如果查询太复杂，我们可以为复杂部分定义别名，并使用Impala的with子句将它们包含在查询中。

语法

以下是Impala中的with子句的语法。

with x as (select 1), y as (select 2) (select * from x union y);

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  | 
| 9  | robert   | 23  | banglore  | 28000  | 
| 2  | Khilan   | 25  | Delhi     | 15000  | 
| 4  | Chaitali | 25  | Mumbai    | 35000  | 
| 7  | ram      | 25  | chennai   | 23000  | 
| 6  | Komal    | 22  | MP        | 32000  | 
| 8  | ram      | 22  | vizag     | 31000  | 
| 5  | Hardik   | 27  | Bhopal    | 40000  | 
| 3  | kaushik  | 23  | Kota      | 30000  | 
+----+----------+-----+-----------+--------+ 
Fetched 9 row(s) in 0.59s

同样，假设我们有另一个名为employee的表，其内容如下 -

[quickstart.cloudera:21000] > select * from employee; 
Query: select * from employee 
+----+---------+-----+---------+--------+ 
| id | name    | age | address | salary | 
+----+---------+-----+---------+--------+ 
| 3  | mahesh  | 54  | Chennai | 55000  | 
| 2  | ramesh  | 44  | Chennai | 50000  | 
| 4  | Rupesh  | 64  | Delhi   | 60000  | 
| 1  | subhash | 34  | Delhi   | 40000  | 
+----+---------+-----+---------+--------+ 
Fetched 4 row(s) in 0.59s

以下是Impala中的with子句的示例。 在本示例中，我们使用with子句显示年龄大于25的员工和客户的记录。

[quickstart.cloudera:21000] > 
   with t1 as (select * from customers where age>25), 
   t2 as (select * from employee where age>25) 
   (select * from t1 union select * from t2);

执行时，上述查询给出以下输出。

Query: with t1 as (select * from customers where age>25), t2 as (select * from employee where age>25) 
   (select * from t1 union select * from t2)
+----+---------+-----+-----------+--------+ 
| id | name    | age | address   | salary | 
+----+---------+-----+-----------+--------+ 
| 3  | mahesh  | 54  | Chennai   | 55000  | 
| 1  | subhash | 34  | Delhi     | 40000  | 
| 2  | ramesh  | 44  | Chennai   | 50000  | 
| 5  | Hardik  | 27  | Bhopal    | 40000  | 
| 4  | Rupesh  | 64  | Delhi     | 60000  | 
| 1  | Ramesh  | 32  | Ahmedabad | 20000  | 
+----+---------+-----+-----------+--------+ 
Fetched 6 row(s) in 1.73s