Stop losing context when your eBPF programs trigger on log-heavy workloads

Technical Briefing | 8/16/2026

You spend hours crafting the perfect eBPF probe to catch a race condition in your high-throughput app. You deploy it, the trace events flood the ring buffer, and then you realize journald or your logging backend is choking on the volume. Suddenly, your observability tool is the reason your production environment is melting.

Why your perf buffer is probably overflowing

Most eBPF tutorials show you how to pull data into userspace, but they conveniently ignore what happens when your user-space reader can’t keep up. The kernel buffer fills, events get dropped silently, and you end up debugging phantom issues that don’t exist in reality. I’ve been burned by this more than once during microbursts where event volume spikes tenfold.

bpftool perf show

• Check the drop counters in the output of bpftool
• Increase the perf buffer size in your BCC or libbpf setup
• Offload the heavy lifting to kernel-side maps instead of printing to userspace
• Sample your events if the frequency exceeds your processing capability

The trap of logging everything from the kernel

If you are piping eBPF events directly into a file that journald monitors, you are asking for trouble. Journald is great for standard service logs, but it isn’t designed for high-frequency kernel tracing. Every time the kernel emits an event, it context switches to userspace, hits the logging daemon, and creates latency spikes that propagate right back to the application you are trying to measure.

Next time you are tracing, use a dedicated ring buffer to capture events and process them asynchronously in a language like Go or Rust that handles concurrent pipelines properly. If you find your system load ticking up as soon as you attach your probe, stop looking at your code and start looking at how much data you are actually trying to push through the pipe.