Real-Time Monitoring with eBPF: Low-Overhead Insights for Linux & K8s

Intro: Why eBPF Is Redefining Observability in 2025

Five years ago, deep observability meant deploying sidecars, heavy agents, and complex pipelines. Fast forward to 2025, and eBPF has quietly become a game changer.
It lets you attach lightweight programs directly to the Linux kernel, collecting network, syscall, and performance data without sidecars and with near-zero overhead.

If you're running Kubernetes, Linux, or microservices at scale, eBPF gives you visibility you didn’t think was possible.

How eBPF Works

At its core, eBPF (extended Berkeley Packet Filter) lets you attach small programs to kernel hooks. These hooks listen to syscalls, network events, or process-level metrics — then export that data without modifying the kernel.

Key Capabilities:

Hook into syscalls → Observe file I/O, network access, and process events.
Monitor network traffic without iptables hacks.
Collect CPU, memory, and latency metrics without sidecar agents.

Unlike traditional monitoring agents, eBPF is event-driven and sandboxed, so it has minimal performance impact.

Why It Matters: Low Overhead, High Fidelity

Legacy monitoring agents often consume 5-15% CPU and add latency to workloads. With eBPF, overhead typically stays under 1%.

For SREs, this means:

You can safely run continuous profiling in production.
No more picking between visibility and performance.
Perfect for scaling environments where sidecars aren’t feasible.

Use Cases for SREs & DevOps

1. Real-Time Container Monitoring

Get process-level insights into running containers without modifying workloads:

Track which processes consume the most CPU or memory.
Monitor container-level network activity.
Detect abnormal syscalls that indicate possible compromises.

2. Network Visibility Without Sidecars

With eBPF, you don’t need Envoy or Istio sidecars just for metrics:

Trace TCP/UDP connections directly at the kernel level.
See service-to-service traffic inside Kubernetes with tools like Cilium Hubble.

3. Profiling High-CPU Processes in Production

Continuous profiling without impacting performance:

sudo perf record -e cycles:u -g -- sleep 5
sudo perf script | ./bcc/tools/flamegraph.py > cpu-flamegraph.svg

Get detailed call stacks to find exactly where CPU cycles are burning.

Tooling Ecosystem Worth Knowing

Tool	Best For	Why It’s Useful
Pixie	Kubernetes observability	Zero-instrumentation, instant insights
Cilium Hubble	Network tracing & policies	Perfect for service-to-service traffic
Parca	Continuous profiling	Low overhead CPU/memory profiling
BCC Tools	Kernel-level debugging	Great for tracing syscalls & IO

These tools integrate easily into existing pipelines and make eBPF accessible to teams without kernel expertise.

Case Study: Using eBPF to Detect Kubernetes DNS Latency

Scenario:
A fintech platform noticed intermittent slowdowns in their checkout API. Traditional APM tools showed “database issues,” but digging deeper revealed DNS resolution spikes.

Solution with Pixie + eBPF:

Attached hooks to DNS-related syscalls.
Observed increased query response times from CoreDNS pods.
Rolled out caching tweaks and reduced p95 DNS latency by 65%.

Without eBPF, debugging this issue would have taken days instead of hours.

eBPF Hook Architecture

[ App ] → [ eBPF Hook in Kernel ] → [ Metrics Pipeline ] → [ Observability Tools ]

eBPF Hooks capture events at kernel level.
Data flows directly into tracing, monitoring, or profiling tools.
No sidecars. No intrusive agents. No massive infra overhead.

Key Takeaways

eBPF delivers deep visibility into Linux & Kubernetes environments with minimal performance cost.
Use it for container monitoring, network tracing, and continuous profiling without modifying workloads.
Tools like Pixie, Cilium Hubble, and Parca make eBPF practical for SREs and DevOps teams.
Real-world debugging becomes faster, cheaper, and safer when your observability is kernel-native.

In 2025, eBPF isn’t just a buzzword — it’s becoming the foundation of modern observability.