System design interviews determine career trajectories at the world’s top technology companies. Unlike coding interviews that test algorithmic problem-solving in isolation, system design interviews evaluate how you architect real-world systems under realistic constraints. The questions appear deceptively simple, ‘Design Twitter’ or ‘Design a URL shortener,’ but answering them well requires demonstrating trade-off analysis, scalability thinking, and communication skills that only come from deliberate practice.
How to Approach System Design Questions
Before diving into specific questions, understanding the evaluation framework that interviewers use improves preparation efficiency. Interviewers assess four primary dimensions: problem scoping and requirement clarification, high-level architecture and component identification, deep technical dive into critical components, and discussion of trade-offs, bottlenecks, and scaling strategies. To build a foundation before practicing these questions, check out the best system design books that cover these concepts comprehensively.
The biggest mistake candidates make is jumping immediately into solutions without clarifying requirements. Interviewers deliberately present ambiguous problems to evaluate your ability to ask clarifying questions. Establishing functional requirements, non-functional requirements like expected scale and latency, and constraints like budget or technology limitations should consume the first five to ten minutes of any system design interview.
Question 1: Design a URL Shortening Service (Like Bitly)
This classic question evaluates your understanding of key generation, database design, and caching strategies. Begin by clarifying requirements: how many URLs will be shortened daily, how long should shortened URLs remain valid, do we need analytics on click-through rates, and what are the latency requirements for redirection.
A strong answer includes a high-level architecture with an API gateway, application servers, a key generation service, a database storing URL mappings, and a cache layer. The deep dive should cover key generation algorithms like base62 encoding of auto-incrementing IDs versus MD5 hashing with collision handling, database schema design including indexing strategies, cache eviction policies like LRU for hot URLs, and redirection mechanisms using HTTP 301 versus 302 status codes with trade-offs for each.
Scaling Considerations
Discuss horizontal scaling of application servers behind load balancers, database sharding strategies partitioned by key range or hash, cache distribution using consistent hashing, and rate limiting to prevent abuse. Address single points of failure by introducing database replication, multi-region deployment for global availability, and monitoring systems to track performance metrics and detect anomalies.
Question 2: Design a Distributed Cache (Like Redis or Memcached)
This question tests your knowledge of distributed systems fundamentals including consistency, availability, partition tolerance trade-offs. Start by defining requirements: what data types does the cache support, what eviction policies are needed, should the cache be durable or in-memory only, and what consistency guarantees are required.
Architecture components include client libraries with consistent hashing for shard selection, cache servers distributed across multiple nodes, a coordination service like ZooKeeper for cluster membership, and optional persistence layers for durability. Deep dive into consistent hashing implementation to minimize key redistribution when nodes are added or removed, eviction policies including LRU, LFU, and TTL-based expiration, replication strategies for fault tolerance with trade-offs between synchronous and asynchronous replication, and handling cache misses through cache-aside, read-through, or write-through patterns.
Question 3: Design a Rate Limiter
Rate limiting prevents system abuse and ensures fair resource allocation. Clarify whether rate limiting is per user, per API key, or per IP address, what time windows apply such as requests per second versus per hour, and whether limits should be hard or soft with burst allowances.
Implement rate limiting using token bucket algorithm for smooth rate enforcement with burst capacity, leaky bucket for strict rate enforcement, or fixed window counters with sliding window variations to avoid boundary condition issues. Store rate limit counters in Redis for shared state across application servers, implement distributed rate limiting to handle multi-region deployments, and provide clear error responses with headers indicating remaining quota and reset time.
Question 4: Design a News Feed System (Like Facebook or Twitter)
News feed generation combines fanout strategies, ranking algorithms, and real-time updates. Define functional requirements around posting content, following users, viewing personalized feeds, and non-functional requirements including feed generation latency, consistency requirements for new posts, and expected daily active users.
Architecture includes a post service handling content creation, a graph database storing social connections, a fanout service pushing posts to follower feeds, a feed cache storing pre-generated feeds, and a ranking service applying relevance algorithms. Compare fanout-on-write where posts are pushed to all follower feeds immediately versus fanout-on-read where feeds are generated on demand with hybrid approaches for users with millions of followers to avoid overwhelming the fanout service.
Question 5: Design a Notification System
Multi-channel notification systems deliver messages through email, SMS, push notifications, and in-app alerts. Clarify supported notification types, delivery guarantees needed, personalization requirements, and rate limits to prevent notification fatigue.
Components include a notification service coordinating delivery, channel-specific handlers for email, SMS, and push, a template service for personalization, a priority queue for delivery scheduling, and a tracking database logging delivery status. Address at-least-once versus exactly-once delivery semantics with idempotency keys to prevent duplicate notifications, implement retry logic with exponential backoff for failed deliveries, and provide user preferences for notification frequency and channel selection.
Question 6: Design a Web Crawler
Web crawlers gather data from millions of websites while respecting politeness policies. Define crawl frequency, URL frontier management, duplicate detection strategies, and how to handle dynamic content requiring JavaScript execution.
Architecture includes a URL frontier prioritizing which URLs to crawl next, fetcher workers retrieving content, a content parser extracting links and data, a duplicate detector using bloom filters or checksums, and a distributed storage system for crawled content. Implement politeness policies respecting robots.txt, enforcing crawl delays, and managing crawler load to avoid overwhelming target servers.
Question 7: Design a Video Streaming Platform (Like YouTube)
Video platforms involve content upload, transcoding, storage, and adaptive bitrate streaming. Clarify video quality options, expected concurrent viewers, geographic distribution of users, and content delivery latency requirements.
Components include upload services handling large files with chunked uploads and resumable transfers, transcoding pipelines converting videos to multiple resolutions and formats, object storage like S3 for video files, a CDN distributing content globally with edge caching, and streaming servers implementing HLS or DASH protocols for adaptive bitrate delivery. Discuss thumbnail generation, video metadata indexing for search, recommendation algorithms, and content moderation systems.
Continuing Your Preparation
Practicing these questions builds pattern recognition for common system design problems. The best preparation combines reading about architectures in technical blogs and documentation, drawing system diagrams to visualize component interactions, discussing designs with peers for feedback on trade-offs, and studying real-world systems through engineering blogs from companies like Netflix, Uber, and Meta. Complementing practice with YouTube channels and courses for system design provides visual explanations and alternative perspectives that reinforce written learning.
Remember that system design interviews evaluate thought process more than perfect solutions. Demonstrating structured thinking, asking clarifying questions, discussing trade-offs explicitly, and adapting designs based on interviewer feedback matter more than memorizing specific architectures. With consistent practice using realistic questions, you can build the confidence and skills needed to succeed in even the most challenging system design interviews.
