Introduction to System Design
System design is the process of defining architecture, components, interfaces, and data for a system to satisfy specified requirements. It’s critical for creating scalable, reliable, and maintainable software systems that can handle modern computing demands. Good system design decisions early on prevent costly rewrites later and enable future growth.
Core Concepts and Principles
| Concept | Description |
|---|---|
| Scalability | Ability to handle growing amounts of work by adding resources |
| Reliability | System continues functioning under failure conditions |
| Availability | Proportion of time a system is functional and working |
| Maintainability | Ease with which a system can be modified and improved |
| Latency | Time required to perform an action or produce a result |
| Throughput | Number of operations a system can handle per unit time |
| Fault Tolerance | Ability to continue operating despite failures |
| Consistency | All nodes see the same data at the same time |
| Partitioning | Dividing datasets across multiple resources |
| CAP Theorem | Systems can have at most two of: Consistency, Availability, Partition tolerance |
System Design Process
Requirements Clarification
- Identify functional requirements (features)
- Define non-functional requirements (performance, scalability, reliability)
- Establish constraints and assumptions
Capacity Estimation & Constraints
- Traffic estimates (QPS, DAU)
- Storage requirements
- Bandwidth estimates
- Memory requirements
System Interface Definition
- Define API endpoints
- Specify request/response formats
High-Level Design
- Create core components diagram
- Establish data flow
Detailed Design
- Deep dive into critical components
- Choose technologies and tradeoffs
Bottlenecks & Solutions
- Identify potential system bottlenecks
- Propose mitigation strategies
Key Components and Architecture Patterns
Client-Server
- Separates user interface concerns from data storage and processing
- Examples: Web applications, mobile apps with backend servers
Layered Architecture
- Presentation Layer: User interface, handles user interaction
- Business Layer: Business logic, application processing
- Data Access Layer: Data persistence and retrieval
- Database Layer: Actual data storage
Microservices
- Small, autonomous services working together
- Independent deployment and scaling
- Service boundaries aligned with business domains
Event-Driven Architecture
- Components communicate through events
- Loosely coupled, highly scalable
- Good for real-time systems and asynchronous processing
Service-Oriented Architecture (SOA)
- Services communicate over network using standard protocols
- More coarse-grained than microservices
- Often implemented with enterprise service bus
Scalability Techniques
Horizontal vs. Vertical Scaling
| Horizontal Scaling | Vertical Scaling |
|---|---|
| Add more machines | Add more power to existing machines |
| Easier to scale dynamically | Limited by hardware capacity |
| Higher fault tolerance | Single point of failure |
| Network latency concerns | No network latency between components |
| Data consistency challenges | Easier data consistency |
| Examples: Cassandra, MongoDB | Examples: MySQL, Oracle |
Techniques
- Load balancing: Distribute traffic across servers
- Sharding: Partition data across multiple databases
- Replication: Copy data across multiple nodes
- Denormalization: Redundant data to avoid joins
- CDN: Cache static content closer to users
- Asynchronous processing: Offload time-consuming tasks
- Service discovery: Dynamically locate service instances
Database Design and Selection
Types of Databases
| Type | Examples | Best For |
|---|---|---|
| Relational | MySQL, PostgreSQL | Structured data, ACID transactions |
| NoSQL Document | MongoDB, CouchDB | Semi-structured data, flexible schema |
| NoSQL Key-Value | Redis, DynamoDB | High-throughput, simple data models |
| NoSQL Column | Cassandra, HBase | Time-series, write-heavy workloads |
| NoSQL Graph | Neo4j, Amazon Neptune | Connected data, complex relationships |
| Search Engines | Elasticsearch | Full-text search, log analytics |
| Time Series | InfluxDB, TimescaleDB | IoT data, monitoring metrics |
Database Scaling
- Master-Slave Replication: Read from slaves, write to master
- Master-Master Replication: Write to any node
- Sharding: Horizontal partitioning of data
- Federation: Split databases by function
- Denormalization: Add redundant data to reduce joins
- SQL Tuning: Optimize queries and indexes
Caching Strategies
Cache Locations
- Client-side: Browser cache
- CDN: Edge caching
- Application server: Local memory cache
- Distributed cache: Redis, Memcached
- Database cache: Query and buffer cache
Caching Patterns
- Cache-Aside: Application checks cache before database
- Read-Through: Cache handles fetching from database
- Write-Through: Data written to cache and database
- Write-Behind: Data written to cache, asynchronously to database
- Write-Around: Data written to database, bypassing cache
Cache Invalidation
- TTL (Time-To-Live): Expire after set time
- LRU (Least Recently Used): Evict least used items first
- Event-based invalidation: Invalidate on data change
Load Balancing
Algorithms
- Round Robin: Requests distributed sequentially
- Least Connections: Directs to server with fewest connections
- Least Response Time: Directs to server with fastest response
- IP Hash: Same client IP always goes to same server
- URL Hash: Same URL path always goes to same server
- Weighted methods: Servers assigned different capacities
Load Balancer Types
- Layer 4 (Transport): Directs based on IP/port
- Layer 7 (Application): Directs based on content (HTTP headers, URLs)
- Hardware: Dedicated appliances (F5, Citrix)
- Software: HAProxy, NGINX, AWS ELB
API Design
REST Principles
- Stateless: Server stores no client state
- Resource-based: URLs represent resources
- Standard HTTP methods: GET, POST, PUT, DELETE
- HATEOAS: Hypermedia links in responses
- Representation: Resources have multiple formats
GraphQL Benefits
- Single endpoint for all resources
- Clients specify exactly what they need
- Reduces over/under-fetching of data
- Strong typing system
API Gateway Functions
- Request routing
- API composition
- Authentication/Authorization
- Rate limiting
- Monitoring and analytics
- Protocol translation
Microservices Architecture
Characteristics
- Single Responsibility: One service, one function
- Loose Coupling: Minimal dependencies between services
- Independent Deployment: Services deployed separately
- Decentralized Data: Each service manages its own data
- Resilience: Failure isolation
Communication Patterns
- Synchronous: Request/response (REST, gRPC)
- Asynchronous: Message queues (RabbitMQ, Kafka)
- Service Discovery: Find service instances dynamically
- API Gateway: Single entry point for clients
Challenges
- Distributed transaction management
- Service coordination
- Network latency
- Operational complexity
- Monitoring and debugging
Security Considerations
- Authentication: Verify user identity (OAuth, JWT)
- Authorization: Control access to resources
- Encryption: In-transit (TLS/SSL) and at-rest
- Rate Limiting: Prevent abuse
- Input Validation: Sanitize all inputs
- CORS: Control cross-origin requests
- Security Headers: Prevent common web vulnerabilities
- Logging & Monitoring: Detect suspicious activities
Common System Design Challenges and Solutions
| Challenge | Solution |
|---|---|
| Single Point of Failure | Redundancy, failover systems |
| Data Consistency | Choose appropriate consistency model (strong, eventual) |
| Slow Database Queries | Indexing, denormalization, caching |
| Handling Spikes | Auto-scaling, rate limiting, queuing |
| Cold Start | Warm-up procedures, pre-computing |
| Network Congestion | CDN, data compression, request batching |
| Cascading Failures | Circuit breakers, bulkheads, timeouts |
| Monitoring at Scale | Aggregation, sampling, distributed tracing |
Best Practices
- Start Simple: Begin with monolith, decompose as needed
- Design for Failure: Assume components will fail
- Use Asynchronous Processing: Decouple time-intensive operations
- Implement Monitoring: Metrics, logs, alerts, dashboards
- Automate Testing: Unit, integration, and performance tests
- Document Architecture: Keep diagrams and decisions up-to-date
- Infrastructure as Code: Automate infrastructure provisioning
- Use Feature Flags: Control feature rollout
- Progressive Delivery: Canary releases, blue-green deployments
- Establish SLOs/SLAs: Define reliability targets
Resources for Further Learning
Books:
- “Designing Data-Intensive Applications” by Martin Kleppmann
- “System Design Interview” by Alex Xu
- “Building Microservices” by Sam Newman
- “Clean Architecture” by Robert C. Martin
Online Resources:
- System Design Primer (GitHub)
- AWS Architecture Center
- Google Cloud Architecture Framework
- Microsoft Azure Architecture Center
- High Scalability Blog
Practice Platforms:
- LeetCode System Design
- Grokking the System Design Interview
- InterviewBit System Design
Open Source Examples:
- Netflix Technology Blog
- Uber Engineering Blog
- Airbnb Engineering Blog
This cheat sheet provides a foundation for approaching system design problems methodically. Remember that system design involves tradeoffs—there’s rarely a single “correct” solution, but rather designs that best meet specific requirements and constraints.