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There are so many footguns[1] in async python but it really has stolen the zeitgeist of modern python web. Synchronous django has a lot to commend it. If you deploy it reasonably well (workers, behind a caching reverse proxy etc) it's easy to operate in production even under duress.
It's not going to be the right stack for long lived websocket connections or whatever but for a CRUD-ish or enterprise app, often very productive.
[1] buffer bloat is too easy to sleep walk into
queue = asyncio.Queue() # oops
await asyncio.gather(*(fetch(item) for item in items)) # look mum! no outbound sockets left or maybe even no file descriptors
data = requests.get(url).json() # oops! we're blocking the event loop
I can go on and on about async python (cancellation bugs, leaking a task - that has a cataclysmic failure mode where if you forget to hold a reference to your asyncio.create_task() then it's all weak refs in that machinery so your task can get garbage collected before it ran or completed in production! super tricky forensics. Then there's all the obvious stuff like race conditions, new ways of creating deadlocks, blah blah i really can go on for days.
Everytime I have to deal with websockets in Django, I want to rip my eyeballs out.
Most of the time what I really want is just push support, and don't need bidirectional communication, i.e. SSE. But it's the same thing: both are a pain to work with in Django. I find django-channels so cumbersome.
So, for my latest projects, I've ended up offloading this push functionality to a service like Centrifugo. Django talks to Centrifugo, and clients connect to it as well. It makes Django still be django, without worrying about django-channels, ASGI and all of that complicated setup. I've been very, very, very happy with this decision, even if I do have some additional complexity to articulate the two. It is nowhere near as confusing and convoluted as django-channels and uvicorn/daphne, though.
As for async python in general, fully agree. Every couple of years I decide to give it a go with some new tool or framework, thinking surely now it'll finally just click and I'll get with the times. Every time I feel disappointed with footguns and absolutely terrible ergonomics. No, thanks.
Please go on. I also feel like asyncio is a big hack. Even just accidentally blocking the event loop is way too easy. And I couldn't believe it when I read that you need to store a reference to a task in a set to keep it from accidentally getting canceled. How did this become the main stack of AI backends? It's like node but slower AND much easier to mess up because of synchronous IO.
I agree that there's plenty of footguns with async python, but I think the hype is a bit overblown. A few sensible defaults in the standard library would go a long way, e.g. requiring a maxsize argument to Queue (or None if you really need it to be unbounded). The "accidental blocking" thing seems like less of design issue - it has much more to do with python's super long history as a sync-first language. Taking a step back, though, async is popular because it is genuinely extremely useful! If you need to do a bunch of work and that work is mostly IO-bound, then it makes sense to live in an async world.
I think people in the Python world just don't yet have a good mental model of async either. I recently have had conversations with a number of people who have switched to FastAPI because "it's faster" even when they're using it to serve ML models which are compute bound and there's nothing you can await on.
async python is always slower than just naively using threads. I don't think there is any application in production anywhere that would not prove me right on this. the complete lack of any kind of scheduling is a disaster. unless you know exactly what I mean by a scheduler please don't reply to this.
I would love a blog post on this, in case you ever considered writing one!
Yep, if you are going to make asynchronous programming, you need either a small, plain, and obvious layer that calls insulated synchronous code or some stack with high quality assurances.
I have been using Django since 0.95 and I haven't seen anything which is so flexible with amazing DSLs while also making it easy to understand the magic behind it.
For the last 10 years, even in a Golang stack or Java stack, I still use Django for models and migration. I even have generators which generate Gorm (or other framework) DAO or Java hibernate classes using Django models.
With LLMs, it becomes easier since I can now write all the model, custom querysets in Django, ask the LLM to generate Golang DAO, setters and getters... and test the query against the Django generated queries for completeness.
Atlas, sqlx, sqlc and all other ORM like things in golang cannot do migrations the way Django does.
> I still use Django for models and migration
There are dozens of us! It's a great db management toolkit. I've used it to much success many times for things like managing migrations from mysql to postgres and php to python.
My opinions:
- Django apps are an anitipattern for large internal / single purpose products due to migration overhead as FKs cross application boundaries. I will die on this hill. No team is ever disciplined enough to keep apps as boundaries for relationships and constraints. Strong contrast to the rails crowd that doesn't rely on referential integrity in the db by default where this isn't "a thing".
- Goose[0] migrations in Go are really great but you have to let go of the dsl and the idea that your ORM drives your migrations, as you explicitly called out. Laravel[1] is on par with django IMO and a delight to use when in php-land. I've not tried to repurpose it like I have with Django and sqlalchemy.
- sqlalchemy and alembic is a great toolchain outside of django that get's a bit of a bad wrap / confusion from django devs. it gives you that same ability to drive the changes from the classes / structs without having to drag around all of django. It having more verbose
I've never done it, but now that you say it, it makes a lot of sense. Using
Django just to manage the database and do migrations, even behind other language stacks.
You also get the Django admin interface for free.
I once tried using SqlAlchemy but I couldn't help asking myself why it felt so complicated compared to Django.
> Some light load testing (with (ab -n 1000 -c 1) shows that right now we can serve about 2-3 requests per second (on a ~$10/month VM).
> After turning on template caching, it seems like the site can now pretty easily handle 12 requests per second or so without using all of the CPU. I have not carefully benchmarked the before and after but it seems like it’s made a pretty big difference.
That seems crazy low, I think there has to be something else going on here.
Those numbers sound... Single-threaded. Like they're using the development runserver instead of uwsgi or gunicorn.
Yeah... in the place I worked, for a while, they didn't have a package index for Python packages (similar to PyPI), so, I wanted to write one. At the time I had a love-hate relationship with Ada, so, after trying to do something with Python and thinking how much resources I would have to ask for and whether I'll need load balancing etc... I checked what Ada's (somewhat unfortunately named AWS...) would need to be used as that kind of index. Suffices to say that I wouldn't need any of the "reverse proxy" servers, no caching, no load-balancers... It would be fast enough to service a company with thousands of employees on a very modest h/w setup.
Using Django is like trying to walk on a highway, with a crutch. Even though it has some convenience features, it's just so impossibly slow you would have to invest a lot of engineering time and resources to mitigate that slowness.
If Django could run reasonable numbers of requests on early-200s0 servers... what has gone wrong since?
I imagine even a $10 VPS should be a much more powerful machine than a high-end server from 2005 or so.
It's because -c 1 only makes 1 request at a time, which is not representative of a real website load. This was also brought up on lobsters a few days ago https://lobste.rs/c/lctz1y
That’s a number I would been disappointed with 25 years ago, running Perl CGI on a 700MHz Pentium III.
Wow. With a web service in Go or any similar language that would measure in the thousands, at least.
Copying my comment from lobste.rs here:
I don't think the performance issues here are a result of Django. The author says only 2-3 requests per second (rps). Even in debug mode just using django runserver I can get 60~ rps on my $WORK repo. Part of this is likely their choice of only using '-c 1', which doesn't accurately represent a web workload.
For example on a local dev server I get (in format -c n. rps)
1. 58
2. 61
3. 55
as expected because we only have 1 worker, in debug with little caching.
but when looking at an example production config we see
1. 1: 11
2. 2: 23
3. 5: 54
4. 10: 95
5. 20: 153
6. 30: 165
7. 40: 177
8. 50: 192
and so on....
All of these are far in excess of the 2-3 observed by the author.
Note these will heavily depend on how many database hits you have per request as that is the majority of workload, as well as your http server such as using http 1/2/3, and the TLS settings used etc...
Good ole Django. Worked with a number of frameworks (tm), but nothing really quite scratches my itch like Django does. I still find the ORM and database migration system unmatched.
I found Django a bit hard to get on with vs. other frameworks and I've used Rails, .NET MVC and Express (and friends). I just found more friction trying to achieve X for any given X for some reason. Not sure why.
Not to be a downer since a lot of JEvans’ content is great. People should really give .NET a try. I don’t know if we are just old greybeard curmudgeons who look at this and go “is this a new thing? haven’t we been doing this for decades?”, but .NET out of the box with a few packages for databases, logging, etc is so pleasant you don’t even think about it. Maybe it’s just not a vocal community idk.
At this point Django also qualifies as "for decades" - most (maybe all?) of what's in this post applied back when it was new. The basic design has been stable for a very long time.
To be fair, for grey beards, python predates .net and I was using Django in 2008, which predates .net mvc.
.NET also comes with "telemetry" built-in that sends your data to Microsoft without asking for your consent:
https://github.com/dotnet/sdk/issues/6145
I wouldn't want to run software from someone who thinks taking such liberties with my machine and my data is OK.
> so pleasant you don’t even think about it
I'm sure Microsoft would prefer that people don't think about Microsoft is doing to them. But when do you think about it, you might reconsider using Microsoft software.
agreed 100%, the most under-appreciated programming ecosystem of them all.
It's also the only one that has support for making truly native apps for all four major platforms: Windows, Linux, MacOS and iOS. If you're willing to put in the work and make separate, native UIs for each, C# is the only way to go.
It's a rich enough language that it binds reasonably well to both Java (on Android) and Objective C / Swift (on iOs / MacOS), and having the core and UI layers of your app share the same language enables much higher fidelity bindings than what you can get out of E.G. UniFFI. And as a bonus, the same core is also re-usable on the server, and even in Web Assembly, if that's the way you want to go.
There used to be some issues around Storyboards and such on Apple platforms, but if you go nibless (which you should anyway in the age of AI), that's no longer a problem.
For Django's default template language, does it still have these limitations?
1. Brackets aren't allowed to help with boolean expressions like {% if a and (b or c) %}
2. You can't do basic arithmetic like {{ x * 2 }}, but you're allowed to do {{ x | add:"2" }}. There's hacks to multiply using {% widthratio a 1 b %} or division with {% widthratio a b 1 %} though (https://stackoverflow.com/questions/18350630/multiplication-...).
3. You can't assign expressions to variables like {% with x = a or b %}, so you have to repeat yourself.
4. You can't capture HTML generated from template code in a variable to pass into a partial template to write slots-style HTML components e.g. {% capture x %}Hello {{ username }}{% endcapture %}{{ include "partials/header.html" with body_html=x }}.
5. You can't pass variables to model methods.
I understand there's a philosophy that templates shouldn't contain complex logic, but I find the above pretty arbitrary and leads to code that's harder to maintain. Addition is okay but not multiplication? Boolean logic is okay but not with brackets? I often have to puzzle out some way to get my code to work that goes against what I'd normally want to do, some I'm forced to duplicate template code because you can't put expressions in variables or move basic one-off logic into views (which has poor locality https://htmx.org/essays/locality-of-behaviour/ and makes it harder to move template snippets between pages).
It's like hiding the kitchen knives because they might be misused.
Is Jinja2 a practical alternative or there's friction to using it?
> Is Jinja2 a practical alternative or there's friction to using it?
jinja2 is drop in by changing the template backend. You can actually run both at the same time (just can't mix them, ofc).
The migration is fairly simple and well documented.
The problem may be the lack of jinja support for 3rd party django apps (mostly legacy). So make sure they support it first.
> It's like hiding the kitchen knives because they might be misused.
I used to agree with this more strongly but over time decided the Django model was actually more effective as your cue to ask whether you should have been writing a Python function instead and calling it (which has been super easy since something like 1.4 or so IIRC).
Unless you have a small, very diligent development team it always seemed to end up with people coming up with thickets of template logic which were harder to understand and test but because they’d grown so complex people would act like it’d be an excessive amount of work to switch. I had a few projects where I delivered order of magnitude performance improvements while replacing hundreds of lines of ugly code, especially since using Python directly made things like caching and more complex formatting much easier.
> the site can now pretty easily handle 12 requests per second.
Right prod architecture (nginx -> gunicorn -> django, with nginx serving static assets), decent PRAGMAs for sqlite3 and caching for generic content should improve the RPS one or two orders of magnitude.
I mostly use Go + SQLite for all the things I used to use Rails, JavaScript, or Python for.
I find python django wastes too much resources, just look at memory usage.
One of my web app backend (go) is serving approx 100 req/s right now and i look at pprof i see it's not bottlenecked by CPU but mostly IO and i love this.
Writing concurrent code in Go is easy, the code i wrote 10yrs ago still compiles with no issue! This is why i am never gonna switch.
My go apps use very little memory, so we can scale to many users for very cheap.
For larger apps i use postgres (why? replication is easy using pgfailover, high demand apps need multiple api servers so it's out of process db like postgres is fine) but most of my web app use HTMX and if we need some reactivity, i use react (simply due to react experience from work)
For our maintenance calorie tracking app, which is free and has no ads, we have to use as few resources as possible as we scale to thousands of users: macrocodex (which figures out maintenance calories from weight and calorie intake). We initially used Haskell.
Later, it became slow and cumbersome to develop in (developing on an Apple Silicon Mac and deploying to x64 is a pain), even though I liked writing Haskell code. I even tried nix and wasted a day on that! I had a choice between OCaml and Rust. I picked Rust and never looked back.
The algorithm serves in 0.1 ms on Rust. In Haskell, it was 0.2 ms, and memory usage was twice that of Rust. There are many optimization possible in Rust which i didn't do (for sake of simplicity) yet i received good performance.
Yeah, I use Docker to compile Rust, but it's pretty fast, much faster than what I had with Haskell, so the developer experience is great.
By switching to Rust, the LOC dropped to half of what we had in Haskell.
project turned out to be successful. It has already produced guaranteed weight loss or weight gain for many people.
So I set out to create an algorithmic workout app, for which I am using Rust and Go. The mobile app is in Flutter.
I dont understand the comparison of Go even with Sqlite and Rails or Django?
one is a programming language with a db server and the others are full stack web frameworks.
How fast can you create (without LLMs) an authentication system plus a form and saving the answers in DB (only for authenticated users) with sane default good secure protections in Go and Sqlite?
And assuming you are amazing at Go (and probably you are) then ask the same question above for any average Go developer vs any average Django or Rails developer.
Appart from the fact that you find that Python wastes too much memory, what is your point ?
I think that any person choosing Django knows that Python by nature will not be the most efficient language.
Apart from that Django is battle-tested and can help bring a stable "product" quite quickly.
My experience from running a django app with thousands of migrations and dozen of apps
- Testing involving models is slow. It runs all your migration. Python's own "Unittest" is fast for functions not involving models.
- The suggested pattern for business logic is "active record", which is putting functions about a model alongside the model itself. Good for clean case. Doesn't really work when your operation involve multiple models.
- But if you put function about a model inside a model, the migration doesn't serialize the function into migration history (I could be wrong)
- "Data" migration is handy when you want enum-like data in database available to all teammates. e.g. list of currencies
- It is very tempting for new team member just to create an django app, with its own view and model, because it feels like starting a fresh without needing to care about existing business context. On the other hand, designing conceptual boundary between apps is very important. Because starting app is easy, and often (not always) wrong.
- The squash migration utils are limited because I guess of relatively low usage. It squashes linear migration history (with manual curation of starting and ending migration node I think? Not sure about latest state). I wrote a util to squash whole migration history by finding linear segments and squash those segment of history one by one. Didn't release it, but wrote some notes https://gist.github.com/kmcheung12/08b4c234b38c23efd43550da9...
You can run with --no-migrations (https://pytest-django.readthedocs.io/en/latest/database.html...) and default the test DB to be in memory (works at least for PG and SQLite) to make it quite a bit faster.
> as a meta comment: I’ve been working on talking about my programming opinions by just saying “THING does not feel good to me, I prefer OTHER THING instead”. That post I linked to says that function-based views are the “right way”. I’m not very invested in whether it’s “right”, but it’s validating to know that other people feel similarly to me about inheritance
I admire this about Julia a lot. Her texts and zines are exploring software in a way that encourages curiosity rather than promoting a singular point of view.
Do not use the development http server in a production setting. Use gunicorn or some equivalent.
I had the same issue with incredibly low throughput on beefy machines and it's because the dev server implementation is single threaded and does not do concurrency at all.
Switch to gunicorn.
Better yet, put gunicorn behind nginx, make sure all static assets are served by nginx, add appropriate Cache Control headers. Furthermore understand and use Django’s page caching.
The Django filter syntax with the double underscores is like fingernails on a chalkboard to me. I find it insane that they didn't just use operator overloading to create a real query expression language.
I think you can do that with querysets, Q, and F. The kwargs are a limited convenience.
When it comes to thinking about performance, the first thing to do is install the Django debug toolbar and use that to work out what's going on with your views and the queries they trigger.
Django is nice, but I'm currently migrating a project from Django to Go because Django is too large. I would love to stay with it, but simply assimilating the docs has been a chore. The PDF is 3000 pages, and I love working with tools where all the parts can fit in my head.
> 12 requests per second
You can definitely get more than that in Django. We could much more that back 15 years ago on very basic hardware, rendering templates and doing semi complex UIs.
Is Django still a solid choice in 2026 for new backend projects? Have loved the orm, admin, etc.
duplicate of https://news.ycombinator.com/item?id=49006691
Web dev is just mind-numbingly boring.
Studying CS to be a web dev is like studying Physics to turn on the oven.
automatic migrations terrifies me. i use elixir/ecto. just write a migration!
besides, some times you might want more than one schema for a db table (e.g. one with minimal information, for menus, dropdowns etc, and one with full information for your full CRUD operations)
Holy 2003 that website