聚合函数是将多条记录聚合为一条,而窗口函数是每条记录都会执行

win_fn()over (partition by order by frame) as new_row,

partition:分区,窗口函数在不同分组分别执行

order:排序

frame:在分区滑动窗口,可使用一般聚合函数比如avg等

固定窗口函数

Name	Description
CUME_DIST() over_clause	Cumulative distribution value
DENSE_RANK() over_clause	Rank of current row within its partition, without gaps
FIRST_VALUE(expr) [null_treatment] over_clause	Value of argument from first row of window frame
LAG(expr [, N[, default]]) [null_treatment] over_clause	Value of argument from row lagging current row within partition
LAST_VALUE(expr) [null_treatment] over_clause	Value of argument from last row of window frame
LEAD(expr [, N[, default]]) [null_treatment] over_clause	Value of argument from row leading current row within partition
NTH_VALUE(expr, N) [from_first_last] [null_treatment] over_clause	Value of argument from N-th row of window frame
NTILE(N) over_clause	Bucket number of current row within its partition.
PERCENT_RANK() over_clause	Percentage rank value
RANK() over_clause	Rank of current row within its partition, with gaps
ROW_NUMBER() over_clause	Number of current row within its partition

创建测试数据框

create table ss.test(id INT,c1 INT,c2 INT
)insert into ss.test (c1,c2) values(2,6);
insert into ss.test (c1,c2) values(1,3);
insert into ss.test (c1,c2) values(1,2);
insert into ss.test (c1,c2) values(1,1);
insert into ss.test (c1,c2) values(2,4);
insert into ss.test (c1,c2) values(2,4);

c2	c1
6	2
3	1
2	1
1	1
4	2
4	2

测试

序号函数——row_number() / rank() / dense_rank()

比较区别

row_number不对重复值处理
rank()重复值会并列,后面的值按整体的序号
dense_rank()重复值会并列,后面的值不按整体的序号

select c1,c2,row_number() over (partition by c1 order by c2 asc) as 'new' from ss.test; # 按c1分组,c2排序,给出每行在分组中的序号

c1	c2	new
1	1	1
1	2	2
1	3	3
2	4	1
2	4	2
2	6	3

select c1,c2,rank() over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,求排名

c1	c2	new
1	1	1
1	2	2
1	3	3
2	4	1
2	4	1
2	6	3

select c1,c2,dense_rank() over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,有重复的排名只计算一次

c1	c2	new
1	1	1
1	2	2
1	3	3
2	4	1
2	4	1
2	6	2

应用分组topn

select * from (select c1,c2,row_number() over (partition by c1 order by c2 asc) as 'new' from ss.test) as temp where new <= 2 ;

c1	c2	new
1	1	1
1	2	2
2	4	1
2	4	2

分布函数——percent_rank()/cume_dist()。

percent_rank() : (rank - 1) / (rows - 1),rank为RANK()函数产生的序号，rows为当前窗口的记录总行数
cume_dist():分组内小于等于当前rank值的行数/分组内总行数
select c1,c2,percent_rank() over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,求排名百分比

c1	c2	new
1	1	0
1	2	0.5
1	3	1
2	4	0
2	4	0
2	6	1

select c1,c2,cume_dist() over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,小于等于当前rank行的百分比

c1	c2	new
1	1	0.3333333333333333
1	2	0.6666666666666666
1	3	1
2	4	0.6666666666666666
2	4	0.6666666666666666
2	6	1

应用

查看分区后,小于当前值占的百分比,比如小于某个人考试成绩的百分比

前后函数——lead(n)/lag(n)

lead(n):都是错行取值
lag(n):都是错行取值
select c1,c2,lead(c2) over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,错一行取值

c1	c2	new
1	1	2
1	2	3
1	3
2	4	4
2	4	6
2	6

select c1,c2,lag(c2) over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,错一行取值

c1	c2	new
1	1
1	2	1
1	3	2
2	4
2	4	4
2	6	4

应用

按时间排序后,查询两笔订单之间的时间差

取记录函数——first_val(expr)/last_val(expr)/nth_value(expr)

select c1,c2,first_value(c2) over (partition by c1 order by c2 asc) as 'new' from ss.test;# c1分组,c2排序,取每组c2第一个值

c1	c2	new
1	1	1
1	2	1
1	3	1
2	4	4
2	4	4
2	6	4

select c1,c2,last_value(c2) over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,取每组c2最后一个值

c1	c2	new
1	1	1
1	2	2
1	3	3
2	4	4
2	4	4
2	6	6

select c1,c2,nth_value(c2,2) over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,取每组c2第二个值

c1	c2	new
1	1
1	2	2
1	3	2
2	4	4
2	4	4
2	6	4

应用

取分区后指定位置的值,比如取每个班第二名的成绩

顺序分组

select c1,c2,ntile(2) over (partition by c1 order by c2 asc) as 'new' from ss.test; # c1分组,c2排序,按排序进行分组

c1	c2	new
1	1	1
1	2	1
1	3	2
2	4	1
2	4	1
2	6	2

滑动frame

frame的语法是BETWEEN frame_start AND frame_end

WINDOW w AS (partition by c1 order by c2 asc ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING)
生成一个frame在partitioner分区里滑动
rows BETWEEN 1 PRECEDING AND 1 FOLLOWING 当前行的前一行和后一行,共三行记录
rows UNBOUNDED FOLLOWING 当前行到分区后的最后一行
INTERVAL 7 DAY PRECEDING 一周以内的

窗口命名

1.窗口名称
使用WINDOW w AS将替换over后面的语句,让语句更加清晰

select c1,c2,cume_dist() over w as 'new' from ss.testWINDOW w AS (partition by c1 order by c2 asc);

参考:
https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html
https://www.cnblogs.com/DataArt/p/9961676.html

MySQL Window Function Descriptions相关推荐

MySQl Window Function Concepts
create table test(user_id int1,user_class int1,user_amount int1 );insert into test values (1,1,10); ...
窗口函数（Window Function）
文章目录窗口函数概述窗口函数和聚合函数的区别使用要点应用场景语法结构非聚合窗口函数 rank dense_rank row_number cume_dist percent_rank fi ...
Vue父组件调用子组件的方法并传参的两种方式（用$refs.refName.functionName、window.function）
如需了解儿子怎么控制老子的,传送门:https://s-z-q.blog.csdn.net/article/details/120094689 父组件father.vue <template&g ...
mysql开启function,Mysql自定義函數（function）
語法自定義函數也需要相應的要求,語法如下: CREATE FUNCTION(參數列表) RETURNS返回值類型函數體刪除: DROPFUNCTION 調用自定義函數語法: SELECT (pa ...
窗函数（Window Function）在信号处理当中的应用
窗函数(Window Function)在信号处理当中的应用 1. 从两个重要极限到时域低通滤波器两个重要极限数学里常常会把两个非常重要而且非常常见的极限放在一起,并称他们为两个重要极限. 第一个 ...
mysql8 create table 语法错误_MySQL8.0 Window Function 剖析
title: $MySQL8.0 Window Functions 剖析 author: $马腾什么是window function window function 是在满足某种条件的记录集合上执行 ...
mysql window系统备份远程数据库到本地
使用方法:创建test.bat文件,保存以下内容,修改配置后双击打开 @echo offREM 声明采用UTF-8编码 chcp 65001echo. echo MySQL数据库备份脚本 echo. ...
窗函数(window function)
原文地址:窗函数(window function) 作者:了凡春秋窗函数是频谱分析中一个重要的部分,窗函数修正了由于信号的非周期性并减小了频谱中由于泄露而带来的测量不准确性. 快速傅里叶变换假定了时 ...
4.2.8. Window Function Calls
4.2.8. Window Function Calls 4.2.8.窗口函数调用 A window function call represents the application of an ag ...

MySQL Window Function Descriptions