django-async-orm VS aiosql

I'm not sure if its official. Would love some more guidance/clarity/docs/funding from the django foundation on what it looks like to migrate legacy code to the new ways.

The rednaks repo works great for just giving the new async stuff a go. If everything else is also using async.

I did some experimentation with this. And its a pain trying to migrate a production application that uses gevent and psycogreen2.

The documentation on the code migration path is pretty sparse.

The main hiccup that I ran into was psycogreen2 not being supported.

https://stackoverflow.com/questions/67735453/django-async-or...

https://github.com/rednaks/django-async-orm/discussions/9

I prefer to use aiosql https://nackjicholson.github.io/aiosql/ to organize my SQL and have it in a SQL folder. It looks like this where colons specify variables:

I tend to do a lot of data-heavy projects, so I tend to eschew ORM-style code and use a project called aiosql to bind raw SQL to python methods, and offload as much expensive computation to the DB as possible. If I'm prototyping an endpoint (e.g. calculating percentiles for some midsized time-series data), and just need a non-performant working placeholder, it's extremely easy to dump a SQL table to pandas and yeet something together in a few lines - then smoothly replace it with a more performant SQL query down the road. Highly contextual move, but I find it to be an awesome balancing point between flexibility, scalability, performance, productivity, etc.

As one of the rare Python developers who actually like SQL, my favourite database library is aiosql

I've been doing something in this vein for a big personal project, using this python library: https://nackjicholson.github.io/aiosql/.

In short, I'm using a run of the mill stack (Caddy/Gunicorn/Flask/Postgres) - but with the twist that all my core logic is defined in plaintext SQL files, which get bound into namespaced Python methods by aiosql. Routing, error handling, templating, etc. are all done in Python - but all data manipulation and processing are outsourced to the DB level. All database object definitions are laid out in a massive, idempotent "init_db" method that gets called at launch, so I can essentially point the app at a fresh instance of Postgres and rebuild from scratch. The design is primarily driven by my personal distaste for ORMs, but I've found it extremely beneficial in terms of rigid typing, integrity checks, and performance.

Definitely check out https://nackjicholson.github.io/aiosql/ if you want to stick with SQL

Have a look at this library: https://nackjicholson.github.io/aiosql/ It lets you keep your SQL queries in separate files and handles all of the parameterising for you.

I took that approach on my latest Flask project and it’s gone quite swimmingly. The problem I ran into was that a lot of the ecosystem, and therefore documentation, blog posts, helper libraries, etc., are all written under the assumption that you’re using an ORM. It took a while to figure out how to work around that, but once I did, I was home clear.

I also used a helper library to automatically map namespaced .sql files onto python functions with various return types, which made the development process way more elegant: https://nackjicholson.github.io/aiosql/. Absolute game changer if you plan to go this route - can’t recommend it highly enough.

I've been using FastAPI for some time, and now I'm using it as a full web framework (not just for REST APIs). I like writing SQL without ORMs, so the combination of aiosql[0] + FastAPI + Jinja2 works great. Add HTMX[1] and even interactive websites become easy.

That's in fact the stack I am using to build https://drwn.io/ and I couldn't enjoy it more.

Thanks Sebastián for creating it!

[0] https://github.com/nackjicholson/aiosql