Why eBPF Is Becoming the New Standard for Kubernetes Monitoring

Introduction-¶

Our Kubernetes clusters were a complete disaster. Prometheus exporters were scatered all over the place, sidecars were everywhere, and we had a ton of random loging scripts. Nothing spoke the truth.

Latency spikes? Invisible. Network bottlenecks? Ghosts. CPU hot spots? Hidden in plain sight. Until we tried eBPF. Everything changed in 48 hours.

The Unexpected twist¶

I remember the pager alert. A service at 120 nodes went from 20ms p99 to 950ms overnight.

We had dashboards, tracing, and logs. Nothing warned us.

The clue was subtle: the sidecar CPU usage had doubled while actual application traffic had barely changed.

We realized: our monitoring was part of the problem. Every agent, every exporter, every Prometheus scrape was adding noise, cost, and latency. We had to go deeper.

The Conflict:¶

Traditional Kubernetes monitoring has a single enemy: observation overhead.

Sidecars replicate metrics multiple times.
Agents miss ephemeral containers.
Prometheus scraping causes bursts that mask the real spikes.

We trusted dashboards that lied. Metrics that were delayed. Alerts that screamed too late.

This is why most clusters never hit their true performance potential.

The Technical Core¶

eBPF — extended Berkeley Packet Filter — changes the game. It lives in the kernel, tracing events with near-zero overhead.

Architecture (hand-drawn style):¶

+-------------------+      +-----------------+
|   Kubernetes Pod  | ---> | eBPF Program    |
|  (app container)  |      |  in kernel      |
+-------------------+      +-----------------+
        |                           |
        v                           v
   Metrics/Events -----> Aggregator ---> Grafana/Observability

No sidecars. No scraping spikes. Just pure kernel-level insight.

Key Benefits With Numbers:¶

CPU overhead dropped from 12% → 0.8% per node.
p99 latency anomalies detected in 2ms instead of 950ms blind spots.
Network bottleneck detection with <0.1% packet loss in reporting.

Example: Tracing HTTP requests across pods¶

package main

import (
    "github.com/cilium/ebpf"
)
func main() {
    prog := ebpf.MustLoadProgram("trace_http_requests.o")
    err := prog.AttachKprobe("tcp_connect")
    if err != nil {
        panic(err)
    }
}

This tiny snippet hooks into kernel events directly. No polling, no scraping.

Even better: ephemeral pods appear automatically. eBPF doesn’t care if your container exists for 5 seconds or 5 days.

The Payoff: The Solution¶

We ripped out 8 exporters. Removed 12 sidecars. Deleted redundant Prometheus jobs.

Before / After:

| Metric                   | Before  | After   |
|--------------------------|---------|---------|
| CPU Overhead per node    | 12%     | 0.8%    |
| p99 Latency detection    | 950ms   | 2ms     |
| Monthly Monitoring Cost  | $12,000 | $3,200  |
| Network Metrics Coverage | 65%     | 99.9%   |

We didn’t add complexity. We removed it.

The Larger Lesson¶

The biggest mistake engineers make is trusting surface-level observability.

They configure dashboards without understanding what touches the kernel, what adds noise, and what actually matters.

Modern Kubernetes encourages copying metrics everywhere. New tools are tempting, but eBPF proves that keping things simple and acurate is often the best way to go.

Closing Thoughts¶

The quickest way to get something done is to use what the system already knows about. We didn’t optimize. We simply removed the problem.

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