Async Django in Production

https://jonathanadly.com/is-async-django-ready-for-prime-time

by jonathan-adly • 2 years ago

50 45 2 years ago

We have traditionally used Django in all our projects. We believe it is one of the most underrated, beautifully designed, rock solid framework out there.

However, if we are to be honest, the history of async usage in Django wasn't very impressive. You could argue that for most products, you don’t really need async. It was just an extra layer of complexity without any significant practical benefit.

Over the last couple of years, AI use-cases have changed that perception. Many AI products have calling external APIs over the network as their bottleneck. This makes the complexity from async Python worth considering. FastAPI with its intuitive async usage and simplicity have risen to be the default API/web layer for AI projects.

I wrote about using async Django in a relatively complex AI open source project here: https://jonathanadly.com/is-async-django-ready-for-prime-tim...

tldr: Async django is ready! there is a couple of gotcha's here and there, but there should be no performance loss when using async Django instead of FastAPI for the same tasks. Django's built-in features greatly simplify and enhance the developer experience.

So - go ahead and use async Django in your next project. It should be a lot smoother that it was a year or even six months ago.

fulafel4• 2 years ago

(Disclaimer: I haven't used Django in a long time)

Can you expand more on why these AI cases make the complexity tradeoff different?

I'd imagine think waiting on a 3rd party LLM API call would be computationally very inexpensive compared to what's going on at the business end of that API call. Further lowering the cost, Django is usually configured to use multiple threads and/or processes so that this blocking call won't keep a CPU idle, no?

flowingfocus• 2 years ago

The explanation is in the article. Tldr is for sync functions, the CPU is blocked, with async functions, once the await statement is reached, other stuff can be handled in between

roughly• 2 years ago

Can you expand more on why these AI cases make the complexity tradeoff different?

They’re very slow. Like, several seconds to get a response slow. If you’re serving a very large number of very fast requests, you can argue that the simplicity of the sync model makes it worth it to just scale up the number of processes required to serve that many requests, but the LLM calls are slow enough that it means you need to dramatically scale up the number of serving processes available if you’re going to keep the sync model, and that’s mostly to have CPUs sitting around idle waiting for the LLM to come back. The async model can also let you parallelize calls to the LLMs if you’re making multiple independent calls within the same request - this can cut multiple seconds off your response time.

UltraSane2• 2 years ago

async is just parallelized waiting and LLMs make CPUs wait ages for responses to async calls to LLMs allow for much higher CPU utilization.

mulmboy• 2 years ago

Yeah the article doesn't even mention "what about more threads". Responses to your comment suggest there's broadly a lack of clarity around what Async gets you especially vs threads.

If the alternative to Async is more processes, rather than threads, a clear benefit is reduced memory usage and reduced process startup time.

andrewstuart• 2 years ago

Async Django is a bit of a puzzle …. who is it for?

People who like synchronous Python can use Django.

People who like asynch Python can use Starlette - the async web server also written by the guy who wrote Django.

It’s not clear why Django needs to be async, especially when I get the sense there’s developers who like async and developers who prefer synch. It’s a natural fit for Django to fulfill the sync demand and Starlette to fulfill the async. They’re both good.

senko2• 2 years ago

Starlette and Django are wildly different.

Django has a batteries-includes approach, benefits from tighter integration of orm, auth, form handling, etc, and has a huge 3rd party ecosystem.

First-class async support in Django allows Django users to avoid jumping through hoops (celery, channels, ...) for longer-running requests, something especially noticable if you're calling any kind of AI service.

globular-toast4• 2 years ago

How much of that is relevant if you're going to be using django-ninja (pydantic) and the whole app has to be async, though?

Django is fine for writing a thin CRUD layer around a database. It makes the easy stuff easy. But doesn't seem to help much for the hard stuff and often actively hinders it.

Really the main reason for Django is its ORM and migrations. It's basically the other Python ORM (next to SQLAlchemy) but, unlike SQLAlchemy, it's not designed to be used standalone. In my experience I find Django (and active record ORMs in general) easier for people to get started with, but massively limiting in long run for complex domains.

coffeefirst• 2 years ago

As a long time Django user (haven’t tried async in prod yet) the appeal is to have the full Django toolkit and be able to set something up async _if_ needed.

Which is a very Django way to think: lots of tools ready to go, use only what you need.

simonw4• 2 years ago

Minor correction: Starlette was started by Tom Christie who is also the creator of Django REST Framework, but he didn't start the Django project itself.

Django having async support means you can use the Django ORM, and the Django request/response cycle, and generally not need to write your async and your sync web code using slightly different APIs.

singularity2001• 2 years ago

When will it be common knowledge that introducing async function coloring (on the callee side, instead of just 'go' on the call side) was the biggest mistake of the decade?

gnlampx• 2 years ago

This will happen when someone writes a new green thread framework and convinces van Rossum to preside over the corporate-sponsored integration into Python.

Four releases later async will be marketed as "this has been a mistake" and the green threads will be the best thing ever. Thousands of blog posts will be written and everyone has to rewrite their code bases. Repeat for goroutines etc.

simonw4• 2 years ago

As Python grows GIL-free threading and machines continue to grow more cores do you think we'll be able to skip green threads and use regular threads instead?

the__alchemist2• 2 years ago

I feel like I'm a loner; the odd man out regarding this in rust. I can no longer relate to my (online/OSS) peers, nor use the same libraries they do. I almost wish sync and async rust were separate languages. The web and embedded communities are almost fully committed. I stopped participating in the rust OSS embedded community for this reason.

I haven't tried Async Django, (But use normal Django on a few work and hobby project) and am hesitant based on my experience in rust, and not finding a fault I think coloring my Python/Django code would be advantageous to do.

petters• 2 years ago

Colored functions can be nice. It makes the type system give a hint that a function could take a long time. You can then e.g. avoid holding locks when awaiting.

But there is a big downside, that's true.

Eikon• 2 years ago

I feel I may be an outlier here, but I love async functions (I mostly use Rust). I intuitively understand what will block and what won't, and can generally gauge the performance impact. I believe the few downsides are far outweighed by the massive benefits.

Async functions allow me to build massively parallel and concurrent systems with ease - it's beautiful.

In comparison, I'm not as fond of Go's approach to concurrency, which feels less elegant to me.

UltraSane2• 2 years ago

I found learning async Python to be very painful for months until I gained an intuitive mental model of the async code flow and then it all just started to "click" for me. I just think of async as parallelized waiting.

10000truths2• 2 years ago

Function coloring is only a symptom of a larger problem. The problem is that libraries implementing databases and network protocols are designed in such a way that they tightly couple themselves to a particular type of I/O dispatch. Ideally, libraries wouldn't be dispatching any I/O at all! Instead, they'd have a sans-I/O [0] implementation, where the caller creates a state machine object, feeds it a byte buffer, gets a byte buffer, and then the caller has to handle reading/writing those buffers from/to a socket or disk. A well-designed sans-IO library wouldn't care whether you're using it with sync I/O, or async I/O, or with gevent, or with a homegrown event loop built on epoll or io_uring or kqueue or IOCP.

[0] https://sans-io.readthedocs.io/

surajrmal2• 2 years ago

It's not always that simple. For a complicated self contained state machine this might work, but what about a situation where you are coordinating interactions with multiple distinct actors? You need a primitive to wait. At that point you need to pick a threading model.

surajrmal2• 2 years ago

In languages where there is already considerable overhead I agree with you, but go style coroutines are not really a competitive option for some use cases. The alternative is callbacks or something else which is a lightweight abstraction on top of an event loop.

jfasydfw• 2 years ago

gevent was created over a decade ago to solve this.

python users only have themselves to blame for ignoring it.

westurner• 2 years ago

From "Asyncio, twisted, tornado, gevent walk into a bar" (2023) https://news.ycombinator.com/item?id=37227567 :

IIRC the history of the async things in TLA in order: Twisted (callbacks), Eventlet (for Second Life by Linden Labs), tornado, gevent; gunicorn, Python 3.5+ asyncio, [uvicorn,]

Async/await > History: https://en.wikipedia.org/wiki/Async/await

pacifika• 2 years ago

Learned quite a bit thanks!

olgeni• 2 years ago

I was using Daphne and then found out that it read the whole files in a POST before passing it along - needless to say I am no longer using Daphne \o/

beestripes• 2 years ago

Nearly all of the work done on Django ninja is by someone who lists their location on GitHub as Kharkiv. I hope that’s not current.

Async Django in Production

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