GROUP BY的扩展主要包括ROLLUP，CUBE，GROUPING SETS三种形式。
ROLLUP
rollup相对于简单的分组合计增加了小计和合计，解释起来会比较抽象，下面我们来看看具体事例。
例1，统计不同部门工资的总和和所有部门工资的总和。
SQL> select deptno,sum(sal) from emp group by rollup(deptno);
DEPTNO SUM(SAL)
---------- ----------
10 8750
20 10875
30 9400
29025
例2，该例中先对deptno进行分组，再对job进行分组
SQL> select deptno,job,sum(sal) from emp group by rollup(deptno,job);
DEPTNO JOB SUM(SAL)
---------- --------- ----------
10 CLERK 1300 --10号部门中JOB为CLERK的工资的总和
10 MANAGER 2450
10 PRESIDENT 5000
10 8750 --10号所有工种工资的总和
20 CLERK 1900
20 ANALYST 6000
20 MANAGER 2975
20 10875
30 CLERK 950
30 MANAGER 2850
30 SALESMAN 5600
30 9400
29025 --所有部门，所有工种工资的总和
13 rows selected.
如果要用普通的分组函数实现，可用UNION ALL语句：
--实现单个部门，单个工种的工资的总和
select deptno,job,sum(sal) from emp group by deptno,job union all
--实现单个部门工资的总和
select deptno,null,sum(sal) from emp group by deptno union all
--实现所有部门工资的总和
select null,null,sum(sal) from emp order by 1,2
下面我们分别来看看两者的执行计划及统计信息，
ROLLUP语句：
Execution Plan
-----------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 11 | 132 | 3 (34)| 00:00:01 |
| 1 | SORT GROUP BY ROLLUP| | 11 | 132 | 3 (34)| 00:00:01 |
| 2 | TABLE ACCESS FULL | EMP | 14 | 168 | 2 (0)| 00:00:01 |
-----------------------------------------------------------------------------
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
2 consistent gets
0 physical reads
0 redo size
895 bytes sent via SQL*Net to client
519 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
13 rows processed
UNION ALL语句：
Execution Plan ----------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ----------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 15 | 150 | 9 (34)| 00:00:01 | | 1 | SORT ORDER BY | | 15 | 150 | 8 (75)| 00:00:01 | | 2 | UNION-ALL | | | | | | | 3 | HASH GROUP BY | | 11 | 132 | 3 (34)| 00:00:01 | | 4 | TABLE ACCESS FULL| EMP | 14 | 168 | 2 (0)| 00:00:01 | | 5 | HASH GROUP BY | | 3 | 15 | 3 (34)| 00:00:01 | | 6 | TABLE ACCESS FULL| EMP | 14 | 70 | 2 (0)| 00:00:01 | | 7 | SORT AGGREGATE | | 1 | 3 | | | | 8 | TABLE ACCESS FULL| EMP | 14 | 42 | 2 (0)| 00:00:01 | -----------------------------------------------------------------------------
Statistics ---------------------------------------------------------- 0 recursive calls 0 db block gets 6 consistent gets 0 physical reads 0 redo size 895 bytes sent via SQL*Net to client 519 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 1 sorts (memory) 0 sorts (disk) 13 rows processed
不难看出，相同的功能实现，ROLLUP相对于UNION ALL效率有了极大的提升。
CUBE
cube相对于rollup，结果输出更加详细。
例1，在本例中还不是很明显。
SQL> select deptno,sum(sal) from emp group by cube(deptno);
DEPTNO SUM(SAL)
---------- ----------
29025
10 8750
20 10875
30 9400
例2，相对于rollup，cube还对工种这一列进行了专门的汇总。
SQL> select deptno,job,sum(sal) from emp group by cube(deptno,job);
DEPTNO JOB SUM(SAL)
---------- --------- ----------
29025
CLERK 4150
ANALYST 6000
MANAGER 8275
SALESMAN 5600
PRESIDENT 5000
10 8750
10 CLERK 1300
10 MANAGER 2450
10 PRESIDENT 5000
20 10875
20 CLERK 1900
20 ANALYST 6000
20 MANAGER 2975
30 9400
30 CLERK 950
30 MANAGER 2850
30 SALESMAN 5600
18 rows selected.
GROUPING SETS
GROUPING SETS相对于ROLLUP和CUBE，结果是分类统计的，可读性更好一些。
例1：
SQL> select deptno,job,to_char(hiredate,'yyyy')hireyear,sum(sal) from emp group by grouping sets(deptno,job,to_char(hiredate,'yyyy'));
DEPTNO JOB HIRE SUM(SAL)
---------- --------- ---- ----------
CLERK 4150
SALESMAN 5600
PRESIDENT 5000
MANAGER 8275
ANALYST 6000
30 9400
20 10875
10 8750
1987 4100
1980 800
1982 1300
1981 22825
例2：
SQL> select deptno,job,sum(sal) from emp group by grouping sets(deptno,job);
DEPTNO JOB SUM(SAL)
---------- --------- ----------
CLERK 4150
SALESMAN 5600
PRESIDENT 5000
MANAGER 8275
ANALYST 6000
30 9400
20 10875
10 8750
8 rows selected.
对于该例，如何用UNION ALL实现呢？
select null deptno,job,sum(sal) from emp group by job union all select deptno,null,sum(sal) from emp group by deptno;
两者的执行计划及统计信息分别如下：
GROUPING SETS：
Execution Plan
--------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 11 | 352 | 10 (20)| 00:00:01 |
| 1 | TEMP TABLE TRANSFORMATION | | | | | |
| 2 | LOAD AS SELECT | SYS_TEMP_0FD9D6795_E71F79 | | | | |
| 3 | TABLE ACCESS FULL | EMP | 14 | 168 | 2 (0)| 00:00:01 |
| 4 | LOAD AS SELECT | SYS_TEMP_0FD9D6796_E71F79 | | | | |
| 5 | HASH GROUP BY | | 1 | 19 | 3 (34)| 00:00:01 |
| 6 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6795_E71F79 | 1 | 19 | 2 (0)| 00:00:01 |
| 7 | LOAD AS SELECT | SYS_TEMP_0FD9D6796_E71F79 | | | | |
| 8 | HASH GROUP BY | | 1 | 26 | 3 (34)| 00:00:01 |
| 9 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6795_E71F79 | 1 | 26 | 2 (0)| 00:00:01 |
| 10 | VIEW | | 1 | 32 | 2 (0)| 00:00:01 |
| 11 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6796_E71F79 | 1 | 32 | 2 (0)| 00:00:01 |
--------------------------------------------------------------------------------------------------------
Statistics
----------------------------------------------------------
4 recursive calls
24 db block gets
17 consistent gets
3 physical reads
1596 redo size
819 bytes sent via SQL*Net to client
519 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
8 rows processed
UNION ALL：
----------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
----------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 8 | 65 | 6 (67)| 00:00:01 |
| 1 | UNION-ALL | | | | | |
| 2 | HASH GROUP BY | | 5 | 50 | 3 (34)| 00:00:01 |
| 3 | TABLE ACCESS FULL| EMP | 14 | 140 | 2 (0)| 00:00:01 |
| 4 | HASH GROUP BY | | 3 | 15 | 3 (34)| 00:00:01 |
| 5 | TABLE ACCESS FULL| EMP | 14 | 70 | 2 (0)| 00:00:01 |
----------------------------------------------------------------------------
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
4 consistent gets
0 physical reads
0 redo size
819 bytes sent via SQL*Net to client
519 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
8 rows processed
和rollup不同的是，grouping sets的效率竟然比同等功能的union all语句低，这实现有点出乎意料。看来，也不可盲目应用Oracle提供的方案，至少，在本例中是如此。