Scalable Coding—Find the Error
summary
Shows concrete failures and fixes: regex backtracking can explode latency (a hand-rolled split is ~8× faster than the regex example), throwing an exception costs about 7 µs (~200k CPU cycles) and at scale turns microcosts into outages, and a million-run comparison yields ~2 ms vs ~70 s for error-code vs expensive path in the demo. Recommends measurable fixes: use enums/constants instead of hardcoded strings, make operations idempotent or queue-based for creates, prefer SLO-embedded alerts, and always benchmark libraries and error paths.
AI-written summary · about
chapters (4)
related talks
33mWhy 10x Code Velocity Means 10x Operational Risk Without Preparation
52mPerformance & Stability Anti patterns
37mSolving Imaginary Scaling Issues, at Scale
42mHow difficult can it be to write efficient code?
09mHow to answer tricky questions the SRE way
19mAccelerating Code Quality with DORA Metrics
57mVerification, Cost, and Speed: What's Next for AI Coding at Scale
30mAvoiding software fails: few metrics to improve application reliability
