Impala GROUP BY子句与SELECT语句协作使用，以将相同的数据排列到组中。

语法

以下是GROUP BY子句的语法。

select data from table_name Group BY col_name;

例

假设我们在数据库my_db中有一个名为customers的表，其内容如下 -

[quickstart.cloudera:21000] > select * from customers; 
Query: select * from customers 
+----+----------+-----+-----------+--------+ 
| id | name     | age | address   | salary | 
+----+----------+-----+-----------+--------+ 
| 1  | Ramesh   | 32  | Ahmedabad | 20000  | 
| 2  | Khilan   | 25  | Delhi     | 15000  | 
| 3  | kaushik  | 23  | Kota      | 30000  | 
| 4  | Chaitali | 25  | Mumbai    | 35000  | 
| 5  | Hardik   | 27  | Bhopal    | 40000  | 
| 6  | Komal    | 22  | MP        | 32000  | 
+----+----------+-----+-----------+--------+ 
Fetched 6 row(s) in 0.51s

您可以使用GROUP BY查询获得每个客户的工资总额，如下所示。

[quickstart.cloudera:21000] > Select name, sum(salary) from customers Group BY name;

执行时，上述查询给出以下输出。

Query: select name, sum(salary) from customers Group BY name 
+----------+-------------+ 
| name     | sum(salary) | 
+----------+-------------+ 
| Ramesh   | 20000       | 
| Komal    | 32000       | 
| Hardik   | 40000       | 
| Khilan   | 15000       | 
| Chaitali | 35000       | 
| kaushik  | 30000       |
+----------+-------------+ 
Fetched 6 row(s) in 1.75s

假设此表有多个记录，如下所示。

+----+----------+-----+-----------+--------+ 
| id | name     | age | address   | salary | 
+----+----------+-----+-----------+--------+ 
| 1  | Ramesh   | 32  | Ahmedabad | 20000  |
| 2  | Ramesh   | 32  | Ahmedabad | 1000   | 
| 3  | Khilan   | 25  | Delhi     | 15000  | 
| 4  | kaushik  | 23  | Kota      | 30000  | 
| 5  | Chaitali | 25  | Mumbai    | 35000  |
| 6  | Chaitali | 25  | Mumbai    | 2000   |
| 7  | Hardik   | 27  | Bhopal    | 40000  | 
| 8  | Komal    | 22  | MP        | 32000  | 
+----+----------+-----+-----------+--------+

现在，您可以使用Group By子句，如下所示，考虑重复的记录条目，获取员工的总工资。

Select name, sum(salary) from customers Group BY name;

执行时，上述查询给出以下输出。

Query: select name, sum(salary) from customers Group BY name 
+----------+-------------+ 
| name     | sum(salary) | 
+----------+-------------+ 
| Ramesh   | 21000       | 
| Komal    | 32000       | 
| Hardik   | 40000       | 
| Khilan   | 15000       | 
| Chaitali | 37000       | 
| kaushik  | 30000       | 
+----------+-------------+
Fetched 6 row(s) in 1.75s