How extended Berkeley Packet Filter allows you to run sandboxed programs in a privileged context without modifying kernel source code.

eBPF: The Future of Kernel Observability

Extended Berkeley Packet Filter (eBPF) is revolutionizing how we observe and interact with the Linux kernel. This powerful technology allows you to run sandboxed programs in the kernel without modifying kernel source code.

What is eBPF?#

eBPF is a virtual machine in the Linux kernel that allows you to run programs in a restricted, sandboxed environment. These programs can be attached to various kernel hooks to observe and modify system behavior.

Key Use Cases#

• Performance Monitoring: Track system calls, network packets, and function calls
• Security: Implement runtime security policies
• Networking: Programmable packet processing
• Tracing: Low-overhead system tracing

Example: Tracing System Calls#

#include <linux/bpf.h>
#include <bpf/bpf_helpers.h>

SEC("tracepoint/syscalls/sys_enter_execve")
int trace_execve(void *ctx) {
    char msg[] = "execve called";
    bpf_trace_printk(msg, sizeof(msg));
    return 0;
}

char _license[] SEC("license") = "GPL";

Tools and Ecosystem#

• BCC: Python-based toolkit for eBPF programs
• bpftrace: High-level tracing language
• Cilium: eBPF-powered networking and security
• Falco: Runtime security monitoring

Conclusion#

eBPF represents the future of kernel observability, providing unprecedented visibility into system behavior with minimal overhead.

Production Notes 1#

For eBPF: The Future of Kernel Observability, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.

Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.

Production Notes 2#

For eBPF: The Future of Kernel Observability, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.

Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.

Production Notes 3#

For eBPF: The Future of Kernel Observability, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.

Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.