kvdi VS prometheus

It became too much for me to continue doing on my own, but people still ask for help and features til this day. https://github.com/kvdi/kvdi

https://github.com/kvdi/kvdi - Feel free to DM if interested

If you want to sink into Kubernetes, I've been desperately seeking more contributors on kvdi for a while. It's a virtual desktop infrastructure running on K8s. Tons of features to be added, tons of issues/requests to be addressed. It just don't pay the bills and I got a full-time day job. But if you are interested in hacking on it, let me know.

I had to do some modifications to get it working on my pi cluster. But I've used this successfully: https://github.com/kvdi/kvdi

I think in the particular case I care most about I wouldn't have to worry about it. The big players all have equivalent offerings with larger budgets, better features, and insane pricing. It's a VDI running on k8s.

I tried to use pion for the audio functionality in this project at first, but my head had trouble wrapping around how it could be implemented easily on the platform in use (k8s). That being said, seeing this post made me want to potentially give it another go.

This is an example multi-stage Dockerfile that does it

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.