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scaling

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Use when traffic is growing or about to spike and the system bends under concurrency — deciding what to add and in what order (cache, connection pool, async queue, read replica, more instances) and proving it with a load test against explicit RPS and p95 targets rather than guessing. NOT making one slow request faster or profiling an N+1 (that is `performance`), NOT race-free Redis caches, locks and queue semantics (that is `redis`).

skills/scaling/SKILL.md

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# Scaling: survive many requests at once

Performance makes one request faster. Scaling makes many requests survive at the same time. Different problem, different toolbox — don't reach for this one when a single endpoint is slow for a single user (that is `../performance/SKILL.md`).

The whole job in one line: **diagnose the bottleneck, apply the cheapest lever that moves it, re-measure under load.** Repeat until the next bottleneck appears or you hit your target.

**Prime directive: never add infrastructure without a measurement first.** A replica, a queue, or a third app instance you bought on a hunch costs money every month and usually moves the wrong tier. Measure, then add.

## Start here — order of operations

Levers are ordered by payoff per dollar. Caching is nearly free and wins biggest; replicas and autoscaling cost forever. Climb the ladder in order; stop the moment the symptom clears.

| Symptom | Likely bottleneck tier | First lever | Sibling that wires it |
|---|---|---|---|
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