Serverless Computing: Pros, Cons, Patterns
Serverless Computing: Pros, Cons, Patterns Serverless computing lets developers run code without managing servers. In practice, you write small functions that respond to events, and the cloud provider handles hardware, runtime, scaling, and security patches. This model fits many tasks, from API endpoints to data pipelines. It changes how teams think about architecture and cost, often making experimentation faster and ops lighter. Pros Automatic scaling and high availability Pay only for what you use, with no idle capacity Faster time to market and simpler operations Lower maintenance burden, less server management Easier experimentation with small, focused services Cons Cold starts can add latency for infrequent tasks Vendor lock-in and fewer choices for runtime control Execution limits and stateless design requirements Observability, debugging, and tracing can be harder Local development and end-to-end testing can be tricky Patterns Event-driven microservices: connect services with events or queues so parts can run independently and react to changes. Backend for frontend (BFF): tailor endpoints for specific apps to reduce overfetching and improve UX. Scheduled tasks: use a timer service for daily data processing, backups, or reports. API gateway with functions: HTTP requests route to small functions behind an API layer for scalability. Fan-out and fan-in: trigger several functions from one event and gather results later to reduce latency and improve throughput. Orchestration and durable workflows: coordinate long tasks with state, retries, and checkpoints. Edge patterns: run lightweight functions closer to users to cut response time in parts of the world. Example scenarios help planning. A storefront might have a function that validates a cart, another that charges a card, and a third that updates inventory. A daily job might aggregate analytics and archive logs. By keeping functions small and stateless, teams can scale quickly without managing servers. ...