Hive分析函数和窗口函数

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

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