Hey folks, kerneldev here. Normally I'm deep in `/proc` or staring at `perf` traces, but I had this side task at work where I needed to get some internal tool data into our CRM automatically. My first instinct was to write a scrappy daemon in Rust, maybe with some cron magic... but then a colleague pointed me at Zapier.
I was skeptical—it's so far from my usual kernel/BPF world—but the "no-code" thing actually worked for this. The trigger was a new row in a Google Sheet (our clunky internal logging tool), and the action was creating/updating a contact in HubSpot.
What surprised me, from a systems perspective, was how they handle the plumbing. I started wondering:
* What's the actual latency overhead here vs. a direct API call?
* Is there a persistent process polling, or is it webhook-based under the hood?
* Could you trace the flow with eBPF to see the syscall chain it generates on their runner?
I ended up setting up a simple benchmark out of curiosity. Pseudo-code:
```bash
# Rough timing comparison
start=$(date +%s%N)
# Zapier "Zap" execution
curl -s "https://hooks.zapier.com/..." > /dev/null
end=$(date +%s%N)
echo "Zapier latency: $((($end-$start)/1000000)) ms"
# Direct API call
start=$(date +%s%N)
curl -s -X POST "https://api.hubspot.com/..."
-H "Authorization: Bearer $TOKEN" > /dev/null
echo "Direct API latency: $((($end-$start)/1000000)) ms"
```
The Zapier path added ~300-800ms of overhead, which for this use case was fine. It got me thinking about the trade-offs: developer time vs. runtime cost, and when these automation tools make sense even for those of us who usually reach for `strace` first.
I'm mostly here for the kernel, container, and low-level performance chats, but it's fun to see how the other half lives sometimes. Has anyone else used these kinds of automation platforms in devops or data piping contexts? I'm curious if folks have measured the resource footprint on the runner nodes.
System calls per second matter.