impala with子句

由 jojocool 创建，youj 最后一次修改 2016-12-19 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