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HiveQL Select Join

JOIN是子句用于通过使用共同值组合来自两个表特定字段。它是用来从数据库中的两个或更多的表组合的记录。它或多或少类似于SQL JOIN。

语法

 join_table:     table_reference JOIN table_factor [join_condition]    | table_reference {LEFT|RIGHT|FULL} [OUTER] JOIN table_reference    join_condition    | table_reference LEFT SEMI JOIN table_reference join_condition    | table_reference CROSS JOIN table_reference [join_condition]

示例

我们在本章中将使用下面的两个表。考虑下面的表CUSTOMERS..

 +----+----------+-----+-----------+----------+  | 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 |  +----+----------+-----+-----------+----------+

考虑另一个表命令如下：

 +-----+---------------------+-------------+--------+  |OID  | DATE                | CUSTOMER_ID | AMOUNT |  +-----+---------------------+-------------+--------+  | 102 | 2009-10-08 00:00:00 |           3 | 3000   |  | 100 | 2009-10-08 00:00:00 |           3 | 1500   |  | 101 | 2009-11-20 00:00:00 |           2 | 1560   |  | 103 | 2008-05-20 00:00:00 |           4 | 2060   |  +-----+---------------------+-------------+--------+

有不同类型的联接给出如下：

JOIN
LEFT OUTER JOIN
RIGHT OUTER JOIN
FULL OUTER JOIN

JOIN

JOIN子句用于合并和检索来自多个表中的记录。 JOIN和SQLOUTER JOIN 类似。连接条件是使用主键和表的外键。

下面的查询执行JOIN的CUSTOMER和ORDER表，并检索记录：

 hive> SELECT c.ID, c.NAME, c.AGE, o.AMOUNT     > FROM CUSTOMERS c JOIN ORDERS o     > ON (c.ID = o.CUSTOMER_ID);

成功执行查询后，能看到以下回应：

 +----+----------+-----+--------+  | ID | NAME     | AGE | AMOUNT |  +----+----------+-----+--------+  | 3  | kaushik  | 23  | 3000   |  | 3  | kaushik  | 23  | 1500   |  | 2  | Khilan   | 25  | 1560   |  | 4  | Chaitali | 25  | 2060   |  +----+----------+-----+--------+

LEFT OUTER JOIN

HiveQL LEFT OUTER JOIN返回所有行左表，即使是在正确的表中没有匹配。这意味着，如果ON子句匹配的右表0（零）记录，JOIN还是返回结果行，但在右表中的每一列为NULL。

LEFT JOIN返回左表中的所有的值，加上右表，或JOIN子句没有匹配的情况下返回NULL。

下面的查询演示了CUSTOMER 和ORDER 表之间的LEFT OUTER JOIN用法：

 hive> SELECT c.ID, c.NAME, o.AMOUNT, o.DATE     > FROM CUSTOMERS c     > LEFT OUTER JOIN ORDERS o     > ON (c.ID = o.CUSTOMER_ID);

成功执行查询后，能看到以下回应：

 +----+----------+--------+---------------------+  | ID | NAME     | AMOUNT | DATE                |  +----+----------+--------+---------------------+  | 1  | Ramesh   | NULL   | NULL                |  | 2  | Khilan   | 1560   | 2009-11-20 00:00:00 |  | 3  | kaushik  | 3000   | 2009-10-08 00:00:00 |  | 3  | kaushik  | 1500   | 2009-10-08 00:00:00 |  | 4  | Chaitali | 2060   | 2008-05-20 00:00:00 |  | 5  | Hardik   | NULL   | NULL                |  | 6  | Komal    | NULL   | NULL                |  | 7  | Muffy    | NULL   | NULL                |  +----+----------+--------+---------------------+

RIGHT OUTER JOIN

HiveQL RIGHT OUTER JOIN返回右边表的所有行，即使有在左表中没有匹配。如果ON子句的左表匹配0（零）的记录，JOIN结果返回一行，但在左表中的每一列为NULL。

RIGHT JOIN返回右表中的所有值，加上左表，或者没有匹配的情况下返回NULL。

下面的查询演示了在CUSTOMER和ORDER表之间使用RIGHT OUTER JOIN。

 hive> SELECT c.ID, c.NAME, o.AMOUNT, o.DATE     > FROM CUSTOMERS c     > RIGHT OUTER JOIN ORDERS o     > ON (c.ID = o.CUSTOMER_ID);

成功执行查询后，能看到以下回应：

 +------+----------+--------+---------------------+  | ID   | NAME     | AMOUNT | DATE                |  +------+----------+--------+---------------------+  | 3    | kaushik  | 3000   | 2009-10-08 00:00:00 |  | 3    | kaushik  | 1500   | 2009-10-08 00:00:00 |  | 2    | Khilan   | 1560   | 2009-11-20 00:00:00 |  | 4    | Chaitali | 2060   | 2008-05-20 00:00:00 |  +------+----------+--------+---------------------+

FULL OUTER JOIN

HiveQL FULL OUTER JOIN结合了左边，并且满足JOIN条件合适外部表的记录。连接表包含两个表的所有记录，或两侧缺少匹配结果那么使用NULL值填补

下面的查询演示了CUSTOMER 和ORDER 表之间使用的FULL OUTER JOIN：

 hive> SELECT c.ID, c.NAME, o.AMOUNT, o.DATE     > FROM CUSTOMERS c     > FULL OUTER JOIN ORDERS o     > ON (c.ID = o.CUSTOMER_ID);

成功执行查询后，能看到以下回应：

 +------+----------+--------+---------------------+  | ID   | NAME     | AMOUNT | DATE                |  +------+----------+--------+---------------------+  | 1    | Ramesh   | NULL   | NULL                |  | 2    | Khilan   | 1560   | 2009-11-20 00:00:00 |  | 3    | kaushik  | 3000   | 2009-10-08 00:00:00 |  | 3    | kaushik  | 1500   | 2009-10-08 00:00:00 |  | 4    | Chaitali | 2060   | 2008-05-20 00:00:00 |  | 5    | Hardik   | NULL   | NULL                |  | 6    | Komal    | NULL   | NULL                | | 7    | Muffy    | NULL   | NULL                |   | 3    | kaushik  | 3000   | 2009-10-08 00:00:00 |  | 3    | kaushik  | 1500   | 2009-10-08 00:00:00 |  | 2    | Khilan   | 1560   | 2009-11-20 00:00:00 |  | 4    | Chaitali | 2060   | 2008-05-20 00:00:00 |  +------+----------+--------+---------------------+

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