client-go VS kubernetes

The Kubernetes clients (e.g., Go client) support developers with both methods to connect to a cluster, as we can see in the following examples.

My suggestion would be to start looking at things like https://github.com/kubernetes/client-go first in order to get a feel for the API and how data plane k8s components interact with the apiserver (it's the same thing that kubelet uses). Then move on to trying to build your own k8s operator to get a feel for how people expand and customize k8s functionality without having to modify upstream at all. IMO the codebase itself is too messy and in constant flux to make too much sense of it unless you are planning to contribute to upstream.

GitHub related issue https://github.com/kubernetes/client-go/issues/735

CUE is cool and all but as soon as I start writing real code structures I want to reach for client-go.

I'm... really not sure I agree with this, from a philosophical point of view. It feels like this is making "eh, we'll just upgrade our Go version next quarter" too easy; ultimately some responsibility toward updating your application's Go version to work with what new dependencies require should fall on Us, the application developers. Sure, we're bad at it. Everyone's lived through running years-old versions of some toolchain. But I think this just makes the problem worse, not better.

Its compounded by the problem that, when you're setting up a new library, the `go` directive in the mod file defaults to your current toolchain; most likely a very current one. It would take a not-insignificant effort on the library author's part to change that to assert the true-minimum version of Go required, based on libraries and language features and such. That's an effort most devs won't take on.

I'd also guess that many developers, up-to this point if not indefinitely because education is hard, interpreted that `go` directive to mean more-of "the version of go this was built with"; not necessarily "the version of go minimally required". There are really major libraries (kubernetes/client-go [1]) which assert a minimum go version of 1.20; the latest version (see, for comparison, the aws-sdk, which specifies a more reasonable go1.11 [2]). I haven't, you know, fully audited these libraries, but 1.20 wasn't exactly a major release with huge language and library changes; do they really need 1.20? If devs haven't traditionally operated in this world where keeping this value super-current results in actually significant downstream costs in network bandwidth (go1.20 is 100mb!) and CI runtime, do we have confidence that the community will adapt? There's millions of Go packages out there.

Or, will a future version of Go patch a security update, not backport it more than one version or so, and libraries have to specify the newest `go` directive version, because manifest security scanning and policy and whatever? Like, yeah, I get the rosy worldview of "your minimum version encodes required language and library features", but its not obvious to me that this is how this field is, or even will be, used.

Just a LOT of tertiary costs to this change which I hope the team has thought through.

[1] https://github.com/kubernetes/client-go/blob/master/go.mod#L...

[2] https://github.com/aws/aws-sdk-go/blob/main/go.mod

I looked at dynamic package, but it seems like it needs GroupVersionResource, which is different for, say, Service objects and Deployment objects. Also when I pass schema.GroupVersionResource{Group: "apps", Version: "v1"} it doesn't find anything, when I pass schema.GroupVersionResource{Version: "v1"} it finds only namespace object and also doesn't looks for labels, though I provided label options:

I'm writing a script that uses the k8s.io/client-go library (godocs here) to manipulate Deployments. In particular, I want to add a label selector to every Deployment in my cluster. Deployment label selectors are immutable. So my approach is to:

Actually, kubebuilder is not using the standard Go libraries, but one using reflection to dynamically resolve the client based on the type you hand it (which is arguably better). The "official" client is k8s.io/client-go.

Then on June 18th, 2022, I got a chance to meet our mentors and the other mentee of OpenELB (the mentee and the mentors of OpenFunction were also there). There I was informed about how to start working on the project, so I started learning about using the Kubernetes API client. After experimenting with the official Kubernetes Client, I learned that it's not very feasible to use that for dealing with CRDs (custom resource definitions), so I explored the controller-runtime client as per what I found in many sources, and found that it was a great fit for the backend of our project. During that time, I also built a simple project to see if everything would work as expected or not (as this was the first time I dealt with a Kubernetes client, I considered that debugging would be easier in a smaller project).

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

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Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

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.