semantic-release VS fastapi

Using Conventional Commits ⭐ as a standard for your commit messages, makes Semantic Versioning 🔖 as easy as can be, with tools like Conventional Changelog 📄 Standard Version 🔖 and Semantic Release 📦🚀

Auto-tagging a project, Renovate or Dependabot can do this. With a Git Workflow and another tool like semantic-release you can do this. This behavior is a “gymnastic” to do on the CI/CD of your project but it’s not complicated. For example with GitLab CI, you can verify the pipeline run on the default branch of your project :

I'm trying to improve the security behind our release process, which uses semantic-release. During this process, it creates a change log which is committed to the repo, publishes a package and a few other things.

Conventional commits specification contains a set of rules for creating an explicit commit history, which makes it easier to write automated tools on top of, for example, semantic release. You can manually follow this convention in your project or use a tool to assist you, such as Commitizen.

When working with JavaScript projects, managing version numbers and commit messages is important for the maintainability of the project. Since 2020 I have been the main developer of Atomic Calendar Revive a highly customisable Home Assistant calendar card, I found maintaining versions and releases to be cumbersome until recently. In this article, I will introduce the commitizen and semantic-release packages for creation or appropriate commit messages and semantic versioning. I will also provide examples of how I am currently using these packages to streamline my release workflow and project maintenance.

To further streamline deployments, I introduced semantic-release. This tool automates commit tagging and tracks changes since the previous version. As a result, deployments now occur only when new tags are present, saving us valuable minutes.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

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.