Design System Interview Questions and Answers
Master design system interview questions with our comprehensive guide. From monoliths to microservices, learn to build and articulate complex software architectures for your system design interview.
📋 Table of Contents
Core System Design Questions
Interview Success Guide
Essential Design System Interview Questions
🎯 What You'll Master
This comprehensive guide covers the most important design system interview questions asked at top tech companies like Google, Amazon, Facebook, Netflix, and Microsoft. Each question includes detailed explanations, architectural diagrams, and real-world implementation strategies.
💡 Quick Navigation: Use the table of contents above to jump to specific topics, or read through sequentially for complete preparation.
📋 Quick Navigation
Core System Design Questions:
- → URL Shortener Design
- → Chat System Design
- → Social Media Feed
- → Video Streaming Service
- → Ride-Sharing Service
Design Fundamentals:
Interview Success Guides:
💡 Tip: Click any link above to jump directly to that section!
1. Design a URL Shortener (like bit.ly)
Key Focus Areas: Scalability, Database Design, Caching Strategy
System Requirements
- Shorten long URLs to 6-7 character codes
- Handle 100M URLs per day
- Support custom aliases
- Provide analytics and click tracking
High-Level Architecture
- Load Balancer: Distribute traffic across multiple servers
- Application Servers: Handle URL encoding/decoding logic
- Database: Store URL mappings (consider NoSQL for scale)
- Cache Layer: Redis/Memcached for frequently accessed URLs
- Analytics Service: Track clicks and generate reports
Key Design Decisions
Encoding Algorithm: Use Base62 encoding (a-z, A-Z, 0-9) for short URLs. Consider using a counter-based approach with multiple servers or MD5 hashing with collision handling.
Database Schema: Primary table with columns: short_url, long_url, created_at, expires_at, user_id. Consider partitioning by creation date.
2. Design a Chat System (like WhatsApp)
Key Focus Areas: Real-time Communication, Message Delivery, Scalability
Core Features
- One-on-one messaging
- Group chats (up to 500 members)
- Online presence indicators
- Message delivery confirmation
- Push notifications
Technical Architecture
- WebSocket Servers: Maintain persistent connections for real-time messaging
- Message Queue: Apache Kafka for reliable message delivery
- User Service: Handle authentication and user management
- Notification Service: Send push notifications for offline users
- Media Service: Handle file uploads and media sharing
Message Flow Design
Online Users: Direct WebSocket delivery with acknowledgment
Offline Users: Store in message queue, deliver via push notification, sync on reconnection
Group Messages: Fan-out approach - replicate message to all group members' message queues
3. Design a Social Media Feed (like Twitter)
Key Focus Areas: Timeline Generation, Content Ranking, Scalability
System Requirements
- Users can post tweets (280 characters)
- Follow/unfollow other users
- Generate personalized timeline
- Handle 300M active users
- Support trending topics
Feed Generation Strategies
Pull Model (Timeline on Read):
- Generate timeline when user requests it
- Query posts from all followed users
- Pros: Less storage, good for users with many followers
- Cons: Slow timeline generation, high read latency
Push Model (Timeline on Write):
- Pre-compute timeline when posts are created
- Store timeline in cache for each user
- Pros: Fast timeline retrieval
- Cons: High storage cost, expensive for celebrity users
Hybrid Approach: Use push model for regular users, pull model for celebrities with millions of followers
4. Design a Video Streaming Service (like YouTube)
Key Focus Areas: Content Delivery, Video Processing, Global Scale
Core Components
- Upload Service: Handle video file uploads
- Video Processing Pipeline: Transcode videos to multiple formats/resolutions
- CDN (Content Delivery Network): Distribute videos globally
- Metadata Database: Store video information, user data, comments
- Search Service: Enable video discovery
Video Processing Workflow
- User uploads video to upload service
- Video stored in distributed file system (HDFS/S3)
- Processing queue triggers transcoding jobs
- Generate multiple formats: 360p, 720p, 1080p, 4K
- Upload processed videos to CDN
- Update metadata database with video information
Scalability Considerations
Storage: Use distributed file systems, implement data replication across multiple data centers
Bandwidth: Leverage CDN for global content delivery, implement adaptive bitrate streaming
Processing: Use distributed computing frameworks like Apache Spark for parallel video processing
5. Design a Ride-Sharing Service (like Uber)
Key Focus Areas: Location Services, Matching Algorithm, Real-time Updates
Core Services
- User Service: Manage riders and drivers
- Location Service: Track real-time locations
- Matching Service: Connect riders with nearby drivers
- Trip Service: Manage ride lifecycle
- Payment Service: Handle transactions
- Notification Service: Send updates to users
Location Tracking & Matching
Geospatial Indexing: Use QuadTree or Geohash to efficiently find nearby drivers
Real-time Updates: WebSocket connections for live location tracking
Matching Algorithm: Consider factors like distance, driver rating, estimated arrival time, and surge pricing
Database Design
User Table: user_id, name, phone, email, user_type (rider/driver)
Trip Table: trip_id, rider_id, driver_id, start_location, end_location, status, fare
Location Table: driver_id, latitude, longitude, timestamp (frequently updated)
💡 Pro Tips for System Design Interviews
- Start with Requirements: Always clarify functional and non-functional requirements before diving into design
- Estimate Scale: Calculate read/write QPS, storage requirements, and bandwidth needs
- High-Level Design First: Draw the major components and data flow before detailing individual services
- Address Bottlenecks: Identify potential failure points and discuss solutions like caching, load balancing, and replication
- Consider Trade-offs: Discuss CAP theorem implications, consistency vs. availability choices
- Think About Monitoring: Include logging, metrics, and alerting in your design
🔧 Common Design Patterns
Load Balancing Patterns
- Round Robin: Distribute requests evenly across servers
- Weighted Round Robin: Assign different weights based on server capacity
- Least Connections: Route to server with fewest active connections
- Geographic: Route based on user location for reduced latency
Caching Strategies
- Cache-Aside: Application manages cache explicitly
- Write-Through: Write to cache and database simultaneously
- Write-Behind: Write to cache first, database later
- Refresh-Ahead: Proactively refresh cache before expiration
Database Patterns
- Master-Slave: One write node, multiple read replicas
- Master-Master: Multiple write nodes with conflict resolution
- Sharding: Horizontal partitioning across multiple databases
- Federation: Split databases by function/feature
📈 Scaling Strategies
Vertical vs Horizontal Scaling
Vertical Scaling (Scale Up):
- Add more power (CPU, RAM) to existing machines
- Pros: Simple, no code changes required
- Cons: Hardware limits, single point of failure, expensive
Horizontal Scaling (Scale Out):
- Add more machines to the resource pool
- Pros: No hardware limits, fault tolerant, cost-effective
- Cons: Complex architecture, data consistency challenges
When to Scale Different Components
- CPU-bound: Add more application servers, implement load balancing
- Memory-bound: Implement caching layers, optimize data structures
- I/O-bound: Use CDNs, implement database replication
- Network-bound: Use compression, implement edge caching
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