The Vanishing CPU: A ClickHouse Case Study on Debugging with Kernel Memory Reclaim in the Clouds

Operating Systems intermediate operating-systems 4 min read March 16, 2026

Picture this: ClickHouse Cloud on GCP encounters random, unresponsive pods where CPU spikes to 100% and signals go unheard. It isn’t a single buggy line of code; it’s production hell where typical tracing fails 1. This isn’t just a bug hunt; it’s a crash course in reading the orchestra of a cloud kernel and learning when memory reclaim can masquerade as a user-space problem 1.

Illustration: ClickHouse: Kernel memory reclaim can cause producti

Hook and Stakes

In the real world, a data analytics service suddenly loses responsiveness across multiple pods. The team leans on familiar tools, but the signals don’t cooperate. The pressure isn’t only latency—it’s gotta keep dashboards live and customers honest. This story starts with a 3am paging event and ends with a kernel-level windfall of insights that change how production is approached forever. The ClickHouse incident becomes the blueprint: when CPU is pegged and standard tracing stalls, the root cause can hide in kernel memory reclaim behavior, especially in cloud kernels 1 .

The Hunt: From Signals to Silence

You start with the basics—identify the offender with top or htop to pick out the PID quietly siphoning cycles 2 . Then peek into /proc/PID/status to confirm the process state and activity. If the stack stays murky, attach strace -p PID to watch system calls in real time and spot surprising stalls 3 . When user-space traces reach a dead end, a deeper look into the kernel story is required; attaching a debugger like gdb -p PID can reveal where the thread is blocked, especially if it’s stuck waiting on kernel resources 5 . The journey often reveals a tension between what the app does and what the kernel memory manager is doing behind the curtain 1 .

The Twist: Kernel Memory Reclaim in the Spotlight

The twist is counterintuitive: production delays may resemble a stubborn user-space bug, but the culprit is memory reclaim throttling or livelocks within the kernel. Modern cloud kernels can exhibit intermittent reclaim behavior, particularly under memory pressure or unusual memory reclaim policies like MGLRU, which can mask as unresponsive CPU behavior 1 . To uncover this, engineers turn to kernel-space tracing and sampling tools— perf and flame-graph style visualizations help map long-latency stalls back to memory reclamation paths 6 7 . When signals and standard tracing fail, kernel tracing becomes essential, and reproducible stress tests validate hypotheses before changing rollout plans 1 .

Resolution: From Diagnosis to Guardrails

Resolution hinges on disciplined instrumentation and controlled restarts. If a process remains unresponsive to SIGKILL , the immediate action is to terminate with care, then inspect logs for patterns that hint at resource contention, memory pressure, or I/O blocking. The ClickHouse team confirmed that kernel memory reclaim issues required rigorous testing and staged rollouts before stabilizing production; journalism aside, the practical takeaway is to craft reproducible workloads that stress memory reclaim in staging before a cloud rollout 1 . Real-World Case Study ClickHouse ClickHouse Cloud on GCP experienced random, unresponsive pods where CPU usage spiked to 100% and could not be profiled with standard tools, forcing manual restarts; investigation revealed intermittent, cloud-specific kernel behavior affecting memory reclaim. Key Takeaway: Kernel memory reclaim can cause production delays that resemble user-space issues; when signals and traditional tracing fail, kernel tracing (bpftrace, perf) and reproducible stress tests are essential; modern kernel features like MGLRU can mitigate stubborn livelocks, but cloud-provider kernel differences require careful rollout and testing.

CPU Debugging Flow

graph TD A[High CPU process] --> B[Identify PID with top/htop] B --> C[Inspect /proc/PID/status] C --> D[strace -p PID]--> E[If kernel stall detected, attach gdb -p PID] E --> F[Decide kill vs. continue] F --> G[Roll out mitigations via staged testing] Did you know? MGLRU, a modern memory reclaim strategy, aims to reduce livelocks in cloud kernels, but it requires careful rollout and testing across provider variants Key Takeaways Identify high-CPU processes with top/htop Observe /proc/PID/status for state and activity Trace system calls with strace -p PID and inspect kernel stalls Use kernel tracing (bpftrace, perf) for deeper visibility Test changes with reproducible stress tests before rollout References 1 The case of the vanishing CPU: A Linux kernel debugging story article 2 Linux kernel article 3 Strace repository 4 Virtual memory article 5 Linux kernel source repository 6 Perf tools repository 7 FlameGraph repository 8 Process (computing) article 9 bpftrace repository 10 Linux performance analysis with Brendan Gregg repository Share This Ever wondered why a CPU spike hides in the kernel? 🔧 ClickHouse faced random, unresponsive pods as CPU hit 100% and signals failed.,The root cause often lurks in kernel memory reclaim, not in application code.,Kernel tracing plus reproducible stress tests turn the tide—staged rollouts seal the deal. Dive into the full story and learn how to defend production against stubborn livelocks. #SoftwareEngineering #SystemDesign #BackendDevelopment #CloudComputing #LinuxKernel #PerformanceTuning #Observability #DevOps undefined function copySnippet(btn) { const snippet = document.getElementById('shareSnippet').innerText; navigator.clipboard.writeText(snippet).then(() => { btn.innerHTML = ' '; setTimeout(() => { btn.innerHTML = ' '; }, 2000); }); }

System Flow

graph TD A[High CPU process] --> B[Identify PID with top/htop] B --> C[Inspect /proc/PID/status] C --> D[strace -p PID]--> E[If kernel stall detected, attach gdb -p PID] E --> F[Decide kill vs. continue] F --> G[Roll out mitigations via staged testing]

Did you know? MGLRU, a modern memory reclaim strategy, aims to reduce livelocks in cloud kernels, but it requires careful rollout and testing across provider variants

References

Wrapping Up

The moral: production reliability hinges on looking both above and below the user-space surface. Instrumentation, reproducible tests, and staged deployments make the difference between a one-off fix and a durable solution. Talk less, trace more, and test in prod-like environments.

Satishkumar Dhule
Satishkumar Dhule
Software Engineer
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