client-go VS proposals

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).

Check out temporal.io open source project. It was built at Uber for large scale business-level processes. So any data pipelines are low-rate use cases by definition.

With KuFlow it is also possible to work with serverless workflows apart from Temporal.io, we explain it in this blog entry, but in summary, almost as a no-code tool, the correct use It would be a rather low-code tool; in just a matter of minutes with our drag-and-drop tool, you can have a workflow that interacts with one or more users of the organization.

Otherwise you may want to look into Kafka or Fluvio to ensure that task runs at least once. If you're doing something like batch operations as a background task, Temporal is another great option.

The runtime is developed using Temporal, which is one of the main tools that we are currently using at KuFlow. Thanks to, all the workflow executions are robust: your application will be durable, reliable, and scalable.

Hmmm I got confused by the name. I thought it's related to https://temporal.io/

Have you looked at temporal.io open source platform? It uses event sourcing as an implementation detail. But it greatly simplifies the user experience compared to "raw event sourcing."

Rewrite Airflow on top of temporal.io. This way, you get unlimited scalability and very high reliability out of the box and would be able to innovate on the features that matter for DE.

Hi all, founder @ Retool here. Over the past year, we’ve been working on Retool Workflows; a fast way for engineers to automate tasks with code. We started building the product because we ourselves (as developers) were looking for something in-between writing cron jobs (which involves a lot of boilerplate) and Zapier (which oftentimes isn’t customizable enough, since it doesn’t _really_ support writing code).

Workflows is a code-first automation tool: you’re _expected_ to write code, but we handle all the boilerplate for you. For example: out-of-the-box integration with 80+ resources (you probably don’t want to be trying to figure out OAuth 2.0 with Salesforce!), monitoring and observability (so you can see the output of every run in the past, and immediately be notified if something goes wrong), and permissions (e.g. some Okta groups can see the outputs of Workflows, but can’t change the code itself).

Right now, the product is cloud-only, but we’re hard at work at an on-prem, self-hosted version (in a Docker image). If you’re interested in that version, feel free to email us at [email protected]. We aim to get it out in the next few weeks. Self-hosted Retool is responsible for a large portion of our usage today, and we’re excited to be supporting Workflows too.

All Retool plans now include 1GB of Workflows throughput, which we think is quite generous (80% of active Workflows users are below 1GB). We don’t bill by run at all, so you’re welcome to run as many workflows as you want.

We use a bunch of interesting technology for Workflows; we are, for example, using Temporal (https://temporal.io/) under the hood. That’s something we’re going to be writing a blog post about later. (We’ve been hard at work on the launch, hah.)

In such a diverse world, it would be boring to have a single way of doing things. That's why at KuFlow we support different ways to implement the logic of our processes and tasks. And in this post, we will talk about one of them, the orchestration through Temporal, which gives us a powerful way to manage our workflows.