performance-reliability

Performance & Reliability

Performance and reliability must be designed, measured, and protected. Do not optimize blindly or defer obvious scalability risks.

Budgets

• Define expected latency, throughput, payload size, memory, storage, and concurrency for high-impact flows.
• User-facing flows should consider p95 and p99 latency, not only averages.
• Establish performance budgets for page weight, render time, API latency, and query count when applicable.
• If no budget exists, propose one before making risky design choices.

Hot Paths

• Avoid N+1 queries, unbounded loops with I/O, repeated serialization, and large synchronous work on request paths.
• Paginate or stream large datasets. Do not load unbounded result sets into memory.
• Load the collection-standards skill when implementation, QA automation, or DevOps scripting processes repeated collections, joins lists, scans logs, builds command matrices, or must prove O(n) or bounded complexity.
• Keep expensive work outside user-facing request paths through queues, jobs, or precomputation.
• Measure before and after performance changes and report the evidence.

Caching

• Cache only with clear ownership, TTL, invalidation strategy, and stale-data behavior.
• Do not cache sensitive data unless storage, access, and expiration controls are explicit.
• Prefer simple, observable cache strategies before multi-layer cache complexity.
• Document cache keys and invalidation triggers for shared caches.

Reliability

• Design for graceful degradation when dependencies fail.
• Add timeouts, retries, backoff, and circuit breakers for networked dependencies.
• Use bulkheads or queues to protect critical paths from non-critical workload spikes.
• Reliability-sensitive changes need load, stress, or synthetic tests when practical.