Use eBPF in Home Lab Monitoring
If you want reliable home lab eBPF monitoring that mirrors production observability, you’re in the right place—we’ll show a practical approach that gives kernel-level visibility without changing every application.
We use tools like Grafana Beyla and OpenTelemetry to surface network and app behavior directly from the linux kernel. That means quick wins: zero-code visibility for off‑the‑shelf applications and consistent dashboards you can trust when you scale.
Along the way we’ll cover JVM tracing with USDT and uprobes, smarter block I/O timing that cuts overhead, and the pragmatic steps to test and iterate. Expect clear examples, hardware caveats for small boards, and guidance that ties signals back to the system so troubleshooting stays sane over time.
Key Takeaways
- Run Grafana Beyla and OpenTelemetry for fast, kernel‑level observability.
- Get visibility without modifying applications—great for off‑the‑shelf services.
- Use USDT/uprobes for JVM GC timing and kernel tracepoints for process events.
- Prefer per‑CPU histograms for I/O timing to reduce overhead on modern drives.
- Plan and test on your small rig, note hardware caveats, then iterate toward production patterns.
Plan your home lab eBPF monitoring approach
Start by scoping what you actually need to observe—this keeps effort focused and results useful.
We begin by listing the layers to watch: application requests, network protocols, JVM memory and GC, and block I/O. That lets us prioritize the right signals at each layer and target quick wins first.
What you’ll observe
- Application and network: use kernel-level visibility to capture rate, errors, and duration without changing applications.
- JVM: attach to USDT for GC begin/end when available; otherwise fall back to uprobes on key methods.
- Block I/O: record latency distributions and totals—vital for NVMe or high-concurrency setups.
Define the tracepoint list early—examples include sched_process_exit for lifecycle events and request completion points for I/O. Map each traceback to the system view so dashboards make sense when you correlate signals across layers.
Document a baseline: time sources (wall vs boot), resource limits, and expected traffic. Start rollout on a single node or namespace to verify signal quality and overhead before wider deployment.
Capture risks up front—kernel version needs, map memory ceilings, and fail‑open behavior—and note mitigations. For a quick primer on probes and tooling, see start eBPF programming with BCC tools.
Zero-code app and network observability with Beyla and OpenTelemetry
We often want instant visibility without changing running services, so zero-code instrumentation is a fast way to get there.
Grafana Beyla uses ebpf to capture network and app activity at the kernel level and export OpenTelemetry signals or Prometheus metrics. Install is simple — a Helm command deploys Beyla as a DaemonSet and you get node-wide signals within minutes.
What you get quickly
RED metrics and distributed tracing for HTTP and Redis arrive out of the box. Beyla enriches the telemetry with Kubernetes labels and zone metadata so traces correlate across runtimes.
Trade-offs and hardware notes
Zero-code covers most cases, but it omits sensitive details like full SQL queries by design. Native instrumentation still wins for deep business context.
| Aspect | Beyla (zero-code) | Native instrumentation |
|---|---|---|
| Setup | Helm DaemonSet, one command | SDKs per service, code changes |
| Context depth | High-level traces, labeled metadata | Full business context, query text |
| Hardware caveats | May need kernel changes on Raspberry Pi | Runs wherever runtime supports SDK |
Decide the way this tool fits your production approach: use Beyla for breadth and speed, then add manual tracing to a few critical services.
JVM GC and heap usage: USDT, uprobes, and tracepoints for real-time insights
Capturing JVM GC and heap signals gives quick, actionable visibility into memory pressure. We start with USDT probes when available to avoid touching JVM code.
Prototyping with bpftrace
Attach to HOTSPOT_MEM_POOL_GC_BEGIN and _END (mem__pool__gc__begin/end) to print manager and pool names and bytes used. These bpftrace snippets are fast to iterate and show heap trends in real time.
When USDT is disabled
If USDT is off, we place a uprobe on GCTracer::report_gc_heap_summary in libjvm.so. That lets us read a struct field like _used by offset (for example, 32 bytes) to estimate heap usage.
Process lifecycle and production tooling
We catch exits with the sched:sched_process_exit tracepoint so dashboards don’t keep stale series. Then we evolve bpftrace code into a Rust tool using libbpf-rs.
| Stage | What we attach | Output |
|---|---|---|
| Prototype | USDT probes | Heap names, bytes |
| Fallback | uprobes on libjvm.so | Offsets from struct |
| Production | tracepoints + ring buffers | OTLP stream |
We handle namespaces and pid mapping, align bpf_ktime_get_ns with wall clock using boot-time nanoseconds, and validate symbols across versions. Always verify struct layouts per build to avoid bad reads and keep overhead low in the final ebpf program.
High-throughput storage: faster eBPF biolatency for homelab NVMe rigs
Modern NVMe machines push a lot of IO. That exposes where classic biolatency tools fail — and why we need a simpler approach.
What breaks at scale
Traditional tools use two tracepoints and a global start map keyed by request. Each request hits the kernel twice and updates the same hash, causing heavy hashmap contention.
The result: massive CPU processing, higher latency per I/O, and throughput drops on big hardware. In one machine case the classic flow added ~16 microseconds per IO and consumed hundreds of CPUs.
The improved method
We removed the issue tracepoint and the global start map. At completion we read io_start_time_ns directly from the struct request.
That makes one tracepoint per completion and one time read — far less code in the kernel and a much smaller processing footprint.
Per-CPU histogram maps
Switching the histogram to a per-CPU map eliminates atomic contention. The kernel does minimal work; user space aggregates per-CPU buckets later.
| Aspect | Classic | Improved |
|---|---|---|
| Tracepoints | Issue + Complete | Complete only |
| Start storage | Global start map | Read io_start_time_ns from struct |
| Latency cost | ~16 μs per IO | ~271 ns per IO |
| Throughput | Reduced on big machines | Nearly unaffected; 38.5M IOPS & 173 GiB/s tested |
Practical tuning
Adjust map size sparingly — without the start map you need fewer entries. Account for CPU counts and NUMA when sizing per-CPU maps.
Document your time source: use completion time minus io_start_time_ns in nanoseconds so histograms are consistent.
Watch exporter cadence and cleanup loops; many per-CPU maps need careful exporter timing to avoid rare cleanup hiccups. Finally, pick kernel and linux kernel versions that expose the needed struct fields and compile the ebpf program with CO-RE.
Next steps to turn experiments into reliable observability
Treat successful experiments like code—document the command to deploy, the test plan, and the roll-back steps so changes are safe in production.
We keep the zero-code tool for broad coverage, then hand-instrument a few critical applications where method-level context matters. Define SLOs for exporter lag, CPU usage, and data retention so teams agree on acceptable levels.
Validate programs in a staging namespace: check current time alignment, pid mapping, and struct compatibility across versions. Track usage, bytes retained, and bandwidth budgets. Finally, schedule follow-up work—improving per-CPU map cleanup, refining JVM offsets, and capturing case-specific caveats in runbooks—so the approach becomes reliable operational work.
