## What is System Design?
System design is planning how to build large-scale software systems that are reliable, scalable, and maintainable. It involves deciding the architecture, choosing databases, designing APIs, handling failures, and ensuring the system works under real-world conditions.
Think of it as the blueprint for building a skyscraper versus a house. You need to plan for millions of users, data storage, network failures, and growth before writing code.
## Why System Design Matters
**Small Apps**: A simple CRUD app on one server works fine. No complex design needed.
**Large Apps**: Gmail, Netflix, Amazon handle millions of users simultaneously. Without proper system design, they would crash constantly.
Good system design is what separates apps that work for 100 users from apps that work for 100 million users.
## Key Components
**Load Balancers**: Distribute traffic across multiple servers to prevent overload.
**Databases**: Choose between SQL (structured data) and NoSQL (flexible data). Decide on sharding, replication, caching strategies.
**Caching**: Store frequently accessed data (Redis, Memcached) to reduce database load and improve speed.
**Message Queues**: Handle async tasks (RabbitMQ, Kafka). Process emails, notifications, reports in background without blocking users.
**CDN**: Serve static assets (images, videos, CSS) from servers close to users for faster load times.
**Monitoring**: Track system health, errors, performance. Know when things break before users complain.
## Common System Design Patterns
**Microservices**: Split application into small, independent services. Each service handles one thing (authentication, payments, notifications) and scales independently.
**Event-Driven**: Services communicate through events. Order placed triggers payment, inventory update, email notification - all independently.
**Database Replication**: Master handles writes, replicas handle reads. Distributes database load and provides backup if master fails.
**Horizontal Scaling**: Add more servers instead of making one server bigger. No upper limit on capacity.
## Real-World System Design
**Twitter**: Handles 500 million tweets per day. System design decisions:
- Cache timeline in Redis (fetching from database for every user is too slow)
- Fan-out tweets to followers asynchronously (posting should be instant)
- Shard databases by user ID (distribute data across servers)
**Netflix**: Streams to millions simultaneously. System design decisions:
- CDN delivers videos from servers near users
- Microservices architecture (recommendation, playback, billing all independent)
- Auto-scaling adds servers during peak hours (evenings)
## Trade-offs in System Design
Every decision involves trade-offs:
**SQL vs NoSQL**: SQL provides relationships and consistency. NoSQL provides flexibility and scale. Choose based on your data.
**Monolith vs Microservices**: Monoliths are simpler to develop and deploy. Microservices scale better but add complexity.
**Strong Consistency vs Availability**: CAP theorem forces this choice. Banks choose consistency (accurate balances). Social media chooses availability (slightly stale likes counts are fine).
## Common Interview Questions
System design is heavily tested in senior developer interviews:
- Design Twitter
- Design URL shortener (like bit.ly)
- Design Netflix
- Design WhatsApp
These test your ability to think about scale, handle failures, make trade-offs, and communicate technical decisions.
## System Design Process
1. **Understand requirements**: How many users? What features? What scale?
2. **Estimate load**: Requests per second? Data storage needs? Bandwidth?
3. **High-level design**: Draw components (servers, databases, caches, queues)
4. **Detailed design**: How does each component work? What happens when things fail?
5. **Optimize**: Where are bottlenecks? How to improve performance?
## Learning System Design
**Study existing systems**: Read how companies scale (Netflix tech blog, AWS case studies, engineering blogs).
**Practice**: Design systems you use daily. How would you build Instagram? YouTube? Uber?
**Understand fundamentals**: Load balancing, caching, database scaling, CAP theorem, consistency patterns.
## When You Need System Design
**Small projects**: Do not over-engineer. One server, one database is fine initially.
**Growth stage**: Plan for scale when you see traffic consistently growing or expect launches that bring traffic spikes.
**Enterprise**: Always required. Systems must be reliable, handle failures gracefully, and scale with business growth.
## Key Principle
System design is not about knowing every technology. It is about understanding trade-offs, thinking through edge cases, and making informed decisions based on requirements.
A well-designed system handles growth smoothly. A poorly designed system crumbles under load, requiring expensive rewrites. The time invested in design saves exponentially more time later.
