Understanding eBPF: Principles, Applications, Development Process, and Sample Programs

With the rapid growth of cloud services, system observability has become critical; traditional methods require intrusive instrumentation, whereas eBPF offers a zero‑intrusion, no‑probe solution that can monitor kernel events, network traffic, and application performance.

eBPF (extended Berkeley Packet Filter) expands the original BPF beyond packet filtering to support kprobes, tracepoints, and other hook types, allowing custom programs to run in the kernel without modifying its source code.

Typical use cases include application performance monitoring, security event detection, network traffic analysis, container and cloud‑native observability, and extending operating‑system functionality.

The eBPF execution pipeline consists of compiling C code to eBPF bytecode (using LLVM or GCC), loading the bytecode into the kernel via the bpf() system call, verification by the kernel verifier, and runtime execution triggered by kernel events, with data exchanged through maps or perf events.

Development requires a recent Linux kernel (3.18+), LLVM/Clang 11+, libbpf‑devel, kernel‑devel, and a Go environment; the article uses the cilium/ebpf‑go project as an example.

eBPF programs are split into kernel‑space (written in restricted C, defining maps, licenses, and hook functions) and user‑space components (written in any language, here Go) that load the program, configure hooks, and process events.

Sample programs demonstrate attaching an uprobe to the Bash readline function to capture user commands, and tracing TCP connection latency by instrumenting tcp_connect and tcp_rcv_state_process events, with results aggregated in user space.

Portability challenges across kernel versions are addressed by CO‑RE (Compile Once, Run Everywhere) and BTF (BPF Type Format), which provide kernel data‑structure metadata; tools like btfhub can generate minimal BTF files for runtime use.

Security is enforced by the kernel verifier, strict memory access controls, and helper functions, while performance benefits arise from in‑kernel execution that reduces context switches and data copies.

In summary, eBPF has become a powerful observability tool for cloud‑native environments, with expanding applications in performance tuning, security, and potential integration with AI/ML for intelligent system optimization.