Async with Django

Working with Django in ASGI mode can benefit applications with long requests where the synchronous mode may be a bit wasteful, as you would likely need a ton of workers—especially if those workers are processes (e.g., Gunicorn). Your app may also run some fancy new LLM framework which is often async only.

That said, async mode isn't a silver bullet for scaling. In Django 5.1, you still need threads to offload blocking database operations, which remain synchronous by design.

Django provides async query wrappers like aget, acount, and aall, but these are just syntactic sugar for offloading the operation to a worker thread via asgiref.sync_to_async.

You can use the asgiref package's sync_to_async utility to move blocking executions to the worker. By default, that is a ThreadPoolExecutor with one worker. This thread is shared by all sync_to_async calls during the request context, effectively meaning that each request spawns one worker thread for all sync work.

So, to summarize:

• The async main thread handles HTTP calls or non-blocking tasks (e.g., LLM requests, httpx calls...).
• But the moment your view touches the database (or any blocking operation), Django spawns a thread.
• That thread lives for the entire request, holding a DB connection open until the request is complete.

This thread-per-request operation is mostly fine—this has been the default modus operandi for web apps for years, at least before event loops, green threads, and coroutines took over. Of course, it's a bit wasteful, but it's not like we don't do wasteful things as programmers.

So, you can choose between creating a lot of workers or spawning a thread-per-request. Latter is arguably better since with thread you don't have to think about forking and shared memory, and a thread is likely much lighter than, say, a full-blown Gunicorn worker.

Another thing to consider with long requests are of course the DB connections. As said, the DB connection starts when you perform your first operation and stays open for the full request. This is problematic for high-load systems due to the expense of connections to DBs like Postgres.

Django 5.1 introduced the Psycopg 3 connection pooling option, which can help here. During your long request, you likely don't need to keep the connection open at all times, but you can return it to the pool. That's a bit annoying, of course, since you need to manually find where you wait for I/O (like a complex LLM call) outside DB operations for a prolonged time and manually close the connection. It is performant, though, since the actual DB connection doesn't get closed; only the requests handle to it.

It would be fantastic in this era of LLM streaming or long polling if Django supported async DB connections. It would eliminate the need to spawn a thread for each request. Combined with that, it would be fantastic if we were able to configure Django to return an idling DB connection to the Psycopg pool automatically (though I'm not sure how feasible that is).