Hive初识（四）

Hive本质上是一个数据仓库，但不存储数据(只存储元数据(metadata)，Hive中的元数据包括表的名字，表的列和分区及分区及其属性，表的属性（是否为外部表等），表的数据所在目录等)，用户可以借助Hive使用sql对存储在分布式文件系统中的大数据集进行读写

Hive查询语言（HiveQL）是一种查询语言，Hive处理在Metastore(元数据存储)分析结构化数据。

SELECT语句用来从表中检索的数据。WHERE子句中的工作原理类似于一个条件。它使用这个条件过滤数据，并返回给出一个有限的结果。

语法：下面给出的SELECT查询的语法

SELECT [ALL | DISTINCT] select_expr, select_expr, ...
FROM table_reference
[WHERE where_condition]
[GROUP BY col_list]
[HAVING having_condition]
[CLUSTER BY col_list | [DISTRIBUTE BY col_list] [SORT BY col_list]]
[LIMIT number];

示例

举个例子SELECT...WHERE子句。假设employee表有如下Id，Name，Salary，Designation和Dept等字段，生成一个查询检索超过30000薪水的员工详细信息。

+------+--------------+-------------+-------------------+--------+
| ID   | Name         | Salary      | Designation       | Dept   |
+------+--------------+-------------+-------------------+--------+
|1201  | Gopal        | 45000       | Technical manager | TP     |
|1202  | Manisha      | 45000       | Proofreader       | PR     |
|1203  | Masthanvali  | 40000       | Technical writer  | TP     |
|1204  | Krian        | 40000       | Hr Admin          | HR     |
|1205  | Kranthi      | 30000       | Op Admin          | Admin  |
+------+--------------+-------------+-------------------+--------+

下面的查询检索使用上述业务情景的员工详细信息：

SELECT * FROM employee WHERE salary>30000;

成功查询后，能看到以下回应：

+------+--------------+-------------+-------------------+--------+
| ID   | Name         | Salary      | Designation       | Dept   |
+------+--------------+-------------+-------------------+--------+
|1201  | Gopal        | 45000       | Technical manager | TP     |
|1202  | Manisha      | 45000       | Proofreader       | PR     |
|1203  | Masthanvali  | 40000       | Technical writer  | TP     |
|1204  | Krian        | 40000       | Hr Admin          | HR     |
+------+--------------+-------------+-------------------+--------+

下面介绍使用SELECT语句的ORDER BY子句。

示例：假设需要生成一个查询用于检索员工的详细信息。

+------+--------------+-------------+-------------------+--------+
| ID   | Name         | Salary      | Designation       | Dept   |
+------+--------------+-------------+-------------------+--------+
|1201  | Gopal        | 45000       | Technical manager | TP     |
|1202  | Manisha      | 45000       | Proofreader       | PR     |
|1203  | Masthanvali  | 40000       | Technical writer  | TP     |
|1204  | Krian        | 40000       | Hr Admin          | HR     |
|1205  | Kranthi      | 30000       | Op Admin          | Admin  |
+------+--------------+-------------+-------------------+--------+

下面是使用上述业务情景查询检索员工详细信息：

SELECT * FROM employee ORDER BY DEPT;

成功查询后能得到以下回应：

+------+--------------+-------------+-------------------+--------+
| ID   | Name         | Salary      | Designation       | Dept   |
+------+--------------+-------------+-------------------+--------+
|1205  | Kranthi      | 30000       | Op Admin          | Admin  |
|1204  | Krian        | 40000       | Hr Admin          | HR     |
|1202  | Manisha      | 45000       | Proofreader       | PR     |
|1201  | Gopal        | 45000       | Technical manager | TP     |
|1203  | Masthanvali  | 40000       | Technical writer  | TP     |
+------+--------------+-------------+-------------------+--------+

GROUP BY子句用于分类所有记录结果的特定集合列。它被用来查询一组激励。

如果用来产生一个查询以检索每个部门的员工数量。

+------+--------------+-------------+-------------------+--------+
| ID   | Name         | Salary      | Designation       | Dept   |
+------+--------------+-------------+-------------------+--------+
|1201  | Gopal        | 45000       | Technical manager | TP     |
|1202  | Manisha      | 45000       | Proofreader       | PR     |
|1203  | Masthanvali  | 40000       | Technical writer  | TP     |
|1204  | Krian        | 45000       | Proofreader       | PR     |
|1205  | Kranthi      | 30000       | Op Admin          | Admin  |
+------+--------------+-------------+-------------------+--------+ 

下面使用上述业务情景查询检索员工的详细信息。

SELECT Dept,count(*) FROM employee GROUP BY DEPT;

返回结果为：

+------+--------------+
| Dept | Count(*)     |
+------+--------------+
|Admin |    1         |
|PR    |    2         |
|TP    |    3         |
+------+--------------+

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两个或更多的表组合的记录。它或多或少类似于SQL JOIN。

示例：

我们将使用下面两个表，CUSTOMERS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 |
+----+----------+-----+-----------+----------+

ORDERS表

+-----+---------------------+-------------+--------+
|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子句用于合并和检索来自多个表中的记录。JOIN和SQL OUTER 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ORDERS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子句匹配的右表零记录，JOIN还是返回结果行，但在右表中的每一行为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ORDERS表之间的LEFT OUTER JOIN用法：

hive > SELECT c.ID, c.NAME, o.AMOUNT, o.DATE FROME CUSTOMERS c LEFT 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，即使在左表中没有匹配。如果ON子句的左表匹配零记录，JOIN结果返回一行，但在左表中的每一行为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CUSTOMERS和ODERS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CUSTOMERS和ORDERS表之间的FULL OUTER JOIN：

hive > SELCE c.ID, c.NAME, o.AMOUNT, o.DATE FROM CUSTOMERS c FULL OUTER JOIN ODERS 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 |
+------+----------+--------+---------------------+