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
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