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