窗口函数的定义引用一个大佬的定义： a window function calculates a return value for every input row of a table based on a group of rows。窗口函数与与其他函数的区别:

• 普通函数: 作用于每一条记录，计算出一个新列(记录数不变);
• 聚合函数: 作用于一组记录(全部数据按照某种方式分为多组)，计算出一个聚合值(记录数变小);
• 窗口函数: 作用于每一条记录，逐条记录去指定多条记录来计算一个值(记录数不变)。

窗口函数语法结构: 函数名(参数)OVER(PARTITION BY 子句 ORDER BY 子句 ROWS/RANGE子句)

• 函数名:
• OVER: 关键字，说明这是窗口函数，不是普通的聚合函数；
• 子句
  • PARTITION BY: 分组字段
  • ORDER BY: 排序字段
  • ROWS/RANG窗口子句: 用于控制窗口的尺寸边界，有两种(ROW,RANGE)
    • ROW: 物理窗口，数据筛选基于排序后的index
    • RANGE: 逻辑窗口，数据筛选基于值

主要有以下三种窗口函数

• ranking functions
• analytic functions
• aggregate functions

数据加载

from pyspark.sql.types import *


schema = StructType().add('name', StringType(), True).add('department', StringType(), True).add('salary', IntegerType(), True)
df = spark.createDataFrame([
    ("Tom", "Sales", 4500),
    ("Georgi", "Sales", 4200),
    ("Kyoichi", "Sales", 3000),    
    ("Berni", "Sales", 4700),
    ("Guoxiang", "Sales", 4200),   
    ("Parto", "Finance", 2700),
    ("Anneke", "Finance", 3300),
    ("Sumant", "Finance", 3900),
    ("Jeff", "Marketing", 3100),
    ("Patricio", "Marketing", 2500)
], schema=schema)
df.createOrReplaceTempView('salary')
df.show()

+--------+----------+------+
|    name|department|salary|
+--------+----------+------+
|     Tom|     Sales|  4500|
|  Georgi|     Sales|  4200|
| Kyoichi|     Sales|  3000|
|   Berni|     Sales|  4700|
|Guoxiang|     Sales|  4200|
|   Parto|   Finance|  2700|
|  Anneke|   Finance|  3300|
|  Sumant|   Finance|  3900|
|    Jeff| Marketing|  3100|
|Patricio| Marketing|  2500|
+--------+----------+------+

ranking functions

sql	DataFrame	功能
row_number	rowNumber	从1~n的唯一序号值
rank	rank	与denseRank一样，都是排名，对于相同的数值，排名一致。区别：rank不会跳过并列的排名
dense_rank	denseRank	同rank
percent_rank	percentRank	计算公式: (组内排名-1)/(组内行数-1),如果组内只有1行，则结果为0
ntile	ntile	将组内数据排序后，按照指定的n切分为n个桶，该值为当前行的桶号(桶号从1开始)

spark.sql("""
SELECT
    name 
    ,department
    ,salary
    ,row_number() over(partition by department order by salary) as index
    ,rank() over(partition by department order by salary) as rank
    ,dense_rank() over(partition by department order by salary) as dense_rank
    ,percent_rank() over(partition by department order by salary) as percent_rank
    ,ntile(2) over(partition by department order by salary) as ntile
FROM salary
""").toPandas()

	name	department	salary	index	rank	dense_rank	percent_rank	ntile
0	Patricio	Marketing	2500	1	1	1	0.00	1
1	Jeff	Marketing	3100	2	2	2	1.00	2
2	Kyoichi	Sales	3000	1	1	1	0.00	1
3	Georgi	Sales	4200	2	2	2	0.25	1
4	Guoxiang	Sales	4200	3	2	2	0.25	1
5	Tom	Sales	4500	4	4	3	0.75	2
6	Berni	Sales	4700	5	5	4	1.00	2
7	Parto	Finance	2700	1	1	1	0.00	1
8	Anneke	Finance	3300	2	2	2	0.50	1
9	Sumant	Finance	3900	3	3	3	1.00	2

analytic functions

sql	DataFrame	功能
cume_dist	cumeDist	计算公式: 组内小于等于值当前行数/组内总行数
lag	lag	lag(input, [offset,[default]]) 当前index<offset返回defalult(默认defalult=null), 否则返回input
lead	lead	与lag相反

spark.sql("""
SELECT
    name 
    ,department
    ,salary
    ,row_number() over(partition by department order by salary) as index
    ,cume_dist() over(partition by department order by salary) as cume_dist
    ,lag('salary', 2) over(partition by department order by salary) as lag
    ,lead('salary', 2) over(partition by department order by salary) as lead    
    
FROM salary
""").toPandas()

	name	department	salary	index	cume_dist	lag	lead
0	Patricio	Marketing	2500	1	0.500000	None	None
1	Jeff	Marketing	3100	2	1.000000	None	None
2	Kyoichi	Sales	3000	1	0.200000	None	salary
3	Georgi	Sales	4200	2	0.600000	None	salary
4	Guoxiang	Sales	4200	3	0.600000	salary	salary
5	Tom	Sales	4500	4	0.800000	salary	None
6	Berni	Sales	4700	5	1.000000	salary	None
7	Parto	Finance	2700	1	0.333333	None	salary
8	Anneke	Finance	3300	2	0.666667	None	None
9	Sumant	Finance	3900	3	1.000000	salary	None

aggregate functions

只是在一定窗口里实现一些普通的聚合函数。

sql	功能
avg	平均值
sum	求和
min	最小值
max	最大值

spark.sql("""
SELECT
    name 
    ,department
    ,salary
    ,row_number() over(partition by department order by salary) as index
    ,sum(salary) over(partition by department order by salary) as sum
    ,avg(salary) over(partition by department order by salary) as avg
    ,min(salary) over(partition by department order by salary) as min
    ,max(salary) over(partition by department order by salary) as max    
FROM salary
""").toPandas()

	name	department	salary	index	sum	avg	min	max
0	Patricio	Marketing	2500	1	2500	2500.0	2500	2500
1	Jeff	Marketing	3100	2	5600	2800.0	2500	3100
2	Kyoichi	Sales	3000	1	3000	3000.0	3000	3000
3	Georgi	Sales	4200	2	11400	3800.0	3000	4200
4	Guoxiang	Sales	4200	3	11400	3800.0	3000	4200
5	Tom	Sales	4500	4	15900	3975.0	3000	4500
6	Berni	Sales	4700	5	20600	4120.0	3000	4700
7	Parto	Finance	2700	1	2700	2700.0	2700	2700
8	Anneke	Finance	3300	2	6000	3000.0	2700	3300
9	Sumant	Finance	3900	3	9900	3300.0	2700	3900

窗口子句

ROWS/RANG窗口子句: 用于控制窗口的尺寸边界，有两种(ROW,RANGE)

• ROWS: 物理窗口，数据筛选基于排序后的index
• RANGE: 逻辑窗口，数据筛选基于值

语法：OVER (PARTITION BY … ORDER BY … frame_type BETWEEN start AND end)

有以下5种边界

• CURRENT ROW:
• UNBOUNDED PRECEDING: 分区第一行
• UNBOUNDED FOLLOWING: 分区最后一行
• n PRECEDING: 前n行
• n FOLLOWING: 后n行
• UNBOUNDED: 起点

spark.sql("""
SELECT
    name 
    ,department
    ,salary
    ,row_number() over(partition by department order by salary) as index
    ,row_number() over(partition by department order by salary rows between UNBOUNDED PRECEDING and CURRENT ROW) as index1
FROM salary
""").toPandas()

	name	department	salary	index	index1
0	Patricio	Marketing	2500	1	1
1	Jeff	Marketing	3100	2	2
2	Kyoichi	Sales	3000	1	1
3	Georgi	Sales	4200	2	2
4	Guoxiang	Sales	4200	3	3
5	Tom	Sales	4500	4	4
6	Berni	Sales	4700	5	5
7	Parto	Finance	2700	1	1
8	Anneke	Finance	3300	2	2
9	Sumant	Finance	3900	3	3

混合应用

spark.sql("""
SELECT
    name 
    ,department
    ,salary
    ,row_number() over(partition by department order by salary) as index
    ,salary - (min(salary) over(partition by department order by salary)) as salary_diff 
FROM salary
""").toPandas()

	name	department	salary	index	salary_diff
0	Patricio	Marketing	2500	1	0
1	Jeff	Marketing	3100	2	600
2	Kyoichi	Sales	3000	1	0
3	Georgi	Sales	4200	2	1200
4	Guoxiang	Sales	4200	3	1200
5	Tom	Sales	4500	4	1500
6	Berni	Sales	4700	5	1700
7	Parto	Finance	2700	1	0
8	Anneke	Finance	3300	2	600
9	Sumant	Finance	3900	3	1200

参考

• Introducing Window Functions in Spark SQL
• Standard Functions for Window Aggregation (Window Functions
• List Of Spark SQL Window Functions
• 在hive、Spark SQL中引入窗口函数

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