PyO3 Performance Patterns

To optimize PyO3 async callbacks, use the `pyo3_async_runtimes` crate instead of `spawn_blocking` for significantly faster Python callback execution and improved high-throughput asynchronous operation performance.

Deadlocks in PyO3 often occur when `spawn_blocking` is used indiscriminately. Differentiate between long blocking operations like OCR and quick operations like validation to properly manage execution paths and prevent deadlocks.

Correct GIL management requires releasing the Global Interpreter Lock before awaiting async operations and using `Python::attach()` rather than `with_gil()` to ensure safe and efficient execution within PyO3.

Yes, you can apply PyO3 async patterns to improve Python web framework responsiveness by ensuring efficient asynchronous handling for performance-critical Rust backend tasks and high-throughput operations.

The best approach is to differentiate operation length: keep quick tasks like PostProcessor and Validator separate from long operations like OCR to prevent deadlocks and ensure safe `spawn_blocking` usage.