sql中开窗函数的使用

窗口函数

普通的聚集函数只能用来计算一行内的结果，或者把所有行聚集成一行结果。而窗口函数可以跨行计算，并且把结果填到每一行中。

•通过查询筛选出的行的某些部分，窗口调用函数实现了类似于聚集函数的功能，所以聚集函数也可以作为窗口函数使用。窗口函数可以扫描所有的行，根据窗口函数的PARTITION BY选项将查询的行分为一组。

•列存表目前只支持窗口函数rank(expression)和row_number(expression)，以及聚集函数的sum，count，avg，min和max，而行存表没有限制。

•窗口函数需要特殊的关键字OVER语句来指定窗口即触发一个窗口函数。OVER语句用于对数据进行分组，并对组内元素进行排序。窗口函数用于给组内的值生成序号。

•窗口函数中的order by后面必须跟字段名，若order by后面跟数字，该数字会被按照常量处理，对目标列没有起到排序的作用。

窗口函数的语法格式

function_name ( $expression \[, expression ...$ ]) OVER ( window_definition ) function_name ( $expression \[, expression ...$ ]) OVER window_namefunction_name ( * ) OVER ( window_definition ) function_name ( * ) OVER window_name

其中window_definition子句option为：

existing_window_name \] \[ PARTITION BY expression \[, ...\] \] \[ ORDER BY expression \[ ASC \| DESC \| USING operator \] \[ NULLS { FIRST \| LAST } \] \[, ...\] \] \[ frame_clause

frame_clause子句option为：

$RANGE \| ROWS$ frame_start $RANGE \| ROWS$ BETWEEN frame_start AND frame_end

窗口区间支持RANGE、ROWS两种模式，ROWS 以物理单位（行）指定窗口。RANGE将窗口指定为逻辑偏移量。

RANGE、ROWS中可以使用BETWEEN frame_start AND frame_end指定边界可取值。如果省略了frame_end默认为CURRENT ROW。

BETWEEN frame_start AND frame_end取值为：

CURRENT ROW，当前行。

N PRECEDING，当前行向前第n行。

UNBOUNDED PRECEDING，当前PARTITION的第1行。

N FOLLOWING，当前行向后第n行。

UNBOUNDED FOLLOWING，当前PARTITION的最后1行。

示例:

sql 复制代码

create table pinko_0410(
id varchar
,c_type varchar
,create_time date
);

insert into pinko_0410 values ('1','P1','2022-04-01 11:22:33');
insert into pinko_0410 values ('2','P1','2022-04-01 11:22:36');
insert into pinko_0410 values ('3','P1','2022-04-01 11:22:28');
insert into pinko_0410 values ('4','P1','2022-04-01 11:22:57');
insert into pinko_0410 values ('5','P2','2022-04-01 11:22:45');
insert into pinko_0410 values ('6','P2','2022-04-03 11:22:33');
insert into pinko_0410 values ('7','P2','2022-04-03 11:22:38');
insert into pinko_0410 values ('8','P2','2022-04-03 11:22:20');
insert into pinko_0410 values ('9','P2','2022-04-03 11:22:11');

--查询表
select * from pinko_0410;
id	c_type	create_time
5	P2	2022-04-01 11:22:45
8	P2	2022-04-03 11:22:20
2	P1	2022-04-01 11:22:36
7	P2	2022-04-03 11:22:38
4	P1	2022-04-01 11:22:57
9	P2	2022-04-03 11:22:11
3	P1	2022-04-01 11:22:28
6	P2	2022-04-03 11:22:33
1	P1	2022-04-01 11:22:33

--生成行号
select * ,row_number() over() as rn
from pinko_0410;
id	c_type	create_time	rn
1	P1	2022-04-01 11:22:33	1
8	P2	2022-04-03 11:22:20	2
7	P2	2022-04-03 11:22:38	3
2	P1	2022-04-01 11:22:36	4
5	P2	2022-04-01 11:22:45	5
4	P1	2022-04-01 11:22:57	6
9	P2	2022-04-03 11:22:11	7
3	P1	2022-04-01 11:22:28	8
6	P2	2022-04-03 11:22:33	9

--按c_type分组
select * ,row_number() over(partition by c_type) as rn
from pinko_0410;
id	c_type	create_time	rn
8	P2	2022-04-03 11:22:20	1
9	P2	2022-04-03 11:22:11	2
6	P2	2022-04-03 11:22:33	3
7	P2	2022-04-03 11:22:38	4
5	P2	2022-04-01 11:22:45	5
1	P1	2022-04-01 11:22:33	1
2	P1	2022-04-01 11:22:36	2
4	P1	2022-04-01 11:22:57	3
3	P1	2022-04-01 11:22:28	4

--按c_type分组,create_time升序
select * ,row_number() over(partition by c_type order by create_time) as rn
from pinko_0410;
id	c_type	create_time	rn
5	P2	2022-04-01 11:22:45	1
9	P2	2022-04-03 11:22:11	2
8	P2	2022-04-03 11:22:20	3
6	P2	2022-04-03 11:22:33	4
7	P2	2022-04-03 11:22:38	5
3	P1	2022-04-01 11:22:28	1
1	P1	2022-04-01 11:22:33	2
2	P1	2022-04-01 11:22:36	3
4	P1	2022-04-01 11:22:57	4


select * ,max(id) over(partition by c_type) as rn
from pinko_0410;
id	c_type	create_time	rn
1	P1	2022-04-01 11:22:33	4
2	P1	2022-04-01 11:22:36	4
4	P1	2022-04-01 11:22:57	4
3	P1	2022-04-01 11:22:28	4
8	P2	2022-04-03 11:22:20	9
6	P2	2022-04-03 11:22:33	9
5	P2	2022-04-01 11:22:45	9
9	P2	2022-04-03 11:22:11	9
7	P2	2022-04-03 11:22:38	9



select * ,max(id) over(partition by c_type order by create_time) as rn
from pinko_0410;
id	c_type	create_time	rn
5	P2	2022-04-01 11:22:45	5
9	P2	2022-04-03 11:22:11	9
8	P2	2022-04-03 11:22:20	9
6	P2	2022-04-03 11:22:33	9
7	P2	2022-04-03 11:22:38	9
3	P1	2022-04-01 11:22:28	3
1	P1	2022-04-01 11:22:33	3
2	P1	2022-04-01 11:22:36	3
4	P1	2022-04-01 11:22:57	4


select * ,lag(id) over(partition by c_type order by create_time rows between 1 PRECEDING and CURRENT ROW) as rn
from pinko_0410;
id	c_type	create_time	rn
3	P1	2022-04-01 11:22:28
1	P1	2022-04-01 11:22:33	3
2	P1	2022-04-01 11:22:36	1
4	P1	2022-04-01 11:22:57	2
5	P2	2022-04-01 11:22:45
9	P2	2022-04-03 11:22:11	5
8	P2	2022-04-03 11:22:20	9
6	P2	2022-04-03 11:22:33	8
7	P2	2022-04-03 11:22:38	6

select * ,lag(id,2) over(partition by c_type order by create_time rows between 1 PRECEDING and CURRENT ROW) as rn
from pinko_0410;
id	c_type	create_time	rn
5	P2	2022-04-01 11:22:45
9	P2	2022-04-03 11:22:11
8	P2	2022-04-03 11:22:20	5
6	P2	2022-04-03 11:22:33	9
7	P2	2022-04-03 11:22:38	8
3	P1	2022-04-01 11:22:28
1	P1	2022-04-01 11:22:33
2	P1	2022-04-01 11:22:36	3
4	P1	2022-04-01 11:22:57	1

select * ,lag(id,2,'hello') over(partition by c_type order by create_time rows between 1 PRECEDING and CURRENT ROW) as rn
from pinko_0410;
id	c_type	create_time	rn
5	P2	2022-04-01 11:22:45	hello
9	P2	2022-04-03 11:22:11	hello
8	P2	2022-04-03 11:22:20	5
6	P2	2022-04-03 11:22:33	9
7	P2	2022-04-03 11:22:38	8
3	P1	2022-04-01 11:22:28	hello
1	P1	2022-04-01 11:22:33	hello
2	P1	2022-04-01 11:22:36	3
4	P1	2022-04-01 11:22:57	1