[szszrk]

> the lengths devops engineers go to avoid using a real programming language

OK, but, you know... those tools were created by literal devs. Not in yaml, in a "real language"! So apparently devs thought they needed that.

The argument should be towards all people - we love creating new abstractions and "simplify things", but we suck at honest evaluation of the impact of our creations.

Still: I absolutely hate Helm templating and think that the very existence of "helpers" (even in a default chart!) is an abomination.

[kubectl_h]

> OK, but, you know... those tools were created by literal devs.

All `kubectl` does is create REST API calls to the control plane. So to be fair, what I'm grousing about can be accomplished just fine by a developer like me constructing API calls from python to update objects in k8s.

The problem is I work in devops where tooling written in proper languages with standard libraries that have things like useful arrays or robust string manipulation or ergonomic concurrency is a non-starter for some reason. The argument against that being mostly "I have to install the interpereter first".

[szszrk]

Now we get to the point - you are unhappy with how your org/team handles it. Would that be better description? Don't put all DevOps in one basket. It's a messy term anyway.

Many orgs and many teams allow that. That's why alternatives to Helm exist like cdk8s, or Pulumi for rest of infra.

For Kubernetes there is such a variety of tools, it's overwhelming. The only problem to tackle is that Helm/Argo is so popular, that alternatives are hard to sell regardless of real benefits.

Look at how big is a scene of Operators for k8s - this is where the programmable part of k8s went. You configure operators with their own CRDs (usually) that can be static/plain declarative text. Then you write actual code of the operator that deals with all the complexity of enforcing that state. For me this is where typically what you are talking about goes, while high level "non programmable" code sits in yaml in a other repo for others to maintain. Maybe this is where you should also go, standardize your work for others to consume in a form of operator?

I fully get neglect in adopting more complex tooling. sh, curl, sed, awk... those things are present almost everywhere and it's not that hard to get lucky and make a script that will run on almost everything your org has. And it actually might be fine for a decade or so.

I myself could not do code (even scripting) because one of my companies literally treated scripting in anything as development which was strictly regulated (so forbidden for any non dedicated dev role). Or an org that had not a single server in whole DC that has Python 3, years after it's release. Or more recent: some damn Ubuntu LTS that can't be easily upgraded to just 2-3 minor versions that this cool k8s library uses. Maintaining python versions on VMs is a pain in the ass, especially if your org has strict controls. Internet access to pip is not granted as well. UV gets the job done nowadays, if it's allowed, but long story short: that "fear of real language" can be as much lack of knowledge/skills as pragmatism that came from painful experiences.