aiosql
databases
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aiosql | databases | |
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10 | 15 | |
1,243 | 3,697 | |
- | 1.1% | |
8.7 | 6.1 | |
about 2 months ago | 15 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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aiosql
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Don't use your ORM entities for everything ā 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 itās 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 youāre 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 - canāt 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 SebastiƔn for creating it!
[0] https://github.com/nackjicholson/aiosql
databases
- Whats the standard way for interacting with a DB.
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Getting Started with Fast-Api šļø and Dockerš³
In the code ,we are using SQLAlchemy as our ORM(Object Relational Mapper) and Databases as our query builder.
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A Deep Dive into Connecting FastAPI with SingleStore.
MySQL is a powerful and popular database, and it's well-suited for use with Python. In the following section, we are going through how to set up a connection to a MySQL database in an asynchronous manner using the create_asynchrouns_engine function from SQLAlchemy. We'll also use the databases package and the aiomysql extra dependency. We'll also adapt the SQLAlchemy declarative approach to defining our users' table.
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Django 4.1 alpha 1 released
To take an example: I switched a service over from doing synchronous (plain def everywhere) to async (async def and await everywhere, with async DB, and sawā¦ basically no performance improvement.
- PostgREST v9.0.0
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python asyncio, how to prevent the other functions "block" themselves when connecting to db?
No, as I said, async db connection is the key. You can do that with SQLAlchemy using the databases library, but since you are not using SQLAlchemy it makes more sense to use the underlying db driver, aiomysql, directly.
- Database library
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Best approach for async SQLAlchemy in FastAPI
Using the encode/databases library and forgoing SQLAlchemy's ORM
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FastAPI + Ormar + Alembic setup
ormar is a mini async ORM for python. It uses sqlalchemy for building queries, databases for asynchronous execution of queries, and pydantic for data validation. You can create an ormar model and generate pydantic models from it.
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async sqlalchemy question db question
works but there seems to be a difference between the regular slqalchemy syntax from what https://github.com/encode/databases/ is using in the docs?
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.
aiomysql - aiomysql is a library for accessing a MySQL database from the asyncio
django-async-orm - Bringing Async Capabilities to django ORM
asyncmy - A fast asyncio MySQL/MariaDB driver with replication protocol support
fastapi-crudrouter - A dynamic FastAPI router that automatically creates CRUD routes for your models
alembic - A database migrations tool for SQLAlchemy.
Pebble - Java Template Engine
fastapi-users - Ready-to-use and customizable users management for FastAPI
mangum - AWS Lambda support for ASGI applications
openapi-generator - OpenAPI Generator allows generation of API client libraries (SDK generation), server stubs, documentation and configuration automatically given an OpenAPI Spec (v2, v3)