python-error-handling

To handle partial failures in Python batch processing, capture per-item successes and failures while continuing execution. This approach allows large batch imports to process remaining items and return partial results instead of stopping entirely on the first error.

You can validate API input in Python by enforcing early boundary validation to fail fast. Using Pydantic, you can aggregate validation errors and return them together, preventing invalid data from entering your domain logic.

The best way to preserve context when mapping third-party API errors in Python is through exception chaining. By mapping failure modes to specific exception types and chaining them, you maintain the original traceback while adding actionable context to messages.

Yes, you can use Pydantic for converting external data into domain types while handling errors. It provides robust validation at API boundaries, ensuring that invalid data raises specific exceptions early before further processing occurs.

You should design a custom exception hierarchy in Python to map distinct failure modes to specific exception types. Meaningful exceptions with actionable context reduce unexpected crashes and lengthy debugging cycles, making systems easier to operate and maintain.