I'm monitoring a Kubernetes cluster with Grafana, but the usual metrics arrive too slowly to explain short traffic spikes. The current setup relies on Kubernetes Metrics Server, and the standard interval means that brief resource-saturation or network events are often over before they appear on a dashboard.
This is especially important for blockchain nodes, where micro-bursts can coincide with gas-price spikes and transaction-propagation delays. I'm trying to determine whether these incidents come from network congestion, node saturation, or both.
Is adjusting the Metrics Server interval a practical solution, or is it the wrong tool for sub-second visibility? What observability approaches—such as custom Prometheus scraping, OpenTelemetry, eBPF, tracing, or agent-based collection—work well for capturing short-lived CPU, memory, network, and application-performance events without rebuilding official container images or adding invasive application instrumentation?
3 Answers
For transient events, combine normal Prometheus metrics with targeted high-resolution tools. OpenTelemetry tracing can show request and propagation latency, while eBPF-based agents can observe network flows, kernel scheduling, TCP behavior, and process-level resource pressure without changing application images. Use higher sampling or short profiling windows only during an incident; collecting everything at sub-second resolution all the time can create a large storage and CPU burden.
Prometheus scrape intervals can be configured, but the common 15-second interval is intentional and is adequate for many workloads. Metrics Server is mainly designed to provide resource data for Kubernetes features such as autoscaling, not to act as a high-frequency performance profiler. Pushing it toward sub-second collection can add significant load to nodes and the control plane, so it usually isn’t the best place to solve this problem.
That makes sense for normal monitoring, but these brief bursts are exactly what I need to catch. I’ll avoid forcing Metrics Server into a role it wasn’t designed for and look at targeted profiling instead.
Prometheus alone is not ideal for sub-second observability because it is a pull-based time-series system with storage and scrape overhead. Keep it for longer-term trends and alerting, then add focused tools for the short window: distributed tracing for request paths, OpenTelemetry collectors for event telemetry, and eBPF or perf-style profiling for kernel, CPU, and network behavior. Also check whether the event is being aggregated away by dashboard resolution; retaining raw samples briefly or recording them in a specialized event system can make correlation much easier.
The distinction between long-term metrics and short profiling windows is helpful. I’m particularly interested in correlating network-level congestion with node saturation, so combining tracing with eBPF data sounds more useful than simply lowering every scrape interval.
An agent or DaemonSet-based eBPF collector is often a better fit than a sidecar when you need node-wide network and process visibility. It can capture useful signals without injecting instrumentation into every application container.