System Design Fundamentals for Interviews

System design interviews evaluate your ability to architect large-scale distributed systems. Unlike coding interviews that have clear right answers, system design interviews focus on engineering trade-offs and your thought process.

Companies like Google, Facebook, Netflix, and Uber use these interviews to assess senior engineering candidates. The goal isn't to design a perfect system, but to demonstrate how you approach complex technical problems and make informed decisions about scalability, reliability, and performance.

A typical interview follows this structure: requirements gathering (5-10 minutes), high-level design (15-20 minutes), detailed design (15-20 minutes), and scaling considerations (5-10 minutes). Success depends on clear communication and systematic thinking, not memorizing specific architectures.

Successful system design relies on understanding fundamental principles that guide architectural decisions. These principles help you evaluate trade-offs and make informed choices during interviews.

• Scalability: Design systems that handle increased load gracefully
• Reliability: Ensure systems continue functioning despite failures
• Availability: Maximize uptime through redundancy and failover
• Consistency: Manage data integrity across distributed components
• Partition Tolerance: Handle network failures between system components

The famous CAP theorem states you can only guarantee two of three properties: Consistency, Availability, and Partition tolerance. Understanding these trade-offs is crucial for making design decisions. For example, database scaling strategies often involve choosing between strong consistency and high availability.

Scalability patterns are proven architectural approaches for handling increased system load. Master these patterns to demonstrate your understanding of how systems grow from serving hundreds to millions of users.

Horizontal vs Vertical Scaling: Vertical scaling (scaling up) adds more power to existing machines, while horizontal scaling (scaling out) adds more machines. Horizontal scaling is generally preferred for large systems because it's more cost-effective and fault-tolerant, though it introduces complexity in data distribution and consistency.

Microservices Architecture: Breaking monolithic applications into smaller, independent services enables teams to scale different components independently. Each service can use different technologies and scaling strategies. However, microservices vs monoliths involve significant trade-offs in complexity and operational overhead.

Stateless Design: Designing stateless services allows easy horizontal scaling because any server can handle any request. State should be externalized to databases or caches. This pattern is fundamental to modern cloud-native architectures.

Database design often determines system scalability limits. Understanding different database types and scaling strategies is crucial for system design interviews.

SQL vs NoSQL Trade-offs: SQL databases provide ACID guarantees and complex querying capabilities, making them ideal for financial transactions and complex relationships. NoSQL databases sacrifice some consistency for better horizontal scaling and performance with simple queries. The choice depends on your specific use case requirements.

Database Partitioning (Sharding): Sharding distributes data across multiple database instances based on a partition key (like user ID or geographic region). This enables horizontal scaling but complicates queries that span multiple shards. Choose your sharding key carefully to avoid hotspots.

Read Replicas: Creating read-only copies of your database allows you to scale read operations independently from writes. This pattern works well for read-heavy workloads like social media feeds or news sites. However, read replicas introduce eventual consistency challenges.

Caching is one of the most effective ways to improve system performance and reduce database load. Understanding different caching strategies and their trade-offs is essential for system design interviews.

Cache-Aside Pattern: The application manages cache directly, checking the cache before querying the database. If data isn't cached, the application retrieves it from the database and stores it in the cache. This pattern gives fine-grained control but requires careful cache invalidation.

Write-Through vs Write-Behind: Write-through caching writes data to both cache and database simultaneously, ensuring consistency but with higher latency. Write-behind (write-back) caching writes to cache immediately and asynchronously updates the database, improving performance but risking data loss.

Cache Levels: Design multi-level caching strategies with browser cache, CDN, application cache (Redis/Memcached), and database query cache. Each level serves different purposes and has different invalidation requirements.

Load balancing distributes traffic across multiple servers to improve availability and performance. Understanding different load balancing techniques helps you design resilient systems.

Layer 4 vs Layer 7 Load Balancing: Layer 4 (transport layer) load balancers make routing decisions based on IP addresses and ports, offering high performance with low latency. Layer 7 (application layer) load balancers can inspect HTTP headers and content, enabling more sophisticated routing but with higher overhead.

Health Checks and Failover: Implement health checks to detect failed servers and automatically route traffic away from them. Design graceful degradation where the system continues operating with reduced functionality when some components fail.

• Round Robin: Simple rotation through available servers
• Least Connections: Route to server with fewest active connections
• Weighted: Assign different capacities to different servers
• Geographic: Route based on user location for latency optimization

Practice with these common system design questions to build confidence and develop your systematic approach. Focus on the design process, not memorizing specific solutions.

1. Design a URL Shortener (like bit.ly): Focus on encoding algorithms, database schema, caching, and analytics
2. Design a Social Media Feed: Address timeline generation, fanout strategies, and content ranking algorithms
3. Design a Chat System: Consider real-time messaging, presence indicators, message delivery guarantees
4. Design a Video Streaming Service: Think about content delivery, video encoding, recommendation systems
5. Design a Ride-Sharing Service: Handle real-time location tracking, matching algorithms, and payment processing

For each question, start by clarifying requirements, estimate scale (users, requests per second, data volume), then design the high-level architecture before diving into specific components. Always discuss trade-offs and alternative approaches.