Overview
System design case studies provide hands-on learning through real-world scenarios. Each case walks you through designing a complete system from requirements to deployment, covering architectural decisions, trade-offs, and scaling strategies at multiple levels.
🎯 What Makes These Cases Special
Progressive Scaling Philosophy: Unlike many resources that focus only on planet-scale systems, these cases cover five scale levels:
- Startup scale (0–1K users): Simple architecture, single server, minimal infrastructure
- Small scale (1K–10K users): Horizontal scaling, database replication, basic load balancing
- Medium scale (10K–100K users): CDN, distributed caching, auto-scaling, microservices
- Large scale (100K–1M users): Multi-region deployment, sharding, advanced caching
- Planet scale (1M+ users): Global distribution, event-driven architecture, big data analytics
Why multiple scales?
- Learn when to introduce complexity (right-sizing over over-engineering)
- Understand how systems evolve as they grow
- Make cost-effective decisions based on actual requirements
- Practice trade-off analysis at different stages
📋 Case Study Structure
Each case follows a comprehensive framework:
- Problem Statement: Clear description of the system being designed and core requirements
- Requirements Analysis: Functional and non-functional requirements (scale, latency, availability)
- Capacity Estimation: Back-of-the-envelope calculations for traffic, storage, bandwidth, and compute
- High-Level Design: System architecture evolution across all five scale levels
- Detailed Design: Data models, APIs, processing flows, and component interactions
- Scalability Considerations: When and how to introduce caching, sharding, replication, CDN, microservices
- Monitoring & Observability: Metrics, logging, alerting, distributed tracing, incident response
- Testing Strategies: Load testing, stress testing, chaos engineering, integration testing
- Security & Compliance: Authentication, encryption, input validation, regulatory requirements
- Disaster Recovery: Backup strategies, RTO/RPO targets, failover systems, business continuity
- Trade-offs & Alternatives: Different approaches and their pros/cons at various scales
- Practice Exercises: Hands-on challenges to test your understanding
🌐 Technology-Agnostic Approach
Cases focus on architectural thinking, not specific technologies:
- Language-agnostic: Principles apply to any programming language (Java, Go, Python, Node.js, etc.)
- Tech-stack independent: Concepts work with any database, message queue, cache, or framework
- Architecture-first: Emphasis on design patterns, not implementation code
- Vendor-neutral: Discuss components generically (e.g., “cache” not “Redis”, “database” not “PostgreSQL”)
You’ll learn how to think about system design, not just memorize specific technology combinations.
🎓 Learning Objectives
After completing these cases, you’ll be able to:
- Decompose complex requirements into functional and non-functional specifications
- Estimate system capacity for traffic, storage, and compute at different scales
- Design data models and APIs for various system components
- Apply scalability patterns like async processing, sharding, caching, and multi-region deployment
- Balance trade-offs between simplicity vs. scalability, cost vs. performance, build vs. buy
- Ensure reliability through backup strategies, disaster recovery, and incident response
- Design for security with proper authentication, encryption, and compliance
📚 Available Cases
- AI Personal Finance Advisor - Design a system where users upload payment receipts and receive AI-generated financial insights, spending patterns, and personalized budget recommendations. Covers OCR processing, ML inference, multi-region deployment, and financial data compliance.
🔗 Related Content
- Architecture Patterns - Learn C4 Model, DDD, and FSM patterns used in these cases
💡 How to Use These Cases
For Learning:
- Read the problem statement and try designing the system yourself first
- Compare your approach with the provided solution
- Complete the practice exercises to reinforce concepts
- Apply patterns to your own projects
For Interview Preparation:
- Practice articulating your design decisions out loud
- Focus on trade-off discussions and justifying choices
- Work through capacity estimation calculations
- Time yourself (45-60 minutes per case)
For Professional Development:
- Study how systems evolve across scale levels
- Learn when to introduce architectural complexity
- Understand cost implications of different approaches
- Build intuition for real-world system design
🚀 Getting Started
Start with the AI Personal Finance Advisor case - it covers a broad range of system design concepts including document processing, machine learning, distributed systems, and multi-region deployment.
Each case is self-contained, but they build on common patterns and principles from the Architecture section.