fastapi_cache
aiosql
fastapi_cache | aiosql | |
---|---|---|
1 | 10 | |
209 | 1,245 | |
- | - | |
0.0 | 8.7 | |
over 2 years ago | about 2 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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fastapi_cache
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FastAPI framework, high perf, easy to learn, fast to code, ready for production
We are using FastAPI in production.
Some useful links:
- Cookiecutter: https://github.com/tiangolo/full-stack-fastapi-postgresql
- Cache: https://github.com/comeuplater/fastapi_cache
aiosql
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Don't use your ORM entities for everything – embrace the SQL
> 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/
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Project template without ORM
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:
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If you could choose any Python web framework to build APIs for a startup, which one would you choose and why?
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.
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Which not so well known Python packages do you like to use on a regular basis and why?
As one of the rare Python developers who actually like SQL, my favourite database library is aiosql
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Database as Code. Not only migrations
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. :-)
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The Data-Oriented Design Process for Game Development
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.
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Is it bad practice for my flask API to run raw SQL queries against my DB to get/post data?
Definitely check out https://nackjicholson.github.io/aiosql/ if you want to stick with SQL
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Django 4.0 release candidate 1 released
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.
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FastAPI framework, high perf, easy to learn, fast to code, ready for production
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
What are some alternatives?
full-stack-fastapi-template - Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.
databases - Async database support for Python. 🗄
fastapi-users - Ready-to-use and customizable users management for FastAPI
docker-flask-example - A production ready example Flask app that's using Docker and Docker Compose.
django-async-orm - Bringing Async Capabilities to django ORM
fastapi-crudrouter - A dynamic FastAPI router that automatically creates CRUD routes for your models
openapi-generator - OpenAPI Generator allows generation of API client libraries (SDK generation), server stubs, documentation and configuration automatically given an OpenAPI Spec (v2, v3)
Pebble - Java Template Engine
admin - A beautiful and fully-featured administration interface builder for hypermedia APIs
mangum - AWS Lambda support for ASGI applications