rocketry VS fastapi

Intuitive scheduling framework: https://rocketry.readthedocs.io/

One easy option would be to use Rocketry, it's pure Python:

It has bunch of features: logical scheduling syntax, parallelism/concurrency, dynamic parametization, log to database etc. You can read from the docs: https://rocketry.readthedocs.io/

It's the creator of Rocketry, Red Mail and Red Box again. This week I thought to make it easier to integrate command-line programs to your Python applications.

If you don't need fancy UIs (or you can create those yourself), I have heard some people are replacing Airflow with Rocketry. Rocketry is very easy to set up and use and its scheduling mechanics are pretty advanced.

If you need a scheduler (that also supports custom conditions like "run when there is X amount of emails"), I have another library for that: Rocketry

Smtplib is a pain to work with (which is the reason I wrote Red Mail). For the scheduling, I also happen to have a library that is often useful for this sorts of problems: https://rocketry.readthedocs.io

I have been thinking of creating an algo using Rocketry at some point. It's very easy to create custom conditions and combine these with logical statement. For example, you could set your algo (a function) running when volatility is x amount and you have y amount balance.

I have been tackling the same problem and it has recently gained popularilty. In case you want to take a look at Rocketry: https://github.com/Miksus/rocketry

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include: