kube-state-metrics VS skaffold

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.

A nice middle ground is using a tool like Google's Skaffold, which provides "Bazel-like" capabilities for composing Docker images and tagging them based on a number of strategies, including file manifests. In my case, I also use build args to explicitly set versions of external dependencies.

While I am in a Typescript environment with this setup at the moment, my personal experience that Skaffold with Docker has a lighter implementation and maintenance overhead than Bazel. (You also get the added benefit of easy deployment and automatic rebuilds.)

I quite liked using Bazel in a small Golang monorepo, but I ran into pain when trying to do things like include third-party pre-compiled binaries in the Docker builds, because of the unusual build rules convention. The advantage of Skaffold is it provides a thin build/tag/deploy/verify layer over Docker and other container types. Might be worth a look!

Kudos to the Google team building it! https://skaffold.dev

To get a similar experience of preevy up, first we’ll need to split the build and deploy using process or alternatively employ tools that orchestrate build-tag-push-update-sync flow like Skaffold/Tilt.

We will be using docker and microk8s from Canonical. For running our software during development, we will be using skaffold which is a great tool developed by Google.

To add more context, if you are developing containers in a local dev environment, the minimum you should have is the Google Cloud SDK and Skaffold. The SDK will allow you to programmatically interact with Googleapis e.g. auth, services, resources. Skaffold will allow you to build and deploy to the cloud similar to working with a local dev environment.

I have used both Skaffold and Devspace. I prefer the latter.

I wonder if it has some overlap with https://skaffold.dev/.

K3d and Skaffold for local development

skaffold.dev - build in k8s - no more asking for the database password. All the plumbing to the backend is just done so it's easier for them to test and demo any branch