SparkSQL|窗口函数

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

spark.sql("""
SELECTname ,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()


analytic functions

spark.sql("""
SELECTname ,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()


aggregate functions

只是在一定窗口里实现一些普通的聚合函数。

spark.sql("""
SELECTname ,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()


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("""
SELECTname ,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()


混合应用

spark.sql("""
SELECTname ,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()


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