kubespray VS kube-state-metrics

When it came to deployment, I had several options, and I chose the hard way: deploying Kubernetes on bare metal nodes using KubeSpray. Troubleshooting bare metal Kubernetes deployments honed my skills in pinpointing issues. This hands-on experience provided a deep understanding of how each component, like the Control Plane, kubelet, Container Runtime, and scheduler, interacts to orchestrate containers.

Worth noting that if you like ansible, Kubespray has had documented air-gap installation since 2018 https://github.com/kubernetes-sigs/kubespray/commit/963c3479...

calico v3.7.3 kubernetes v1.16.0 installed via kubespray https://github.com/kubernetes-sigs/kubespray

About 1yr ago I ran k8s with 300 nodes using kube spray https://github.com/kubernetes-sigs/kubespray . Never had any real issue with it. We did finally move to the cloud though.

I would highly recommend https://github.com/kubernetes-sigs/kubespray

We build our own distro called Compliant Kubernetes, and we use (a fork of) kubespray to install the base Kubernetes layer. Our distro is entirely open source, so you can use it as a reference, if you want.

If you want to run Kubernetes on VM's you can look into kubespray and play around with the options for installing and checking the configuration after the fact.

git clone https://github.com/kubernetes/kube-state-metrics.git

https://github.com/kubernetes/kube-state-metrics/blob/main/docs/pod-metrics.md According to this github repo completion is responsible of termination date if I correctly understood .

kube-state-metrics

Those are entirely different types of data. You can get that from something like kube-state-metrics

I've never had a cluster of that size, so take it with a grain of salt - but maybe you could try purpose-based sharding? KSM has allowlist and denylist config flags, for configuring which metrics it exposes https://github.com/kubernetes/kube-state-metrics/blob/main/docs/cli-arguments.md

Kubernetes itself will not notify you, the way I've seen people do this, is to use something like kube-state-metrics or node_exporter, export that to Prometheus (or preferrably VictoriaMetrics because Prometheus is terrible IMO), and then setup alarms on that with alertmanager or equivalent, or just look at dashboards regularly with Grafana. Realistically I recommend only setting alerts on disk usage and application/database latency. CPU and memory utilization isn't a great metric to alert on a lot of the time.

Another tip that you could consider spelling out a little more, is to monitor the number of resources created by Kind. This is somewhat mentioned for jobs and services, but any Kind of which thousands of resources are created will put stress on the control-plane. The total number of resources per namespace/cluster can be monitored with kube-state-metrics. KSM does not emit metrics of resources created from CRDs. These metrics can be implemented with KSM's custom resource state metrics: https://github.com/kubernetes/kube-state-metrics/blob/main/docs/customresourcestate-metrics.md

We then have various other Metrics called Kubernetes Workload Metrics. These are the dashboards with names that start with “Kubernetes / Compute Resources / Workload”. These dashboards are specific to the services you are running. They take into account the Kubernetes Workloads in your various namespaces, using kube-state-metrics. For a closer look, check out otel_demo_app_k8s_dashboard.tf.

The first step to optimizing costs is gaining visibility into your costs using tools. Kubernetes provides a Metrics Server and kube-state-metrics that can give you the overall picture of resource utilization by your cluster. There are more tools that provide more granular breakdowns and provide dashboards with business metrics, infra cost, and alerting functionalities. Here are some strategies to optimize your resource utilization and cloud bills on k8s.