窗口函数详细用法

博客园 - 星朝

博客园搬家 JVM启动参数大全 BigDecimal.setScale用法总结 - 星朝 - 博客园 springboot项目上传文件出现临时文件目录为空 springboot中多线程中使用MultipartFile进行异步操作报错，系统找不到指定的文件 java获取本机IP Java获取本机ip和服务器ip Spring中的@Transactional(rollbackFor = Exception.class)属性详解注解@CrossOrigin解决跨域问题 unique key index区别 mysql 创建唯一约束表 MySQL中添加唯一约束和联合唯一约束移动IM开源框架Tigase > Openfire > Ejabberd对比分析 Tigase 8.0开发环境搭建 Tigase手动安装过程几个时序数据库时序数据库入门 MySQL执行计划extra中的using index 和 using where using index 的区别 ETL的详细解释定义

窗口函数详细用法

星朝 · 2020-04-14 · via 博客园 - 星朝

仅做记录，原文：https://blog.csdn.net/scgaliguodong123_/article/details/60135385

窗口函数与分析函数
应用场景：
（1）用于分区排序
（2）动态Group By
（3）Top N
（4）累计计算
（5）层次查询

窗口函数
FIRST_VALUE：取分组内排序后，截止到当前行，第一个值
LAST_VALUE：取分组内排序后，截止到当前行，最后一个值
LEAD(col,n,DEFAULT) ：用于统计窗口内往下第n行值。第一个参数为列名，第二个参数为往下第n行（可选，默认为1），第三个参数为默认值（当往下第n行为NULL时候，取默认值，如不指定，则为NULL）
LAG(col,n,DEFAULT) ：与lead相反，用于统计窗口内往上第n行值。第一个参数为列名，第二个参数为往上第n行（可选，默认为1），第三个参数为默认值（当往上第n行为NULL时候，取默认值，如不指定，则为NULL）

OVER从句
1、使用标准的聚合函数COUNT、SUM、MIN、MAX、AVG
2、使用PARTITION BY语句，使用一个或者多个原始数据类型的列
3、使用PARTITION BY与ORDER BY语句，使用一个或者多个数据类型的分区或者排序列
4、使用窗口规范，窗口规范支持以下格式：
---------------------

(ROWS | RANGE) BETWEEN CURRENT ROW AND (CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)

(ROWS | RANGE) BETWEEN [num] FOLLOWING AND (UNBOUNDED | [num]) FOLLOWING

当ORDER BY后面缺少窗口从句条件，窗口规范默认是 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.

当ORDER BY和窗口从句都缺失, 窗口规范默认是 ROW BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING.

OVER从句支持以下函数，但是并不支持和窗口一起使用它们。
Ranking函数: Rank, NTile, DenseRank, CumeDist, PercentRank.
Lead 和 Lag 函数.

分析函数
ROW_NUMBER() 从1开始，按照顺序，生成分组内记录的序列,比如，按照pv降序排列，生成分组内每天的pv名次,ROW_NUMBER()的应用场景非常多，再比如，获取分组内排序第一的记录;获取一个session中的第一条refer等。
RANK() 生成数据项在分组中的排名，排名相等会在名次中留下空位
DENSE_RANK() 生成数据项在分组中的排名，排名相等会在名次中不会留下空位
CUME_DIST 小于等于当前值的行数/分组内总行数。比如，统计小于等于当前薪水的人数，所占总人数的比例
PERCENT_RANK 分组内当前行的RANK值-1/分组内总行数-1
NTILE(n) 用于将分组数据按照顺序切分成n片，返回当前切片值，如果切片不均匀，默认增加第一个切片的分布。NTILE不支持ROWS BETWEEN，比如 NTILE(2) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3 PRECEDING AND CURRENT ROW)。

Hive2.1.0及以后支持Distinct
在聚合函数（SUM, COUNT and AVG）中，支持distinct，但是在ORDER BY 或者窗口限制不支持。

1	`COUNT(DISTINCT` `a) OVER (PARTITION` `BY` `c)`

Hive 2.2.0中在使用ORDER BY和窗口限制时支持distinct

1	`COUNT(DISTINCT` `a) OVER (PARTITION` `BY` `c` `ORDER` `BY` `d` `ROWS` `BETWEEN` `1 PRECEDING` `AND` `1 FOLLOWING)`

Hive2.1.0及以后支持在OVER从句中支持聚合函数

SELECT rank() OVER (ORDER BY sum(b))

FROM T

GROUP BY a;

测试数据集：

## COUNT、SUM、MIN、MAX、AVG

select

user_id,

user_type,

sales,

sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc) AS sales_1,

sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS sales_2,

sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) AS sales_3,

sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN 3 PRECEDING AND 1 FOLLOWING) AS sales_4,

sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) AS sales_5,

SUM(sales) OVER(PARTITION BY user_type) AS sales_6

from

order_detail

order by

user_type,

sales,

user_id

+

+

| liiu | new | 1 | 2 | 2 | 2 | 4 | 22 | 23 |

| qibaqiu | new | 1 | 2 | 1 | 1 | 2 | 23 | 23 |

| zhangsa | new | 2 | 4 | 4 | 4 | 7 | 21 | 23 |

| wanger | new | 3 | 7 | 7 | 7 | 12 | 19 | 23 |

| lilisi | new | 5 | 17 | 17 | 15 | 21 | 11 | 23 |

| qishili | new | 5 | 17 | 12 | 11 | 16 | 16 | 23 |

| wutong | new | 6 | 23 | 23 | 19 | 19 | 6 | 23 |

| lisi | old | 1 | 1 | 1 | 1 | 3 | 6 | 6 |

| wangshi | old | 2 | 3 | 3 | 3 | 6 | 5 | 6 |

| liwei | old | 3 | 6 | 6 | 6 | 6 | 3 | 6 |

+

注意:

结果和ORDER BY相关,默认为升序

如果不指定ROWS BETWEEN,默认为从起点到当前行;

如果不指定ORDER BY，则将分组内所有值累加;

关键是理解ROWS BETWEEN含义,也叫做WINDOW子句：

PRECEDING：往前

FOLLOWING：往后

CURRENT ROW：当前行

UNBOUNDED：无界限（起点或终点）

UNBOUNDED PRECEDING：表示从前面的起点

UNBOUNDED FOLLOWING：表示到后面的终点

其他COUNT、AVG，MIN，MAX，和SUM用法一样。

## first_value与last_value

select

user_id,

user_type,

ROW_NUMBER() OVER(PARTITION BY user_type ORDER BY sales) AS row_num,

first_value(user_id) over (partition by user_type order by sales desc) as max_sales_user,

first_value(user_id) over (partition by user_type order by sales asc) as min_sales_user,

last_value(user_id) over (partition by user_type order by sales desc) as curr_last_min_user,

last_value(user_id) over (partition by user_type order by sales asc) as curr_last_max_user

from

order_detail;

+

+

| wutong | new | 7 | wutong | qibaqiu | wutong | wutong |

| lilisi | new | 6 | wutong | qibaqiu | qishili | lilisi |

| qishili | new | 5 | wutong | qibaqiu | qishili | lilisi |

| wanger | new | 4 | wutong | qibaqiu | wanger | wanger |

| zhangsa | new | 3 | wutong | qibaqiu | zhangsa | zhangsa |

| liiu | new | 2 | wutong | qibaqiu | qibaqiu | liiu |

| qibaqiu | new | 1 | wutong | qibaqiu | qibaqiu | liiu |

| liwei | old | 3 | liwei | lisi | liwei | liwei |

| wangshi | old | 2 | liwei | lisi | wangshi | wangshi |

| lisi | old | 1 | liwei | lisi | lisi | lisi |

+

## lead与lag

select

user_id,device_id,

lead(device_id) over (order by sales) as default_after_one_line,

lag(device_id) over (order by sales) as default_before_one_line,

lead(device_id,2) over (order by sales) as after_two_line,

lag(device_id,2,'abc') over (order by sales) as before_two_line

from

order_detail;

+

+

| qibaqiu | fds | fdsfagwe | NULL | 543gfd | abc |

| liiu | fdsfagwe | 543gfd | fds | f332 | abc |

| lisi | 543gfd | f332 | fdsfagwe | dfsadsa323 | fds |

| wangshi | f332 | dfsadsa323 | 543gfd | hfd | fdsfagwe |

| zhangsa | dfsadsa323 | hfd | f332 | 65ghf | 543gfd |

| liwei | hfd | 65ghf | dfsadsa323 | fds | f332 |

| wanger | 65ghf | fds | hfd | dsfgg | dfsadsa323 |

| qishili | fds | dsfgg | 65ghf | 543gdfsd | hfd |

| lilisi | dsfgg | 543gdfsd | fds | NULL | 65ghf |

+

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

## RANK、ROW_NUMBER、DENSE_RANK

select

user_id,user_type,sales,

RANK() over (partition by user_type order by sales desc) as r,

ROW_NUMBER() over (partition by user_type order by sales desc) as rn,

DENSE_RANK() over (partition by user_type order by sales desc) as dr

from

order_detail;

+

| user_id | user_type | sales | r | rn | dr |

+

| wutong | new | 6 | 1 | 1 | 1 |

| qishili | new | 5 | 2 | 2 | 2 |

| lilisi | new | 5 | 2 | 3 | 2 |

| wanger | new | 3 | 4 | 4 | 3 |

| zhangsa | new | 2 | 5 | 5 | 4 |

| qibaqiu | new | 1 | 6 | 6 | 5 |

| liiu | new | 1 | 6 | 7 | 5 |

| liwei | old | 3 | 1 | 1 | 1 |

| wangshi | old | 2 | 2 | 2 | 2 |

| lisi | old | 1 | 3 | 3 | 3 |

+

## NTILE

select

user_type,sales,

NTILE(2) OVER(PARTITION BY user_type ORDER BY sales) AS nt2,

NTILE(3) OVER(PARTITION BY user_type ORDER BY sales) AS nt3,

NTILE(4) OVER(PARTITION BY user_type ORDER BY sales) AS nt4,

NTILE(4) OVER(ORDER BY sales) AS all_nt4

from

order_detail

order by

user_type,

sales

+

| user_type | sales | nt2 | nt3 | nt4 | all_nt4 |

+

| new | 1 | 1 | 1 | 1 | 1 |

| new | 2 | 1 | 1 | 2 | 2 |

| new | 3 | 1 | 2 | 2 | 3 |

| new | 5 | 2 | 2 | 3 | 4 |

| new | 5 | 2 | 3 | 3 | 3 |

| new | 6 | 2 | 3 | 4 | 4 |

| old | 1 | 1 | 1 | 1 | 1 |

| old | 2 | 1 | 2 | 2 | 2 |

| old | 3 | 2 | 3 | 3 | 2 |

+

求取sale前20%的用户ID

select

user_id

from

(

select

user_id,

NTILE(5) OVER(ORDER BY sales desc) AS nt

from

order_detail

)A

where nt=1;

## CUME_DIST、PERCENT_RANK

select

user_id,user_type,sales,

CUME_DIST() OVER(ORDER BY sales) AS cd1,

CUME_DIST() OVER(PARTITION BY user_type ORDER BY sales) AS cd2

from

order_detail;

+

| user_id | user_type | sales | cd1 | cd2 |

+

| liiu | new | 1 | 0.3 | 0.2857142857142857 |

| qibaqiu | new | 1 | 0.3 | 0.2857142857142857 |

| zhangsa | new | 2 | 0.5 | 0.42857142857142855 |

| wanger | new | 3 | 0.7 | 0.5714285714285714 |

| lilisi | new | 5 | 0.9 | 0.8571428571428571 |

| qishili | new | 5 | 0.9 | 0.8571428571428571 |

| wutong | new | 6 | 1.0 | 1.0 |

| lisi | old | 1 | 0.3 | 0.3333333333333333 |

| wangshi | old | 2 | 0.5 | 0.6666666666666666 |

| liwei | old | 3 | 0.7 | 1.0 |

+

select

user_type,sales

SUM(1) OVER(PARTITION BY user_type) AS s,

RANK() OVER(ORDER BY sales) AS r,

PERCENT_RANK() OVER(ORDER BY sales) AS pr,

PERCENT_RANK() OVER(PARTITION BY user_type ORDER BY sales) AS prg

from

order_detail;

+

| s | r | pr | prg |

+

| 7 | 1 | 0.0 | 0.0 |

| 7 | 4 | 0.3333333333333333 | 0.3333333333333333 |

| 7 | 6 | 0.5555555555555556 | 0.5 |

| 7 | 8 | 0.7777777777777778 | 0.6666666666666666 |

| 7 | 10 | 1.0 | 1.0 |

| 3 | 1 | 0.0 | 0.0 |

| 3 | 4 | 0.3333333333333333 | 0.5 |

| 3 | 6 | 0.5555555555555556 | 1.0 |

+

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。原文来自 — 版权归原作者所有。

推荐订阅源

博客园 - 星朝