System Design
Scalability, load balancing, CAP theorem, observability, and classic design problems explained step by step.
Courses
Master how microservices talk to each other — synchronous REST and gRPC, async messaging, event-driven patterns, resilience, and distributed consistency.
Learn how to design scalable, reliable, and maintainable systems — from single servers to distributed architectures used by millions of users.
From fundamentals to production — caching strategies, eviction policies, invalidation, Redis, CDN caching, and multi-level architectures with hands-on patterns throughout.
Articles
When a production system breaks at 3am, you need to answer three questions fast: what happened, how bad is it, and where is the bottleneck? …
Scaling is how a system handles more users, data, or traffic. Vertical scaling (scale up) means giving your existing server more resources —…
The CAP theorem states that a distributed system can guarantee at most two of three properties: Consistency (every read returns the latest w…
Docker and Kubernetes are often mentioned together, but they solve different problems. Docker packages your application and its dependencies…
Every new project faces the same question: one big application or many small services? The answer is rarely binary. A monolith ships fast an…
Load balancers distribute incoming traffic across multiple servers so no single machine becomes a bottleneck. But not all load balancers wor…
Every public API needs rate limiting — to prevent abuse, protect downstream services, and ensure fair usage across tenants. Without it, one …
Asynchronous messaging decouples services in time — producers send messages without waiting for consumers. But not all message brokers work …
Picking a database is not about finding the fastest engine — it is about matching storage to the shape of your data. Start with one question…
Microservices give teams independence — but independence without patterns leads to chaos. Six design patterns solve the most common problems…
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