prometheus-operator VS Grafana

The project repository for Prometheus-operator can be found here, The repo defines the CRDs and the controller. You can follow this documentation for the installation. which will require the creation of metrics exporters, node exporters, scrape configurations, etc.

Personally I use kubernetes, k3s is kind of lightweight, with the Prometheus operator.

https://k3s.io/

https://prometheus-operator.dev/

Kubernetes is not for everyone and is far from perfect but you already use Docker and you seem to seek many features offered by Kubernetes.

I see that the operator is a community-run project. Does anybody have any experience (positive or negative) on running the operator itself? I wonder if it is more for multi-tenant Prometheus. For single tenant Prometheus collection, I guess the big gain is abstracting scrape config into ServiceMonitor and PodMonitor resources? Is there anything else that this makes much better?

But in all honesty if you’re on k8s you’re probably best served by the Prometheus operator. It’ll give you all that and more, easily.

Try some monitoring.

It’s a common pattern [1] how else can users override the child objects you’re controller creates?

1 https://github.com/prometheus-operator/prometheus-operator/b...

The Prometheus operator is this one https://github.com/prometheus-operator/prometheus-operator

At a minimum, you want to look at your metrics using the Prometheus Operator. You can write alerts for resource requests.

Prometheus Operator is an independent project from the Prometheus project. I know, it can lead to confusion. In the official README you can find short comparison. Basically, Prometheus Operator does what an operator should do - provides Kubernetes native deployment and management of Prometheus and related monitoring components like Grafana or Alert Manager.

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

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

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.