One of the most persistent headaches in my career was a situation that didn’t seem like a problem at all: sufficient CPU usage. My eyes would be glued to the metrics, CPU hovering around 10-20%, servers seemingly idling happily. Yet, the application was slow, users were complaining, reports were delayed. It was precisely in the midst of this illusion that the “BurnCPU” idea was born.
This idea didn’t just offer me a technical tool; it completely changed my perspective on systems and performance issues. Through years of experience, I’ve come to understand very well that sometimes the simplest-looking solutions hold the deepest lessons.
Performance Issues and Misleading Metrics
For twenty years working in system and network administration, I witnessed the same scene countless times: an application manager would come in saying “the system is slow,” and I’d open the metrics, stating, “CPU 15%, memory 30%, disk I/O low.” Everything would appear fine, but no one was happy. Especially in a production ERP system, month-end reports taking hours or operator screens lagging really made me think.
Once, I saw an instant data stream getting stuck on an internal banking platform. Again, the metrics showed low CPU. If the problem wasn’t the CPU, what was it? This situation convinced me how misleading it could be to only look at CPU percentage. At that moment, I realized that evaluating a system’s performance based on just one metric meant missing the big picture.
Why Did I Need BurnCPU?
Application slowdowns are often not directly related to the CPU. Most of the time, bottlenecks are hidden elsewhere: disk I/O, network latency, database locks, or even poorly written code itself. In my experience, I’ve often seen applications getting stuck due to WAL bloat issues in PostgreSQL or incorrect OOM eviction policy selection in Redis. Similarly, N+1 query problems, a common ORM trap, could leave the CPU idle while suffocating the database server.
Such situations pushed me to look for ways to accurately identify a system’s true performance capacity and bottlenecks. My goal was to definitively understand whether the application was truly bottlenecked by the CPU or by something else. This is where the BurnCPU idea began to sprout.
How Did the BurnCPU Idea Take Shape?
The BurnCPU idea was actually born from a very simple logic: If the system’s CPU is truly idle, what happens when I deliberately keep it busy? If the system slows down even more, the problem was with the CPU. If nothing changes, then it was certain that the CPU was dependent on something else. This “aha!” moment emerged from years of experience and the accumulation of endless performance problems.
Initially, I would try to burn the CPU with simple while true; do :; done & commands. Then, to make this more systematic, I started using tools like stress-ng within systemd unit files.
# systemd unit example
[Unit]
Description=CPU Burner Service
After=network.target
[Service]
ExecStart=/usr/bin/stress-ng --cpu 1 --timeout 300s --metrics-brief
Restart=always
Type=simple
[Install]
WantedBy=multi-user.targetThis simple method helped me identify real bottlenecks many times. For example, in the backend of my own side project, I used this method when integrating an AI model and testing the fallback mechanism between different providers like Gemini Flash, Groq, and Cerebras. When I deliberately burned the CPU, I saw that the latency of some providers increased dramatically, while others remained stable. This allowed me to clearly understand which provider performed better under CPU load.
What BurnCPU Taught Me and Its Impact on My Career
The BurnCPU idea didn’t just provide me with a practical troubleshooting tool; it also taught me a series of important lessons:
- Don’t Be Fooled by Surface Metrics: Metrics are important, but they don’t mean anything on their own. In-depth analysis and critical thinking are always necessary.
- Understand the Entire System: When evaluating a system’s performance, it’s crucial to consider the entire ecosystem (application, database, network, disk) as a whole, rather than focusing on just one component.
- Formulate Hypotheses and Test Them: Troubleshooting should be approached with a scientific method. Formulate a hypothesis, test it, and observe the results. BurnCPU became an important part of this testing process for me.
- Manage Trade-offs: Every architectural decision has a
trade-off. Should we choosemonolithormicroservice? Should we useevent-sourcing? Understanding the potential impacts of these decisions on CPU, network, and I/O became easier with tools like BurnCPU.
This simple idea gradually turned into a philosophy for me. It made me a better system architect and troubleshooter. Now, when I encounter a problem, my first thought is “Why is the CPU low?” and then I question the other layers of the system.
What Do You Think?
These kinds of situations I’ve encountered in my career have always shown me that even the simplest-looking problems can have deep lessons underneath. Have you had similar “BurnCPU” moments in your career that others overlooked but were turning points for you? What was your most expensive mistake or your biggest learning? Share in the comments; your experiences always inspire me.