Step-by-step articles on System Design, part of our Backend Interviews collection.
Showing 10 articles
System design interviews are open-ended on purpose. Interviewers want to see structured thinking and trade-off reasoning more than a "correct" diagram. This framework works for almost any prompt. 1. Clarify requirements (5 minutes) Functional : what must it do...
A classic warm-up question that touches ID generation, read-heavy scaling and caching. Requirements Create a short URL for a long URL; optionally a custom alias and expiry. Redirect quickly (under ~50 ms), very high availability. Scale: ~100M new links per mon...
Rate limiters protect services from abuse and overload. This question tests algorithm choice and distributed-state thinking. Requirements Limit requests per user/API key (for example 100 per minute), with different tiers. Low added latency (a few milliseconds)...
Caching appears in almost every design. Strong answers name the cache layer, the pattern, how data stays fresh and what happens when the cache fails. Layers of caching Browser - Cache-Control headers for static assets and some API responses. CDN - images, scri...
Short answer: a load balancer spreads incoming requests across multiple servers so no single server is overloaded, and routes around servers that fail health checks. Layer 4 vs Layer 7 L4 (transport) : routes TCP/UDP connections by IP and port. Very fast, prot...
"The database is the bottleneck. How do you scale it?" Walk through the options from cheapest to most complex. 1. Optimise and scale up Indexes, query fixes, connection pooling and caching first. Then a bigger machine (vertical scaling). Modern servers are pow...
Short answer: in a distributed system, when a network partition happens (nodes can't talk to each other), you must choose between consistency (every read sees the latest write, or gets an error) and availability (every request gets a non-error response, possib...
Short answer: use a message queue when work doesn't need to finish within the user's request, when producers and consumers should scale and fail independently, or when traffic bursts need buffering. Classic uses Sending emails, generating PDFs, resizing images...
A popular question that combines APIs, queues, third-party integrations and reliability. Requirements Channels: mobile push, email, SMS, in-app. Triggered by other services ("order shipped") and by scheduled campaigns. Respect user preferences and quiet hours;...
Short answer: consistent hashing distributes keys across servers so that adding or removing a server moves only a small fraction of keys, instead of almost all of them. The problem with modulo server = hash(key) % N N = 4 -> 5 servers: about 80% of keys now ma...
