Performance Optimization

When to use

• Improve a measured bottleneck under representative workload.

Not for: debugging for unexplained failures; cost-optimization for spending.

Steps

1. Define the slow operation and target; record workload, environment and measured baseline.

2. Profile and identify dominant CPU, I/O, query, rendering, network or contention costs.

3. Change one justified cause while preserving behavior; bound cache size, invalidation and concurrency where relevant.

4. Compare under the same conditions, inspect variance and regressions, and retain only justified improvements.

Verify

• Comparable before/after measurements exist; correctness and resource regressions are checked.

References

• engineering-principles.md — shared decision boundaries, scope and verification.
• human-audit.md — document consent, readability and human review.
• references/bottlenecks.md — symptom→cause decision tree, frontend/backend bottleneck tables, Core Web Vitals targets, performance budgets
• references/anti-patterns.md — N+1, unbounded fetching, image optimization, re-renders, bundle splitting, caching (with code examples)
• references/database-performance.md — DB-specific: EXPLAIN plan reading, index maintenance, slow-query analysis, N+1 detection, query anti-patterns, connection pooling
• references/sql-optimization.md — SQL-design optimization: composite/covering/partial index strategy, missing-vs-unused index audit, cursor vs offset pagination, materialized views, partitioning