SQL 中的 CARTESIAN JOIN函数

CARTESIAN JOIN或CROSS JOIN从两个或多个联接表中返回记录集的笛卡尔积。

CARTESIAN JOIN - 语法

CARTESIAN JOIN 或 CROSS JOIN 的基本语法如下-

SELECT table1.column1, table2.column2...
FROM  table1, table2 [, table3 ]

CARTESIAN JOIN - 示例

请考虑以下两个表。

无涯教程网

表1 -CUSTOMERS表如下。

+----+----------+-----+-----------+----------+
| 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 | Learnfk    |  24 | Indore    | 10000.00 |
+----+----------+-----+-----------+----------+

表2:ORDERS表如下-

+-----+---------------------+-------------+--------+
|OID  | DATE                | CUSTOMER_ID | AMOUNT |
+-----+---------------------+-------------+--------+
| 102 | 2019-10-08 00:00:00 |           3 |   3000 |
| 100 | 2019-10-08 00:00:00 |           3 |   1500 |
| 101 | 2019-11-20 00:00:00 |           2 |   1560 |
| 103 | 2018-05-20 00:00:00 |           4 |   2060 |
+-----+---------------------+-------------+--------+

现在，让无涯教程使用CARTESIAN JOIN联接这两个表，如下所示:

SQL> SELECT  ID, NAME, AMOUNT, DATE
   FROM CUSTOMERS, ORDERS;

这将产生以下输出-

+----+----------+--------+---------------------+
| ID | NAME     | AMOUNT | DATE                |
+----+----------+--------+---------------------+
|  1 | Ramesh   |   3000 | 2019-10-08 00:00:00 |
|  1 | Ramesh   |   1500 | 2019-10-08 00:00:00 |
|  1 | Ramesh   |   1560 | 2019-11-20 00:00:00 |
|  1 | Ramesh   |   2060 | 2018-05-20 00:00:00 |
|  2 | Khilan   |   3000 | 2019-10-08 00:00:00 |
|  2 | Khilan   |   1500 | 2019-10-08 00:00:00 |
|  2 | Khilan   |   1560 | 2019-11-20 00:00:00 |
|  2 | Khilan   |   2060 | 2018-05-20 00:00:00 |
|  3 | kaushik  |   3000 | 2019-10-08 00:00:00 |
|  3 | kaushik  |   1500 | 2019-10-08 00:00:00 |
|  3 | kaushik  |   1560 | 2019-11-20 00:00:00 |
|  3 | kaushik  |   2060 | 2018-05-20 00:00:00 |
|  4 | Chaitali |   3000 | 2019-10-08 00:00:00 |
|  4 | Chaitali |   1500 | 2019-10-08 00:00:00 |
|  4 | Chaitali |   1560 | 2019-11-20 00:00:00 |
|  4 | Chaitali |   2060 | 2018-05-20 00:00:00 |
|  5 | Hardik   |   3000 | 2019-10-08 00:00:00 |
|  5 | Hardik   |   1500 | 2019-10-08 00:00:00 |
|  5 | Hardik   |   1560 | 2019-11-20 00:00:00 |
|  5 | Hardik   |   2060 | 2018-05-20 00:00:00 |
|  6 | Komal    |   3000 | 2019-10-08 00:00:00 |
|  6 | Komal    |   1500 | 2019-10-08 00:00:00 |
|  6 | Komal    |   1560 | 2019-11-20 00:00:00 |
|  6 | Komal    |   2060 | 2018-05-20 00:00:00 |
|  7 | Learnfk    |   3000 | 2019-10-08 00:00:00 |
|  7 | Learnfk    |   1500 | 2019-10-08 00:00:00 |
|  7 | Learnfk    |   1560 | 2019-11-20 00:00:00 |
|  7 | Learnfk    |   2060 | 2018-05-20 00:00:00 |
+----+----------+--------+---------------------+