Introduction to eBPF network observability on Linux
eBPF network observability on Linux lets you inspect sockets, TCP state, and latency without installing heavy agents. This hands on guide is geared to sysadmins, network engineers, and developers who need low overhead visibility in production.
We cover required tooling, practical examples for socket and TCP tracing, how to export metrics to Prometheus, and steps to visualize and troubleshoot network issues in Grafana. The examples assume Linux kernel 5.4 or later, and root access for loading programs.
Why use eBPF for network visibility
eBPF runs code in kernel context securely, so tracing is more efficient than user space packet captures or poll based agents. You get fine grained context, lower overhead, and the ability to attach to sockets, kprobes, tracepoints, and tc hooks.
For operational teams this means you can capture connection latency, retransmissions, and socket queue sizes in real time, correlate with application metrics, and reduce mean time to resolution for network issues.
Key tools and quick install on Linux
The common toolset includes bpftool, bpftrace, BCC, and optionally Cilium for advanced networking features. bpftool inspects and manages loaded programs, bpftrace provides quick one liners, and BCC contains production ready scripts.
Install essentials on Ubuntu Debian based systems with package manager or build from source if your kernel is newer. Typical packages are linux-headers, clang, llvm, bpftool, and bpftrace.
- bpftool for program inspection and maps
- bpftrace for quick tracing scripts
- BCC for mature tracing utilities and examples
Basic socket and TCP tracing examples
Start with bpftrace to trace connect latency and socket events. Example scripts attach to kprobes or tracepoints to log timestamps and stack identifiers. Keep traces focused to avoid high cardinality in production.
Example patterns include measuring connect time, counting retransmits, and sampling socket queue length. Use maps to aggregate metrics and export periodically rather than streaming raw events.
Exporting eBPF metrics to Prometheus
To get eBPF metrics into Prometheus, expose aggregated counters via an HTTP endpoint. A common pattern is user space exporter that reads eBPF maps with bpftool or libbpf, converts values to Prometheus metrics, and serves them.
Alternatively, use existing exporters such as the BPF Exporter project, or write a small Go or Python service that polls maps, resets values when appropriate, and provides scrape friendly labels.
- Poll map values at a controlled interval, for example 10 seconds
- Reduce label cardinality by aggregating by service port, not by socket id
- Handle map rollovers and provide stable label names for Prometheus
Grafana dashboards and useful metrics
Once metrics are in Prometheus, build Grafana panels for connect latency histograms, retransmit rate, socket drops, and application level error rates. Use heatmaps for latency distributions and rate panels for retransmits per second.
Label strategies matter: include the host and service labels, trim dynamic labels like ephemeral socket ids, and create derived metrics such as p95 and p99 for latency to surface tail behavior.
Running agentless in production, performance and security
Agentless here means no persistent user space probe per application, but you still deploy a small exporter to read eBPF maps. Keep eBPF programs minimal and validate them in staging to control overhead. Monitor CPU usage of the exporter and kernel eBPF verifier logs.
Security considerations include restricting who can load eBPF programs, using signed kernel modules if available, and limiting map size. Use seccomp and capabilities to reduce the attack surface of any exporter process.
Troubleshooting common issues
If bpftool shows verifier rejects, inspect program size and referenced helpers. Kernel versions can change helper availability, so verify compatibility and update clang or libbpf if needed.
Common runtime issues include missing kernel headers, map memory exhaustion, and label cardinality explosions in Prometheus. Use these checks to isolate causes quickly before rolling to production.
- Verifier failures: simplify program logic and test locally
- High cardinality: aggregate labels and reduce dimensions
- Map exhaustion: increase map size or cleanup stale entries
FAQs
The following frequently asked questions cover compatibility, overhead, and deployment patterns for eBPF network observability on Linux.
Answers are concise to help you make operational decisions quickly, and include pointers to tools and configuration choices.
- Q: Which kernel versions support eBPF network tracing?
A: Most modern kernels from 4.19 onward include core eBPF features, but advanced helpers and stability improve in 5.x releases. For production, prefer 5.4 or later and test your specific bpf programs. - Q: Will eBPF tracing add significant CPU overhead?
A: Properly scoped eBPF programs and aggregate maps add minimal overhead. The main cost is program attach points and map polling frequency. Keep sampling rates reasonable and test under representative load. - Q: Can I trace containers and namespaces?
A: Yes, eBPF operates at kernel level and can observe containers by filtering on cgroup or network namespace id. Use cgroup attachment points when you need per container isolation. - Q: How do I avoid Prometheus cardinality problems?
A: Reduce labels, aggregate by service or port, sample histograms rather than emitting per connection labels, and use relabeling rules in Prometheus to drop high cardinality labels.
Conclusion
eBPF network observability on Linux delivers powerful, low overhead visibility into sockets and TCP stacks, making it possible to diagnose latency, packet loss, and retransmits without heavy agents. By combining bpftool, bpftrace, BCC utilities, and a lightweight exporter to Prometheus, teams can collect high fidelity metrics and visualize them in Grafana for operational monitoring and troubleshooting.
Successful deployment requires attention to kernel compatibility, map sizing, and label strategy to avoid performance and storage pitfalls. Start small with targeted traces, validate in staging under realistic load, and incrementally expand coverage. Instrumentation that aggregates in kernel maps and exposes controlled metrics to Prometheus will scale much better than streaming raw events. With these practices you can bring production ready, agentless network observability to Linux servers, reduce time to detect network faults, and correlate network signals with application performance for faster remediation and improved reliability.