Overview

This directory contains hands-on system design study cases and tutorials. Each case walks through the design of a real-world system, covering architectural decisions, trade-offs, and implementation considerations.

🎯 Scope & Focus

These tutorials are language-agnostic and tech-stack independent:

• High-level design: Focus on system architecture, components, and interactions - not implementation code
• Language-agnostic: Principles apply regardless of programming language (Go, Java, Python, Node.js, etc.)
• Tech-stack independent: Concepts work with any technology stack (databases, message queues, caches, etc.)
• Architecture-first: Emphasis on design patterns, scalability strategies, and system trade-offs

You won’t find specific code implementations here - instead, you’ll learn how to design systems that can be implemented in any language or stack.

📏 Scaling Philosophy

Each study case covers multiple scale levels - not just planet-scale systems:

• Startup scale (0–1K users): Single server or small cluster, simple architecture, minimal infrastructure
• Small scale (1K–10K users): Horizontal scaling, database replication, basic load balancing
• Medium scale (10K–100K users): CDN, distributed caching, auto-scaling, consistent hashing
• Large scale (100K–1M users): Geo-replication, microservices, sharding, multi-region deployment
• Planet scale (1M+ users): Global distribution, eventual consistency, advanced automation, big data analytics

Why multiple scales?

• Right-sizing: Not every system needs planet-scale from day one
• Cost-effective: Over-engineering wastes resources and increases complexity
• Progressive scaling: Learn how systems evolve as they grow from startup to planet-scale
• Trade-off awareness: Different scales require different architectural decisions and techniques

Good system design is about choosing the right scale for your requirements, not always building for maximum scale.

Scale categories based on: Omnistrate — Distributed System Design

📋 Purpose

System design study cases help you:

• Learn by doing: Work through real-world design scenarios step-by-step
• Understand trade-offs: Explore different architectural approaches and their implications
• Build intuition: Develop practical experience with system design patterns
• Prepare for interviews: Practice common system design questions in a structured way

🎯 What Belongs Here

Study cases should be:

• Scenario-based: Start with a clear problem statement (e.g., “Design a URL shortener”)
• Step-by-step: Guide the reader through the design process progressively
• Architecture-focused: Include concrete architectural decisions without implementation code
• Technology-neutral: Discuss components generically (e.g., “cache” not “Redis”, “database” not “PostgreSQL”)
• Comprehensive: Cover requirements, architecture, data models, APIs, scalability, and trade-offs

📚 Available Study Cases

• AI-Powered Personal Finance Advisor - Design a system where users upload payment receipts and receive AI-generated financial insights, spending patterns, and budget recommendations

🏗️ Structure of a Study Case

Each study case typically includes:

1. Problem Statement: What system are we designing? What are the core requirements?
2. Requirements Analysis: Functional and non-functional requirements (scale, latency, availability)
3. Capacity Estimation: Back-of-the-envelope calculations to inform design decisions
   • QPS (queries per second) and traffic estimates
   • Storage requirements (data volume, growth rate)
   • Bandwidth and network requirements
   • Peak load calculations and capacity planning
4. High-Level Design: System architecture, major components, data flow
5. Detailed Design: Deep dive into critical components, data models, APIs
6. Scalability Considerations: How to scale from startup → small → medium → large → planet-scale
   • Architectural changes needed at each scale level (0–1K, 1K–10K, 10K–100K, 100K–1M, 1M+)
   • When to introduce caching, sharding, replication, distribution, CDN, microservices
   • Performance characteristics and bottlenecks at each scale
7. Monitoring and Observability: How to monitor system health and performance
   • Key metrics to track (latency, throughput, error rates, resource utilization)
   • Logging strategies (structured logging, log aggregation, retention policies)
   • Alerting and incident response (SLIs, SLOs, SLAs, on-call procedures)
   • Observability tools and dashboards (metrics, logs, traces, distributed tracing)
8. Testing Strategies: How to validate system behavior and resilience
   • Load testing (capacity planning, performance benchmarks)
   • Stress testing (finding breaking points, bottleneck identification)
   • Chaos engineering (failure injection, resilience testing)
   • Integration and end-to-end testing approaches
9. Security & Compliance: How to secure the system and meet regulatory requirements
   • Authentication and authorization (OAuth, JWT, RBAC, session management)
   • Data encryption (in transit: TLS/SSL, at rest: encryption keys, key management)
   • Input validation, sanitization, and protection against common attacks (XSS, SQL injection, CSRF)
   • DDoS protection, rate limiting, and API security
   • Compliance requirements (GDPR, PCI-DSS, HIPAA, SOC 2, data residency)
   • Security testing (penetration testing, vulnerability scanning, security audits)
10. Disaster Recovery & Business Continuity: How to handle failures and maintain availability
    • Backup strategies (frequency, retention policies, backup types: full/incremental/differential)
    • Disaster recovery plans (hot/warm/cold standby, multi-region failover)
    • RTO (Recovery Time Objective) and RPO (Recovery Point Objective) targets
    • Failover systems, redundancy, and high availability architecture
    • Data replication across regions and availability zones
    • Business continuity planning and incident response procedures
11. Trade-offs and Alternatives: Different approaches and their pros/cons at various scales
12. Further Reading: Links to related resources and real-world implementations