aerich VS databases

We have successfully setup out FastAPI + ormar + alembic project. With pydantic support, ormar can generate pydantic models from ormar models. The generated pydantic models can be used by FastAPI during request/response data validation. ormar + alembic is a great duo to writing database code. Another great combination is tortoise ORM + aerich, which I'm planning to write soon.

I also use the source code for aerich a lot. Aerich is similar to alembic but for Tortoise ORM.

In the code ,we are using SQLAlchemy as our ORM(Object Relational Mapper) and Databases as our query builder.

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.

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 saw… basically no performance improvement.

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.

Using the encode/databases library and forgoing SQLAlchemy's ORM

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.

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?