kubernetes VS kine

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.

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.

-- Kine (Kine is not etcd)

Looking at [1], is there any reason why this couldn’t run in something like DynamoDB? I feel like the operations required would fit

https://github.com/k3s-io/kine/blob/master/pkg/drivers/gener...

I don't like some of the decisions that k3s makes which is why I stay away from it, but it's a great project. I'm not using kine yet, but I fantasize about using it exclusively every time I deal with etcd-induced slowness/random CPU spikes on my group of controller nodes. k0s supports kine too of course but I just haven't switched it on yet.

Well in some situations you can also get away with CRDTs or Gossip (SWIM), but yeah in the general sense Paxos (Raft is a Paxos-family algo). There are lots of paxos family algos to choose from[0].

> I’m also currently building a distributed search engine using etcd for the service registry, broker peer announcements, and worker queue and it’s been a good experience so far.

As a random stranger on the internet, please build an abstraction layer around etcd. Even if there's only one implementation, I've found that so many distributed projects that just never reach the scale that etcd is built for would benefit from the option of writing their own drivers (you don't need a full plugin system just a regular abstraction layer).

One example is Kine[0] for k8s -- if k8s had a built in option for writing/reading from something like Postgres from the beginning it would have been a better project for it, IMO.

If you do build a plugin system though, you can pass off the work of maintaining the other implementations!

[0]: https://vadosware.io/post/paxosmon-gotta-concensus-them-all

[1]: https://github.com/k3s-io/kine

I could spent >0s looking into this, but I've been curious how kline implements Etcd's Watch API. For the matter, I don't have much idea in general how kline handles data. Kline would be a great candidate for a blog post or introductory technical article on.

I do quickly see one issue- evidently watches presently use polling. The issue requests use of postgres triggers: https://github.com/k3s-io/kine/issues/20

check out https://github.com/k3s-io/kine

Not to be mistaken for high-availability Dqlite[1], which is one of the options one can run the k3s kubernetes distribution on (instead of etcd), via the Kine etcd shim[2]. Ultimately though the K3s team replaced Dqlite with an embedded etcd to get high-availability[3].

[1] https://dqlite.io/

[2] https://github.com/k3s-io/kine

[3] https://rancher.com/docs/k3s/latest/en/installation/ha-embed...