Why eBPF Is the Future of Observability: A Practical Guide with Go and C
This guide explores how eBPF technology enables deep observability in AI-generated services, providing practical implementation examples using Go and C. It explains how eBPF allows developers to monitor system behavior, trace function calls, and collect performance data with minimal overhead. The article positions eBPF as a key tool for observability in modern, AI-driven applications.
Background
eBPF (extended Berkeley Packet Filter) is a Linux kernel technology that lets you run sandboxed programs inside the operating system kernel without changing kernel source code or loading kernel modules. It was originally built for packet filtering but has become the standard way to add observability, security, and networking features to Linux.
- eBPF allows monitoring of system calls, network events, and application behavior at near-zero overhead — without modifying applications or restarting them.
- Go and C are the main languages used to write eBPF programs: C for the kernel-side logic, Go (via libraries like ebpf-go) for the user-space side that loads and manages the eBPF code.
- The article's core claim: eBPF is ideal for observability in AI-based services because these systems are complex, frequently updated, and hard to instrument with traditional approaches (e.g., manual code injection or sidecars). eBPF gives you deep, safe, always-on visibility without touching the AI model code.
- The practical guide shows how to build a basic eBPF program in Go and C that traces function calls — a pattern used in production tools like Cilium, Falco, and Pixie.