kubernetes VS etcd

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.

sudo dnf -y install curl wget vim ETCD_RELEASE=$(curl -s https://api.github.com/repos/etcd-io/etcd/releases/latest|grep tag_name | cut -d '"' -f 4) echo $ETCD_RELEASE wget https://github.com/etcd-io/etcd/releases/download/${ETCD_RELEASE}/etcd-${ETCD_RELEASE}-linux-amd64.tar.gz tar xvf etcd-${ETCD_RELEASE}-linux-amd64.tar.gz cd etcd-${ETCD_RELEASE}-linux-amd64 sudo mv etcd* /usr/local/bin ls /usr/local/bin /usr/local/bin/etcd --version

Reading the standard library will give you ideas/insight about various Go idiomatic patterns/approaches, and you can see a full website/API implementation in the pkg.go.dev repository (https://github.com/golang/pkgsite). Projects like https://github.com/etcd-io/etcd may be interesting too.

Failure Detection and Recovery It’s not enough to have backup systems. It’s also crucial to detect failures quickly. Modern systems employ monitoring tools and rely on distributed coordination systems such as Zookeeper or etcd to identify faults in real-time: once detected, recovery mechanisms are triggered to restore the service.

Service Discovery: Microservices need to discover and communicate with each other dynamically. Service discovery tools like etcd, Consul, or Kubernetes built-in service discovery mechanisms help locate and connect to microservices running on different nodes within the infrastructure.

APISIX uses etcd to store and synchronize configurations.

etcd is an excellent key-value distributed database used internally by Kubernetes and managed by the CNCF. It's a great option, and that's the reason why Apache APISIX uses it too. Yet, it's not devoid of issues.

Someone on HN (https://news.ycombinator.com/item?id=36682595) suggested etcd (https://etcd.io)

In traditional mode, APISIX stores its configuration in etcd. APISIX offers a rich API to access and update the configuration, the Admin API. In standalone mode, the configuration is just plain YAML. It's the approach for GitOps practitioners: you'd store your configuration in a Git repo, watch it via your favorite tool (e.g., Argo CD or Tekton), and the latter would propagate the changes to APISIX nodes upon changes. APISIX reloads its configuration every second or so.

I am not sure neither. But this might overcome the etcd's soft storage limit of 8GB? [1]

[1] https://github.com/etcd-io/etcd/issues/9771