The honest results table is what makes this post stand out. Showing Spring Boot at 40 percent faster alongside Jenkins at parity and SonarQube slower is far more useful than a single best case number, and the explanation of why the SonarQube wrapper bypasses the cache turns a surprising result into a design lesson. The init container finding is the part I would stress to teams first: cutting JVM boot time from 3.18s to 1.88s while pod readiness stayed at 5s is a clear reminder to measure pod-to-Ready, not just process start. Curious to see the HPA scale-out study you mention, since cache fetch cost against a remote registry is where this approach will win or lose in production.
Thanks for the read :) You're spot-on about the HPA scale-out test. Local kind runs hide network latency, and simultaneous multi-pod bursts against a remote OCI registry introduce real-world fetch and throttling risks. Measuring how archive size impacts fetch time, alongside our fail-open fallback and layer-caching strategies, is the exact stress test I want to tackle before going beta.
OneLifeVibe
The honest results table is what makes this post stand out. Showing Spring Boot at 40 percent faster alongside Jenkins at parity and SonarQube slower is far more useful than a single best case number, and the explanation of why the SonarQube wrapper bypasses the cache turns a surprising result into a design lesson. The init container finding is the part I would stress to teams first: cutting JVM boot time from 3.18s to 1.88s while pod readiness stayed at 5s is a clear reminder to measure pod-to-Ready, not just process start. Curious to see the HPA scale-out study you mention, since cache fetch cost against a remote registry is where this approach will win or lose in production.