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---
name: performance-profiling
description: Measure before optimizing and confirm the gain after.
---
# Performance profiling
Optimization without measurement is guesswork that costs readability. Find the bottleneck, then fix
the bottleneck.
## Measure first
Profile under conditions that resemble production: realistic data volume, realistic concurrency, a
production build. Development builds and toy datasets hide the actual cost.
## Set a target
Define what fast enough means before starting — a latency budget, a percentile, a throughput figure.
Without a target, optimization has no finish line.
## Fix the dominant cost
Work on the largest contributor first. An improvement to code that accounts for two percent of
runtime cannot matter, however large the factor.
## Prefer algorithmic wins
Removing repeated work, unnecessary round trips and quadratic behavior beats micro-optimization.
Caching is a last resort, not a first move: it adds invalidation as a new class of bug.
## Verify and keep the number
Re-measure after the change and record the result. Track the metric so a regression is caught by the
build rather than by users.
