Comment by bb88

You can absolutely scale Django.

The framework itself is not the limiting factor. The main constraint of performance usually comes from Python itself (really slow). And possibly I/O.

There are well established ways to work around that. In practice, lots of heavy lifting happens in the DB, can you can offload workloads to separate processes as well (whether those are Python, Go, Rust, Java etc).

You need to identify the hotspots, and blindly trusting a framework to "do the job for you" (or for that matter, trusting an LLM to write the code for you without understanding the underlying queries) is not a good idea.

I'm not saying you are doing that, but how often do you use the query planner? Whenever I've heard someone saying Django can't scale, it's not Django's fault.

> When you start to run into scaling problems, your solution is within that framework and that becomes a limiting factor from my experience.

Using Django doesn't mean that everything needs to run inside of it. I am working on an API that needs async perf, and I run separate FastAPI containers will still using Django to maintain the data model + migrations.

Occasionally I will drop down to raw SQL, or materialized views (if you are not using them with Django, you are missing out). And the obvious for any Django dev; select_related, prefetch_related, annotate, etc etc.

> Then as a programmer, you have to find workarounds in Django instead of workarounds with programming.

The mental unlock here is: Django is only a convention, not strictly enforced. It’s just Python. You can change how it works.

See the Instagram playbook. They didn’t reach a point where Django stopped scaling and move away from Django. They started modifying Django because it’s pluggable.

As an example, if you’re dealing with complex background tasks, at some point you need something more architecturally robust, like a message bus feeding a pool of workers. One simple example could be, Django gets a request, you stick a message on Azure Service Bus (or AWS SQS, GCP PubSub, etc), and return HTTP 202 Accepted to the client with a URL they can poll for the result. Then you have a pool of workers in Azure Container Apps (or AWS/GCP thing that runs containers) that can scale to zero, and gets woken up when there’s a message on the service bus. Usually I’d implement the worker as a Django management command, so it can write back results to Django models.

Or if your background tasks have complex workflow dependencies then you need an orchestrator that can run DAGs (directed acyclic graph) like Airflow or Dagster or similar.

These are patterns you’d need to reach for regardless of tech stack, but Django makes it sane to do the plumbing.

The lesson from Instagram is that you don’t have to hit a wall and do a rewrite. You can just keep modifying Django until it’s almost unrecognizable as a Django project. Django just starts you with a good convention that (mostly) prevents you from doing things that you’ll regret later (except for untangling cross-app foreign keys, this part requires curse words and throwing things).