Tech Due Diligence ARCH-4: Scalability and Performance Engineering

Design for horizontal scalability from the start. Application servers should be stateless (session state stored externally in Redis or similar), database connections pooled, file uploads directed to object storage (S3, GCS) rather than local disk, and background jobs processed through a queue (SQS, RabbitMQ, Redis) consumed by multiple workers. Build a caching strategy across multiple levels: CDN caching for static assets and frequently accessed content, application-level caching (Redis, Memcached) for computed results and query responses, database query caching for repeated lookups, and HTTP caching headers for cacheable API responses. Monitor cache hit rates and tune TTLs based on data freshness requirements. Run regular load tests using tools like k6, Locust, or Gatling. Define scenarios that mirror real-world usage patterns and projected growth. Establish performance baselines and detect regressions by running load tests in CI/CD or on a regular schedule. Set performance budgets for key user journeys. Define acceptable thresholds - for example, p95 API response under 200ms, page load under 2 seconds - and alert when they are exceeded. Move time-consuming operations to asynchronous processing: email sending, report generation, data processing, webhook delivery, notification dispatch. Use a reliable job queue with retry mechanisms and dead-letter queues for failed jobs. Identify and document known scalability limits honestly. Cover the maximum concurrent users tested, database row counts where performance degrades, external service rate limits that constrain throughput, and how costs scale with load (linearly, sub-linearly, or super-linearly). Create a scalability roadmap that addresses bottlenecks and projects when they will be hit based on growth forecasts. Proactive planning impresses assessors far more than reactive firefighting.