In this lesson, you will learn how to unify disparate kernel-space events and user-space telemetry into cohesive, actionable Grafana dashboards.
When troubleshooting complex performance bottlenecks, the gap between kernel activity (like context switches or irq handling) and application behavior (like thread pool utilization or request latency) is often where the most elusive bugs hide. To bridge this, you must treat your data sources—such as eBPF-exported metrics and Prometheus-scraped application logs—as a unified time-series stream.
Aligning Data Sources
The core strategy for an effective dashboard is temporal correlation. You cannot effectively debug a "thread starvation" issue if your kernel scheduler metrics and your application’s event loop latency are offset by misaligned timestamps.
When building your dashboard, normalize all data into a shared time bucket. For instance, when visualizing Thread Affinity Shifts alongside Application Wait States, ensure both queries utilize the same time_bucket interval in your TSDB (e.g., InfluxDB or Prometheus).
Creating the Correlation View
A powerful approach is to overlay a heatmap of system-wide scheduling delays directly beneath a line graph of your service’s P99 latency. By using a shared instance label or host_id, you can construct a dashboard panel that highlights the "blast radius" of kernel events on user-space performance:
-- Example Prometheus/Grafana query to correlate CPU wait with Request Latency
(rate(sched_delay_nanoseconds_total[1m]) / rate(sched_runs_total[1m]))
/
histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[1m]))
In this setup, you are looking for visual spikes in the scheduler delay that align perfectly with the tail-latency dips in your application. By layering these metrics, you stop guessing whether an application stall is due to logic contention or a low-level interrupt storm. When building these, always leverage Grafana’s "Shared Crosshair" feature, which allows you to scrub across the time axis and see exactly what the kernel was doing at the precise millisecond an application thread stalled.
Key Takeaway: Effective performance visualization requires aligning kernel-level events and user-space telemetry on a synchronized time axis to identify causal relationships between system interference and application throughput.