UnicornTranscoder VS kubebuilder

Just having support for a remote transcoder, along the lines of unicorn transcoder or rffmpeg would be a good first step to let us start homebrewing a solution.

There's this project - https://github.com/UnicornTranscoder/UnicornTranscoder That lets you use a remote transcoder, but idk if the project is maintained anymore.

There are some projects to distribute transcoding jobs like the Unicorn Transcoder (last activity in 2021), Remote Transcoder (last activity in 2017) or the Kubernetes project (last activity in 2020)

Interesting, I wasn't aware they tweaked it so much. Projects like Unicorn Transcoder made it seem like you could just drop-in replace it with a different binary.

There was/is a Project for remote transcoding, the Unicorn Trancoder which might help but the last commit in that project was made in Oct. 2021.

On the other hand, there is a Project called Unicorn Transcoder which is to offload the transcoding job on a different machine. If that works with plex optimization or just the "on-the-fly" transcoder, I don't know.

The kube-shift operator, like many Go-based operators, is built using the Kubebuilder framework. Kubebuilder provides a set of tools to quickly scaffold a new operator project, including the boilerplate for CRDs, controllers, and other necessary components.

Note: The Controller runtime is not the only way one can build a Kubernetes Operator, there are multiple ways to do so such as using the Operator Framework SDK or Kubebuilder, which are both frameworks built on top of the Controller runtime and utilize it under the hood to assist you when building complex Operators. You could even build an application that utilizes the Kubernetes Rest API through client libraries in various languages such as Python, Java, JavaScript etc depending on your tech stack. Find the Full list of Client Libraries on the Kubernetes Documentation. In this article, we will use the Controller runtime because it offers flexibility and provides a hands-on understanding of how Controllers work internally. This approach is ideal for gaining deeper insight into the inner workings of Kubernetes Operators while maintaining the ability to extend or customize as needed.

By default, operators built using Kubebuilder and controller-runtime process a single reconcile request at a time. This is a sensible setting, since it's easier for operator developers to reason about and debug the logic in their applications. It also constrains throughput from the controller to core Kubernetes resources like ectd and the API server.

client-go is the official Golang client for Kubernetes, responsible for interacting with the Kubernetes API server using REST API. In fact, client-go can do almost anything, not just for writing operators. Even the internal implementation of kubectl is based on client-go. As for more specialized frameworks used to write operators, including controller-runtime, kubebuilder, and operator-sdk, they will be introduced later in this series.

And it is only because of the focus on extensibility and interoperability that today, we can run WebAssembly workloads in Kubernetes so seamlessly. SpinKube is an open source stack of projects for running WebAssembly applications. A core piece of the stack is a containerd shim. I remember when containerd was donated to the CNCF in 2017. That took work and collaboration from several companies, most notably Docker, to make happen. SpinKube also depends on CRDs and operators. I recall seeing one the early demos of scaffolding an operator and a CRD in a SIG meeting from Phillip Wittrock, who went on to work on Kubebuilder in a Kubernetes SIG. Kubebuilder is a key piece of SpinKube’s Spin operator development. As I reflect on the last decade, I appreciate every contribution even more deeply.

Kubebuilder, a framework by Kubernetes SIGs, simplifies creating Kubernetes APIs using Custom Resource Definitions.

The Spin operator uses the Kubebuilder framework and contains a Spin App Custom Resource Definition (CRD) and controller. It watches Spin App Custom Resources and realizes the desired state in the K8s cluster. Aside from the immediate benefits gained by running Wasm workloads in k8s, additional optimizations such as Horizontal Pod Scaling (HPA) and k8s Event-driven Autoscaling (KEDA) can be achieved in a pinch.

kubebuilder: brew install kubebuilder

The operator that I've been working on is designed to manage the full lifecycle of a QuestDB database instance, including version and hardware upgrades, config changes, backups, and (eventually) recovery from node failure. I used the Operator SDK and kubebuilder frameworks to provide scaffolding and API support.