May 26, 2021 impala
If the query is too complex, we can define aliases for the complex parts and include them in the query using Impala's with clause.
The following is the syntax of the with clause in Impala.
with x as (select 1), y as (select 2) (select * from x union y);
Suppose we have my_db table called customers in the database database, which reads as follows -
[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
Similarly, suppose we have another table called employee, which is as follows -
[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
The following is an example of the with clause in Impala. I n this example, we use the with clause to display records of employees and customers older than 25.
[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);
When executed, the above query gives the following output.
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