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scaling-load-assumptions

Verify and design for load scaling — data volume, transaction volume, request rate, and user count. Use when sizing infrastructure, choosing data structures, designing pagination and lazy-loading patterns, evaluating dependencies' rate limits, or load-testing before launch. Most scaling failures are load failures: a system that handled X requests/second can't handle 10X. The skill is anticipating the load the system will see and designing or testing for it deliberately.

plugins/HDeibler/universal-design-principles/plugins/process-and-robustness-principles/skills/scaling-load-assumptions/SKILL.md

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# Scaling — load assumptions

The first kind of scaling fallacy: assumptions about how the system will perform under load — data volume, request rate, user count, transaction volume. A system that handles current load gracefully may fail catastrophically at 10x or 100x load. Often the failure isn't a steady degradation but a sharp cliff: things work fine until the moment they don't.

The work of treating load assumptions is partly architectural (designing systems that scale) and partly verification (testing at production-like scale before launch).

## Common load-scaling failures

**Linear search on a growing dataset.** A simple "search through all records" works for small datasets and times out for large ones. Eventually requires indexing or dedicated search infrastructure.

**N+1 query patterns.** A code pattern that issues one database query per item in a list. Fine for 10 items; catastrophic for 10,000.

**Unbounded results.** "Return all matching records" fine for small queries; explodes when "all matching" is millions of rows.