Uncover hidden HPA failures, silent scaling blockers, and advanced debugging techniques for CPU/memory autoscaling in production Kubernetes clusters.

Kubernetes prioritizes stability over immediate responsiveness, often using a 10% tolerance window that can act as a silent blocker to scaling even when an HPA looks healthy.
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샌프란시스코에서 컬럼비아 대학교 동문들이 만들었습니다

Nia: Hey Miles, I was debugging an HPA issue yesterday and discovered something wild - my autoscaler was perfectly healthy, showing all green conditions, but it wasn't scaling at all. Turns out there's this hidden 10% tolerance window that was keeping it from triggering!
Miles: Oh, that's such a classic gotcha! You know, I see this all the time where people expect immediate scaling when CPU hits 85%, but the HPA actually waits until it's outside that 0.9 to 1.1 ratio. So if your target is 85% and you're sitting at 93%, that ratio is about 1.094 - still within tolerance.
Nia: Exactly! And what really threw me was that the HPA status looked completely normal. No error messages, no failed conditions - it just seemed lazy. But there are actually several sneaky reasons why a healthy-looking HPA might not scale, right?
Miles: Right! Between replica limits, unready pods, sync delays, and that tolerance window, there are so many silent blockers. It's like the HPA is being cautious by design, but it doesn't always tell you why it's holding back.
Nia: That's fascinating how Kubernetes prioritizes stability over immediate responsiveness. So let's dive into a systematic approach for diagnosing these silent HPA issues and the specific kubectl commands that reveal what's really happening.