k3s VS pipy-demos

In this demo, we will be using k3d a lightweight wrapper to run k3s (Rancher Lab’s minimal Kubernetes distribution) in docker, to create 4 separate clusters named control-plane, cluster-1, cluster-2, and cluster-3 respectively.

To run the demo locally, we recommend k3d a lightweight wrapper to run k3s (Rancher Lab’s minimal Kubernetes distribution) in docker.

I've been messing around with k8s (k3s) lately, and got to the "issue" of downtime/inconsistencies caused by one of multiple workers being down, who had pods running on them. I found a couple useful parameters here that helped me reduce the time needed to redeploy the old pods on other nodes, as well as stop sending requests to the NotReady node. But that got me thinking, how long should k8s wait before doing these things? Or is there perhaps a better option for increasing avaliability?

Local Kubernetes clusters are clusters that are running on the individual computer of the developer. There are many tools that provide such an environment, such as Minikube, microk8s, k3s, or kind. While they are not all the same, their use as a development environment is quite comparable.

Since the developer is the only one who has to access this cluster for development, local clusters can be a feasible solution for this purpose. Over time, several solutions have emerged that are particularly made for running Kubernetes in local environments. The most important ones are Kubernetes in Docker (kind), MicroK8s, minikube and k3s. For a comparison of these local Kubernetes options, you can look at this post.

With local Kubernetes environments such as minikube or k3s, developers can create their own Kubernetes clusters on their local computers. This often leads to developers struggling with the management and setup of these pared-down Kubernetes technologies that are also not completely realistic compared to “real-world”, cloud-based environments. The upside of this approach is that the developers have full control over their environment and can independently create it whenever they need it.

K3d - a lightweight wrapper to run k3s in docker.

Next, we will demonstrate how to use Pipy to implement a DNS proxy (more accurately, a combination of a proxy and a server) that returns DNS query requests from custom records. At the same time, we will also add a feature to return different DNS records based on the client IP address to achieve intelligent route resolution. The scripts used in the demonstration can be downloaded from here.

The demo source code is available on GitHub and can be downloaded from pipy-sqli-demo repository.

Sample policy and rules are defined in policy.json where we define 4 test accounts and some rules on which clients are allowed to subscribe/publish to which topics. Pipy proxy script will read this configuration file and enforce them before routing traffic to the upstream MQTT server. Refer to Publisher workflow and Subscriber workflow.

Full source of demo is available and can be downloaded from https://github.com/flomesh-io/pipy-demos/tree/main/pipy-redis-sentinel