What We’re Building
In this article, we’ll design a social media platform similar to Twitter/X. This system will handle user posts, following relationships, real-time feeds, and high-traffic scenarios. We’ll use this example to demonstrate the system design process and create a comprehensive checklist that can be applied to any system design problem.
System Overview
Our social media platform will have these core features:
- User Management: Registration, profiles, authentication
- Content Creation: Posts with text, images, videos
- Social Features: Follow/unfollow, likes, comments, retweets
- Feed Generation: Real-time personalized feeds
- Search: Find users and content
- Notifications: Real-time updates for user interactions
Scale Requirements
- Users: 100 million registered users
- Daily Active Users: 20 million
- Posts per day: 50 million
- Read/Write ratio: 80% reads, 20% writes
- Peak QPS: 10,000 requests per second
- Storage: 1TB of data per day
1. Requirements Analysis
Functional Requirements
- Core Features: Define the primary functionality
- User Stories: Document user interactions and workflows
- API Specifications: Define endpoints, request/response formats
- Data Models: Identify entities, relationships, and constraints
- Business Rules: Document domain-specific logic and validations
Non-Functional Requirements
- Performance: Response time, throughput, latency targets
- Scalability: Expected growth, horizontal/vertical scaling needs
- Availability: Uptime requirements, SLA definitions
- Reliability: Fault tolerance, error handling strategies
- Security: Authentication, authorization, data protection
- Maintainability: Code quality, documentation, testing requirements
2. System Architecture Design
High-Level Architecture
┌─────────────────────────────────────────────────────────────┐
│ Load Balancer │
└─────────────────────┬───────────────────────────────────────┘
│
┌─────────────┼─────────────┐
│ │ │
┌───────▼──────┐ ┌───▼──────┐ ┌───▼──────┐
│ Web Tier │ │ API │ │ Cache │
│ (CDN) │ │ Gateway │ │ Layer │
└───────┬──────┘ └───┬──────┘ └───┬──────┘
│ │ │
└─────────────┼─────────────┘
│
┌─────────────┼─────────────┐
│ │ │
┌───────▼──────┐ ┌───▼──────┐ ┌───▼──────┐
│ User │ │ Post │ │ Feed │
│ Service │ │ Service │ │ Service │
└───────┬──────┘ └───┬──────┘ └───┬──────┘
│ │ │
└─────────────┼─────────────┘
│
┌─────────────┼─────────────┐
│ │ │
┌───────▼──────┐ ┌───▼──────┐ ┌───▼──────┐
│ Primary │ │ Secondary│ │ Message │
│ Database │ │ Database │ │ Queue │
└──────────────┘ └──────────┘ └──────────┘
3. Data Design
Database Schema Design
-- Users Table
CREATE TABLE users (
id UUID PRIMARY KEY,
username VARCHAR(50) UNIQUE NOT NULL,
email VARCHAR(255) UNIQUE NOT NULL,
password_hash VARCHAR(255) NOT NULL,
display_name VARCHAR(100),
bio TEXT,
profile_image_url VARCHAR(500),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Posts Table
CREATE TABLE posts (
id UUID PRIMARY KEY,
user_id UUID NOT NULL,
content TEXT NOT NULL,
media_urls TEXT[], -- Array of media URLs
reply_to_post_id UUID,
retweet_of_post_id UUID,
like_count INTEGER DEFAULT 0,
retweet_count INTEGER DEFAULT 0,
comment_count INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (reply_to_post_id) REFERENCES posts(id),
FOREIGN KEY (retweet_of_post_id) REFERENCES posts(id)
);
-- Follows Table
CREATE TABLE follows (
id UUID PRIMARY KEY,
follower_id UUID NOT NULL,
following_id UUID NOT NULL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (follower_id) REFERENCES users(id),
FOREIGN KEY (following_id) REFERENCES users(id),
UNIQUE(follower_id, following_id)
);
-- Likes Table
CREATE TABLE likes (
id UUID PRIMARY KEY,
user_id UUID NOT NULL,
post_id UUID NOT NULL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (post_id) REFERENCES posts(id),
UNIQUE(user_id, post_id)
);
Data Flow Architecture
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Client │───▶│ API │───▶│ Service A │
│ Request │ │ Gateway │ │ │
└─────────────┘ └─────────────┘ └─────────────┘
│
▼
┌─────────────┐
│ Cache │
│ (Redis) │
└─────────────┘
│
▼
┌─────────────┐
│ Database │
│ (PostgreSQL)│
└─────────────┘
4. API Design
RESTful API Endpoints
# Core API Endpoints
/users
GET - List users (with pagination)
POST - Create new user
GET /users/{id} - Get user details
PUT /users/{id} - Update user profile
DELETE /users/{id} - Delete user
/posts
GET - List posts (with filters)
POST - Create new post
GET /posts/{id} - Get post details
PUT /posts/{id} - Update post
DELETE /posts/{id} - Delete post
/feeds
GET /feeds/home - Get user's home feed
GET /feeds/user/{id} - Get user's posts
/follows
POST /follows - Follow a user
DELETE /follows/{id} - Unfollow a user
GET /follows/followers/{id} - Get user's followers
GET /follows/following/{id} - Get users being followed
/likes
POST /likes - Like a post
DELETE /likes/{id} - Unlike a post
# Response Format
{
"data": [...],
"pagination": {
"page": 1,
"limit": 20,
"total": 100,
"pages": 5
}
}
5. Security Design
Authentication & Authorization
┌─────────────────────────────────────────────────────────────┐
│ Security Architecture │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Client │───▶│ API │───▶│ Auth │ │
│ │ (JWT) │ │ Gateway │ │ Service │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ Rate │ │ User │ │
│ │ Limiter │ │ Store │ │
│ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────┘
Security Checklist
- Authentication: JWT, OAuth 2.0, or session-based auth
- Authorization: Role-based access control (RBAC)
- Input Validation: Sanitize all user inputs
- SQL Injection: Use parameterized queries
- XSS Protection: Escape user-generated content
- CSRF Protection: Implement CSRF tokens
- HTTPS: Enforce SSL/TLS encryption
- Rate Limiting: Prevent abuse and DDoS attacks
- Data Encryption: Encrypt sensitive data at rest
- Audit Logging: Track security events
6. Performance & Scalability
Caching Strategy
┌─────────────────────────────────────────────────────────────┐
│ Caching Layers │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ CDN │ │ Application│ │ Database │ │
│ │ Cache │ │ Cache │ │ Cache │ │
│ │ (Static) │ │ (Redis) │ │ (Query) │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
│ Cache Hit Ratio Targets: │
│ • CDN: 90%+ │
│ • Application: 80%+ │
│ • Database: 70%+ │
└─────────────────────────────────────────────────────────────┘
Load Balancing Strategies
- Round Robin: Distribute requests evenly
- Least Connections: Route to least busy server
- IP Hash: Sticky sessions based on client IP
- Weighted Round Robin: Assign weights to servers
- Health Checks: Monitor server health
Database Scaling
┌─────────────────────────────────────────────────────────────┐
│ Database Scaling │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Master │ │ Read │ │ Read │ │
│ │ (Write) │ │ Replica 1 │ │ Replica 2 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │ │ │ │
│ └───────────────────┼───────────────────┘ │
│ │ │
│ ┌─────────▼─────────┐ │
│ │ Load Balancer │ │
│ │ (Read Queries) │ │
│ └───────────────────┘ │
└─────────────────────────────────────────────────────────────┘
7. Monitoring & Observability
Key Metrics to Monitor
- Application Metrics: Response time, error rate, throughput
- Infrastructure Metrics: CPU, memory, disk, network
- Business Metrics: User engagement, conversion rates
- Database Metrics: Query performance, connection pool usage
8. Deployment Strategy
Deployment Options
- Blue-Green Deployment: Zero-downtime deployments
- Canary Deployment: Gradual rollout to users
- Rolling Updates: Update instances one by one
- Feature Flags: Enable/disable features dynamically
10. Documentation
Documentation Checklist
- Architecture Decision Records (ADRs): Document design decisions
- API Documentation: REST API specifications
- Database Schema: ERD and migration scripts
- Deployment Guide: Step-by-step deployment instructions
- Runbook: Operational procedures and troubleshooting
- Testing: Regular disaster recovery drills
11. Key System Design Concepts
Fundamental Principles
- CAP Theorem: Consistency vs Availability vs Partition tolerance
- ACID vs BASE: Database consistency models
- Eventual Consistency: How distributed systems handle consistency
- Sharding Strategies: Horizontal vs vertical partitioning
- Caching Patterns: When and where to cache data
Design Trade-offs
- Consistency vs Performance: Strong consistency vs eventual consistency
- Latency vs Throughput: Optimizing for speed vs volume
- Cost vs Scalability: Balancing resource usage with growth
- Simplicity vs Flexibility: Easy to understand vs adaptable to changes
Conclusion
This system design checklist provides a structured approach for designing scalable, reliable, and maintainable systems. The key is to start simple and iterate based on requirements and constraints.
The social media platform example demonstrates how to apply these principles to real-world problems. Whether you’re building a new system or improving an existing one, this checklist helps ensure you’ve considered all the essential aspects of system design.
Remember that good system design is iterative - start with a simple design and evolve it based on feedback, requirements, and constraints.