debug-toolkit VS opentelemetry-python-contrib

We're very much not a company of one anymore, but I used Unicorn Platform for our startups website (http://robusta.dev)

It's optimized for building a decent looking startup website in half an hour.

We now have an in house designer and frontend team so the whole thing will be replaced soon... But it got us fairly far.

I use it all the time when hiring!

We're open source (https://robusta.dev) and very involved in the kubernetes ecosystem so GitHub history is extremely relevant when we look at candidates.

We'll hire people with no GitHub activity too, but when it's available it's great

Github: https://github.com/robusta-dev/robusta Marketing site: http://robusta.dev/ Docs: https://docs.robusta.dev/master/

We're using Hikaru extensively in Robusta. The best part (well, other then the ease of use) is that Tom is super responsive to issues on GitHub and always happy to help.

To everyone saying that Kubernetes is unnecessary, try implementing autoscaling, service discovery, secrets management, and autohealing in a vendor independent way without it.

Of course none of that is necessary for a self hosted home lab, but neither is gitops.

This is a very nice example of how to set stuff up properly.

OP, I would love to see Robusta (https://robusta.dev) as part of this too. It's definitely in line with your vision of automating everything, as it let's you automate the response to alerts and other events in your cluster. (Disclaimer: I'm one of the maintainers)

In my last blog post, I showed y’all how to instrument Python code with OpenTelemetry (OTel), à la auto-instrumentation. You may also recall from that post that I recommended using the Python auto-instrumentation binary even for non-auto-instrumented libraries, because it abstracts all that pesky OTel config stuff so nicely. When you use it, along with any applicable Python auto-instrumentation libraries (installed courtesy of opentelemetry-bootstrap), it takes care of context propagation across related services for you.

More specifically, auto-instrumentation uses shims or bytecode instrumentation agents to intercept your code at runtime or at compile-time to add tracing and metrics instrumentation to the libraries and frameworks you depend on. The beauty of auto-instrumentation is that it requires a minimum amount of effort. Sit back, relax, and enjoy the show. A number of popular Python libraries are auto-instrumented, including Flask and Django. You can find the full list here.

And sure, there's gaps and those are awful when you get to them. But writing minimal tracing integration is pretty easy. This is the full source of the psycopg2 instrumentation. https://github.com/open-telemetry/opentelemetry-python-contrib/blob/main/instrumentation/opentelemetry-instrumentation-psycopg2/src/opentelemetry/instrumentation/psycopg2/__init__.py