I've been in backend engineering for about 15 years, and whenever I notice a spike in API latency or timeout on requests, I generally follow a routine: check application logs, analyze distributed traces using tools like Jaeger or Datadog APM, and then look at standard system metrics such as top and CloudWatch.
However, I recently faced a puzzling issue where the API latency spiked randomly. My initial checks yielded clean logs, and distributed traces revealed gaps indicating the application was simply 'waiting' without any database or external requests blocking it. Host metrics, including CPU and load, appeared completely normal.
It turned out to be a misconfigured cron script that was launching around 50 heavy worker processes every minute. These daemons would run for about 650ms, significantly taxing the CPU before exiting. Consequently, by the time monitoring tools like top or our system agent, which updates every 15 seconds, checked in, the workers were already gone, misleading the monitoring dashboard into reporting the server as 'idle'. However, the CPU switching during that brief period was disrupting our API requests.
This experience led me to delve into eBPF for a more effective monitoring method, shifting from a polling model that takes snapshots every few seconds to a tracing approach that responds to events in real time. By hooking into kernel tracepoints with eBPF, we could see exactly when these workers were created, allowing us to pinpoint the source of the latency spikes. If anyone's interested, I've been compiling notes and insights on my findings.
8 Answers
To catch those spiky processes, I’d run frequent data-gathering commands to make sure I’m capturing the moments when things start to go wrong. Sometimes, even logging can give you a clue into what's really happening behind the scenes.
This is a great read! It really resonates with Brendan Gregg's work. If you haven't seen it, his insights on Linux performance are a must-watch. Remember, small processes can be a hidden cause of performance issues!
For monitoring issues like this, eBPF can feel like a super power. These days, I generally prefer it over traditional logging methods. It makes the world of observability so much better!
Sounds like classic dev errors! I usually deal with dev cron jobs by adjusting their priority or staggering their execution times. If you have a web server running multiple sites and everyone's triggering tasks simultaneously, you’re definitely asking for trouble.
Your post is interesting, but the AI-generated vibe is hard to ignore. Can you give us a brief summary of your findings?
Yeah, I thought the same! But I think the depth of knowledge shows it's from an experienced source.
I’m interested to know how you ended up using eBPF for monitoring. If you thought cron jobs were the issue, wouldn't checking crontab be a quick way to identify the problematic job?
True, checking crontab would have been the straightforward step. In our case, we didn’t even realize it was cron-related until after observing the worker spikes. It was quite the mystery until we dived deeper with eBPF.
Using Prometheus or tools like CloudWatch can give you better insights, especially when working with Kubernetes, but they still can miss short-lived processes. If something happens between scraping intervals, you're left in the dark!
Honestly, not catching this after so many years in backend engineering is surprising. For inexplicable spikes disappearing from monitoring tools, short-lived processes are often the cause. In the past, it was usually cron jobs.
It does feel like it's structured like AI, but the logic is really clear and makes sense.