aiosql VS best-of-python

aiosql – Simple SQL in Python

> 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.

Show 10 hidden projects... numpy (🥇51 · ⭐ 27K) - The fundamental package for scientific computing with Python. Unlicensed; Blaze GitHub - ml-tooling/best-of-python

You may be interested in this best-of-python list on github.

I suggest looking at this and this github links which group many of the most used/useful python libraries by their category of use.

best-of-python: General overview of Python libraries & tools