🏗️ System Design Fundamentals

Learn to design scalable, reliable, and maintainable systems. System design is crucial for building systems that can handle millions of users and massive data volumes.

Core Concepts

Scalability

  • Vertical Scaling: Better hardware (limited)
  • Horizontal Scaling: More servers (better)

Reliability

  • Redundancy: Eliminate single points of failure
  • Fault Tolerance: System continues despite failures
  • Replication: Data on multiple servers

Performance

  • Latency: Response time
  • Throughput: Requests per second
  • Load Balancing: Distribute traffic

Database Design

  • SQL: ACID, relational
  • NoSQL: Scalable, flexible schema (Cassandra, MongoDB, DynamoDB)
  • Sharding: Partition data across servers
  • Replication: Master-Slave, Master-Master

Caching Strategies

  • Cache Invalidation: TTL, LRU, write-through
  • Cache Layers: Browser, CDN, Application, Database
  • Popular Tools: Redis, Memcached

Message Queues

  • Asynchronous Processing: Kafka, RabbitMQ
  • Data Consistency: Eventually consistent
  • Retry Logic: Handle failures gracefully

API Design

  • REST: Standard web API pattern
  • GraphQL: Query language for APIs
  • Rate Limiting: Protect from abuse
  • Versioning: Maintain backward compatibility

Design Patterns

Microservices vs Monolith

Microservices:

  • ✅ Independent scaling
  • ✅ Technology flexibility
  • ❌ Distributed complexity
  • ❌ Network latency

Monolith:

  • ✅ Simple to start
  • ✅ Better for small teams
  • ❌ Scaling limitations
  • ❌ Technology lock-in

CQRS (Command Query Responsibility Segregation)

  • Separate read and write operations
  • Different data models for each
  • Better scalability and performance

Event Sourcing

  • Store all state changes as events
  • Replay events to reconstruct state
  • Audit trail and temporal queries

Common Architectures

URL Shortener Service

  • Hash function for encoding
  • Database (SQL for URLs, cache for redirects)
  • TTL for expiration
  • Analytics tracking

Social Media Feed

  • Timeline service
  • Caching layer (Redis)
  • Batch pull vs Push notifications
  • Eventually consistent

Real-time Chat

  • Message persistence
  • Presence tracking
  • Read receipts
  • Message queuing for delivery

Tools & Technologies

Load Balancing: Nginx, HAProxy, AWS ELB
Caching: Redis, Memcached, CDN (Cloudflare)
Databases: PostgreSQL, MongoDB, Cassandra
Message Queues: Kafka, RabbitMQ, SQS
Monitoring: Prometheus, ELK Stack, Datadog
Containerization: Docker, Kubernetes

Last updated: April 12, 2026
Difficulty: Intermediate to Advanced