SQL-rollup、cube（hive）
先举个例：
group by WITH ROLLUP
mysql> select dep,pos,avg(sal) from employee group by dep,pos with rollup;
+------+------+-----------+
| dep | pos | avg(sal) |
+------+------+-----------+
| 01 | 01 | 1500.0000 |
| 01 | 02 | 1950.0000 |
| 01 | NULL | 1725.0000 |
| 02 | 01 | 1500.0000 |
| 02 | 02 | 2450.0000 |
| 02 | NULL | 2133.3333 |
| 03 | 01 | 2500.0000 |
| 03 | 02 | 2550.0000 |
| 03 | NULL | 2533.3333 |
| NULL | NULL | 2090.0000 |
首先会根据dep变量，将原始数据分为三个01,02,03三个组，从数据表上看，第5,6行是一个聚合，group by WITH ROLLUP会在每个分组后面加上本组类的信息，di7行数据就是5,6行数据聚合所执行avg(sal)所得的结果，依次类推，02,03也是一样，同时在最后，会将全部的分组聚合。
Hive：
GROUPING SETS
在一个GROUP BY查询中，根据不同的维度组合进行聚合，等价于将不同维度的GROUP BY结果集进行UNION ALL
SELECT
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM lxw1234
GROUP BY month,day
GROUPING SETS (month,day)
ORDER BY GROUPING__ID;
month day uv GROUPING__ID
------------------------------------------------
2015-03 NULL 5 1
2015-04 NULL 6 1
NULL 2015-03-10 4 2
NULL 2015-03-12 1 2
NULL 2015-04-12 2 2
NULL 2015-04-13 3 2
NULL 2015-04-15 2 2
NULL 2015-04-16 2 2
等价于
SELECT
month,
NULL,
COUNT(DISTINCT cookieid) AS uv,
1 AS GROUPING__ID
FROM lxw1234
GROUP BY month
UNION ALL
SELECT
NULL,
day,
COUNT(DISTINCT cookieid) AS uv,
2 AS GROUPING__ID
FROM lxw1234
GROUP BY day再如：
SELECT
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM lxw1234
GROUP BY month,day
GROUPING SETS (month,day,(month,day))
ORDER BY GROUPING__ID;
month day uv GROUPING__ID
------------------------------------------------
2015-03 NULL 5 1
2015-04 NULL 6 1
NULL 2015-03-10 4 2
NULL 2015-03-12 1 2
NULL 2015-04-12 2 2
NULL 2015-04-13 3 2
NULL 2015-04-15 2 2
NULL 2015-04-16 2 2
2015-03 2015-03-10 4 3
2015-03 2015-03-12 1 3
2015-04 2015-04-12 2 3
2015-04 2015-04-13 3 3
2015-04 2015-04-15 2 3
2015-04 2015-04-16 2 3
等价于
SELECT month,
NULL,
COUNT(DISTINCT cookieid) AS uv,
1 AS GROUPING__ID
FROM lxw1234
GROUP BY month
UNION ALL
SELECT NULL,
day,
COUNT(DISTINCT cookieid) AS uv,
2 AS GROUPING__ID
FROM lxw1234
GROUP BY day
UNION ALL
SELECT month,
day,
COUNT(DISTINCT cookieid) AS uv,
3 AS GROUPING__ID
FROM lxw1234
GROUP BY month,day其中的 GROUPING__ID，表示结果属于哪一个分组集合。
CUBE
根据GROUP BY的维度的所有组合进行聚合。
SELECT
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM lxw1234
GROUP BY month,day
WITH CUBE
ORDER BY GROUPING__ID;
month day uv GROUPING__ID
--------------------------------------------
NULL NULL 7 0
2015-03 NULL 5 1
2015-04 NULL 6 1
NULL 2015-04-12 2 2
NULL 2015-04-13 3 2
NULL 2015-04-15 2 2
NULL 2015-04-16 2 2
NULL 2015-03-10 4 2
NULL 2015-03-12 1 2
2015-03 2015-03-10 4 3
2015-03 2015-03-12 1 3
2015-04 2015-04-16 2 3
2015-04 2015-04-12 2 3
2015-04 2015-04-13 3 3
2015-04 2015-04-15 2 3
等价于
SELECT
NULL,
NULL,
COUNT(DISTINCT cookieid) AS uv,
0 AS GROUPING__ID
FROM lxw1234
UNION ALL
SELECT
month,
NULL,
COUNT(DISTINCT cookieid) AS uv,
1 AS GROUPING__ID
FROM lxw1234
GROUP BY month
UNION ALL
SELECT
NULL,
day,
COUNT(DISTINCT cookieid) AS uv,
2 AS GROUPING__ID
FROM lxw1234
GROUP BY day
UNION ALL
SELECT
month,
day,
COUNT(DISTINCT cookieid) AS uv,
3 AS GROUPING__ID
FROM lxw1234
GROUP BY month,dayROLLUP
CUBE的子集，以最左侧的维度为主，从该维度进行层级聚合。
比如，以month维度进行层级聚合：
SELECT
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM lxw1234
GROUP BY month,day
WITH ROLLUP
ORDER BY GROUPING__ID;
month day uv GROUPING__ID
---------------------------------------------------
NULL NULL 7 0
2015-03 NULL 5 1
2015-04 NULL 6 1
2015-03 2015-03-10 4 3
2015-03 2015-03-12 1 3
2015-04 2015-04-12 2 3
2015-04 2015-04-13 3 3
2015-04 2015-04-15 2 3
2015-04 2015-04-16 2 3
可以实现这样的上钻过程：月天的UV->月的UV->总UV--把month和day调换顺序，则以day维度进行层级聚合：
SELECT
day,
month,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM lxw1234
GROUP BY day,month
WITH ROLLUP
ORDER BY GROUPING__ID;
day month uv GROUPING__ID
-------------------------------------------------------
NULL NULL 7 0
2015-04-13 NULL 3 1
2015-03-12 NULL 1 1
2015-04-15 NULL 2 1
2015-03-10 NULL 4 1
2015-04-16 NULL 2 1
2015-04-12 NULL 2 1
2015-04-12 2015-04 2 3
2015-03-10 2015-03 4 3
2015-03-12 2015-03 1 3
2015-04-13 2015-04 3 3
2015-04-15 2015-04 2 3
2015-04-16 2015-04 2 3
可以实现这样的上钻过程：天月的UV->天的UV->总UV
（这里，根据天和月进行聚合，和根据天聚合结果一样，因为有父子关系，如果是其他维度组合的话，就会不一样）