Implementing Alerting on BPF Metrics

Configure threshold-based alerts for critical kernel performance metrics such as disk queue latency.

In this lesson, you will learn how to bridge the gap between raw eBPF observability and operational reliability by configuring threshold-based alerts for critical kernel metrics like disk queue latency.

From Observability to Alerting

eBPF programs are excellent at providing deep, high-resolution visibility into kernel state. However, telemetry is useless if you aren't notified when the system crosses performance boundaries. To build a robust alerting system, we must treat eBPF-derived metrics as first-class citizens within our time-series database (TSDB), then apply conditional logic to trigger notifications.

Thresholds for Disk Queue Latency

Disk queue latency is a classic "critical" metric. If the kernel takes too long to process I/O requests, application performance suffers significantly. By using an eBPF program to trace blk_account_io_completion, we can export a histogram of request latency to a TSDB like Prometheus.

Once the data is flowing, we define a threshold alert. For instance, if the P99 latency of disk operations exceeds 50ms over a 2-minute sliding window, we trigger an incident.

# Example Prometheus alerting rule
groups:
- name: kernel_performance
  rules:
  - alert: HighDiskQueueLatency
    expr: histogram_quantile(0.99, sum by (le) (rate(disk_latency_bucket[2m]))) > 0.05
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: "Disk latency is exceeding threshold"
      description: "P99 disk latency is {{ $value }}s, indicating potential I/O bottlenecks."

Avoiding Alert Fatigue

The power of eBPF allows us to capture more than just simple averages. When setting thresholds, avoid static "magic numbers" that apply to all workloads. Instead, use eBPF to calculate dynamic baselines during "normal" operating hours. By alert-gating based on standard deviations (e.g., alert if current latency is > 3 sigma above the rolling 24-hour mean), you ensure that your alerts are triggered by genuine performance regressions rather than routine, expected spikes in I/O.

Always keep your logic anchored in the specific kernel tracepoints that directly impact user experience; tracking raw queue depth is helpful, but tracking the actual latency experienced by the storage subsystem is actionable.

Key Takeaway: Implement alerting by piping high-fidelity eBPF metrics into your TSDB and using statistical thresholds—rather than arbitrary values—to trigger actionable, low-noise alerts for kernel performance degradation.