Hive分析函数和窗口函数
在Hive 0.11之后支持的，扫描多个输入的行计算每行的结果。通常和OVER,PARTITION BY, ORDER BY, WINDOWING配合使用。和传统的分组结果不一样，传统的结果每组中只有一个结果。分析函数的结果会出现多次，和每条记录都连接输出。
语法形式如下：
Function(arg1,....argn) OVER([PARTITION BY<...>] [ORDER BY<...>] [window_clause])
窗口函数
| 函数名 | 说明 |
|---|---|
| FIRST_VALUE | 取出分组内排序后，截止到当前行，第一个值 |
| LAST_VALUE | 取出分组内排序后，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行的值。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，取默认值) |
| LAG(col,n,DEFAULT) | 与lead相反，用于统计窗口内往下第n个值。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) |
OVER从句
- 使用标准的聚合函数COUNT,SUM,MIN,MAX,AVG
- 使用PARTITION BY语句，使用一个或者多个原始数据类型的列
- 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拍序列
- 使用窗口规范，窗口规范支持一下格式：
(ROW | RANGE) BETWEEN (UNBOUNDED | [num]) PRECEDING AND ([num] PRECEDING | CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)
(ROW | RANGE) BETWEEN CURRENT ROW AND (CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)
(ROW | RANGE) BETWEEN [num] PRECEDING 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
分析函数
| 函数 | 说明 |
|---|---|
| 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除以分组内总行数。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 PERCEDING AND CURRENT ROW) |
Hive2.1.0及以后版本支持distinct
在聚合函数(sum, count, avg)中支持distinct，但是在order by或者 窗口限制中不支持。
conut(distinct a) over(partition by c)
Hive2.1.0以后支持在OVER从句中支持聚合函数
select rank() over(order by sum(b))
Hive2.2.0中在使用ORDER BY和窗口限制时支持distinct
count(distinct a) over (partition by c order by d rows between 1 preceding and 1 following)
通过实例深入理解窗口函数和分析函数
COUNT、SUM、MIN、MAX、AVG案例分析
## 创建数据表
create table orders(
user_id string,
device_id string,
user_type string,
price float,
sales int);
## 添加数据orders.txt
zhangsa test1 new 67.1 2
lisi test2 old 43.32 1
wanger test3 new 88.88 3
liliu test4 new 66.0 1
tom test5 new 54.32 1
tomas test6 old 77.77 2
tomson test7 old 88.44 3
tom1 test8 new 56.55 6
tom2 test9 new 88.88 5
tom3 test10 new 66.66 5
## 开窗函数案例
select
user_id,
user_type,
sales,
-- 默认从起点到当前所有重复行
sum(sales) over(partition by user_type order by sales asc) as sales_1,
-- 从起点到当前所有重复行与sales_1结果相同
sum(sales) over(partition by user_type order by sales asc range between unbounded preceding and current row) as sales_2,
-- 从起点到当前行，结果与sale_1结果不同
sum(sales) over(partition by user_type order by sales asc rows between unbounded preceding and current row) as sales_3,
-- 当前行加上往前3行
sum(sales) over(partition by user_type order by sales asc rows between 3 preceding and current row) as sales_4,
-- 当前范围往上加3行
sum(sales) over(partition by user_type order by sales asc range between 3 preceding and current row) as sales_5,
-- 当前行+往前3行+往后1行
sum(sales) over(partition by user_type order by sales asc rows between 3 preceding and 1 following) as sales_6,
--
sum(sales) over(partition by user_type order by sales asc range between 3 preceding and 1 following) as sales_7,
-- 当前行+之后所有行
sum(sales) over(partition by user_type order by sales asc rows between current row and unbounded following) as sales_8,
--
sum(sales) over(partition by user_type order by sales asc range between current row and unbounded following) as sales_9,
-- 分组内所有行
sum(sales) over(partition by user_type) as sales_10
from
orders
order by
user_type,
sales,
user_id;
##上述查询结果如下：
| user_id | user_type | sales | sales_1 | sales_2 | sales_3 | sales_4 | sales_5 | sales_6 | sales_7 | sales_8 | sales_9 | sales_10 |
|----------|------------|--------|----------|----------|----------|----------|----------|----------|----------|----------|----------|-----------|
| liliu | new | 1 | 2 | 2 | 2 | 2 | 2 | 4 | 4 | 22 | 23 | 23 |
| tom | new | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 4 | 23 | 23 | 23 |
| zhangsa | new | 2 | 4 | 4 | 4 | 4 | 4 | 7 | 7 | 21 | 21 | 23 |
| wanger | new | 3 | 7 | 7 | 7 | 7 | 7 | 12 | 7 | 19 | 19 | 23 |
| tom2 | new | 5 | 17 | 17 | 17 | 15 | 15 | 21 | 21 | 11 | 16 | 23 |
| tom3 | new | 5 | 17 | 17 | 12 | 11 | 15 | 16 | 21 | 16 | 16 | 23 |
| tom1 | new | 6 | 23 | 23 | 23 | 19 | 19 | 19 | 19 | 6 | 6 | 23 |
| lisi | old | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 6 | 6 | 6 |
| tomas | old | 2 | 3 | 3 | 3 | 3 | 3 | 6 | 6 | 5 | 5 | 6 |
| tomson | old | 3 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 3 | 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;
- 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和LAST_VALUE案例分析
select
user_id,
user_type,
sales,
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
orders
order by
user_type,
sales;
##上述查询结果如下:
| user_id | user_type | sales | row_num | max_sales_user | min_sales_user | curr_last_min_user | curr_last_max_user |
| ------- | --------- | ----- | ------- | -------------- | -------------- | ------------------ | ------------------ |
| tom | new | 1 | 1 | tom1 | tom | tom | liliu |
| liliu | new | 1 | 2 | tom1 | tom | tom | liliu |
| zhangsa | new | 2 | 3 | tom1 | tom | zhangsa | zhangsa |
| wanger | new | 3 | 4 | tom1 | tom | wanger | wanger |
| tom3 | new | 5 | 5 | tom1 | tom | tom3 | tom2 |
| tom2 | new | 5 | 6 | tom1 | tom | tom3 | tom2 |
| tom1 | new | 6 | 7 | tom1 | tom | tom1 | tom1 |
| lisi | old | 1 | 1 | tomson | lisi | lisi | lisi |
| tomas | old | 2 | 2 | tomson | lisi | tomas | tomas |
| tomson | old | 3 | 3 | tomson | lisi | tomson | tomson |
LEAD与LAG
select
user_id,
device_id,
sales,
ROW_NUMBER() OVER(ORDER BY sales) AS row_num,
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
orders
order by
sales;
上述查询结果如下
| user_id | device_id | sales | row_num | default_after_one_line | default_before_one_line | after_two_line | before_two_line |
|----------|------------|--------|----------|-------------------------|--------------------------|-----------------|------------------|
| lisi | test2 | 1 | 3 | test6 | test4 | test1 | test5 |
| liliu | test4 | 1 | 2 | test2 | test5 | test6 | abc |
| tom | test5 | 1 | 1 | test4 | NULL | test2 | abc |
| zhangsa | test1 | 2 | 5 | test7 | test6 | test3 | test2 |
| tomas | test6 | 2 | 4 | test1 | test2 | test7 | test4 |
| wanger | test3 | 3 | 7 | test10 | test7 | test9 | test1 |
| tomson | test7 | 3 | 6 | test3 | test1 | test10 | test6 |
| tom2 | test9 | 5 | 9 | test8 | test10 | NULL | test3 |
| tom3 | test10 | 5 | 8 | test9 | test3 | test8 | test7 |
| tom1 | test8 | 6 | 10 | NULL | test9 | NULL | test10 |
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
orders;
##上述查询结果如下
| user_id | user_type | sales | r | rn | dr |
| ------- | --------- | ----- | --- | --- | --- |
| tom1 | new | 6 | 1 | 1 | 1 |
| tom3 | new | 5 | 2 | 2 | 2 |
| tom2 | new | 5 | 2 | 3 | 2 |
| wanger | new | 3 | 4 | 4 | 3 |
| zhangsa | new | 2 | 5 | 5 | 4 |
| tom | new | 1 | 6 | 6 | 5 |
| liliu | new | 1 | 6 | 7 | 5 |
| tomson | old | 3 | 1 | 1 | 1 |
| tomas | old | 2 | 2 | 2 | 2 |
| lisi | old | 1 | 3 | 3 | 3 |
NTILE
select
user_type,sales,
--分组内将数据分成2片
NTILE(2) OVER(PARTITION BY user_type ORDER BY sales) AS nt2,
--分组内将数据分成3片
NTILE(3) OVER(PARTITION BY user_type ORDER BY sales) AS nt3,
--分组内将数据分成4片
NTILE(4) OVER(PARTITION BY user_type ORDER BY sales) AS nt4,
--将所有数据分成4片
NTILE(4) OVER(ORDER BY sales) AS all_nt4
from
orders
order by
user_type,
sales;
##上述查询结果如下
| user_type | sales | nt2 | nt3 | nt4 | all_nt4 |
| --------- | ----- | --- | --- | --- | ------- |
| new | 1 | 1 | 1 | 1 | 1 |
| 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
orders
)A
where nt=1;
## 结果如下
+----------+
| user_id |
+----------+
| tom1 |
| tom3 |
+----------+
CUME_DIST、PERCENT_RANK
select
user_id,user_type,sales,
--没有partition,所有数据均为1组
CUME_DIST() OVER(ORDER BY sales) AS cd1,
--按照user_type进行分组
CUME_DIST() OVER(PARTITION BY user_type ORDER BY sales) AS cd2
from
orders;
## 上述结果如下
+----------+------------+--------+------+----------------------+--+
| user_id | user_type | sales | cd1 | cd2 |
+----------+------------+--------+------+----------------------+--+
| liliu | new | 1 | 0.3 | 0.2857142857142857 |
| tom | new | 1 | 0.3 | 0.2857142857142857 |
| zhangsa | new | 2 | 0.5 | 0.42857142857142855 |
| wanger | new | 3 | 0.7 | 0.5714285714285714 |
| tom2 | new | 5 | 0.9 | 0.8571428571428571 |
| tom3 | new | 5 | 0.9 | 0.8571428571428571 |
| tom1 | new | 6 | 1.0 | 1.0 |
| lisi | old | 1 | 0.3 | 0.3333333333333333 |
| tomas | old | 2 | 0.5 | 0.6666666666666666 |
| tomson | old | 3 | 0.7 | 1.0 |
+----------+------------+--------+------+----------------------+--+
select
user_type,sales,
--分组内总行数
SUM(1) OVER(PARTITION BY user_type) AS s,
--RANK值
RANK() OVER(ORDER BY sales) AS r,
PERCENT_RANK() OVER(ORDER BY sales) AS pr,
--分组内
PERCENT_RANK() OVER(PARTITION BY user_type ORDER BY sales) AS prg
from
orders;
## 上述结果如下
+------------+--------+----+-----+---------------------+---------------------+--+
| user_type | sales | s | r | pr | prg |
+------------+--------+----+-----+---------------------+---------------------+--+
| new | 1 | 7 | 1 | 0.0 | 0.0 |
| new | 1 | 7 | 1 | 0.0 | 0.0 |
| new | 2 | 7 | 4 | 0.3333333333333333 | 0.3333333333333333 |
| new | 3 | 7 | 6 | 0.5555555555555556 | 0.5 |
| new | 5 | 7 | 8 | 0.7777777777777778 | 0.6666666666666666 |
| new | 5 | 7 | 8 | 0.7777777777777778 | 0.6666666666666666 |
| new | 6 | 7 | 10 | 1.0 | 1.0 |
| old | 1 | 3 | 1 | 0.0 | 0.0 |
| old | 2 | 3 | 4 | 0.3333333333333333 | 0.5 |
| old | 3 | 3 | 6 | 0.5555555555555556 | 1.0 |
+------------+--------+----+-----+---------------------+---------------------+--+
参考博文
http://blog.csdn.net/scgaliguodong123_/article/details/60135385
