Web APIs

Back-End APIs Scalable Architecture Patterns Building scalable back-end APIs means designing for growth from day one. When traffic rises, you don’t want to rewrite the system. The core idea is to keep services small, stateless, and easy to replace. This guide shows practical patterns you can apply today. Stateless design and horizontal scaling A stateless service handles each request independently. It makes auto-scaling simple and predictable. Use JWTs or tokens for auth, and store user data in external stores like caches or databases. A load balancer can spread requests across healthy instances, and you can add more instances as load grows. Keep sessions outside the process, and idempotent operations to prevent duplicates. ...

Backend Web Servers Architecture APIs and Scaling Building a solid backend means more than writing code. It is about designing how servers, APIs, and data work together. A simple app grows fast, and a clear structure helps it stay fast and reliable. This article looks at common parts, how to design APIs, and how to scale when traffic rises. Architecture components Edge and gateway: DNS and a load balancer direct traffic to healthy services. Stateless application servers: workers run without long memory of past requests, so they scale easily. Data layer: databases, caches, and queues store and move information. Caching: an in‑memory layer (like Redis) speeds repeated reads. Messaging and queues: services talk through events and jobs without waiting. Observability: logs, metrics, and traces show what happens in real time. API design REST and GraphQL cover most needs. REST is simple and cache friendly; GraphQL gives precise data with fewer calls. Versioning and contracts keep clients stable when servers change. Consistency matters: decide between strong consistency for critical data, or eventual consistency for speed. Scaling and reliability Horizontal vs vertical: add more servers (horizontal) rather than just bigger machines (vertical) for better resilience. Load balancing spreads work across instances and helps tolerate failures. Auto-scaling groups adjust capacity based on demand, using metrics like latency and queue length. Caching reduces load on databases; set clear invalidation rules to avoid stale data. Data replication and read replicas help serve users fast across regions. Circuit breakers and retries protect a system from cascading failures. Deployment and security Blue/green or canary deployments minimize risk when releasing new code. TLS, rate limiting, and proper authentication keep data safe. Regular backups and tested recovery plans guard against data loss. Example flow: a user request hits the edge, moves through a load balancer to a stateless API service, checks the cache, and only if the cache misses does it query the database, then stores the result back in cache for next time. ...