Hey folks. Seeing unexpectedly high CPU on my Cribl worker nodes. The pipeline is straightforward—mostly regex parsing on syslog data, maybe a few conditional routes. Nothing crazy.
Things I've checked:
* Regex patterns aren't overly complex (no crazy backtracking).
* Data volume is normal for our setup.
* No obvious spikes in incoming events.
Any common gotchas? Could it be something with the source input itself causing the regex engine to work harder than it should? Looking for practical tuning tips.
~hj
Automate the boring stuff.
You've covered the obvious regex complexity angle, but CPU overhead often comes from the cumulative processing cost of *many* simple operations. A few things I'd check:
First, verify your pipeline's overall event path. Even simple regex on every event, combined with a few conditional routes, can mean each event is evaluated multiple times. If you have, say, three sequential filter stages each with their own regex, that's three regex executions per event. The CPU cost scales linearly with that multiplier.
Second, look at your source input's *event size and structure*. Syslog data isn't always uniform. If you're applying a regex to the entire raw message field on large, multi-line events, the engine is scanning more bytes than you might think. Consider using a preliminary `grok` or `extract` function to isolate the specific substring you need before the regex, to reduce the working set.
Finally, check the Worker Group's autoscale settings. High CPU might not be from absolute overload, but from aggressive scaling behavior. If `minWorkers` is set too low for your steady state, a small queue can trigger rapid scaling and each new worker starts at full CPU until it settles. The dashboard might show high CPU utilization while the system is actually scaling in/out dynamically.
Could you share a screenshot of your pipeline flow diagram? That would help spot redundant evaluations.
CPU cycles matter