kube-prometheus VS sloth

The last one is mostly an observability stack with Prometheus, Metric server, and Prometheus adapter to have excellent insights into what is happening on the cluster. You can reuse the same stack for autoscaling by repurposing all the data collected for monitoring.

kustomize Kube Prometheus

On the other hand, the Kube-prometheus project provides documentation and scripts to operate end-to-end Kubernetes cluster monitoring using the Prometheus Operator, making easier the process of monitoring the Kubernetes cluster.

For our load testing we’ve deployed Temporal on Kubernetes, and we’re using MySQL for the persistence backend. The MySQL instance has 4 CPU cores and 32GB RAM, and each Temporal service (Frontend, History, Matching, and Worker) has 2 pods, with requests for 1 CPU core and 1GB RAM as a starting point. We’re not setting CPU limits for our pods—see our upcoming Temporal on Kubernetes post for more details on why. For monitoring we’ll use Prometheus and Grafana, installed via the kube-prometheus stack, giving us some useful Kubernetes metrics.

The 2 most common approaches I have seen are kube-prometheus-stack and kube-prometheus..

- I'm missing a lot of the Grafana dashboards that are provisioned during the deployment, not sure why as it has worked before, and wanted to add them after install... I believe it's different ConfigMaps like the one in kube-prometheus but I was wondering if there's a way to force provisioning them all again at once (multiple k8s, node_exporter, vm, etc)?

Check out the kube Prometheus project -- https://github.com/prometheus-operator/kube-prometheus It's a bit heavy, but the included recording rules and dashboards give you a great start at understanding your cluster.

The actual link to the prometheus/grafana bundle: https://github.com/prometheus-operator/kube-prometheus

Here’s a list of what’s installed: https://github.com/prometheus-operator/kube-prometheus/tree/main/manifests

It’s a CLI that allows developers to embed SLO annotation into GO code as comments and generate Prometheus alert groups when paired with Sloth, https://github.com/slok/sloth.

SLOTH: https://github.com/slok/sloth

Note: Although it’s outside of the scope of this post to dig deep into this topic, in case you’re curious, you can check out what an OpenSLO YAML definition looks like here.

The actual hard part is standardizing all teams on SLI/SLO-based thinking. For that we're looking at tools like Sloth.

Have a look at sloth (https://github.com/slok/sloth) which will help you generate SLOs and error budgets given a PromQL query. This might be easier than trying to calculate it yourself. Plus, it's "metrics as code" and OpenSLO spec compliant.

If you are in k8s and use Prometheus you could take a look at sloth: https://github.com/slok/sloth which can either generate the rules/alerts for you, or can run as an operator and allows you to write SLOs as k8s kinds.

Check out https://github.com/slok/sloth