python-core-development

Dataclasses combine type annotations with automatic `__init__`, `__repr__`, and `__eq__` methods to create validated data containers. Define fields with type hints, optionally add validation logic in `__post_init__`, and enforce type safety through static checkers like mypy to catch errors before runtime.

Protocols enable structural subtyping—objects matching a Protocol's interface satisfy it without explicit inheritance. Unlike abstract base classes, protocols don't require registration or inheritance, making them ideal for decoupled, flexible APIs where any object with matching methods qualifies automatically.

Use `async def` to define coroutines and `await` to pause execution while I/O completes without blocking. Combine with `asyncio.gather()` or `asyncio.create_task()` to run multiple operations concurrently, improving throughput for network requests, file reads, or other I/O-bound tasks.

Yes. Implement validation in the `__post_init__` method to check field values after initialization and raise custom exceptions for invalid data. This provides fail-fast error handling with descriptive messages, keeping validation logic within the dataclass itself.

Custom exceptions communicate domain-specific errors and enable precise error handling in calling code. Create them for library APIs, service clients, or business logic to distinguish your component's failures from Python's built-in errors, improving code clarity and error recovery.

Basic async/await knowledge—coroutines, `await`, and event loop concepts—is sufficient for simple I/O-bound tasks. Start with single-threaded async functions and standard patterns like `asyncio.run()`; deeper asyncio features are optional for most data models and service clients.