📅 最后修改于: 2020-11-30 05:06:22 🧑 作者: Mango
如果查询过于复杂，我们可以为复杂零件定义别名，并使用Impala的with子句将它们包括在查询中。
以下是Impala中with子句的语法。
with x as (select 1), y as (select 2) (select * from x union y);
假设我们在数据库my_db中有一个名为客户的表，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
以同样的方式，假设我们还有一个名为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with子句的示例。在此示例中，我们使用with子句显示年龄在25岁以上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，上面的查询给出以下输出。
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