←back to thread

Python has had async for 10 years – why isn't it more popular?

(tonybaloney.github.io)
320 points willm | 1 comments | 02 Sep 25 17:24 UTC | HN request time: 0s | source
Show context
PaulHoule ◴[02 Sep 25 17:34 UTC] No.45106346[source]▶
I went through a phase of writing asyncio servers for my side projects. Probably the most fun I had was writing things that were responsive in complex ways, such as a websockets server that was also listening on message queues or on a TCP connection to a Denon HEOS music player.

Eventually I wrote an "image sorter" that I found was hanging up when the browser was trying to download images in parallel, the image serving should not have been CPU bound, I was even using sendfile(), but I think other requests would hold up the CPU and would be block the tiny amount of CPU needed to set up that sendfile.

So I switched from aiohttp to the flask API and serve with either Flask or Gunicorn, I even front it with Microsoft IIS or nginx to handle the images so Python doesn't have to. It is a minor hassle because I develop on Windows so I have to run Gunicorn inside WSL2 but it works great and I don't have to think about server performance anymore.

replies(2): >>45106551 #>>45109325 #
tdumitrescu ◴[02 Sep 25 17:47 UTC] No.45106551[source]▶
That's the main problem with evented servers in general isn't it? If any one of your workloads is cpu-intensive, it has the potential to block the serving of everything else on the same thread, so requests that should always be snappy can end up taking randomly long times in practice. Basically if you have any cpu-heavy work, it shouldn't go in that same server.
replies(5): >>45106641 #>>45107229 #>>45108270 #>>45109384 #>>45113420 #
1. PaulHoule ◴[02 Sep 25 20:00 UTC] No.45108270[source]▶
My system is written in Python because it is supported by a number of batch jobs that use code from SBERT, scikit-learn, numpy and such. Currently the server doesn't do any complex calculations but under asyncio it was a strict no-no. Mostly it does database queries and formats HTML responses but it seems like that is still too much CPU.

My take on gunicorn is that it doesn't need any tuning or care to handle anything up to the large workgroup size other than maybe "buy some more RAM" -- and now if I want to do some inference in the server or use pandas to generate a report I can do it.

If I had to go bigger I probably wouldn't be using Python in the server and would have to face up to either dual language or doing the ML work in a different way. I'm a little intimidated about being on the public web in 2025 though with all the bad webcrawlers. Young 'uns just never learned everything that webcrawler authors knew in 1999. In 2010 there were just two bad Chinese webcrawlers that never sent a lick of traffic to anglophone sites, but now there are new bad webcrawlers every day it seems.