SQL stored procedure try catch odd behavior when called from Python

Title: Understanding SQL Stored Procedure TRY/CATCH Odd Behavior when Called from Python
Introduction:
SQL Server provides a powerful mechanism for handling errors within stored procedures using the TRY/CATCH block. However, when calling SQL Server stored procedures from Python, you may encounter some odd behavior with error handling. In this tutorial, we will explore these nuances and provide examples to help you understand how to work with TRY/CATCH in this context.
Prerequisites:
Scenario:
We'll use a simple example to illustrate the odd behavior of TRY/CATCH in SQL Server when called from Python. We'll create a stored procedure that intentionally throws an error and observe how it behaves when invoked from Python.
Odd Behavior:
When you execute the stored procedure from SQL Server Management Studio (SSMS), you will receive the expected error message "An error occurred: Divide by zero error encountered." This is due to the division by zero error handled in the CATCH block.
However, when calling the same stored procedure from Python using pyodbc or pymssql, you may notice that the error message is not captured as expected, and the result may be "None."
Explanation:
The odd behavior is due to the way SQL Server handles errors within stored procedures when called from external applications like Python. The TRY/CATCH block in SQL Server captures the error, but it does not pass the error message back to the calling application by default. Instead, it continues the execution flow.
To capture the error message when calling the stored procedure from Python, you can modify the procedure to return the error message explicitly.
Now, the modified Python code captures and prints the error message as expected.
Conclusion:
When calling SQL Server stored procedures from Python, be aware of the peculiar behavior of TRY/CATCH error handling. To capture and handle error messages properly, you may need to modify the stored procedure to explicitly return error information. This ensures that you can effectively handle errors in your Python applications when interacting with SQL Server stored procedures.
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