kopf VS locust

If you're looking for more examples beyond what was shown and referenced above, I recommend exploring other popular tools that make use Python Kubernetes client, such kopf - the library for creating Kubernetes operators. I also find it very useful to take a look at tests of the library itself, as it showcases its intended usage such this client test suite.

Depending on how you want to configure your selecting logic, it can be solved by mutating admission webhooks for the pods. For example, in Kopf, the simplest approach would be:

Hello. Kopf (a framework to write Kubernetes operators in Python) 1.31 is released and has finally got admission webhooks — https://github.com/nolar/kopf/releases/tag/1.31.0. I would appreciate some feedback from experienced operator developers on how easy or hard it is to write webhooks now, and what is missing and makes it inconvenient. The docs: https://kopf.readthedocs.io/en/stable/admission/ For a brief preview, it looks like this:

Correct, using generic resources with Client.watch should work. In general to create more complex operators I would recommend to check out kopf.

I'm assuming this breaks a ton more than just my project (https://github.com/locustio/locust/actions/runs/4687344723/jobs/8315803536)

If, like me, AGPL isn't your cup of tea, you can look at vegeta or locust which are both MIT.

locust if you can code

Agreed with a lot of the points here, like starting small with a single piece of your API, then slowly expanding your tests once you’re comfortable that you know what you’re doing.

Note that if you use the Locust framework to write your load tests in Python, it takes care of measuring and reporting the latency and throughput for you. It’s really nice.

https://locust.io/

For my application, to tune these parameters I used a load testing tool. I built a script using Locust.