kubectl VS kubernetes

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

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.

The single most important development in hosting since the invention of EC2 is defined by its own 3-letter acronym: k8s. Kubernetes has won the “container orchestrator” space, becoming the default way that teams across industries are managing their compute nodes and scheduling their workloads, from data pipelines to web services.