Integrating eBPF with Time-Series Databases

In this lesson, you will learn how to bridge the gap between low-level kernel observability and high-level infrastructure monitoring by streaming real-time eBPF metrics directly into Prometheus or VictoriaMetrics.

The Observability Bridge

While eBPF excels at capturing high-fidelity events like system calls, context switches, or disk I/O, these metrics exist transiently in kernel maps. To make this data actionable for long-term trend analysis and alerting, we must export it to a time-series database (TSDB).

The integration process follows a standard pattern:

1. Extraction: A userspace agent (typically written in Go or C++) attaches to your eBPF kprobes or uprobes.
2. Aggregation: The agent reads from BPF_MAP_TYPE_HASH or BPF_MAP_TYPE_PERCPU_ARRAY, summarizing the raw events into structured metrics.
3. Exposition: The agent implements a local HTTP endpoint (e.g., /metrics) that exposes the current values in the Prometheus exposition format.

Concrete Implementation

Rather than pushing data manually, we use the prometheus/client_golang library to serve the map contents. For example, if you are tracking the latency of a specific kernel function via a histogram map, your userspace collector loops through the map and updates a Prometheus Gauge:

// Example: Exposing a kernel-latency map to Prometheus
for {
    val, _ := bpfMap.Lookup(key)
    gauge.Set(float64(val)) // Updates the metric for Prometheus scraper
    time.Sleep(1 * time.Second)
}

By setting up a Prometheus or VictoriaMetrics server to scrape this agent, you transform volatile kernel snapshots into persistent historical data. This allows you to correlate "hidden" kernel bottlenecks—such as thread starvation or context switching overhead—directly against application-level latency spikes in your standard dashboard.

When integrating, ensure you use BPF_MAP_TYPE_PERCPU_ARRAY to avoid contention during map updates, as this prevents your observability tool from becoming a source of performance degradation in high-throughput production environments.

Key Takeaway: Integrating eBPF with a TSDB enables you to move from ephemeral debugging to sustained observability by exposing kernel map data as queryable metrics in Prometheus or VictoriaMetrics.