kube-state-metrics VS node_exporter

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.

$ wget https://github.com/prometheus/node_exporter/releases/download/v1.7.0/node_exporter-1.7.0.linux-amd64.tar.gz $ tar -xzvf node_exporter-1.7.0.linux-amd64.tar.gz

Node Exporter

The Prometheus node_exporter can provide this information, doesn't require root. You could run it as a systemd user unit if you don't have root.

I personally find it best to learn through experimentation. Start with reading a bit about Prometheus and Grafana through their docs, and then familiarise yourself with setting up a local Prometheus + Grafana instance either locally or with docker using docker-compose, along with something to generate /metrics endpoint for Prometheus to scrape from such as a custom Prometheus exporter in Python or using Node Exporter.

Your best bet might be to fork node_exporter to get you more verbose socket stats: https://github.com/prometheus/node_exporter/blob/master/collector/sockstat_linux.go

I use Grafana + Prometheus + Node Exporter.

Crazy idea, can't NX devices run Docker? I wonder if the node_exporter would work.

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.

You may setup node exporter and collect metrics with prometheus for example. Its not quite "simple" way, but still you may find it useful.