Seeing a lot of timeouts when our XSOAR playbooks call the CrowdStrike Falcon API. Happens on `device-count` and `detection-search` especially during peak ingestion.
* Timeout set to 120s, but still fails.
* Retries help, but slow down automation.
* Our volume is high, but within documented limits.
What's the actual ROI on tweaking timeouts vs. batching calls? Anyone solved this with:
* Specific instance sizing for the XSOAR engine?
* Adjusting CrowdStrike API priority parameters?
* A proxy layer for connection pooling?
—CR
Ask me about hidden egress costs.
Interesting. We've had similar timeouts and ended up adding a simple delay between batches in our playbooks, even with retries. It feels janky but it stopped the worst of it.
For the ROI question, tweaking timeouts just seemed to move the failure point for us. Batching calls was a bigger win, but we had to rewrite a few playbooks.
Did you check if your XSOAR engine is hitting CPU limits during those peaks? That was our hidden culprit.
CloudNewbie
Good call on checking the XSOAR engine CPU. We missed that at first and were chasing API configs. It was absolutely a factor.
We also saw timeouts just shifting around with longer settings. Batching was the real fix, but like you said, it meant reworking playbook logic. Not fun.
Did your delay help with the CPU spikes, or was scaling the engine the final step?
That timeout issue on device-count and detection-search rings a bell. We're still setting up our pipelines, but we saw something similar even at lower volumes. Tweaking timeouts just made things hang longer before failing for us, too.
I'm curious about the proxy layer idea for connection pooling. Has anyone tried something like that with a service like HAProxy in front of XSOAR? I'm wondering if it helps more with the API rate limits or the actual connection overhead.
rookie
Yep, CPU limits on the engine are such a common tripwire. It's easy to blame the external API first. We found the same thing - adding a delay did help smooth out the CPU spikes a bit by preventing a stampede of concurrent calls. It didn't eliminate the need for proper engine sizing, but it bought us time to plan the upgrade without the automation falling over every afternoon.
~Harry
Totally agree on batching being the real fix, even though it's a pain.
The delay did help our CPU spikes, but only as a temporary bandage. It smoothed the curve enough to stop the immediate fires. But scaling the engine was the final, permanent step. The delay just proved we were asking the current box to do too much at once.
data over opinions