HiveQL Select-Joins子句
JOIN是一个使用每个表中共同值组合两个表的特定字段的子句。它用于在数据库中组合两个或多个表中的记录。
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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以下两个表。考虑以下名为CUSTOMERS的表。
+----+----------+-----+-----------+----------+
| 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 |
+----+----------+-----+-----------+----------+
考虑如下表ORDERS:
+-----+---------------------+-------------+--------+
|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 |
+-----+---------------------+-------------+--------+
以下给出了不同类型的连接:
- JOIN
- LEFT OUTER JOIN
- RIGHT OUTER JOIN
- FULL OUTER JOIN
JOIN
JOIN子句用于组合和检索多个表中的记录。 JOIN与SQL中的OUTER JOIN相同。必须使用表的主键和外键来建立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 |
+----+----------+-----+--------+
LEFT OUTER JOIN
HiveQL LEFT OUTER JOIN返回左侧表中的所有行,即使右侧表中没有匹配项也是如此。 这意味着,如果ON子句在右侧表中匹配0(零)条记录,则JOIN仍然在结果中返回一个行,但每个来自右侧表的列都为NULL。
LEFT JOIN(左连接)返回左表中的全部值,以及右表中的符合连接条件的匹配值,无匹配时返回NULL。
下面的查询演示了CUSTOMER和ORDER表之间的LEFT OUTER JOIN(左外连接):
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 |
+----+----------+--------+---------------------+
RIGHT OUTER JOIN(右外连接)
HiveQL中的RIGHT OUTER JOIN(右外连接)返回所有来自右表的行,即使左表没有匹配项。 如果ON子句在左表中匹配了0(零)条记录,则JOIN仍将在结果中返回一行,但左表中每列都是NULL。
RIGHT JOIN(右连接)返回右表中的全部值,以及左表中的符合连接条件的匹配值,无匹配时返回NULL。
下面的查询演示了CUSTOMER和ORDER表之间的RIGHT OUTER JOIN(右外连接):
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 |
+------+----------+--------+---------------------+
FULL OUTER JOIN(完全外连接)
HiveQL中的FULL OUTER JOIN(完全外连接)组合左右外部表的记录,满足JOIN条件。连接表包含两个表中的所有记录或填充两边缺少的匹配项的NULL值。
下面的查询演示了CUSTOMER和ORDER表之间的FULL OUTER JOIN(完全外连接):
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 |
+------+----------+--------+---------------------+