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Common Interview Patterns

System design interviews often follow predictable patterns. Understanding these common topics and solution approaches is key to success.

Interview Framework​

Step 1: Requirements Clarification​

  • Functional requirements: What the system must do
  • Non-functional requirements: Performance, scalability, reliability
  • Constraints: Budget, timeline, team size
  • Assumptions: Document and validate assumptions

Step 2: System Design​

  • High-level architecture: Major components and interactions
  • Data model: Database schema and relationships
  • API design: Key endpoints and data flow
  • Scalability approach: Horizontal vs vertical scaling

Step 3: Deep Dive​

  • Component design: Detailed component architecture
  • Data flow: Request/response lifecycle
  • Bottleneck analysis: Identify and address bottlenecks
  • Failure scenarios: Error handling and recovery

Step 4: Trade-offs Discussion​

  • Technology choices: Justify your selections
  • Performance vs complexity: Balance competing requirements
  • Cost considerations: Infrastructure and operational costs
  • Future evolution: How the system can grow

Common Question Types​

Social Media Platforms​

Examples: Twitter, Facebook, Instagram Key Challenges:

  • High write throughput
  • Timeline generation
  • Feed ranking
  • Real-time notifications

Solution Patterns:

  • Write-optimized databases: Cassandra, DynamoDB
  • Timeline generation: Fan-out on write
  • Feed ranking: Machine learning pipelines
  • Real-time updates: WebSockets, push notifications

URL Shortening Services​

Examples: Bitly, TinyURL Key Challenges:

  • High read/write ratio
  • Unique ID generation
  • Analytics tracking
  • Custom URLs

Solution Patterns:

  • Base62 encoding: Compact URL representation
  • Distributed ID generation: Snowflake, UUID
  • Caching: Redis for popular URLs
  • Analytics: Event processing pipelines

Messaging Systems​

Examples: WhatsApp, Slack Key Challenges:

  • Real-time delivery
  • Message persistence
  • Offline support
  • Group messaging

Solution Patterns:

  • WebSocket connections: Real-time communication
  • Message queues: RabbitMQ, Kafka
  • Push notifications: Firebase, APNs
  • Presence tracking: Redis, databases

Video Streaming Platforms​

Examples: YouTube, Netflix Key Challenges:

  • High bandwidth requirements
  • Content delivery optimization
  • Recommendation algorithms
  • Digital rights management

Solution Patterns:

  • CDN distribution: Edge server caching
  • Adaptive bitrate: Multiple quality levels
  • Recommendation engines: Machine learning
  • DRM: Content encryption and licensing

Ride-sharing Services​

Examples: Uber, Lyft Key Challenges:

  • Real-time location tracking
  • Driver-rider matching
  • Surge pricing
  • Payment processing

Solution Patterns:

  • Geospatial indexing: Quad trees, H3
  • Real-time matching: WebSocket, push notifications
  • Dynamic pricing: Algorithmic pricing models
  • Payment integration: Stripe, PayPal APIs

Design Patterns​

Caching Patterns​

  • Cache-aside: Application manages cache
  • Read-through: Cache manages reads
  • Write-through: Cache manages writes
  • Write-behind: Asynchronous writes

Database Patterns​

  • Master-slave: Read scaling
  • Sharding: Horizontal scaling
  • Eventual consistency: Distributed systems
  • Polyglot persistence: Multiple databases

Communication Patterns​

  • Request-response: Traditional HTTP
  • Event-driven: Message queues
  • Streaming: Real-time data
  • Pub-sub: Decoupled communication

Scalability Patterns​

  • Load balancing: Traffic distribution
  • Horizontal scaling: Adding more machines
  • Microservices: Service decomposition
  • Serverless: Function-based scaling

Common Technologies​

Databases​

  • SQL: PostgreSQL, MySQL
  • NoSQL: MongoDB, Cassandra, DynamoDB
  • In-memory: Redis, Memcached
  • Search: Elasticsearch, Solr

Message Queues​

  • RabbitMQ: Traditional messaging
  • Kafka: High-throughput streaming
  • SQS: Cloud-based queuing
  • Redis Pub/Sub: Simple pub-sub

Caching​

  • Redis: In-memory data store
  • Memcached: Simple caching
  • CDN: Edge caching
  • Application cache: Local caching

Load Balancers​

  • NGINX: Web server and balancer
  • HAProxy: Dedicated balancer
  • AWS ALB: Cloud-based balancing
  • Envoy: Modern proxy

Estimation Techniques​

Capacity Planning​

  • QPS estimation: Queries per second
  • Storage estimation: Data growth projections
  • Bandwidth estimation: Network requirements
  • Memory estimation: Cache and application memory

Performance Metrics​

  • Latency: Response time requirements
  • Throughput: Requests per second
  • Availability: Uptime requirements
  • Consistency: Data consistency needs

Cost Estimation​

  • Infrastructure costs: Servers, storage, network
  • Operational costs: Monitoring, maintenance
  • Scaling costs: Growth projections
  • Optimization opportunities: Cost reduction

Common Pitfalls​

Requirements Misunderstanding​

  • Solution: Ask clarifying questions
  • Example: "What's the expected scale?"
  • Impact: Wrong architecture choices

Over-engineering​

  • Solution: Start simple, evolve complexity
  • Example: Don't use distributed systems for simple problems
  • Impact: Unnecessary complexity and cost

Ignoring Trade-offs​

  • Solution: Explicitly discuss trade-offs
  • Example: "We choose consistency over availability"
  • Impact: Missing key design considerations

No Failure Analysis​

  • Solution: Consider failure scenarios
  • Example: "What if the database goes down?"
  • Impact: Unreliable system design

Preparation Tips​

Study Common Systems​

  • Social media: Twitter, Facebook
  • E-commerce: Amazon, eBay
  • Streaming: YouTube, Netflix
  • Messaging: WhatsApp, Slack

Practice Framework​

  • Time management: 45-60 minutes per problem
  • Whiteboard practice: Draw diagrams clearly
  • Communication: Explain your thought process
  • Questions: Ask clarifying questions

Technical Knowledge​

  • Databases: SQL vs NoSQL, scaling
  • Networking: HTTP, TCP/IP, DNS
  • Caching: Strategies and patterns
  • Load balancing: Algorithms and configurations

Key Takeaway: System design interviews test your ability to think systematically about complex problems, make informed trade-offs, and communicate your design decisions clearly.