async-worker | Microframework para escrever handlers | Reactive Programming library

Here's how the event-driven asynchronous servers generally work:

• Start a process and listen to incoming requests. Utilizing the event notification API of the operating system makes it very easy to serve thousands of clients from single thread/process.
• Since there's only one process managing all the connections, you don't want to perform any slow (or blocking) tasks in this process. Because then it will block the program for every client.
• To perform blocking tasks, the server delegates the tasks to "workers". Workers can be threads (running in the same process) or separate processes (or subprocesses). Now the main process can keep on serving clients while workers perform the blocking tasks.

How does Waitress handle concurrent tasks?

Pretty much the same way I just described above. And for workers it creates threads, not processes.

how the python GIL affects them

Waitress uses threads for workers. So, yes they are affected by GIL in that they aren't truly concurrent though they seem to be. "Asynchronous" is the correct term.

Threads in Python run inside a single process, on a single CPU core, and don't run in parallel. A thread acquires the GIL for a very small amount of time and executes its code and then the GIL is acquired by another thread.

But since the GIL is released on network I/O, the parent process will always acquire the GIL whenever there's a network event (such as an incoming request) and this way you can stay assured that the GIL will not affect the network bound operations (like receiving requests or sending response).

On the other hand, Python processes are actually concurrent: they can run in parallel on multiple cores. But Waitress doesn't use processes.

Should you be worried?

If you're just doing small blocking tasks like database read/writes and serving only a few hundred users per second, then using threads isn't really that bad.

For serving a large volume of users or doing long running blocking tasks, you can look into using external task queues like Celery. This will be much better than spawning and managing processes yourself.