT);
INSERT INTO student VALUES
('Chun', 'SQL', 75), ('Chun', 'Tuning', 73),
('Esben', 'SQL', 43), ('Esben', 'Tuning', 31),
('Kaolin', 'SQL', 56), ('Kaolin', 'Tuning', 88),
('Tatiana', 'SQL', 87), ('Tatiana', 'Tuning', 83);
~~~
下面两个查询可以分别返回按 test 和 name 分区处理的平均数：
~~~
SELECT name, test, score, AVG(score) OVER (PARTITION BY test)
AS average_by_test FROM student;
+---------+--------+-------+-----------------+
| name | test | score | average_by_test |
+---------+--------+-------+-----------------+
| Chun | SQL | 75 | 65.2500 |
| Chun | Tuning | 73 | 68.7500 |
| Esben | SQL | 43 | 65.2500 |
| Esben | Tuning | 31 | 68.7500 |
| Kaolin | SQL | 56 | 65.2500 |
| Kaolin | Tuning | 88 | 68.7500 |
| Tatiana | SQL | 87 | 65.2500 |
| Tatiana | Tuning | 83 | 68.7500 |
+---------+--------+-------+-----------------+
SELECT name, test, score, AVG(score) OVER (PARTITION BY name)
AS average_by_name FROM student;
+---------+--------+-------+-----------------+
| name | test | score | average_by_name |
+---------+--------+-------+-----------------+
| Chun | SQL | 75 | 74.0000 |
| Chun | Tuning | 73 | 74.0000 |
| Esben | SQL | 43 | 37.0000 |
| Esben | Tuning | 31 | 37.0000 |
| Kaolin | SQL | 56 | 72.0000 |
| Kaolin | Tuning | 88 | 72.0000 |
| Tatiana | SQL | 87 | 85.0000 |
| Tatiana | Tuning | 83 | 85.0000 |
+---------+--------+-------+-----------------+
~~~
## 用例
### RANK
描述：
RANK() 可以显示给定行的序号，从1开始，顺序以 ORDER BY 字段排序后的序列为准。
语法：
~~~
RANK() OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)
~~~
例子
~~~
CREATE TABLE student(course VARCHAR(10), mark int, name varchar(10));
INSERT INTO student VALUES
('Maths', 60, 'Thulile'),
('Maths', 60, 'Pritha'),
('Maths', 70, 'Voitto'),
('Biology', 60, 'Bilal'),
('Biology', 70, 'Roger');
SELECT RANK() OVER (PARTITION BY course ORDER BY mark) AS rank,
DENSE_RANK() OVER (PARTITION BY course ORDER BY mark) AS dense_rank,
ROW_NUMBER() OVER (PARTITION BY course ORDER BY mark) AS row_num,
course, mark, name from student;
+------+------------+---------+---------+------+---------+
| rank | dense_rank | row_num | course | mark | name |
+------+------------+---------+---------+------+---------+
| 1 | 1 | 1 | Maths | 60 | Thulile |
| 1 | 1 | 2 | Maths | 60 | Pritha |
| 3 | 2 | 3 | Maths | 70 | Voitto |
| 1 | 1 | 1 | Biology | 60 | Bilal |
| 2 | 2 | 2 | Biology | 70 | Roger |
+------+------------+---------+---------+------+---------+
~~~
### CUME_DIST
描述：
CUME_DIST() 可以返回一行数据的累积分布（cumulative distribution）。计算公式如下
`(number of rows <= current row) / (total rows)`
语法：
~~~
CUME_DIST() OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)
~~~
例子：
~~~
create table t1 (
pk int primary key,
a int,
b int
);
insert into t1 values
( 1 , 0, 10),
( 2 , 0, 10),
( 3 , 1, 10),
( 4 , 1, 10),
( 8 , 2, 10),
( 5 , 2, 20),
( 6 , 2, 20),
( 7 , 2, 20),
( 9 , 4, 20),
(10 , 4, 20);
select pk, a, b,
rank() over (order by a),
percent_rank() over (order by a),
cume_dist() over (order by a)
from t1;
+----+------+------+--------------------------+----------------------------------+-------------------------------+
| pk | a | b | rank() over (order by a) | percent_rank() over (order by a) | cume_dist() over (order by a) |
+----+------+------+--------------------------+----------------------------------+-------------------------------+
| 1 | 0 | 10 | 1 | 0.0000000000 | 0.2000000000 |
| 2 | 0 | 10 | 1 | 0.0000000000 | 0.2000000000 |
| 3 | 1 | 10 | 3 | 0.2222222222 | 0.4000000000 |
| 4 | 1 | 10 | 3 | 0.2222222222 | 0.4000000000 |
| 5 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 6 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 7 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 8 | 2 | 10 | 5 | 0.4444444444 | 0.8000000000 |
| 9 | 4 | 20 | 9 | 0.8888888889 | 1.0000000000 |
| 10 | 4 | 20 | 9 | 0.8888888889 | 1.0000000000 |
+----+------+------+--------------------------+----------------------------------+-------------------------------+
select pk, a, b,
percent_rank() over (order by pk),
cume_dist() over (order by pk)
from t1 order by pk;
+----+------+------+-----------------------------------+--------------------------------+
| pk | a | b | percent_rank() over (order by pk) | cume_dist() over (order by pk) |
+----+------+------+-----------------------------------+--------------------------------+
| 1 | 0 | 10 | 0.0000000000 | 0.1000000000 |
| 2 | 0 | 10 | 0.1111111111 | 0.2000000000 |
| 3 | 1 | 10 | 0.2222222222 | 0.3000000000 |
| 4 | 1 | 10 | 0.3333333333 | 0.4000000000 |
| 5 | 2 | 20 | 0.4444444444 | 0.5000000000 |
| 6 | 2 | 20 | 0.5555555556 | 0.6000000000 |
| 7 | 2 | 20 | 0.6666666667 | 0.7000000000 |
| 8 | 2 | 10 | 0.7777777778 | 0.8000000000 |
| 9 | 4 | 20 | 0.8888888889 | 0.9000000000 |
| 10 | 4 | 20 | 1.0000000000 | 1.0000000000 |
+----+------+------+-----------------------------------+--------------------------------+
select pk, a, b,
percent_rank() over (partition by a order by a),
cume_dist() over (partition by a order by a)
from t1;
+----+------+------+-------------------------------------------------+----------------------------------------------+
| pk | a | b | percent_rank() over (partition by a order by a) | cume_dist() over (partition by a order by a) |
+----+------+------+-------------------------------------------------+----------------------------------------------+
| 1 | 0 | 10 | 0.0000000000 | 1.0000000000 |
| 2 | 0 | 10 | 0.0000000000 | 1.0000000000 |
| 3 | 1 | 10 | 0.0000000000 | 1.0000000000 |
| 4 | 1 | 10 | 0.0000000000 | 1.0000000000 |
| 5 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 6 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 7 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 8 | 2 | 10 | 0.0000000000 | 1.0000000000 |
| 9 | 4 | 20 | 0.0000000000 | 1.0000000000 |
| 10 | 4 | 20 | 0.0000000000 | 1.0000000000 |
+----+------+------+-------------------------------------------------+----------------------------------------------+
~~~
## 总结
* 支持 ROWS 和 RANGE类型的Frame
* 各种类型的Frame界定都支持，包括 RANGE PRECEDING | FOLLOWING n个Frame范围（不同于PostgreSQL 和 SQL Server）
* RANGE类型的Frame 还不支持 DATE[TIME] 数据类型和四则运算，但MDEV-9727正在进行开发
* 还不支持 GROUPS类型的Frame（但好像还没有流行的数据库有支持这玩意的）
* 不支持禁用Frame（好像也没其他数据库支持）
* 不支持显式的 NULLS FIRST 和 NULLS LAST
* 不支持窗口函数嵌套处理（就是VALUE_OF(expr AT row_marker [, default_value) 这种语法）
* 下面这些窗口函数都支持：
* “Streamable（流式）” 窗口函数：ROW_NUMBER, RANK, DENSE_RANK
* 一旦分区中的行数知道后，就可以流式处理的窗口函数：PERCENT_RANK, CUME_DIST, NTILE
* 目前支持窗口函数的聚合函数有： COUNT, SUM, AVG, BIT_OR, BIT_AND, BIT_XOR
* 包含 DISTINCT 子句的聚合函数不支持作为窗口函数
大家可以在[这里](https://mariadb.com/kb/en/mariadb/window-functions/)查看具体支持哪些函数以及未来新增了哪些函数