Learn scalable system architecture and ace design interviews
Horizontal scaling adds more machines; vertical scaling adds more power to existing ones. Choose based on cost, complexity, and traffic patterns.
Distributes incoming traffic across multiple servers to ensure no single server bears too much load. Algorithms: Round Robin, Least Connections, IP Hash.
Store frequently accessed data in fast storage (Redis, Memcached). Strategies: Cache-Aside, Write-Through, Write-Back. Always plan for cache invalidation.
SQL for structured data with ACID guarantees. NoSQL for flexible schemas, high throughput, and horizontal scaling. Choose based on your data model.
Decompose large applications into small, independent services. Each service owns its data and communicates via APIs or message queues.
CAP theorem: you can only guarantee two of Consistency, Availability, Partition Tolerance. Understand trade-offs before designing your system.
Design a system like bit.ly that shortens long URLs and redirects users. Consider: hashing, redirects, analytics, scale.
Design a real-time messaging system like WhatsApp. Consider: WebSockets, message delivery guarantees, group chats, media storage.
Design a news feed system like Facebook. Consider: fan-out on write vs read, ranking algorithms, pagination, real-time updates.
Design a system like Uber. Consider: location tracking, driver matching, surge pricing, trip management, real-time GPS updates.
Always clarify functional and non-functional requirements first. Ask about scale, latency, consistency needs before designing anything.
Sketch out your APIs and core entities early. This forces you to think clearly about what the system does before you worry about how.
Identify bottlenecks early. Where will load concentrate? Which components need to scale independently? Plan for 10x your initial estimate.
There's no perfect design. Interviewers want to hear you reason about trade-offs: consistency vs availability, latency vs throughput, cost vs performance.