aiosql VS full-stack-fastapi-template

> resort to raw SQL

I'm the opposite, I would rather write SQL than "resorting to" ORM queries, which is why my favourite libraries are aiosql[1] in Python, Hugsql[2] in Clojure and similar: write the queries as SQL in .sql files, which then get exposed as functions to your code.

[1] https://nackjicholson.github.io/aiosql/

[2] https://www.hugsql.org/

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

Only slightly off-topic, poking around in there led me to aiosql, which takes an idea I'd had and jumps forward a good long way. :-)

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

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

This project is a modification of the authentication flow of the awesome repository made by tiangolo at full-stack-fastapi-postgresql

FastAPI with a PostgreSQL database: https://github.com/tiangolo/full-stack-fastapi-postgresql/tree/master

Ha! I do write SQL since that's where I cut my teeth many years ago. But I mostly use stored procedures where possible. I prefer not to use ORMs - sorry I don't find that side work... I am not saying Django's was bad or inferior, just do not prefer it. For FastAPI - maybe you missed the various repos with everything you mentioned was missing (there are great ones directly from the maintainer as well as others). No more glue than what you find in all the modules in a large Django project, just maybe in different forms and flavors. Besides, we're here to talk about Rust, making me wonder why we're debating two Python projects. Yes, I fell in love with Django, the romance faded in 2018, and I moved on. Feel free to enjoy using it - I'm not trying to sway you away from it!

I've just recently switched to a structure that follows Netflix's Dispatch application after starting with https://github.com/tiangolo/full-stack-fastapi-postgresql and it feels way better and organized.

Honestly your easiest option for data gathering would be to create google spreadsheets/forms and give each municipality access. For a custom data entry platform I suggest looking for templates like this one and learning how to add custom logic to the boilerplate: https://github.com/tiangolo/full-stack-fastapi-postgresql

I would encourage you to take a look at this repo: https://github.com/tiangolo/full-stack-fastapi-postgresql This is a boilerplate of an application made with fastapi, prepared by the creator of the fastapi himself. You can even set it up yourself locally and have a look how it’s organised. I know it has a lot of different services included, but I find the fastapi part itself to be well thought. Inside the api directory you can notice another folder named api_v1, so you can have multiple versions of your API routes when needed, with the general code in other places that is more generic and can be reused in all your different API versions. The schemas are separated from the models and models itself have different classes depending on what you would actually like to do with the data. The migrations are managed with alembic based on schemas rather than models itself. The settings are a python class that implicitly reads the .env file in your project’s directory. And many, many other interesting patterns to explore. Too much to write in one comment to be honest.