tortoise-orm VS SQLAlchemy

Wouldn't know. However, some standalone projects are attempting to replicate the same experience, check Tortoise ORM - https://github.com/tortoise/tortoise-orm.

TortoiseORM: I personally really like this Django-inspired async ORM; it's clean and nice to use.

To do this, we can use the Tortoise-ORM. Begin by installing the package:

If I was answering this question but for python, I'd recommend something like prefect, boto3, or tortoise-orm -- not extremely complex and with a pretty comprehensible featureset.

If you're just starting with relational databases i suggest you use an ORM an object relational mapper. Which allows you to use simple python to make all database interactions. And i recommend using tortoise ORM.

Does anybody have any experience with that? Can I use Django in my asyncio application without *actually using Django? Or should sth like Tortoise ORM be preferred?

Tortoise ORM looks a lot like the django ORM https://tortoise.github.io

Thanks, it's great to see Django adding slowly but surely in all sorts of places.

By the way, have you looked at tortoise orm, which is a fully async python ORM with very Django-like syntax? [1]

[1] https://github.com/tortoise/tortoise-orm

SQLAlchemy Documentation. "SQLAlchemy ORM." https://www.sqlalchemy.org/.

Let’s start with a simple example of a data pipeline that reads data from a CSV file, processes it, and stores the result in a database. We’ll use Pandas for data manipulation and SQLAlchemy to interact with an SQL database.

We will define our database schema using SQLAlchemy. First, we will store a subset of the information in a single table called documents. This is to test that our database configuration is correct and avoid storing nested data now. The code is fairly simple to create a SQLite3 database with SQLAlchemy:

We've put a special focus on making sure Drizzle, Prisma, SQLAlchemy and Django ORMs work well with our platform. Common administrative and data exploration tools like DataGrip, pgAdmin and TablePlus have been put through the wringer to resolve any compatibility hiccups seen over the past few months.

SQLAlchemy: Python SQL toolkit and ORM (Object Relational Mapper). Use it to work with databases with Python objects.

We'll use the Flask-SQLAlchemy package to manage the database. This package provides SQLAlchemy support to Flask projects. That includes the Python SQL toolkit and the ORM.

SQLite for the database

I'm not familiar with the library either, but that seems to be a SQL expression executed on the database server. It's basically a copy-paste from the official documentation[0]. So no, not a lambda expression, because it's not computed in Python.

As to the extra parentheses: I bet that's a force-of-habit thing to prevent potential issues. For example, it seems Sqlite requires them for exactly this kind of default definition[1]. It could also read to nasty bugs when the lack of parentheses in the resulting SQL could result in a different parse than expected[2]. Adding them just-to-be-safe isn't the worst thing to do.

[0]: https://docs.sqlalchemy.org/en/13/core/metadata.html

[1]: https://github.com/sqlalchemy/sqlalchemy/issues/4474

[2]: https://github.com/sqlalchemy/sqlalchemy/issues/5344

Depending on the progress of your application, you may want to migrate to another framework faster than Django. Django’s ORM is not exactly the fastest out there, and, at the time of writing, it is not asynchronous. You might want to consider giving sqlalchemy, ponyorm a try.

SQLAlchemy is a widely used database toolkit for Python, providing a SQL abstraction layer covering most, if not all, your relational database needs. It's often used together with popular frameworks such as Flask and FastAPI, using either its Core library and/or its ORM components.