Everyone's quick to slap on another security layer in their pipelines, especially with tools like Absolute Secure Access promising to lock down your ephemeral environments. But all that deep packet inspection, TLS termination/re-encryption, and heuristic analysis has to come from somewhere. That somewhere is your runtime's performance.
I'm running self-hosted GitHub runners on decent metal, and the difference between a clean Ubuntu image and one loaded with this class of security proxy is palpable. We're talking about adding potentially hundreds of milliseconds of handshake and validation overhead to every service-to-service call. In a microservices pipeline where a single integration test might make hundreds of internal HTTP requests, this doesn't just add up—it multiplies.
Has anyone actually put numbers to this? Not the vendor's "negligible single-digit ms" claim, but real measurements from a staging or pre-prod pipeline? I'm particularly interested in the latency introduced per hop, and the aggregate impact on total pipeline duration. My own crude testing shows a 15-20% increase in end-to-end test suite runtime, which for a 30-minute pipeline is a serious tax just for the "privilege" of more observability.
```bash
# A simple loop to compare, run from within the secured environment vs. a baseline
for i in {1..100}; do
curl -o /dev/null -s -w "%{time_total}n" http://internal-service:8080/health
done | awk '{sum+=$1} END {print "Avg:", sum/NR}'
```
If you're just running linters, maybe you don't care. But if your CI is actually building and validating integrated systems, this overhead directly impacts developer velocity and infrastructure cost. I'm skeptical the trade-off is worth it for most teams who already have segmented networks and decent secrets management.
null
Great question, and something that doesn't get measured enough. Your 15-20% figure rings true from what I've seen in community discussions.
The per-hop latency can be surprisingly variable depending on the security tool's architecture. I've heard from teams where the main hit wasn't on the handshake, but on the heuristic analysis for *specific* payload sizes. It created a "jumpy" latency profile that was harder to tune for.
Did you test with consistent, small payloads vs. larger ones? That's where the numbers often get interesting, and it might explain the multiplier effect you're seeing in a microservices chain.
Keep it civil, keep it real.
That's a good point about payload sizes. I've been trying to measure this on our staging Airflow workers, and I'm seeing something similar with large JSON payloads, maybe 1MB or more. The processing time for heuristic checks seems to spike unpredictably, not linearly. It makes our DAG runtimes really inconsistent.
How do you even begin to tune for that kind of "jumpy" latency? Do you just bake in huge buffer times for your task timeouts, or is there a smarter way to handle it? I'm worried about setting SLAs when the baseline is so shaky.
rookie