常常写SQL语句的人应该知道Group by语句的主要使用方法是进行分类汇总，以下是一种它最常见的使用方法（依据部门、职位分别统计业绩）：

SELECT  a.dname,b.job,SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP  BY a.dname,b.job;

DNAME          JOB          SUM_SAL
-------------- --------- ----------
SALES          MANAGER         2850
SALES          CLERK            950
SALES          SALESMAN        5600
ACCOUNTING     MANAGER         2450
ACCOUNTING     PRESIDENT       5000
ACCOUNTING     CLERK           1300
RESEARCH       MANAGER         2975
RESEARCH       ANALYST         6000
RESEARCH       CLERK           1900

这时候，假设有人跑过来跟你说：我除了以上数据之外，还要每一个部门总的业绩以及全部部门加起来的业绩，这时候你非常可能会想到例如以下的笨方法（union all）：

select * from (
SELECT  a.dname,b.job,SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP  BY a.dname,b.job
UNION ALL
--实现了部门的小计
SELECT  a.dname,NULL, SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP  BY a.dname
UNION ALL
--实现了全部部门总的合计
SELECT  NULL,NULL, SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno)
order by dname;

DNAME          JOB          SUM_SAL
-------------- --------- ----------
ACCOUNTING     CLERK           1300
ACCOUNTING     MANAGER         2450
ACCOUNTING     PRESIDENT       5000
ACCOUNTING                     8750
RESEARCH       CLERK           1900
RESEARCH       MANAGER         2975
RESEARCH       ANALYST         6000
RESEARCH                      10875
SALES          CLERK            950
SALES          MANAGER         2850
SALES          SALESMAN        5600
SALES                          9400
                              29025

union all 合并笨办法产生的运行计划
-------------------------------------------------------------------------------
Plan hash value: 2979078843
-------------------------------------------------------------------------------
| Id  | Operation              | Name | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |      |    29 |   812 |    23  (22)| 00:00:01 |
|   1 |  SORT ORDER BY         |      |    29 |   812 |    23  (22)| 00:00:01 |
|   2 |   VIEW                 |      |    29 |   812 |    22  (19)| 00:00:01 |
|   3 |    UNION-ALL           |      |       |       |            |          |
|   4 |     HASH GROUP BY      |      |    14 |   756 |     8  (25)| 00:00:01 |
|*  5 |      HASH JOIN         |      |    14 |   756 |     7  (15)| 00:00:01 |
|   6 |       TABLE ACCESS FULL| DEPT |     4 |    88 |     3   (0)| 00:00:01 |
|   7 |       TABLE ACCESS FULL| EMP  |    14 |   448 |     3   (0)| 00:00:01 |
|   8 |     HASH GROUP BY      |      |    14 |   672 |     8  (25)| 00:00:01 |
|*  9 |      HASH JOIN         |      |    14 |   672 |     7  (15)| 00:00:01 |
|  10 |       TABLE ACCESS FULL| DEPT |     4 |    88 |     3   (0)| 00:00:01 |
|  11 |       TABLE ACCESS FULL| EMP  |    14 |   364 |     3   (0)| 00:00:01 |
|  12 |     SORT AGGREGATE     |      |     1 |    39 |            |          |
|* 13 |      HASH JOIN         |      |    14 |   546 |     7  (15)| 00:00:01 |
|  14 |       TABLE ACCESS FULL| DEPT |     4 |    52 |     3   (0)| 00:00:01 |
|  15 |       TABLE ACCESS FULL| EMP  |    14 |   364 |     3   (0)| 00:00:01 |
-------------------------------------------------------------------------------

事实上，假设你知道Group By的Rollup扩展的话，这样的需求仅仅是小case：

SELECT  a.dname,b.job, SUM(b.sal) sum_sal
FROM dept a,emp b 
WHERE a.deptno = b.deptno
GROUP  BY ROLLUP(a.dname,b.job);

DNAME          JOB          SUM_SAL
-------------- --------- ----------
SALES          CLERK            950
SALES          MANAGER         2850
SALES          SALESMAN        5600
SALES                          9400
RESEARCH       CLERK           1900
RESEARCH       ANALYST         6000
RESEARCH       MANAGER         2975
RESEARCH                      10875
ACCOUNTING     CLERK           1300
ACCOUNTING     MANAGER         2450
ACCOUNTING     PRESIDENT       5000
ACCOUNTING                     8750
                              29025

rollup写法产生的运行计划
-----------------------------------------------------------------------------
Plan hash value: 1037965942
-----------------------------------------------------------------------------
| Id  | Operation            | Name | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |      |    14 |   756 |     8  (25)| 00:00:01 |
|   1 |  SORT GROUP BY ROLLUP|      |    14 |   756 |     8  (25)| 00:00:01 |
|*  2 |   HASH JOIN          |      |    14 |   756 |     7  (15)| 00:00:01 |
|   3 |    TABLE ACCESS FULL | DEPT |     4 |    88 |     3   (0)| 00:00:01 |
|   4 |    TABLE ACCESS FULL | EMP  |    14 |   448 |     3   (0)| 00:00:01 |
-----------------------------------------------------------------------------

能够发现，这样的方法不但SQL书写方便，性能也能得到提高。

这时候，假设又有人跑过来说：除了以上数据，他还须要每一个职位总的业绩，你仅仅要把rollup换成cube就能够了，例如以下所看到的：

-- CUBE分组
SELECT  a.dname,b.job, SUM(b.sal) sum_sal
FROM dept a,emp b 
WHERE a.deptno = b.deptno
GROUP  BY CUBE(a.dname,b.job);

DNAME          JOB          SUM_SAL
-------------- --------- ----------
                              29025
               CLERK           4150
               ANALYST         6000
               MANAGER         8275
               SALESMAN        5600
               PRESIDENT       5000
SALES                          9400
SALES          CLERK            950
SALES          MANAGER         2850
SALES          SALESMAN        5600
RESEARCH                      10875
RESEARCH       CLERK           1900
RESEARCH       ANALYST         6000
RESEARCH       MANAGER         2975
ACCOUNTING                     8750
ACCOUNTING     CLERK           1300
ACCOUNTING     MANAGER         2450
ACCOUNTING     PRESIDENT       5000

从上面能够看出：cube比rollup的展现的粒度更细一些。

这时候，假设又有人跑过来说：他不须要那么细的数据，仅仅须要汇总的数据，能够使用Grouping Sets：

---GROUPING SETS分组
SELECT to_char(b.hiredate,'yyyy') hire_year,a.dname,b.job, SUM(sal) sum_sal
FROM dept a,emp b 
WHERE a.deptno = b.deptno
GROUP BY GROUPING SETS(to_char(b.hiredate,'yyyy'),a.dname,b.job);

HIRE DNAME          JOB          SUM_SAL
---- -------------- --------- ----------
1987                                4100
1980                                 800
1982                                1300
1981                               22825
     ACCOUNTING                     8750
     RESEARCH                      10875
     SALES                          9400
                    CLERK           4150
                    SALESMAN        5600
                    PRESIDENT       5000
                    MANAGER         8275
                    ANALYST         6000