โ10-31-2023 11:39 AM
3 weeks ago
Hi,
One way to address the example provided in your screenshot is by using a combination of a python f-string and a Common Table Expression like shown below. This is assuming that in reality the two tables are different unlike in the provided screenshot. If the query is only against a single table then there is no need for the CTE and the predicate variables can be applied directly against the table.
min_value = 10
max_value = 20
sql_statement = f"""with users_with_filter as (
select user_id from users where user_id between {min_value} and {max_value}
)
select *
from users as u1
inner join users_with_filter as u2
on u1.user_id = u2.user_id
"""
spark.sql(sql_statement).show()
3 weeks ago
Hi @Daniel3 , Hi , Certainly! It looks like youโre trying to use variables in your Spark SQL query.
Letโs address this and improve your code.
String Formatting:
You can use Pythonโs string formatting to insert variables into your query.
Using Parameters:
Another approach is to use parameters. You can set the parameters and refer to them in your query.
Note on Performance:
Remember to adapt the code to your specific use case and data set. If you encounter any issues or need further assistance, feel free to ask! ๐
3 weeks ago
Hello Kaniz, Thanks!
Can I get any reference URL for this content, so that i can see the examples on how to use variables in spark SQL.
3 weeks ago
Hi,
One way to address the example provided in your screenshot is by using a combination of a python f-string and a Common Table Expression like shown below. This is assuming that in reality the two tables are different unlike in the provided screenshot. If the query is only against a single table then there is no need for the CTE and the predicate variables can be applied directly against the table.
min_value = 10
max_value = 20
sql_statement = f"""with users_with_filter as (
select user_id from users where user_id between {min_value} and {max_value}
)
select *
from users as u1
inner join users_with_filter as u2
on u1.user_id = u2.user_id
"""
spark.sql(sql_statement).show()
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