HiveQL Select Join

JOIN是子句用於通過使用共同值組合來自兩個表特定字段。它是用來從數據庫中的兩個或更多的表組合的記錄。它或多或少類似於SQL JOIN。

語法

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

我們在本章中將使用下面的兩個表。考慮下面的表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 |
+----+----------+-----+-----------+----------+

考慮另一個表命令如下：

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

有不同類型的聯接給出如下：

• JOIN
• LEFT OUTER JOIN
• RIGHT OUTER JOIN
• FULL OUTER JOIN

JOIN

JOIN子句用於合併和檢索來自多個表中的記錄。 JOIN和SQLOUTER JOIN 類似。連接條件是使用主鍵和表的外鍵。

下面的查詢執行JOIN的CUSTOMER和ORDER表，並檢索記錄：

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

成功執行查詢後，能看到以下回應：

+----+----------+-----+--------+
| 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，即使是在正確的表中沒有匹配。這意味着，如果ON子句匹配的右表0（零）記錄，JOIN還是返回結果行，但在右表中的每一列爲NULL。

LEFT JOIN返回左表中的所有的值，加上右表，或JOIN子句沒有匹配的情況下返回NULL。

下面的查詢演示了CUSTOMER 和ORDER 表之間的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);

成功執行查詢後，能看到以下回應：

+----+----------+--------+---------------------+
| 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返回右邊表的所有行，即使有在左表中沒有匹配。如果ON子句的左表匹配0（零）的記錄，JOIN結果返回一行，但在左表中的每一列爲NULL。

RIGHT JOIN返回右表中的所有值，加上左表，或者沒有匹配的情況下返回NULL。

下面的查詢演示了在CUSTOMER和ORDER表之間使用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);

成功執行查詢後，能看到以下回應：

+------+----------+--------+---------------------+
| 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結合了左邊，並且滿足JOIN條件合適外部表的記錄。連接表包含兩個表的所有記錄，或兩側缺少匹配結果那麼使用NULL值填補

下面的查詢演示了CUSTOMER 和ORDER 表之間使用的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);

成功執行查詢後，能看到以下回應：

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