Universal-Kubernetes-Helm-Charts VS prometheus

I would point you to what I call the "Universal Helm Charts" and some examples of how to use them.

Shameless Plug: Here's one of my dashboards I made for Ingress-Nginx, which is my recommended border router/gateway into all the services. It adds deep robust metrics and configurability, and if you've got years of experience with Nginx also, it allows you rich complex customization via nginx's configuration structure via kubernetes annotations. Besides that I have open-source helm charts which are easy to use, boilerplates showing how to use them, a volume autoscaler to automatically resize your disks as they get full, and a blog where I share various of my experience which is a companion blog to my upcoming book of the same name. Hope this helps! Feel free to ask if you have any further questions.

You may want to check out some other of my Helm Boilerplates to explain and highlight how using subcharts works. This is a companion repo to my upcoming DevOps + Kubernetes book. You also might like to check out my set of open-source universal helm charts which are published in a helm registry right now that you can leverage and has many industry best-practices built into it, such as anti-affinity rules, pod disruption budgets, horizontal pod autoscaling, ingress, service support, etc.

References: I use docker and Kubernetes daily. I currently manage numerous clusters and maintain pipelines for hundreds of microservices as I type this. I've been converting microservices into Docker images for companies hundreds if not thousands of times by now over the last bunches of years. I am also an avid and passionate open-source evangelist and Kubernetes/DevOps consultant. I author some Kubernetes controllers such as the Volume Autoscaler and have a set of Open Source Helm Charts and I love to contribute code/fixes wherever I run into issues.

If it helps at all I have some universal helm charts that have a template for easily deploying your application as a deployment or statefulset. You will notice I don’t even have a daemonset chart because it doesn’t make sense to. https://github.com/DevOps-Nirvana/Universal-Kubernetes-Helm-Charts

See: Open-Source Universal Helm Charts See: Boilerplates of using Open-Source Helm Charts (as a sub-chart)

Helm charts - https://github.com/DevOps-Nirvana/Universal-Kubernetes-Helm-Charts Example using helm charts as sub charts - https://github.com/DevOps-Nirvana/Helm-Chart-Boilerplates/tree/master/boilerplate-echoserver

A open-source set of Universal Helm Charts with tons of best-practices baked into it such as autoscaling, PDBs, labeling, and an standardized set of "universal" templates that allows you to pivot between templates easily (meaning, you can easily make a deployment into a Statefulset or a Cronjob). Yes, I need to add more documentation, I know. I'm busy :P

Universal Kubernetes Helm Charts

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.