System Design Fundamentals
Core Design Principles
Scalability
System grows efficiently with data and users.
- Horizontal scaling (add servers)
- Load distribution
- Caching strategies
Reliability
System continues operating despite failures.
- Redundancy
- Health checks
- Graceful degradation
Maintainability
System is easy to update and evolve.
- Clear architecture
- Good documentation
- Modular design
Key Components
API Design
Clear contracts between services.
Database Selection
- SQL: Relational, structured data
- NoSQL: Flexible, unstructured data
- Cache: Fast reads, temporary data
Message Queues
Decouple services, handle async operations.
Service A → Queue → Service B
Load Balancing
Distribute traffic across servers.
Caching Layer
Reduce database load, improve latency.
Design Process
1. Understand Requirements
- Users, requests, data volume
- Consistency vs availability tradeoffs
- Latency and throughput needs
2. High-Level Design
- API endpoints
- Database schema
- Service boundaries
3. Deep Dive
- Data models
- Scaling strategies
- Bottleneck identification
4. Optimization
- Caching strategies
- Database optimization
- Network efficiency
Trade-offs
Consistency vs Availability
- Strong consistency: Every read returns latest data
- Eventual consistency: Faster reads, temporary stale data
Latency vs Throughput
- Low latency: Fast responses
- High throughput: Many requests processed
Cost vs Performance
- High cost for top performance
- Optimization helps, but has limits
System Design Interview Tips
- Ask clarifying questions
- Start with simple design, then scale
- Discuss tradeoffs openly
- Consider both technical and business aspects
- Think about failure scenarios
- Propose monitoring and observability
Common Patterns
- Microservices: Independent services
- Event-driven: Async message processing
- CQRS: Separate read and write models
- Sharding: Data partitioning for scale