kubectl VS helm

Check https://github.com/kubernetes/kubectl/issues/151

I have read this issue, and up to now it seems not possible to change the kubectl context via an env var: https://github.com/kubernetes/kubectl/issues/1154

However, I would encourage people to take a look at what the code looks like before assuming the Go developer experience on this was positive. Bear in mind that's just the top level kubectl command and some helper functions, the subcommand definitions take up a several more files split into a few more packages. Then you're still not even done, because code that uses the parsed flags still has to redundantly check things that couldn't be enforced at the type level, something Go folks like to pretend is a good thing for some reason.

kubectl is for sure battle tested, but it involves very Kubernetes specific implementations and is going to be too complicated for the first pointer

> context.Background() is typically only used when one doesn’t care about the result. If you did care about the result, you should be passing the parent context to preserve the circuit breaker timeout in case the operation takes too long.

Not necessarily. You would use context.Background in a test situation. It's also commonly used for short-lived applications like a CLI invocation. You can see kubectl uses context.Background quite a lot: https://github.com/kubernetes/kubectl/search?q=context.backg...

> I think the level of pain you experience from mutable references in Rust depends on if you’re coming from an OOP or FP background. I have a FP background and so the patterns I use to build code already greatly restrict mutation. You can usually change code that updates data immutably (creating a new copy of it) with mutable code in rust because the control flow of your program already involves passing that new version back to the caller which also satisfies the borrow checker in most situations.

There has to be a better solution to needlessly copying data.

This is particularly useful for academic purposes, and makes somehow convinient to get the yaml template of k8s objects. I was looking for this as well due to an upcoming ckad test i have. Unfourtunately due to not being considered best practice the request for it was dismissed. https://github.com/kubernetes/kubectl/issues/1073

I see, apologies, I did misunderstand. This is actually a known race condition between kubectl (or even helm, or any Kube API client) issuing the requests to deploy CRs that depend on CRDs while those CRDs are still being installed on the API server. Simply put, kubectl makes these requests too quickly. There is no solution to this currently aside from deploying CRDs separately from the resources they expose. See this kubectl issue: https://github.com/kubernetes/kubectl/issues/1117, and there are some links in the comments to other issues echoing the same problem in helm and elsewhere.

Go add --no-really-all if you really want it: https://github.com/kubernetes/kubectl

You could take a look at how kubectl actually does it: https://github.com/kubernetes/kubectl/blob/master/pkg/cmd/label/label.go

helm

Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...

Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.

Helm (Get from here https://helm.sh/)

It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.

Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!

Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.

We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).

Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.

Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.

helm: brew install helm