Posts

Every API needs rate limiting. Without it, a single misbehaving client can overwhelm your service, intentional attacks become trivial, and cost management becomes impossible. But implement it poorly, and you’ll frustrate legitimate users while barely slowing down bad actors. Let’s explore rate limiting patterns that actually work. The Fundamentals: Why Rate Limit? Rate limiting serves multiple purposes: Protection: Prevent abuse, DDoS attacks, and runaway scripts Fairness: Ensure one client can’t monopolize resources Cost control: Limit expensive operations (API calls, LLM tokens, etc.) Stability: Maintain consistent performance under load Algorithm 1: Token Bucket The token bucket is the most flexible approach. Imagine a bucket that fills with tokens at a steady rate. Each request consumes a token. If the bucket is empty, the request is denied. ...

Traditional request-response architectures work well until they don’t. When your services grow, synchronous calls create tight coupling, cascading failures, and bottlenecks. Event-driven architecture (EDA) offers an alternative: systems that react to changes rather than constantly polling for them. What Is Event-Driven Architecture? In EDA, components communicate through events — immutable records of something that happened. Instead of Service A calling Service B directly, Service A publishes an event, and any interested services subscribe to it. ...

A practical guide to load balancing — algorithms, health checks, sticky sessions, and patterns for keeping your services up when traffic spikes.

A practical guide to backups and disaster recovery — automated backup strategies, testing your restores, and building systems that survive the worst.

A practical guide to caching — when to cache, what to cache, and how to avoid the gotchas that make caching the second hardest problem in computer science.

A practical guide to database migrations — tools, patterns, and strategies for evolving your schema safely in production.

A practical guide to managing multiple environments — configuration strategies, promotion workflows, and patterns that prevent ‘works on my machine’ disasters.

A practical guide to logging — structured formats, log levels, correlation IDs, and patterns that make debugging production issues bearable.

Practical REST API design patterns — naming conventions, versioning strategies, error handling, and pagination that scales.

A practical guide to Git branching strategies — from trunk-based development to GitFlow, with patterns that scale from solo projects to large teams.