SQL GROUP BY

SQL GROUP BY语句与 SELECT 语句配合使用，用来将相同的数据进行分组。此 GROUP BY 语句跟在 SELECT 语句的 WHERE 子句之后，在 ORDER BY 子句之前。

语法

GROUP BY 语句的基本语法如下。

SELECT column1, column2
FROM table_name
WHERE [ conditions ]
GROUP BY column1, column2
ORDER BY column1, column2

GROUP BY 语句必须跟在 WHERE 语句中的条件之后，并且必须在 ORDER BY 子句之前。顺序很重要，如果顺序错误的话，sql是会报错的。

示例

CUSTOMERS 表

+----+----------+-----+-----------+----------+
| ID | NAME     | AGE | ADDRESS   | SALARY   |
+----+----------+-----+-----------+----------+
|  1 | Ramesh   |  32 | Ahmedabad |  2000.00 |
|  2 | Khilan   |  25 | Delhi     |  1500.00 |
|  3 | kaushik  |  23 | Kota      |  2000.00 |
|  4 | Chaitali |  25 | Mumbai    |  6500.00 |
|  5 | Hardik   |  27 | Bhopal    |  8500.00 |
|  6 | Komal    |  22 | MP        |  4500.00 |
|  7 | Muffy    |  24 | Indore    | 10000.00 |
+----+----------+-----+-----------+----------+

如果要想知道每个客户的工资总额，那么可以使用 GROUP BY 对SALARY字段进行分组，然后使用聚合函数SUM进行组内相加。

SQL> SELECT NAME, SUM(SALARY) FROM CUSTOMERS
   GROUP BY NAME;

结果如下

+----------+-------------+
| NAME     | SUM(SALARY) |
+----------+-------------+
| Chaitali |     6500.00 |
| Hardik   |     8500.00 |
| kaushik  |     2000.00 |
| Khilan   |     1500.00 |
| Komal    |     4500.00 |
| Muffy    |    10000.00 |
| Ramesh   |     2000.00 |
+----------+-------------+

现在，让我们看一个表，其中 CUSTOMERS 表的NAME字段有重复的

+----+----------+-----+-----------+----------+
| ID | NAME     | AGE | ADDRESS   | SALARY   |
+----+----------+-----+-----------+----------+
|  1 | Ramesh   |  32 | Ahmedabad |  2000.00 |
|  2 | Ramesh   |  25 | Delhi     |  1500.00 |
|  3 | kaushik  |  23 | Kota      |  2000.00 |
|  4 | kaushik  |  25 | Mumbai    |  6500.00 |
|  5 | Hardik   |  27 | Bhopal    |  8500.00 |
|  6 | Komal    |  22 | MP        |  4500.00 |
|  7 | Muffy    |  24 | Indore    | 10000.00 |
+----+----------+-----+-----------+----------+

现在，如果要想知道每个客户的工资总额，那么使用 GROUP BY 语句

SQL> SELECT NAME, SUM(SALARY) FROM CUSTOMERS
   GROUP BY NAME;

结果如下

+---------+-------------+
| NAME    | SUM(SALARY) |
+---------+-------------+
| Hardik  |     8500.00 |
| kaushik |     8500.00 |
| Komal   |     4500.00 |
| Muffy   |    10000.00 |
| Ramesh  |     3500.00 |
+---------+-------------+