Building Millisecond-Level Observability with Prometheus + eBPF

1. Why eBPF Observability

Traditional observability solutions rely on application instrumentation and sidecar proxies, introducing significant performance overhead and blind spots. eBPF allows safe execution of sandboxed programs within the kernel, capturing system calls, network packets, and file operations without modifying application code or introducing additional latency.

2. Three-Layer Monitoring with eBPF

We split observability into network, storage, and application layers. The network layer tracks TCP connection latency and retransmission rates via eBPF; the storage layer monitors file I/O and page cache hit rates; the application layer uses eBPF uprobes to capture key function call chains.

3. Prometheus Federation Architecture

For multi-cluster scenarios, we adopted the Prometheus federation model: each cluster runs an independent Prometheus Server collecting eBPF-exported metrics, with a central Prometheus aggregating them for a global cross-cluster view.

eBPF Map Operations Example

package main

import (
    "fmt"
    "unsafe"
)

// #include <linux/bpf.h>
import "C"

func loadBpfMap(mapFd int) {
    var key, value uint64
    for i := 0; i < 1024; i++ {
        key = uint64(i)
        err := bpfMapLookupElem(mapFd, unsafe.Pointer(&key), unsafe.Pointer(&value))
        if err == nil {
            fmt.Printf("map[%d] = %d\n", key, value)
        }
    }
}

func bpfMapLookupElem(fd int, key, value unsafe.Pointer) error {
    _, _, err := syscall.Syscall(syscall.SYS_BPF, C.BPF_MAP_LOOKUP_ELEM, key, value)
    return err
}

Prometheus Federation Configuration

scrape_configs:
  - job_name: 'federation'
    scrape_interval: '15s'
    honor_labels: true
    metrics_path: '/federate'
    params:
      'match[]':
        - '{__name__=~"eBPF_.*"}'
        - '{__name__=~"node_.*"}'
    static_configs:
      - targets:
        - 'cluster-1-prometheus:9090'
        - 'cluster-2-prometheus:9090'
        - 'cluster-3-prometheus:9090'

4. Summary

eBPF pushes observability granularity from the process level to the kernel level. Combined with the Prometheus ecosystem, it enables truly millisecond-level non-intrusive monitoring. This system has been running stably in our production environment for over six months, reducing average alert response time by 60%.