pipy VS k3s

Pipy is an open-source, lightweight, high-performance, modular, programmable, cloud-native network stream processor that is ideal for a variety of use cases ranging from (but not limited to) edge routers, load balancers & proxy solutions, API gateways, static HTTP servers, service mesh sidecars, and other applications. Previously we have covered several use cases, and in this blog post will continue our learning of Pipy by implementing an HTTP Tunnel functionality by only using the PipyJS scripting language.

If you are interested in learning more about SMI-compatible Service Mesh, FSM, and Programmable Proxy, visit our website Flomesh.io for more detailed documentation, tutorials, and use cases.

But as the complexity of your apps and architecture grows, you might require more fine–grain control over your services. And if your organization requires Zero Trust and has a need for end–to–end encryption like mTLS, you should consider a service mesh like osm-edge a light weight, ultra fast, low resources, highly extensible, Service Mesh Interface (SMI) compatible, built for edge and cloud computing service mesh. When you have communication between services (east–west traffic), a service mesh allows you to control traffic at that level.

Below we list the most remarkable changes in the Core API and PipyJS. For a detailed change log, refer to Pipy Release Page

Flomesh Service Mesh(FSM) from Flomesh is Kubernetes North-South traffic manager, provides Ingress controllers, Gateway API, Load Balancer, and cross-cluster service registration and service discovery. FSM uses Pipy - a programmable network proxy, as its data plane and is suitable for cloud, edge, and IoT.

FSM is another open-source product from Flomesh for Kubernetes north-south traffic, gateway api contoller, and multi-cluster management. FSM uses Pipy, a programmable proxy at its core, and provides an Ingress controller, Gateway API controller, load balancer, cross-cluster service registration discovery, and more.

osm-edge is built on top of Open Service Mesh (OSM) v1.1.0 codebase and is a lightweight service mesh for resource-sensitive cloud environments and edge computing scenarios. It uses osm as the control plane and Pipy as the data plane and features high performance, low resources, simplicity, ease of use, scalability, and compatibility (x86/arm64 support).

We're very happy to announce the release of osm-edge 1.1. This release is the culmination of months of effort from the flomesh.io team and we are very excited to unveil it today. osm-edge is built on top of Open Service Mesh v1.1.0 codebase and is built purposely for edge computing and uses lightweight, high-performant, cloud native, and programmable proxy Pipy as its data plane and sidecar proxy.

Below we list the most remarkable changes in the Core API and PipyJS. For detailed change log refer to Pipy Release Page

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.