Hive教程

HiveQL SELECT JOIN

JOIN 是一个子句,用于通过使用每个表的公共值来组合两个表中的特定字段。用于合并数据库中两个或多个表的记录。

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

我们将在本章中使用以下两个表格。考虑下表名为 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 ORDERS,如下所示:
+-----+---------------------+-------------+--------+ 
|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   | 
+-----+---------------------+-------------+--------+
有以下不同类型的连接:
加入 左外连接 右外连接 完全外连接

加入

JOIN 子句用于合并和检索多个表中的记录。 JOIN 与 SQL 中的 OUTER JOIN 相同。将使用表的主键和外键引发 JOIN 条件。
以下查询在 CUSTOMER 和 ORDER 表上执行 JOIN,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,您将看到以下响应:
+----+----------+-----+--------+ 
| ID | NAME     | AGE | AMOUNT | 
+----+----------+-----+--------+ 
| 3 | kaushik  | 23 | 3000   | 
| 3  | kaushik  | 23  | 1500   | 
| 2  | Khilan   | 25 | 1560   | 
| 4  | Chaitali | 25  | 2060   | 
+----+----------+-----+--------+

左外连接

HiveQL LEFT OUTER JOIN 返回左表中的所有行,即使右表中没有匹配项。这意味着,如果 ON 子句匹配右表中的 0(零)条记录,则 JOIN 仍会在结果中返回一行,但右表中的每一列都为 NULL。
LEFT JOIN 返回左表中的所有值,加上右表中匹配的值,如果没有匹配的 JOIN 谓词,则返回 NULL。
以下查询演示了 CUSTOMER 和 ORDER 表之间的 LEFT OUTER JOIN:
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,您将看到以下响应:
+----+----------+--------+---------------------+ 
| 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                | 
+----+----------+--------+---------------------+

右外连接

HiveQL RIGHT OUTER JOIN 返回右表中的所有行,即使左表中没有匹配项。如果 ON 子句匹配左表中的 0(零)条记录,则 JOIN 仍会在结果中返回一行,但左表中的每一列都为 NULL。
RIGHT JOIN 返回右表中的所有值,加上左表中匹配的值,如果没有匹配的连接谓词,则返回 NULL。
以下查询演示了 CUSTOMER 和 ORDER 表之间的 RIGHT OUTER JOIN。
notranslate"> 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,您将看到以下响应:
+------+----------+--------+---------------------+ 
| 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 | 
+------+----------+--------+---------------------+

完全外连接

HiveQL FULL OUTER JOIN 结合了满足 JOIN 条件的左右外部表的记录。连接表包含两个表中的所有记录,或者为任一侧缺失的匹配项填充 NULL 值。
以下查询演示了 CUSTOMER 和 ORDER 表之间的 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,您将看到以下响应:
+------+----------+--------+---------------------+ 
| 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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