Performance Principles
General Principles
- Optimize for correct, clear code first; then optimize hot paths.
- Measure before optimizing when possible.
- Prefer algorithmic improvements over micro-optimizations.
Common Pitfalls
- N+1 queries in database access.
- Unbounded in-memory collections for large datasets.
- Synchronous calls in latency-sensitive paths.
- Repeated expensive computations without caching.
Guidelines by Layer
Backend
- Use efficient query patterns and indices.
- Batch calls where practical.
- Apply backpressure and rate limiting where needed.
Frontend
- Avoid unnecessary re-renders.
- Lazy-load heavy components and routes.
- Cache data appropriately (client & server).
Monitoring & Metrics
- Ensure critical paths have basic instrumentation:
- Latency, error rate, throughput.
- Define performance budgets (e.g., 95th percentile latency thresholds).
When to Escalate
- If a change risks breaching performance budgets.
- If complexity grows significantly (e.g., big-O behavior worsens).
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