apiclarity VS CPython

We'll install APIClarity into a Kubernetes cluster to test our API documentation. We're using a Kind cluster for demonstration purposes. Of course, if you have another Kubernetes cluster up and running elsewhere, all steps also work there.

If you go to APIClarity, the first thing you’ll see is the source code (Figure 1), followed by some documentation at the bottom.

This feature is available in the open-source tool APIClarity, as part of the OpenClarity initiative.

Hi, I would also like to add another tool I'm contributing to at work (cisco) called APIClarity [1]. It aims at reconstructing swagger specifications of REST microservices running in K8S, but can also be run locally.

This is a challenging task and we don't support OpenAPI v3 specs yet (we are working on it).

Feel free to have a look, and get ideas from it :)

We'll also be presenting it at next Kubecon 2022.

[1]: https://github.com/openclarity/apiclarity

For example, APIClarity is a tool that observes all of the API traffic within your Kubernetes environment. Based on traffic observation, APIClarity infers an OpenAPI description for those APIs. This is especially helpful if the API creator never defined or provided such a description. It also surfaces potential problems with existing APIs, such as requests made to undocumented, shadow APIs or continued use of deprecated, zombie APIs. If you’re getting started on the path toward OAS compliance, then tools like APIClarity can be a great source of insight and observability.

The fundamental first step to solving this problem is to create an API spec and use it to audit and document the APIs your apps use. Ideally, we would create an API spec simply by observing API traffic in real-world applications. In the past, there was no simple, scalable, and open-source tooling capable of doing this. Now, we have APIClarity—an open-source API traffic visibility tool for Kubernetes (K8s) clusters. It’s purpose-built to address the gap and enable API reconstruction through observation.

Thankfully, Python, the go-to coding language for loads of developers, is here to save the day. It's got some awesome features for diving into text sentiment analysis. With cool libraries like Tweepy, we can sift through X(Twitter) data and snag those interesting tweets about Bitcoin. And then there's TextBlob, a clever tool for understanding the sentiment in text. When it's time to clean up and organize all that data, libraries like pandas and numpy are there to help out. And let's not forget about matplotlib, the master of visualisations that can help us see the trends in sentiment crystal clear. Armed with these tools, developers can really dig deep into social media data and figure out what the general public thinks about Bitcoin.

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When working in Visual Studio Code (VS Code), create a new Python file for our music player project. It's helpful to have separate files for different parts of your project.

> It provides a meaningful performance improvement for at least one popular platform (realistically, on the order of 5%).

At first it will not provide a large boost, but it will set the foundations for larger gains in subsequent releases. They link a list of some proposed improvements already underway, with improvement estimates, at https://github.com/python/cpython/issues/115802

After that, with the basics of software engineering understood, I would move on to a wider use language, with a bigger ecosystem to employ, most likely Python. This would expose me to large system design / distributed systems and architectural challenges...

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