eBPF: High-Performance Monitoring in Linux

Actualizado: 2026-05-03

eBPF (Extended Berkeley Packet Filter) has changed the way developers and system administrators monitor, analyse, and optimise performance in Linux. It allows running custom programs directly in the kernel without modifying its source code or loading modules, combining high performance with verified safety.

Key takeaways

• eBPF extends the classic BPF filter to run complex, verified programs in the Linux kernel.
• It operates via a virtual machine in the kernel; the safety verifier ensures the program cannot damage the system.
• It allows monitoring system calls, network events, hardware events, and I/O latencies.
• The most relevant tools are BCC, bpftrace, Cilium, Falco, and Katran.
• CPU cost is significantly lower than traditional monitoring solutions based on external probes.

What eBPF is and where it comes from

eBPF originates from BPF (Berkeley Packet Filter), designed in 1992 to filter network packets on Unix systems. The extended version radically broadens that foundation:

• It allows running much more complex and versatile programs in the kernel.
• It supports multiple event types: system calls, hardware events, block operations, network packets.
• It includes a formal verifier that rejects any program that could cause an infinite loop or access unauthorised memory.

The result is a technology that gives operators real-time visibility into what happens inside the kernel without the stability risk of modifying the kernel directly.

How it works

The execution flow of an eBPF program follows five steps:

1. The developer writes the program in C (or Rust with some libraries) using the eBPF API.
2. The compiler (LLVM/clang) transforms it into eBPF bytecode.
3. The loading tool (bpftool, bpftrace, or others) sends the bytecode to the kernel.
4. The kernel verifier checks the program is safe (no infinite loops, no out-of-bounds accesses, no unauthorised calls).
5. The kernel JIT-compiles the bytecode to native code and executes it in the eBPF virtual machine.

Results are communicated to user space via eBPF maps — high-speed shared data structures — for analysis and visualisation.

Key benefits

Four properties make eBPF a differentiating tool:

• Performance: programs run directly in the kernel with minimal overhead. Analysing millions of events per second has a much lower CPU impact than monitoring solutions based on external agents.
• Flexibility: any operator can write a custom program for their specific use case without waiting for the kernel or monitoring tools to support it natively.
• Safety: the formal verifier prevents an eBPF program from damaging the system or compromising stability. Operators can deploy code without full root access.
• Portability: eBPF programs are architecture-independent and can run on different Linux systems with few or no modifications.

These properties make eBPF the foundation of modern container and Kubernetes observability tools, in line with what Pixie for Kubernetes observability analyses.

Ecosystem tools and projects

The eBPF ecosystem has grown rapidly:

BCC (BPF Compiler Collection) is the reference collection of tools and libraries for developing and running eBPF programs. Provides C and Python APIs, and a set of pre-built tools like execsnoop (process execution tracing), opensnoop (file open monitoring), and biolatency (disk block I/O latency).

bpftrace is a high-level tracing tool with an awk-like domain-specific language. Especially useful for ad-hoc analysis and production debugging without compiling a full program.

Cilium uses eBPF for security, observability, and load balancing in container networks. Implements API-level security policies and monitors traffic in real time — it’s the reference CNI for Kubernetes in environments that prioritise network observability.

Falco (CNCF) uses eBPF for real-time intrusion detection and behaviour monitoring. It fires alerts when it detects suspicious activity: unexpected syscalls, accesses to critical files, or anomalous network behaviour.

Katran is Meta’s high-performance load-balancing library using eBPF to implement efficient kernel-level distribution algorithms.

Primary use cases

eBPF applies in the following contexts:

• System performance monitoring and analysis (syscall latency, per-process CPU usage, disk I/O).
• High-speed network traffic analysis and packet filtering.
• System call tracing for malicious behaviour detection.
• Granular security policy and access control implementation.
• Kernel-level load balancing without passing through user space.

This capability complements alerting in Prometheus: while Prometheus captures aggregated metrics in user space, eBPF adds kernel-granularity visibility that no external agent can match.

Conclusion

eBPF represents a paradigm shift in Linux observability: from external probes with high overhead to verified programs executed directly in the kernel. For teams that need high-resolution visibility into latencies, system calls, and network traffic, eBPF is the most powerful tool in the Linux ecosystem and the foundation on which modern cloud-native observability solutions are built.