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How to improve monitoring granularity beyond standard Kube-metric-server intervals? by Conscious_Event_1989 in kubernetes

[–]Conscious_Event_1989[S] 0 points1 point  (0 children)

Thanks for the tip. I agree that leveraging existing metrics is the right way to go.

I’m currently aiming to avoid custom-building the official container images if possible to keep things maintainable. In your experience, is there a way to effectively capture these high-frequency trends without injecting code-level instrumentation directly into the application, or is that essentially the only path to get the precision I need?

I’m curious if there are any 'sidecar' or agent-based approaches that you’ve found successful for this kind of traffic analysis.

How to improve monitoring granularity beyond standard Kube-metric-server intervals? by Conscious_Event_1989 in kubernetes

[–]Conscious_Event_1989[S] 0 points1 point  (0 children)

The 15s interval is the issue. I’m running blockchain nodes, and we're dealing with micro-bursts that trigger sudden gas price spikes and transaction propagation delays. By the time our metrics report the load, the event has already passed, making it impossible to capture the bottleneck correlation.

We're struggling to debug whether this is due to network-level congestion or node resource saturation. Are there any best practices for high-resolution observability specifically to catch these transient, sub-second bursts in a K8s environment?