system-design-expert

System Design Expert

Architecture Patterns

Pattern	Use When	Trade-offs
Monolith	Small team, MVP, simple domain	Easy to deploy, hard to scale
Microservices	Large team, complex domain, scale	Flexible, complex ops
Serverless	Event-driven, variable load	Pay per use, cold starts

Scalability Checklist

Horizontal Scaling

• Stateless application (no local sessions)
• Load balancer configured
• Database connection pooling
• Shared cache (Redis)
• CDN for static assets

Database Scaling

• Read replicas for read-heavy
• Sharding for write-heavy
• Caching layer (Redis)
• Indexing optimized

Common Patterns

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│   Client    │────▶│ Load Balancer│────▶│  App Server │
└─────────────┘     └──────────────┘     └──────┬──────┘
                                                 │
                    ┌────────────────────────────┼────────────────────────────┐
                    │                            │                            │
               ┌────▼────┐                 ┌─────▼─────┐               ┌──────▼──────┐
               │  Cache  │                 │  Database │               │ File Storage│
               │ (Redis) │                 │(PostgreSQL)│               │    (S3)     │
               └─────────┘                 └───────────┘               └─────────────┘

Estimation Guidelines

Users	Architecture	Database	Cache
< 1K	Monolith	Single DB	Optional
1K-10K	Monolith + Cache	DB + Read replica	Redis
10K-100K	Microservices	Sharded DB	Redis Cluster
> 100K	Distributed	Multiple DBs	Distributed Cache

API Design

# RESTful conventions
GET    /users          # List
GET    /users/:id      # Get one
POST   /users          # Create
PUT    /users/:id      # Update (full)
PATCH  /users/:id      # Update (partial)
DELETE /users/:id      # Delete

# Pagination
GET /users?page=1&limit=20

# Filtering
GET /users?status=active&role=admin

# Response format
{
  "data": [...],
  "meta": { "total": 100, "page": 1 }
}