2023.11.16 hivesql高阶函数之开窗函数


--数据准备
create table students(
    s_id int,
    s_name string,
    subject string,
    score double,
    class string
)row format delimited
fields terminated by '\t';
 
--加载数据
load data inpath '/input/score.txt' into table students;
 
--验证数据
select * from students;

3.开窗函数之排序


--查询最高分的学生
select max(score) from students; -- 99
-- 聚合函数配合over()使用,也可以叫开窗函数
 
--查询最高分的学生,并附上他的名字
select s_name,
       max(score) over()   --每一个学生都会匹配一个最高分,数据不正确
from students;

-- 排序开窗函数: row_number rank dense_rank

-- 排序函数必须配合over(order by 排序字段 asc|desc)

row_number: 巧记: 1234 特点: 唯一且连续

dense_rank: 巧记: 1223 特点: 并列且连续

rank : 巧记: 1224 特点: 并列不连续

需求:用三种排序方法查询学生的语文成绩排名,并降序显示


select s_name,subject,score,
       row_number() over (order by score desc ) ,--唯一且连续
       dense_rank() over (order by score desc ) ,--并列且连续
       rank() over (order by score desc ) --并列不连续
from students
where subject = '语文';

4.开窗函数分组

-- 开窗函数分组

-- 注意不能用group by ,需要使用partition by,可以理解成partition by是group by的子句

-- 演示排序函数和分组配合使用: 先分组再组内排序

需求:按照科目来分类,使用三种排序方式来排序学生的成绩


select *,
       row_number() over (partition by subject order by score desc ),
       dense_rank() over (partition by subject order by score desc ),
       rank() over (partition by subject order by score desc )
from students;

5.聚合函数与分组配合使用


-- 演示聚合函数和分组配合使用
-- 普通分组
select s_name,
       max(score)
from students group by s_name;

查询每个学生的信息,按照文理科分类,以及平均分


-- 开窗分组
select *,
       avg(score) over(partition by class)
from students;

6.聚合函数同时和分组以及排序关键字配合使用


-- 演示聚合函数同时和分组以及排序关键字配合使用
-- 数据准备
---建表并且加载数据
create table website_pv_info(
   cookieid string,
   createtime string,   --day
   pv int
) row format delimited
fields terminated by ',';
 
-- 建表
create table website_url_info (
    cookieid string,
    createtime string,  --访问时间
    url string       --访问页面
) row format delimited
fields terminated by ',';
 
-- 加载数据
load data inpath '/input/website_pv_info.txt' into table website_pv_info;
load data inpath '/input/website_url_info.txt' into table website_url_info;
 
-- 查询数据
select * from website_pv_info;
select * from website_url_info;

--需求1：求出每个用户的总pv数,展示所有信息默认第一行到最后一行

cookie是记住用户记录的一个文件,代表一个用户


select *,
       sum(pv) over (partition by cookieid) 
from website_pv_info;

--需求2：求出每个用户截止到当天，累积的总pv数默认第一行到当前行

--sum(...) over( partition by... order by ... )，在每个分组内，连续累积求和


select *,
       sum(pv) over (partition by cookieid order by createtime)
from website_pv_info;

做题思路,开窗函数核心:保证输出结果的记录数和输入的数据记录数一致

7.窗口范围控制

rows between
   - preceding：往前
   - following：往后
   - current row：当前行
   - unbounded：起点
   - unbounded preceding 表示从前面的起点第一行
   - unbounded following：表示到后面的终点最后一行

1.默认第一行到当前行


select cookieid,createtime,pv,
       sum(pv) over(partition by cookieid order by createtime) as pv1
from website_pv_info;

2.第一行到当前行,等效于rows between ..不写,默认就是第一行到当前行


select cookieid,createtime,pv,
       sum(pv) over(partition by cookieid order by createtime
           rows between unbounded preceding and current row) as pv2
from website_pv_info;

3.向前3行到当前行


--向前3行至当前行
select cookieid,createtime,pv,
       sum(pv) over(partition by cookieid order by createtime
           rows between 3 preceding and current row) as pv4
from website_pv_info;

1+5+7=13 , 1+5+7+3=16 , 5+7+3+2=17 , 7+3+2+4=16 , 3+2+4+4=13

相当于查询今天以及前三天的总浏览量,在现实中常称为网站的'最近3天访问量'.

4.向前3行向后1行


select cookieid,createtime,pv,
       sum(pv) over(partition by cookieid order by createtime
 rows between 3 preceding and 1 following) as pv5
from website_pv_info;

1+5+7+3+2=18 , 5+7+3+2+4=21

5.当前行到最后一行,第一行到最后一行


--当前行至最后一行
select cookieid,createtime,pv,
       sum(pv) over(partition by cookieid order by createtime 
rows between current row and unbounded following) as pv6
from website_pv_info;
 
--第一行到最后一行 也就是分组内的所有行
select cookieid,createtime,pv,
       sum(pv) over(partition by cookieid order by createtime 
rows between unbounded preceding  and unbounded following) as pv6
from website_pv_info;

8.其他函数

1.ntile平分:

注意ntile规则:尽量平均分配，优先满足最小(编号1)的桶，彼此最多不相差1个。

其他开窗函数: ntile lag和lead first_value和last_value

ntile(x)功能: 将分组排序之后的数据分成指定的x个部分（x个桶）
注意ntile规则:尽量平均分配，优先满足最小(编号1)的桶，彼此最多不相差1个。

lag: 用于统计窗口内往上第n行值
lead:用于统计窗口内往下第n行值

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

注意: 窗口函数结果都是单独生成一列存储对应数据


-- 演示ntile
--把每个分组内的数据均匀分为3桶
SELECT
    cookieid,
    createtime,
    pv,
    ntile(3) OVER(PARTITION BY cookieid ORDER BY createtime) AS rn2
FROM website_pv_info
ORDER BY cookieid,createtime;

--需求：统计每个用户pv数最多的前3分之1天。
--理解：将数据根据cookieid分根据pv倒序排序排序之后分为3个部分取第一部分


with tmp as (SELECT
     cookieid,
     createtime,
     pv,
     NTILE(3) OVER(PARTITION BY cookieid ORDER BY pv DESC) AS rn
 FROM website_pv_info)
SELECT * from tmp where rn =1;


 
--lag 用于统计窗口内往上第n行值
select cookieid, createtime, url,
    row_number() over (partition by cookieid order by createtime) rn,
    lag(createtime, 1) over (partition by cookieid order by createtime) la1,
    lag(createtime, 2, '2000-01-01 00:00:00') over (partition by cookieid order by createtime) la2
from website_url_info;
 
 
--lead 用于统计窗口内往下第n行值
select cookieid, createtime, url,
    row_number() over (partition by cookieid order by createtime) rn,
    lead(createtime, 1) over (partition by cookieid order by createtime) la1,
    lead(createtime, 2, '2000-01-01 00:00:00') over (partition by cookieid order by createtime) la2
from website_url_info;
 
 
--FIRST_VALUE 取分组内排序后，截止到当前行，第一个值
select cookieid, createtime, url,
    row_number() over (partition by cookieid order by createtime) rn,
    first_value(url) over (partition by cookieid order by createtime) fv
from website_url_info;
 
 
--LAST_VALUE  取分组内排序后，截止到当前行，最后一个值
select cookieid, createtime, url,
    row_number() over (partition by cookieid order by createtime) rn,
    last_value(url) over (partition by cookieid order by createtime rows between unbounded preceding and unbounded following) fv
from website_url_info;

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原文地址：https://blog.csdn.net/m0_49956154/article/details/134434820