Top 50 System Design Interview Questions and Answers

If you’re gearing up for a tech interview at a top company like Google, Amazon, Meta, or Microsoft, mastering system design questions is essential. These interviews test your ability to architect scalable, reliable systems under real-world constraints. Whether you’re a seasoned engineer or just starting to prepare, this guide dives deep into the top 50 system design interview questions frequently asked in 2025, based on insights from FAANG interviews and industry experts. To kickstart your preparation with hands-on practice, sign up for our free courses on system design fundamentals and get the latest updates delivered straight to your inbox.

Understanding System Design Interviews

System design interviews evaluate how you approach building large-scale systems. Unlike coding problems, there’s no single “right” answer—it’s about your reasoning, tradeoffs, and ability to handle ambiguity. According to a 2025 report from Interviewing.io, over 70% of senior engineering candidates at FAANG companies face at least one system design round, with questions often drawn from real-world products like social media feeds or e-commerce platforms.

In these interviews, you’ll typically spend 45-60 minutes discussing a problem. Start by clarifying requirements: Who are the users? What’s the scale (e.g., 1 million daily active users)? Focus on high-level design first, then drill into components like databases, caching, and load balancing. Always justify choices—e.g., why SQL over NoSQL?

How to Prepare for System Design Interviews

Preparation is key to confidence. Begin with core concepts: scalability (horizontal vs. vertical), availability (99.99% uptime), and consistency (CAP theorem). Practice diagramming systems using tools like Draw.io.

• Study Patterns: Learn common designs like microservices, event-driven architectures, and sharding.
• Mock Interviews: Simulate sessions on platforms like Pramp or Interviewing.io.
• Resources: For foundational skills, explore our DSA course to strengthen algorithms, or the web development course for full-stack insights.

Aim for 20-30 practice questions. Track tradeoffs: For instance, caching improves speed but adds complexity. If you’re aiming for a comprehensive prep, check out our master DSA, web dev, and system design bundle.

Categories of System Design Questions

Questions often fall into categories like web applications, distributed systems, and infrastructure. We’ll cover 50 real questions asked in interviews, grouped for clarity, with in-depth answers including high-level designs, key components, and tradeoffs.

Web and Social Media Systems (Questions 1-10)

1. Design a URL Shortener (like TinyURL)

This classic question, asked at Google and Amazon, tests hashing and database choices.

High-level design: Users submit a long URL; the system generates a short one (e.g., tinyurl.com/abc123) via base-62 encoding of a unique ID. Redirects handle lookups.

Components:

• API server for shortening/redirecting.
• Database (e.g., Cassandra for scalability) to store mappings.
• Cache (Redis) for frequent redirects.

Tradeoffs: Collision handling with custom hashing vs. auto-increment IDs. Scale to 100M URLs/day requires sharding. Analytics? Add counters.

Actionable advice: Use MD5 hashing but truncate to avoid collisions. For practice, simulate with our crash course.

2. Design Pastebin

Frequently at Meta: Store and share text snippets.

Design: Frontend uploads text; backend generates ID, stores in blob storage (S3), and metadata in SQL.

Components: Load balancer, app servers, NoSQL for metadata.

Tradeoffs: Expiration via TTL in Redis. Security: Encrypt sensitive pastes.

3. Design Instagram

Amazon and Meta favorite: Photo sharing with feeds.

Design: Users upload photos to S3; metadata in DynamoDB. Feeds use fanout-on-write for celebrities.

Components: CDN for images, Kafka for notifications.

Tradeoffs: Read-heavy feeds favor eventual consistency.

4. Design a Web Crawler

Google staple: Crawl web pages efficiently.

Design: Seed URLs in queue (RabbitMQ), workers fetch/parse, store in indexed DB (Elasticsearch).

Components: Politeness policies to avoid overloading sites.