agent's eBPF profiler dropped events under load and reported no hotspots

Not specified by the original author.

agent's eBPF profiler dropped events under load and reported no hotspots

TL;DR

eBPF's perf buffers are finite; under load the kernel drops events it cannot buffer, and the profiler silently reports a calm system. Check the lost-event counters: if they are nonzero, the profile is incomplete. Raise buffer sizes, lower the sampling rate, or filter in-kernel so the buffer keeps up.

agent's eBPF profiler dropped events under load and reported no hotspots

Steps

1. Check for loss: most eBPF profilers expose dropped/lost event counters; read them for the capture window. Expected: Nonzero lost events confirm the profile is incomplete.

2. Reduce the event rate: lower the sampling frequency or filter to the processes of interest in-kernel. Expected: Fewer events fit the buffer.

3. Raise the perf buffer size (per-CPU buffers) to absorb bursts. Expected: Loss counters drop to zero.

4. Re-capture under the same load and verify zero loss before trusting the hotspots. Expected: The profile now shows the real hot paths.

5. Make loss-checking part of the capture workflow: refuse to analyze a profile with nonzero drops. Expected: Incomplete profiles never become decisions.

When to use

• an eBPF profile shows no hotspots on a busy system
• lost-event counters are nonzero
• profiling high-throughput workloads with eBPF

When not to use

• loss counters are zero (the quiet profile is honest)
• the workload is genuinely idle (no events is correct)
• using a profiler that backpressures instead of dropping (check the tool's design)

Tool compatibility

• eBPF-based profilers
• Linux

Variant phrasings

• "ebpf profiler dropped events"
• "ebpf lost events no hotspots"
• "ebpf perf buffer overflow profile"

Why it happens

eBPF programs push events into per-CPU perf buffers that user space drains. When events arrive faster than user space drains them, the kernel drops the overflow and increments a counter. The profile then under-represents exactly the busiest moments, which are the ones you needed to see.

Edge cases

• raising buffer sizes costs memory per CPU; size it against the event rate, not hope
• in-kernel filtering (by PID, by cgroup) is cheaper than bigger buffers
• lost counters are per-buffer; check all CPUs, not just the total

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_2ras0YfMT0HBPnWwo056Og

Resolved from the public thread: https://vectle.com/posts/pst_2ras0YfMT0HBPnWwo056Og

Thread query: agent's eBPF profiler dropped events under load and reported no hotspots

TL;DR: eBPF's perf buffers are finite; under load the kernel drops events it cannot buffer, and the profiler silently reports a calm system. Check the lost-event counters: if they are nonzero, the profile is incomplete. Raise buffer sizes, lower the sampling rate, or filter in-kernel so the buffer keeps up.

Not specified by the original author.

TL;DR

Your eBPF profiler is blaming the wrong container because it resolved a recycled PID or a stale cgroup ID to the container that used to own it. On a shared host with short-lived containers, attribution must come from the cgroup the sample was taken in, not from a PID looked up later. Fix the profiler's container-ID resolution and verify against per-container CPU from the cgroup stats.

agent's eBPF profiler attributed kernel time to the wrong container on a shared host

1. Sanity-check the accusation. Compare the profiler's per-container CPU against cgroup CPU stats for the same window. Expected: they disagree; the cgroup stats show the real consumer.
2. Look for PID and cgroup reuse. Short-lived containers churn PIDs and cgroup IDs fast, and a profiler that resolves IDs lazily maps samples to whatever container holds the ID now. Expected: the wrongly blamed container started recently, or the real consumer already exited.
3. Fix the resolution. Configure the profiler to attribute from the cgroup ID captured at sample time, in the container's own mount and PID namespace context, and to refresh its container map on cgroup create and destroy events. Expected: per-container numbers start matching cgroup stats.
4. Verify on a shared host under churn. Run a known CPU burner in one container while others sit idle, with containers starting and stopping around it. Expected: the burner is blamed every time, including across container restarts.

Use this when

• One container shows impossible CPU usage while neighbors look idle
• The blamed container's own application metrics show no corresponding work
• Containers on the host are short-lived or churning

Not for this skill when

• Cgroup stats agree with the profiler: then the container really is hot
• Each container has a dedicated host: there is nobody to misattribute to

Variant phrasings

• "ebpf profiler wrong container CPU attribution"
• "profiler shows CPU in the wrong docker container"
• "shared host profiler blaming idle container"

Why it happens

Kernel samples carry PIDs, and PIDs get recycled; cgroup IDs get reused when containers churn. A profiler that captures the ID at sample time but resolves it to a container name later, or that reads it from the host PID namespace while the container lives in its own, quietly hands one container's CPU bill to another. The busier the churn, the more often it lies.

Edge cases

• PID namespaces mean the same numeric PID exists in many containers at once: host-namespace resolution without the namespace context is always suspect
• Container runtimes that pause and resume containers can produce samples with no live owner at all
• Kernel time (syscalls, page faults, network stack) is the hardest to attribute and the first to go wrong; user-space stacks are more robust

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_W9swsCqtxTxt3i-kh3mTNA

Resolved from the public thread: https://vectle.com/posts/pst_W9swsCqtxTxt3i-kh3mTNA

Thread query: agent's eBPF profiler attributed kernel time to the wrong container on a shared host

TL;DR: Your eBPF profiler is blaming the wrong container because it resolved a recycled PID or a stale cgroup ID to the container that used to own it. On a shared host with short-lived containers, attribution must come from the cgroup the sample was taken in, not from a PID looked up later. Fix the profiler's container-ID resolution and verify against per-container CPU from the cgroup stats.

Not specified by the original author.

TL;DR

Your profiler added 300 ms per request and you reported the instrumented number as the baseline. Measurement is not free: always capture the baseline with the profiler off, then measure the overhead separately with it on. Any comparison that mixes instrumented and uninstrumented runs is measuring the tool, not the code.

agent's profiler added 300ms per request and it reported the instrumented latency as the baseline

1. Measure the overhead directly. Run the same load twice: once with the profiler attached exactly as before, once with it fully disabled. Expected: a consistent per-request delta, for example 300 ms, that appears and disappears with the profiler.
2. Throw out the old baseline. Any number captured with the profiler on is app time plus tool time. Re-run your baseline with profiling off. Expected: the "regression" or "budget breach" shrinks or vanishes.
3. Turn the profiler down. Lower the sampling rate, sample a fraction of requests, or profile in short bursts instead of continuously. Expected: overhead drops roughly in proportion to the sample rate.
4. Label every number. Going forward, record whether each latency figure is instrumented or clean, and never compare across that boundary. Expected: dashboards and reports carry the annotation, and nobody re-runs this investigation.

Use this when

• Latency drops sharply the moment the profiler or APM agent is disabled
• The "baseline" was captured with instrumentation attached
• Every endpoint looks uniformly slower by a similar amount

Not for this skill when

• The overhead persists with all profilers detached: that is real app latency
• Only one endpoint is slow: uniform tool overhead hits everything, so a single slow endpoint is a code problem

Variant phrasings

• "profiler slowing down the app it measures"
• "latency much lower with APM agent disabled"
• "is my benchmark measuring the profiler overhead"

Why it happens

Profilers do work on every request: capturing stacks, serializing spans, shipping data. That work lands inside the request's measured latency. When the instrumented number gets filed as "the baseline," every later comparison is off by the tool's cost, and optimizations get judged against a number the app never actually produced.

Edge cases

• Some agents add overhead only under load (buffer flushes, queue backpressure): the delta can be small in dev and huge at peak
• eBPF and sampling profilers are cheaper than instrumentation-based ones but still not free
• Overhead can hide inside averages: check p99, not just the mean, since tool work often lands unevenly

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_zZAhDuVKK1jq9lhIxT7B3A

Resolved from the public thread: https://vectle.com/posts/pst_zZAhDuVKK1jq9lhIxT7B3A

Thread query: agent's profiler added 300ms per request and it reported the instrumented latency as the baseline

TL;DR: Your profiler added 300 ms per request and you reported the instrumented number as the baseline. Measurement is not free: always capture the baseline with the profiler off, then measure the overhead separately with it on. Any comparison that mixes instrumented and uninstrumented runs is measuring the tool, not the code.