Hive函数06_多维计算函数（grouping sets，cube，roll up）

一、GROUPING SETS

GROUPING SETS作为GROUP BY的子句，允许开发人员在GROUP BY语句后面指定多个统计选项，可以简单理解为多条group by语句通过union all把查询结果聚合起来结合起来，下面是几个实例可以帮助我们了解，

1.基础语法

grouping sets语句	等价hive语句
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id))	SELECT device_id,null,null,count(user_id) FROM test_xinyan_reg group by device_id
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id,os_id))	SELECT device_id,os_id,null,count(user_id) FROM test_xinyan_reg group by device_id,os_id
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id,os_id),(device_id))	SELECT device_id,os_id,null,count(user_id) FROM test_xinyan_reg group by device_id,os_id UNION ALL SELECT device_id,null,null,count(user_id) FROM test_xinyan_reg group by device_id
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id),(os_id),(device_id,os_id),())	SELECT device_id,null,null,count(user_id) FROM test_xinyan_reg group by device_id UNION ALL SELECT null,os_id,null,count(user_id) FROM test_xinyan_reg group by os_id UNION ALLSELECT device_id,os_id,null,count(user_id) FROM test_xinyan_reg group by device_id,os_id UNION ALL SELECT null,null,null,count(user_id) FROM test_xinyan_reg

2.案例:分组汇公司的每一年的收入

我们大家也都很熟悉GROUP BY子句来实现聚合表达式，但是如果打算在一个结果集中包含多种不同的汇总结果，可能会比较麻烦。我将举例展示给大家使用GROUPING SETS操作符来完成这个“混合的结果集”。
如果你想要多种不同组合的聚合时，一般有两种方式：

a.将不懂组合聚合的结果集UNIONALL在一起。

b.使用 GROUPING SETS操作符，结合GROUP BY一起在一个语句中实现。

Query 1. 汇总每年收入

SELECT 
	YEAR( OrderDate ) AS OrderYear,
	SUM( SubTotal ) AS Income 
FROM
	Sales.SalesOrderHeader 
GROUP BY
	YEAR ( OrderDate ) 
ORDER BY
	OrderYear;

根据这个结果集，可知该公2005到2008年的收入情况。这类数据信息对于商业分析来说很常见。
如果你想要更多关于收入的信息，比如其他汇总条件，你必须要重新运行一个GROUP BY子句。比如查询返回公司每个月的收入情况。查询语句如下：

Query 2. 公司每个月的收入

SELECT YEAR
	( OrderDate ) AS OrderYear,
	MONTH ( OrderDate ) AS OrderMonth,
	SUM( SubTotal ) AS Income 
FROM
	Sales.SalesOrderHeader 
GROUP BY
	YEAR ( OrderDate ),
	MONTH ( OrderDate ) 
ORDER BY
	OrderYear,
	OrderMonth;

结果集格式如下：

这个结果集要比之前的更详细一点。可以得到具体某个月的收入汇总。显然GROUP BY 后面的列越多其越详细，结果一般也越多（除非有传递依赖键）。

如果你仔细观察两个查询，你会发现他们都是根据个子的分组表达式进行分组汇总的。前面的是按照年，后面的是按照年和月。

假如我想查看两种汇总结果在一个结果集中应该怎么处理那？为了实现这个目标，我们前面说了两个方案，方案1就是使用UNION ALL,代码如下：

Query 3. 公司收入（每年|每月）

SELECT 
	YEAR( OrderDate ) AS OrderYear,
	NULL AS OrderMonth,-- 虚拟列
	SUM( SubTotal ) AS Incomes 
FROM
	Sales.SalesOrderHeader 
GROUP BY
	YEAR ( OrderDate ) 
	UNION ALL
SELECT 
	YEAR( OrderDate ) AS OrderYear,
	MONTH ( OrderDate ) AS OrderMonth,
	SUM( SubTotal ) AS Incomes 
FROM
	Sales.SalesOrderHeader 
GROUP BY
	YEAR ( OrderDate ),
	MONTH ( OrderDate ) 
ORDER BY
	OrderYear,
	OrderMonth;

其中红色框内为按照年的汇总数据。蓝色框内为按照年和月的分组汇总。
如图所示两个结果集被合并在一起了。
注意。此时NULL出现在里面，使用NULL作为假列（虚拟列）来标识order year分组的结果。因为按年分组没有这个列。

尽管你已经获得了想要的结果，但是这样需要完成两次的语句，接下来我们尝试一下grouping sets，
目的就是 “更少代码，相同结果"

SELECT 
	YEAR( OrderDate ) AS OrderYear,
	MONTH ( OrderDate ) AS OrderMonth,
	SUM( SubTotal ) AS Incomes 
FROM
	Sales.SalesOrderHeader 
GROUP BY
	GROUPING SETS 
	( YEAR ( OrderDate ), -- 1st grouping SET 
	( YEAR ( OrderDate ),
	MONTH ( OrderDate ) ) -- 2nd grouping SET );

结果集跟之前的一模一样。但是新的代码要少很多。GROUPING SETS 操作符要和GROUP BY 子句在一起使用。并且允许我们可以做一个多分组的查询。尽管如此，我们要仔细检查指定的分组集。例如假如一个分组包含两个列，假设列A和B，两个列都需要包含在括号内：(column A, column B)。如果没有括号，这个子句将会被定义为独立的分组，结果就不同了。
上面的结果集：

顺便说一下，如果我们打算聚合整个结果集（不分组聚合所有数据），只需要添加有一个空的括号在分组集里面即可。查询语句如下:

SELECT YEAR
	( OrderDate ) AS OrderYear,
	MONTH ( OrderDate ) AS OrderMonth,
	SUM( SubTotal ) AS Incomes 
FROM
	Sales.SalesOrderHeader 
GROUP BY
	GROUPING SETS ( YEAR ( OrderDate ), -- 1st grouping set
	( YEAR ( OrderDate ), MONTH ( OrderDate ) ), -- 2nd grouping set
	( ) 								-- 3rd grouping set (grand total)
	);

注意最下方的42行，年月都为null，这个查询汇总了郑铁的所有收入，因为没有进行任何分组。

注意，需要强调一个十强，一定要确保分组列字段部位NULL，因此NULLS不能被用作分组列在GROUPING SETS中使用。如果非要那个为空字段，需要使用 GROUPING 或者 GROUPING_ID 函数判断是否NULL来自GROUPING SETS 操作符。

二、CUBE

cube简称数据魔方，可以实现hive多个任意维度的查询，cube(a,b,c)则首先会对(a,b,c)进行group by，然后依次是(a,b),(a,c),(a),(b,c),(b),©,最后在对全表进行group by，他会统计所选列中值的所有组合的聚合

select device_id,os_id,app_id,client_version,from_id,count(user_id) 
from test_xinyan_reg 
group by device_id,os_id,app_id,client_version,from_id with cube;

等价于以下sql

SELECT device_id,null,null,null,null ,count(user_id) FROM test_xinyan_reg 
group by device_id
UNION ALL
SELECT null,os_id,null,null,null ,count(user_id) FROM test_xinyan_reg 
group by os_id
UNION ALL
SELECT device_id,os_id,null,null,null ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id
UNION ALL
SELECT null,null,app_id,null,null ,count(user_id) FROM test_xinyan_reg 
group by app_id
UNION ALL
SELECT device_id,null,app_id,null,null ,count(user_id) FROM test_xinyan_reg 
group by device_id,app_id
UNION ALL
SELECT null,os_id,app_id,null,null ,count(user_id) FROM test_xinyan_reg 
group by os_id,app_id
UNION ALL
SELECT device_id,os_id,app_id,null,null ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id,app_id
UNION ALL
SELECT null,null,null,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by client_version
UNION ALL
SELECT device_id,null,null,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by device_id,client_version
UNION ALL
SELECT null,os_id,null,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by os_id,client_version
UNION ALL
SELECT device_id,os_id,null,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id,client_version
UNION ALL
SELECT null,null,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by app_id,client_version
UNION ALL
SELECT device_id,null,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by device_id,app_id,client_version
UNION ALL
SELECT null,os_id,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by os_id,app_id,client_version
UNION ALL
SELECT device_id,os_id,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id,app_id,client_version
UNION ALL
SELECT null,null,null,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by from_id
UNION ALL
SELECT device_id,null,null,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,from_id
UNION ALL
SELECT null,os_id,null,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by os_id,from_id
UNION ALL
SELECT device_id,os_id,null,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id,from_id
UNION ALL
SELECT null,null,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by app_id,from_id
UNION ALL
SELECT device_id,null,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,app_id,from_id
UNION ALL
SELECT null,os_id,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by os_id,app_id,from_id
UNION ALL
SELECT device_id,os_id,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id,app_id,from_id
UNION ALL
SELECT null,null,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by client_version,from_id
UNION ALL
SELECT device_id,null,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,client_version,from_id
UNION ALL
SELECT null,os_id,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by os_id,client_version,from_id
UNION ALL
SELECT device_id,os_id,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id,client_version,from_id
UNION ALL
SELECT null,null,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by app_id,client_version,from_id
UNION ALL
SELECT device_id,null,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,app_id,client_version,from_id
UNION ALL
SELECT null,os_id,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by os_id,app_id,client_version,from_id
UNION ALL
SELECT device_id,os_id,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg 
group by device_id,os_id,app_id,client_version,from_id
UNION ALL
SELECT null,null,null,null,null ,count(user_id) FROM test_xinyan_reg

三、ROLL UP

rollup可以实现从右到左递减多级的统计，显示统计某一层次结构的聚合。

select device_id,os_id,app_id,client_version,from_id,count(user_id) 
from test_xinyan_reg 
group by device_id,os_id,app_id,client_version,from_id
with rollup;