Async/IO and Parallelism

Why, then, is Async/IO new and trendy if it less parallel than multiprocessing?

The two solve different problems. Asyncio allows writing asynchronous code sans the "callback hell". await allows the use of constructs like loops, ifs, try/except, and so on, with automatic task switching at await points. This enables servicing a large number of connections without needing to spawn a thread per connection, but with maintainable code that looks as if it were written for blocking connections. Thus asyncio mostly helps with the code whose only bottleneck is waiting for external events, such as network IO and timeouts.

Multiprocessing, on the other hand, is about parallelizing execution of CPU-bound code, such as scientific calculations. Since OS threads do not help due to the GIL, multiprocessing spawns separate OS processes and distributes the work among them. This comes at the cost of the processes not being able to easily share data - all communication is done either by serialization through pipes, or with dedicated proxies.

A multi-threaded asyncio-style framework is possible in theory - for example, Rust's tokio is like that - but would be unlikely to bring performance benefits due to Python's GIL preventing utilization of multiple cores. Combining asyncio and multiprocessing can work on asyncio code that doesn't depend on shared state, which is supported by asyncio through run_in_executor and ProcessPoolExecutor.

Gunicorn can help you:

gunicorn module:app --bind 0.0.0.0:8080 --worker-class aiohttp.GunicornWebWorker --workers 4