System Design
System design is the process of defining architecture, components, interfaces, and data for a system to satisfy specified requirements. This module covers the principles, patterns, and practices for designing scalable, reliable, and efficient systems.
Module Contents
Learning Resources
Introduction to System Design
System design is a critical skill for software engineers, especially those working on large-scale applications. It involves making decisions about architecture, components, interfaces, and data models to meet functional and non-functional requirements.
Why System Design Matters
- Enables building scalable systems that can handle growth
- Ensures reliability and fault tolerance
- Optimizes performance and efficiency
- Facilitates maintenance and future development
- Critical for technical interviews at top tech companies
System Design Process
- Requirement Clarification: Understand functional and non-functional requirements
- System Interface Definition: Define what APIs are expected from the system
- Back-of-the-envelope Estimation: Estimate scale, storage, bandwidth needs
- Data Model Definition: Define how data will be stored and accessed
- High-level Design: Outline the core components and their interactions
- Detailed Design: Dive deeper into critical components
- Identifying and Resolving Bottlenecks: Address scalability, single points of failure, etc.
Design Principles
Several key principles guide effective system design. Understanding and applying these principles helps create robust, maintainable, and scalable systems.
SOLID Principles
- S - Single Responsibility: A class should have only one reason to change
- O - Open/Closed: Software entities should be open for extension but closed for modification
- L - Liskov Substitution: Objects of a superclass should be replaceable with objects of a subclass without affecting correctness
- I - Interface Segregation: Many client-specific interfaces are better than one general-purpose interface
- D - Dependency Inversion: Depend on abstractions, not concretions
CAP Theorem
In a distributed system, you can only guarantee two of the following three properties:
- Consistency: All nodes see the same data at the same time
- Availability: Every request receives a response (success or failure)
- Partition Tolerance: The system continues to operate despite network partitions
Separation of Concerns
Divide a system into distinct sections, each addressing a separate concern. This improves modularity and makes the system easier to understand, develop, and maintain.
Don't Repeat Yourself (DRY)
Every piece of knowledge or logic should have a single, unambiguous representation within a system. Reduces redundancy and makes maintenance easier.
Keep It Simple, Stupid (KISS)
Systems work best when they are kept simple rather than made complex. Simplicity should be a key goal in design, and unnecessary complexity should be avoided.
Scalability
Scalability is the capability of a system to handle a growing amount of work, or its potential to be enlarged to accommodate that growth.
Vertical Scaling (Scaling Up)
Adding more power (CPU, RAM) to an existing machine.
- Pros: Simple to implement, no distribution complexity
- Cons: Limited by hardware, single point of failure, expensive
Horizontal Scaling (Scaling Out)
Adding more machines to a system to handle increased load.
- Pros: Theoretically unlimited, cost-effective, fault-tolerant
- Cons: Increased complexity, data consistency challenges
Load Balancing
Distributing network traffic across multiple servers to ensure no single server bears too much load.
- Algorithms: Round Robin, Least Connections, IP Hash, etc.
- Benefits: Improved responsiveness, availability, and fault tolerance
Database Scaling
Techniques to scale database systems for handling large volumes of data and traffic.
- Replication: Creating copies of data across multiple machines
- Sharding: Partitioning data across multiple databases
- Denormalization: Adding redundant data to reduce join operations
- NoSQL: Using non-relational databases for specific use cases
Caching
Storing frequently accessed data in memory to reduce database load and improve response times.
- Types: Application cache, Database cache, CDN, etc.
- Strategies: Cache-aside, Write-through, Write-behind, etc.
- Tools: Redis, Memcached, etc.
Case Studies
Analyzing real-world system designs helps understand practical applications of design principles and patterns. Here are some common system design case studies:
1. URL Shortener (like bit.ly)
A service that converts long URLs into shorter, more manageable links.
Key Components:
- API Gateway for handling requests
- URL generation service (hash function or counter-based)
- Database to store URL mappings
- Redirection service
- Analytics service (optional)
Challenges:
- Generating unique, short, non-predictable URLs
- Handling high read-to-write ratio
- Ensuring availability and low latency
2. Social Media Feed
A system that generates and delivers personalized content feeds to users.
Key Components:
- User service for profile management
- Post service for creating and storing content
- Feed generation service
- Notification service
- Media storage and CDN
Challenges:
- Real-time feed updates
- Handling millions of concurrent users
- Content ranking and personalization
- Efficient storage and retrieval of media
3. Distributed File Storage
A system for storing, retrieving, and managing files across multiple servers.
Key Components:
- Client interface (API, web, desktop)
- Metadata service for file information
- Storage nodes for actual file data
- Replication and consistency manager
- Authentication and authorization service
Challenges:
- Ensuring data durability and availability
- Handling large files efficiently
- Maintaining consistency across replicas
- Scaling metadata operations
Practice Problems
Test your system design skills with these practice problems. Each problem requires you to design a system that meets specific requirements and constraints.
1. Design a Rate Limiter
Design a system that limits the number of requests a client can send to an API within a time window.
2. Design a Web Crawler
Design a system that can crawl and index billions of web pages efficiently.
3. Design a Distributed Cache
Design a distributed in-memory cache system like Memcached or Redis.
4. Design a Notification System
Design a scalable notification system that can send millions of notifications across multiple channels (email, SMS, push).