autometrics-rs VS helm-charts

I’ve been working on an open source project built on OpenTelemetry and Prometheus client libraries (https://autometrics.dev). The DX of the OTel libraries is pretty painful in all the languages we’ve used. Granted, we’re using them for metrics while traces are more common, but still.

I think the fundamental issue for the DX is that it’s trying to do everything everyone might want out of all of the observability signals. That’s a useful and laudable goal but it means that everything is configurable and relatively difficult to use.

If you go with Prometheus, we’re building Autometrics to make producing and querying metrics easier for developers. It makes it trivial to instrument functions to track the request rate, error rate, and latency and then writes PromQL for you. The Autometrics libraries are thin layers on top of existing Prometheus and OpenTelemetry libraries.

Autometrics is an open source observability framework that makes it trivial to add useful metrics to your code and writes Prometheus queries for you to help you understand the data. This feature shows the power that comes from pairing code instrumentation with automatically writing queries (and the queries it writes are a whole lot more complicated than what you'd want to write by hand!).

How do people tend to get metrics off of embedded devices?

I’m working on https://github.com/autometrics-dev/autometrics-rs and people asked whether it could be used in embedded contexts but I wasn’t sure how you’d hook up the device to something like Prometheus.

I just opened these two issues for [supporting `prometheus-client`](https://github.com/fiberplane/autometrics-rs/issues/25) and another for [supporting exemplars](https://github.com/fiberplane/autometrics-rs/issues/26).

Prometheus can be deployed using the Prometheus Helm Chart. This helm chart contains a lot of features such as the already mentioned Push Gateway, Alert Manager and so on. For simplicity reasons of this tutorial I will not show all the Helm chart configuration but you can see a real example used by me here.

Building upon Bartłomiej Płotka's insightful blog on Prometheus and its passthrough agent mode, this post dives into implementing multi-cluster Prometheus support. Notably, the official inclusion of support in the widely-used kube-prometheus-stack came with the release in July 2023, making it easier to extend Prometheus monitoring across clusters.

The first thing you'll need, of course, is at least one backend to store metrics. So install Prometheus now:

For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements

kube-prometheus-stack is a Helm chart that contains several components to monitor the Kubernetes cluster, along with Grafana dashboards Grafana Dashboards to visualize the data. This option will be used in this article.

This one I highly recommend: https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack

We use kube-prometheus-stack for metrics and have added the K8s views dashboards from grafana-dashboards-kubernetes. You should check out the k8s-views-global dashboard. I believe it's just what you are looking for.

You should take a look at "kube-prometheus-stack". It not only includes prometheus, node-exporter and Grafana but also a ton of preconfigured alerts and dashboards. Will save you a lot of work!

Since their are different ways to install what exactly did you install? Vanilla charts , stack, operator? https://github.com/prometheus-community/helm-charts/tree/main/charts