Posts

In distributed systems, failures are inevitable. A single slow or failing service can cascade through your entire architecture, turning a minor issue into a major outage. Circuit breakers prevent this by detecting failures and stopping the cascade before it spreads. The Problem: Cascading Failures Imagine Service A calls Service B, which calls Service C. If Service C becomes slow: Requests to C start timing out Service B’s thread pool fills up waiting for C Service B becomes slow Service A’s threads fill up waiting for B Your entire system grinds to a halt One slow service just took down everything. ...

Deploying code and releasing features don’t have to be the same thing. Feature flags let you ship code to production while controlling who sees it, when they see it, and how quickly you roll it out. This separation is transformative for both velocity and safety. Why Feature Flags? Traditional deployment: code goes live, everyone gets it immediately, and if something breaks, you redeploy. With feature flags: Deploy anytime — code exists in production but isn’t active Release gradually — 1% of users, then 10%, then 50%, then everyone Instant rollback — flip a switch, no deployment needed Test in production — real traffic, real conditions, controlled exposure A/B testing — compare variants with actual user behavior Basic Implementation Start simple. A feature flag is just a conditional: ...

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.