Architecture
Designing systems that survive contact with real load and real teams.
Watch it animated → Interactive, click-through walkthroughs for Architecture concepts.Basic
What "Architecture" Even Means Software architecture is the high-level shape of a system - the major parts and how they talk - and the small set of decisions that are expensive to change later; this guide gives you the mental model from scratch.
Intermediate
Monolith vs Microservices, Straight What a monolith and microservices each actually are, the real strengths and real costs of both, and a clear way to choose instead of cargo-culting the architecture everyone's blogging about.
Caching, Explained A cache is a copy of an expensive-to-produce answer kept somewhere fast so you don't redo the work. This guide covers what a cache really is, where caches live, and why keeping them accurate is the genuinely hard part.
Event-Driven Architecture Systems that talk by emitting events instead of calling each other directly: queues, pub/sub, and choreography versus orchestration.
The Twelve-Factor App The canonical checklist for an app that is actually shippable and scalable: config in the environment, stateless processes, logs as streams, and more.
API Gateway, Explained A single front door in front of many backend services - what an API gateway actually does, and when it earns its keep versus when it's overkill.
Advanced
Designing for Scale (Load Balancing & Statelessness) How to take on more load without falling over: scale out instead of just up, make your servers stateless so any box can handle any request, put a load balancer in front, and push the parts that can't be cloned - sessions, the database, the cache - out to the edges.
Designing for Failure (Retries, Timeouts & Circuit Breakers) How to build distributed systems that bend instead of break: assume failure everywhere, add timeouts and backoff retries and circuit breakers, then fail soft with degradation, fallbacks, and bulkheads so one sick dependency can't sink the whole ship.
The CAP Theorem In a distributed system, a network partition forces a choice between consistency and availability - what CAP actually claims, why the choice is unavoidable, and what it looks like in real databases.