kops VS kube-state-metrics

The general problem of patching resource definitions that are not fully under your control has also been recognized for some time. This is true of default resources created and updated by cluster maintenance tools (e.g. kOps), or by public helm charts that you use to install common services and operators (e.g. nginx-ingress, cert-manager, and so on). High quality charts will allow you to override the configuration of important components such as service account references, but some simpler charts offer much less configuration.

The kops documentation is not very clear about this.

Very fast progress and great article with lots of useful information. I found myself nodding a lot having recently seen GitLab Dedicated https://docs.gitlab.com/ee/subscriptions/gitlab_dedicated/ being developed that had my similar challenges. I wonder what other people think of Kops https://kops.sigs.k8s.io/

note: I work with kube on a daily basis. All current clusters under my care use a managed backplane service though there are a number of small (3+ years-old) clusters that I still keep an eye on that were initialized using kops.

kOps with your own instances.

Companies run their own clusters (sometimes for cost reasons), using tools like kops, and kubeadm to set up their own clusters.

curl -Lo kops https://github.com/kubernetes/kops/releases/download/$(curl -s https://api.github.com/repos/kubernetes/kops/releases/latest | grep tag_name | cut -d '"' -f 4)/kops-linux-amd64 chmod +x kops sudo mv kops /usr/local/bin/

Kops is a cluster setup and management command line tool that deploys a Kubernetes cluster to AWS. It provides configuration abstractions such as manifest YAML files that facilitate node and components configuration. And like Ansible, it will provide dry-run capabilities and ensures idempotency of changing the nodes.

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.