1. Key Goals of System Design
Scalability: The system's ability to handle increased load by adding resources. Includes vertical scaling (more power to a single node) and horizontal scaling (adding more nodes).
Reliability: Ensures the system continues functioning even in the presence of failures using redundancy and replication.
Maintainability: Designing systems that are easy to update, debug, and extend with minimal disruption.
Performance: Achieving low latency (response time) and high throughput (amount of work done per unit of time).
2. Architectural Patterns
Layered Architecture: Organizes the application into layers (presentation, business logic, data access) to separate concerns.
Microservices: A collection of small, independent services communicating over a network. Each service focuses on a single business capability.
Event-Driven Architecture: Uses events to trigger and communicate between services. Common in systems requiring real-time updates.
MVC/MVVM: Patterns for separating concerns in UI-driven applications. MVC splits into Model, View, Controller, while MVVM adds ViewModel for binding.
3. Database Design
SQL vs NoSQL: SQL databases are relational and structured, while NoSQL databases are designed for scalability and unstructured data (e.g., MongoDB, Cassandra).
Database Normalization and Denormalization: Normalization reduces redundancy, while denormalization improves read performance at the cost of storage.
Indexes and Query Optimization: Indexes speed up data retrieval, but excessive indexing can slow down writes.
Caching: Temporary storage (e.g., Redis) for quick access to frequently used data, reducing database load.
4. Scalability and Load Balancing
Load Balancers: Distribute incoming traffic across servers (e.g., HAProxy, AWS ELB) to prevent overloading a single server.
Horizontal Scaling: Adding more instances to handle increased load.
CDNs: Serve static resources (images, CSS, JS) from edge servers closer to the user, reducing latency.
5. System Communication
APIs: REST uses stateless HTTP methods, while GraphQL provides flexible querying with a single endpoint.
Inter-Service Communication: Synchronous methods like REST/gRPC and asynchronous methods like Kafka or RabbitMQ for decoupled systems.
Rate Limiting and Circuit Breakers: Control traffic to avoid overwhelming services and gracefully degrade when dependent services fail.
6. Security Considerations
Authentication: Verifying user identity using methods like OAuth 2.0 or JSON Web Tokens (JWT).
Secure Data: Encrypting data in transit (SSL/TLS) and at rest (database encryption).
OWASP Best Practices: Avoiding vulnerabilities like SQL injection, XSS, and CSRF through secure coding practices.
7. High-Level Design Workflow
Requirements Gathering: Identifying what the system must do (functional) and how it must perform (non-functional).
System Components: Breaking the system into manageable parts like front-end, back-end, and database.
Database Schema Design: Creating a structure to store and relate data (e.g., ER diagrams).
Data Flow: Visualizing how data moves through the system using sequence or data flow diagrams.
Performance Strategies: Techniques like caching, load balancing, and database sharding to handle scaling.
8. Common Interview Systems
URL Shortener: Maps long URLs to short keys using a hash function or a key-value store.
E-Commerce Platform: Requires user management, product catalogs, and order processing with payment integration.
Social Media Feed: Handles personalized newsfeeds, requiring real-time updates and ranking algorithms.
Messaging System: Delivers real-time communication using WebSockets or message brokers like RabbitMQ.
9. Tools and Techniques
UML Diagrams: Represent system components, processes, and relationships visually for better understanding.
Tools: Draw.io, Lucidchart for creating system design diagrams.
Cloud Platforms: Familiarity with AWS (e.g., EC2, S3), Azure, or GCP services for hosting scalable applications.
10. Java-Specific Considerations
Frameworks: Spring Boot for building robust back-end applications and Hibernate for ORM.
Threads and Concurrency: Managing threads efficiently using executors or modern thread pools.
Logging and Monitoring: Tools like SLF4J and Prometheus for system observability and debugging.