30 System Design Lessons Every Engineer Should Know
30 System Design Lessons Every Engineer Should Know
System design is the backbone of every scalable and reliable software product. Whether you're building a new system, improving an existing one, or preparing for interviews, these 30 lessons will help you master the fundamentals. Each lesson includes practical examples and real-world solutions.
1. Clarify Requirements First
Always understand both functional and non-functional requirements before designing.
Example: E-commerce Platform
- Functional: Users browse, add to cart, checkout
- Non-functional: Handle 10,000 concurrent users, load under 3 seconds
- Solution: Use horizontal scaling and caching
Example: Ride-Sharing App
- Functional: Book rides, track drivers, pay
- Non-functional: 99.99% uptime, 1,000 rides/second
- Solution: Load testing (JMeter) to validate capacity
Reference: Functional vs Non-Functional Requirements
2. Assume Everything Will Fail
Design for fault tolerance from day one.
Example: Payment System
- Problem: Gateway fails during checkout
- Solution: Retry logic with exponential backoff + fallback payment method
Example: Database Failure
- Problem: Database goes down
- Solution: Master-Slave replication for automatic failover
Reference: Fault Tolerance in Distributed Systems
3. Don't Over-Engineer
Add functionality only when necessary (YAGNI principle).
Example: Social Media App
- Problem: Building "Stories" before core features work
- Solution: Start with MVP (posting, commenting), iterate based on feedback
Example: Food Delivery
- Problem: Complex recommendation engine before smooth ordering
- Solution: Focus on core features first (ordering, tracking)
Reference: YAGNI Principle
4. It's All About Tradeoffs
There's no perfect solution—balance based on requirements.
Example: Consistency vs. Availability
- Payment systems: Choose SQL (consistency)
- Social feeds: Choose NoSQL (availability)
- Framework: Use CAP theorem to decide
Example: Latency vs. Accuracy
- Problem: Real-time analytics need speed
- Solution: Use approximation algorithms (HyperLogLog)
Reference: CAP Theorem Explained
5. Scale Horizontally, Not Vertically
Add more servers instead of bigger servers.
Example: Web Application
- Problem: Single server can't handle traffic
- Solution: Kubernetes auto-scaling (add more servers)
Example: Database
- Problem: Single DB server overloaded
- Solution: Database sharding across multiple servers
Reference: Horizontal vs Vertical Scaling
6. Use Load Balancers
Distribute traffic and ensure high availability.
Example: E-commerce
- Problem: Sales traffic overwhelms single server
- Solution: AWS Elastic Load Balancer distributes across servers
Example: API Service
- Problem: One server becomes bottleneck
- Solution: Round-robin load balancing
Reference: What is a Load Balancer?
7. SQL for Structured Data
Use SQL databases for structured data and ACID transactions.
Example: Banking System
- Need: Accurate, consistent transactions
- Solution: PostgreSQL for ACID compliance
Example: Inventory Management
- Need: Accurate stock tracking
- Solution: MySQL for data consistency
Reference: ACID Properties in Databases
8. NoSQL for Unstructured Data
Use NoSQL when dealing with flexible schemas.
Example: Social Media Feed
- Problem: Varying post structures (text, images, videos)
- Solution: MongoDB for flexible schema
Example: Real-Time Analytics
- Problem: Large volumes of log data
- Solution: Cassandra for high write throughput
Reference: NoSQL Databases Explained
9. Shard Your Database
Scale SQL databases horizontally.
Example: E-Commerce
- Problem: Millions of orders won't fit in one DB
- Solution: Shard by customer region (North, South, etc.)
Example: Social Network
- Problem: Millions of users
- Solution: Shard by user ID ranges
Reference: What is Database Sharding?
10. Index Your Queries
Optimize read queries with proper indexing.
Example: Product Search
- Problem: Slow search queries
- Solution: Add index on product name column
Example: User Login
- Problem: Slow authentication
- Solution: Add index on email column
Reference: Database Indexing Explained
11. Implement Rate Limiting
Prevent system overload and DOS attacks.
Example: API Service
- Problem: Single user spamming requests
- Solution: Limit to 100 requests/minute per user
Example: Login System
- Problem: Brute force attacks
- Solution: Limit to 5 login attempts/minute per IP
Reference: Rate Limiting Explained
12. Use WebSockets for Real-Time
Enable instant communication.
Example: Chat Application
- Need: Real-time messaging
- Solution: WebSockets for instant delivery
Example: Live Notifications
- Need: Instant user updates
- Solution: WebSockets to push notifications
Reference: WebSockets vs HTTP
13. Health Checks Matter
Detect failures early with heartbeats.
Example: Microservices
- Problem: Service goes down silently
- Solution: Kubernetes liveness probes
Example: Load Balancer
- Problem: Server becomes unresponsive
- Solution: Heartbeats to remove unhealthy servers
Reference: Health Checks in Microservices
14. Message Queues for Async
Decouple services with asynchronous communication.
Example: Order Processing
- Problem: Payment processing blocks everything
- Solution: RabbitMQ to decouple payment and fulfillment
Example: Email Notifications
- Problem: Sending emails slows main app
- Solution: Kafka to queue and process async
Reference: Message Queues Explained
15. Partition Large Datasets
Split data for better performance.
Example: Log Storage
- Problem: Terabytes of logs
- Solution: Partition by date (daily partitions)
Example: User Data
- Problem: Millions of profiles
- Solution: Shard by geographic region
Reference: Data Partitioning and Sharding
16. Denormalize for Reads
Optimize read-heavy workloads.
Example: Product Catalog
- Problem: Slow reads due to joins
- Solution: Store category names in product table
Example: Social Feed
- Problem: Joins slow down feed
- Solution: Store user details in post table
Reference: Database Denormalization
17. Event-Driven Architecture
Decouple systems with events.
Example: Order Processing
- Problem: Tight coupling between payment and shipping
- Solution: Kafka for event-driven decoupling
Example: User Registration
- Problem: Welcome email slows registration
- Solution: Trigger async event for emails
Reference: Event-Driven Architecture
18. CDNs Reduce Latency
Serve content closer to users globally.
Example: E-Commerce Images
- Problem: Slow loading for global users
- Solution: Cloudflare CDN to cache images worldwide
Example: Video Streaming
- Problem: High latency for videos
- Solution: CDN caches videos near users
Reference: What is a CDN?
19. Write-Through Cache
Keep cache and database in sync for write-heavy apps.
Example: Inventory Management
- Problem: Frequent stock updates
- Solution: Redis write-through cache
Example: Session Management
- Problem: Frequent session updates
- Solution: Write-through ensures consistency
Reference: Write-Through Caching
20. Read-Through Cache
Reduce database load for read-heavy apps.
Example: Product Catalog
- Problem: Frequent product reads
- Solution: Read-through cache updates on misses
Example: News Feed
- Problem: Constant article reads
- Solution: Cache reduces DB load
Reference: Read-Through Caching
21. Blob Storage for Media
Store files, images, and videos efficiently.
Example: Photo Sharing
- Problem: Millions of photos in database
- Solution: AWS S3 blob storage
Example: Video Streaming
- Problem: Large video files
- Solution: Blob storage for efficient streaming
Reference: What is Blob Storage?
22. Replicate Everything
Avoid single points of failure.
Example: Database
- Problem: Server failure
- Solution: MySQL Master-Slave replication
Example: File Storage
- Problem: File server fails
- Solution: HDFS with replication
Reference: Data Replication Explained
23. Autoscaling for Spikes
Handle traffic surges automatically.
Example: E-Commerce
- Problem: Black Friday traffic spike
- Solution: AWS Auto Scaling adds servers
Example: Streaming Service
- Problem: Live event surge
- Solution: Auto-scale to handle load
Reference: What is Autoscaling?
24. Async for Background Tasks
Don't block the main flow.
Example: Email Notifications
- Problem: Emails slow down app
- Solution: Kafka message queue
Example: Data Processing
- Problem: Large datasets block app
- Solution: Celery background workers
Reference: Asynchronous Processing
25. Make It Idempotent
Simplify retries and error handling.
Example: Payment Processing
- Problem: Retry creates duplicate charges
- Solution: Use unique transaction ID
Example: Order Placement
- Problem: Retry creates duplicate orders
- Solution: Idempotency key prevents duplicates
Reference: Idempotency in APIs
26. Microservices Over Monoliths
Scale and maintain independently.
Example: E-Commerce
- Problem: Monolith hard to scale
- Solution: Split into orders, payments, inventory services
Example: Ride-Sharing
- Problem: Growing complexity
- Solution: Separate ride matching, payments, notifications
Reference: Microservices vs Monolith
27. API Gateway for Microservices
Centralize routing and security.
Example: E-Commerce
- Problem: Clients call multiple services
- Solution: Kong API gateway routes requests
Example: Banking
- Problem: Different APIs everywhere
- Solution: Gateway unifies and secures APIs
Reference: What is an API Gateway?
28. Circuit Breaker Pattern
Prevent cascading failures.
Example: Payment Service
- Problem: Failing service takes down everything
- Solution: Hystrix circuit breaker isolates failure
Example: Inventory Service
- Problem: Failing inventory breaks orders
- Solution: Circuit breaker contains damage
Reference: Circuit Breaker Pattern
29. Design Clear APIs
Consistency and security matter.
Example: E-Commerce API
- Problem: Inconsistent design confuses developers
- Solution: RESTful principles + OpenAPI spec
Example: Banking API
- Problem: Security gaps expose data
- Solution: OAuth2 + HTTPS
Reference: API Design Best Practices
30. Data Lakes & Warehouses
Analytics at scale.
Example: E-Commerce Analytics
- Need: Sales insights
- Solution: Snowflake data warehouse
Example: Social Media Analytics
- Need: Analyze unstructured data
- Solution: AWS S3 data lake
Reference: Data Lake vs Data Warehouse
Key Takeaways
✅ Always clarify requirements first ✅ Design for failure from the start ✅ Keep it simple—avoid over-engineering ✅ Every decision involves tradeoffs ✅ Scale horizontally when possible ✅ Cache intelligently based on workload ✅ Decouple with events and queues ✅ Monitor everything with health checks
Remember: There's no one-size-fits-all solution. Choose patterns based on your specific requirements, constraints, and tradeoffs.