Tools: eBPF for SREs: Observability Without Agents (2026)

Tools: eBPF for SREs: Observability Without Agents (2026)

The Agent Problem

What eBPF Actually Is

What You Can Observe

A Practical Example: Detecting Slow HTTP Requests

Tools Worth Knowing

The Tradeoffs

When eBPF Shines

When eBPF Is Wrong

Getting Started Traditional monitoring means shipping an agent with every service. That agent: eBPF says: what if the kernel itself could emit observability data? eBPF (extended Berkeley Packet Filter) lets you run sandboxed programs inside the Linux kernel without recompiling or loading modules. It was originally for packet filtering. Now it powers Cilium, Pixie, Falco, and dozens of other tools. From an SRE perspective: you get deep visibility into syscalls, network traffic, process behavior, and filesystem operations with zero code changes to your applications. All of this without modifying your application code. Traditional approach: instrument your HTTP framework with OpenTelemetry, deploy a collector, ship traces. No code changes. No restarts. Real-time visibility. 1. Pixie (now part of New Relic) Use eBPF for infrastructure and network. Use OpenTelemetry for application logic. They complement each other. The future of observability is kernel-native. Agent-based tools will still exist, but the gap will keep shrinking. Written by Dr. Samson Tanimawo

BSc · MSc · MBA · PhD

Founder & CEO, Nova AI Ops. https://novaaiops.com Templates let you quickly answer FAQs or store snippets for re-use. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse

Code Block

Copy

network: - every TCP connection (src, dst, bytes, duration) - DNS queries and response times - TLS handshake failures - HTTP request/response cycles application: - function call latencies (uprobes) - memory allocations - lock contention - GC pauses security: - syscall audit trails - privilege escalations - suspicious file access - container escape attempts performance: - CPU scheduling delays - I/O wait time per process - disk latency histograms - page fault patterns network: - every TCP connection (src, dst, bytes, duration) - DNS queries and response times - TLS handshake failures - HTTP request/response cycles application: - function call latencies (uprobes) - memory allocations - lock contention - GC pauses security: - syscall audit trails - privilege escalations - suspicious file access - container escape attempts performance: - CPU scheduling delays - I/O wait time per process - disk latency histograms - page fault patterns network: - every TCP connection (src, dst, bytes, duration) - DNS queries and response times - TLS handshake failures - HTTP request/response cycles application: - function call latencies (uprobes) - memory allocations - lock contention - GC pauses security: - syscall audit trails - privilege escalations - suspicious file access - container escape attempts performance: - CPU scheduling delays - I/O wait time per process - disk latency histograms - page fault patterns # Install bpftrace sudo apt install bpftrace # Trace every HTTP response larger than 1MB sudo bpftrace -e ' uprobe:/usr/lib/libssl.so:SSL_write { @http_writes[pid] = count(); @http_bytes[comm] = sum(arg2); } ' # Install bpftrace sudo apt install bpftrace # Trace every HTTP response larger than 1MB sudo bpftrace -e ' uprobe:/usr/lib/libssl.so:SSL_write { @http_writes[pid] = count(); @http_bytes[comm] = sum(arg2); } ' # Install bpftrace sudo apt install bpftrace # Trace every HTTP response larger than 1MB sudo bpftrace -e ' uprobe:/usr/lib/libssl.so:SSL_write { @http_writes[pid] = count(); @http_bytes[comm] = sum(arg2); } ' - Adds memory overhead - Needs to be updated - Gets out of date - Breaks with kernel upgrades - Needs instrumentation code - Auto-instruments every service in your K8s cluster - No code changes, no sidecars - Full HTTP, MySQL, Postgres, DNS tracing - Open source - Network observability + security policy enforcement - Replaces kube-proxy - Hubble UI for service-to-service traffic visualization - Runtime security detection - "Alert if a process inside a container spawns a shell" - Writes rules in YAML - Continuous profiling via eBPF - See CPU flame graphs across your entire fleet - Identify the most expensive code paths - Security-focused eBPF tracing - Detects privilege escalations, cryptojacking, suspicious syscalls - Zero app code changes - Near-zero overhead (kernel-level efficiency) - Unified view across languages (Go, Python, Java, Rust, all seen the same way) - No agent lifecycle to manage - Requires Linux 4.14+ (5.0+ preferred) - Steep learning curve for custom probes - Limited visibility into in-process logic (you see syscalls, not business logic) - eBPF verifier rejects programs for subtle reasons - Network debugging: "Why is service A slow to reach service B?" - Security auditing: "What containers are making unexpected syscalls?" - Performance profiling: "Where is the cluster CPU time actually going?" - Incident forensics: "Reconstruct the syscall timeline during the outage" - Business logic observability you still need OpenTelemetry for spans - Application errors your logs and exception tracking still matter - Multi-region correlation eBPF is node-local - Deploy Pixie in a dev cluster (1-line install) - Open the UI, watch real-time HTTP traffic - Try a bpftrace one-liner to trace a specific syscall - Read the Cilium + Hubble docs - Replace one agent-based tool with its eBPF equivalent