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).