6 ms·

Async Django in Production

50 points by jonathan-adly 2y ago

jonathan-adly 2y 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-time 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.
- fulafel 2y 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 2y 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
    - fulafel 2y ago
      Indeed it says "It enhances performance in areas where tasks are waiting for IO to complete by allowing the CPU to handle other tasks in the meantime". To restate my comment: I argued (1) this CPU cost would be very marginal compared to the LLM API compute cost, and (2) the CPU blocking claim doesn't really hold, due to the wonders of threads and processes.
      - simonw 2y ago
        There are two ways to call out to an externally hosted LLM via an HTTP API: 1. A blocking call, which can take 3-10 seconds. 2. A streaming call, which can also take 3-10 seconds but where the content is streaming directly to you as it is generated (and you may be proxying it through to your user). In both cases you risk blocking a thread or process for several seconds. That's the problem asyncio solves for you - it means you could have hundreds (or even thousands) of users all waiting for the response to that LLM call without needing hundreds or thousands of blocked threads/processes.
        fulafel 2y ago
        Reading this my first reaction was that my questions still holds. Unless the slowness of the LLMs is of such a magnitude that having a thread or process waiting on the API call would substantially increase its cost, which I guess would mean the LLM server endpoint would be doing very heavy queuing and/or multitasking instead utilizing a powerful compute element for 90% of the call duration. Or maybe the disconnect is where I'm taking for granted that "cost of parked thread" is the same as worrying about the nr of parked threads? Maybe everyone uses Django setups where it's nontrivial to add memory, increase serverless platform limits, etc if you get the happy problem of 10k concurrent users? Or maybe people don't know that the nr of threads/processes you can have on Linux is much more than hundreds or thousands. Or maybe there's some Python or Django specific limits to this.
        simonw 2y ago
        Maybe I need to update my mental model of how many threads is too many on Linux (and benchmark the impact on the GIL here, which should at least be released for I/O network waits).
  - roughly 2y 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.
  - UltraSane 2y 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 2y 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.
    - zbentley 2y ago
      > a clear benefit is reduced memory usage and reduced process startup time Not necessarily true. Many process-parallel Python environments support using fork(2) for parallelism (multiprocessing, gunicorn, celery). For similar processes (e.g. parallel waiting on RPCs) that removes the memory overhead. It also largely mitigates startup time costs (especially if forks are reused for multiple requests, which they are in most forking contexts). While there is debate and grumbling in the Python community about fork(2)’s rough edges re: signals/threads/MacOS, these issues are usually handled inside parallelism-management library code and rarely concern application level developers.
andrewstuart 2y 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.
- senko 2y 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-toast 2y 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.
    - senko 2y ago
      > if you're going to be using django-ninja (pydantic) This assumes that people don't do multi-page apps or sites any more, which ... isn't true. And I believe django-ninja replaces forms/serialization/deserialization and routing, while nicely integrating with everything else. > Django is fine for writing a thin CRUD layer around a database. In my dozen or so years with Django, I confess I did more than a few thin CRUD layers around a database. But also worked on billing systems for telecoms, insurance provider API services, live/on demand audio/video streaming services, a bunch of business process apps, AI codegen tools, and other web apps and API backends that were way more than thin CRUD layers around databases. Django was rarely a hindrance. In fact, Python being sync-only (or Django not supporting async) was usually more of a hindrance that anything Django specific. > 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. In my exprience the only situations where Django's ORM doesn't help much is when you have a lot of business logic in your database (views, stored procedures), or the database is organized in a way that's not Django's preffered way. Still works, mind you, just not as great a experience. However, the vast majority of projects I've encountered have none of those. Otherwise, I've found its ORM quite powerful, and easy to drop down to raw() in cases where you really need it (which was maybe 1% on the projects I've worked).
singularity2001 2y 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 2y 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.
  - simonw 2y 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?
    - deleted 2y ago
      [deleted]
- the__alchemist 2y 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 2y 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.
pacifika 2y ago
Learned quite a bit thanks!
olgeni 2y 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 2y 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.

50 points by jonathan-adly 2y 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-time 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.

fulafel 2y 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 2y 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
  - fulafel 2y ago
    Indeed it says "It enhances performance in areas where tasks are waiting for IO to complete by allowing the CPU to handle other tasks in the meantime". To restate my comment: I argued (1) this CPU cost would be very marginal compared to the LLM API compute cost, and (2) the CPU blocking claim doesn't really hold, due to the wonders of threads and processes.
    - simonw 2y ago
      There are two ways to call out to an externally hosted LLM via an HTTP API: 1. A blocking call, which can take 3-10 seconds. 2. A streaming call, which can also take 3-10 seconds but where the content is streaming directly to you as it is generated (and you may be proxying it through to your user). In both cases you risk blocking a thread or process for several seconds. That's the problem asyncio solves for you - it means you could have hundreds (or even thousands) of users all waiting for the response to that LLM call without needing hundreds or thousands of blocked threads/processes.
      - fulafel 2y ago
        Reading this my first reaction was that my questions still holds. Unless the slowness of the LLMs is of such a magnitude that having a thread or process waiting on the API call would substantially increase its cost, which I guess would mean the LLM server endpoint would be doing very heavy queuing and/or multitasking instead utilizing a powerful compute element for 90% of the call duration. Or maybe the disconnect is where I'm taking for granted that "cost of parked thread" is the same as worrying about the nr of parked threads? Maybe everyone uses Django setups where it's nontrivial to add memory, increase serverless platform limits, etc if you get the happy problem of 10k concurrent users? Or maybe people don't know that the nr of threads/processes you can have on Linux is much more than hundreds or thousands. Or maybe there's some Python or Django specific limits to this.
        simonw 2y ago
        Maybe I need to update my mental model of how many threads is too many on Linux (and benchmark the impact on the GIL here, which should at least be released for I/O network waits).
- roughly 2y 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.
- UltraSane 2y 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 2y 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.
  - zbentley 2y ago
    > a clear benefit is reduced memory usage and reduced process startup time Not necessarily true. Many process-parallel Python environments support using fork(2) for parallelism (multiprocessing, gunicorn, celery). For similar processes (e.g. parallel waiting on RPCs) that removes the memory overhead. It also largely mitigates startup time costs (especially if forks are reused for multiple requests, which they are in most forking contexts). While there is debate and grumbling in the Python community about fork(2)’s rough edges re: signals/threads/MacOS, these issues are usually handled inside parallelism-management library code and rarely concern application level developers.

andrewstuart 2y 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.

senko 2y 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-toast 2y 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.
  - senko 2y ago
    > if you're going to be using django-ninja (pydantic) This assumes that people don't do multi-page apps or sites any more, which ... isn't true. And I believe django-ninja replaces forms/serialization/deserialization and routing, while nicely integrating with everything else. > Django is fine for writing a thin CRUD layer around a database. In my dozen or so years with Django, I confess I did more than a few thin CRUD layers around a database. But also worked on billing systems for telecoms, insurance provider API services, live/on demand audio/video streaming services, a bunch of business process apps, AI codegen tools, and other web apps and API backends that were way more than thin CRUD layers around databases. Django was rarely a hindrance. In fact, Python being sync-only (or Django not supporting async) was usually more of a hindrance that anything Django specific. > 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. In my exprience the only situations where Django's ORM doesn't help much is when you have a lot of business logic in your database (views, stored procedures), or the database is organized in a way that's not Django's preffered way. Still works, mind you, just not as great a experience. However, the vast majority of projects I've encountered have none of those. Otherwise, I've found its ORM quite powerful, and easy to drop down to raw() in cases where you really need it (which was maybe 1% on the projects I've worked).

singularity2001 2y 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 2y 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.
- simonw 2y 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?
  - deleted 2y ago
    [deleted]
the__alchemist 2y 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 2y 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.

pacifika 2y ago

Learned quite a bit thanks!

olgeni 2y 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 2y 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.