feat: new library System Design Interview Props
Category: system_design
Date: 2026-03-28
feat: new library System Design Interview Props
Problem Statement:
Design a system to manage a library of props used in System Design Interview (SDI) mock interviews. The system should allow for easy addition, deletion, and retrieval of props, as well as efficient searching and filtering.
Requirements:
Functional Requirements
- Prop Management: Allow users to add, delete, and update props in the library.
- Prop Retrieval: Provide efficient methods for retrieving props by name, category, or tags.
- Search and Filter: Implement search functionality for props based on user input (e.g., keyword search).
- User Roles: Support multiple user roles (e.g., admin, moderator, user) with varying levels of access.
Non-functional Requirements
- Scalability: Design the system to handle a large number of users and props.
- Performance: Ensure fast response times for prop retrieval and search operations.
- Data Consistency: Implement mechanisms to ensure data consistency across the system.
- Security: Protect user data and prevent unauthorized access.
High-Level Architecture:
- Frontend: Design a user-friendly interface using a web framework (e.g., React, Angular) to interact with the API.
- API Gateway: Implement an API gateway (e.g., NGINX, AWS API Gateway) to manage incoming requests and route them to the correct microservices.
- Microservices:
- Prop Service: Responsible for prop data storage, retrieval, and management.
- Search Service: Handles search and filtering operations.
- Authentication Service: Manages user authentication and authorization.
- Database: Design a database schema to store prop data, user information, and search indices.
Database Design:
- Prop Table: Store prop metadata (e.g., name, description, tags).
- User Table: Store user information (e.g., username, password, role).
- Search Index: Create a search index (e.g., Elasticsearch) to facilitate fast search and filtering operations.
Scaling Strategy:
- Horizontal Scaling: Use a load balancer to distribute incoming requests across multiple instances of the Prop Service and Search Service.
- Vertical Scaling: Increase the instance size of the services as needed to handle high traffic.
- Caching: Implement caching mechanisms (e.g., Redis, Memcached) to reduce database queries and improve performance.
Bottlenecks:
- Database Queries: Optimizing database queries and indexing can help alleviate bottlenecks.
- Search Operations: Implementing efficient search algorithms and indexing can improve search performance.
- User Load: Scaling the Authentication Service and implementing caching can help handle high user loads.
Trade-offs:
- Complexity vs. Simplicity: Balancing the complexity of the system design with the need for simplicity and ease of use.
- Scalability vs. Performance: Optimizing the system for scalability may compromise performance, and vice versa.
- Security vs. Accessibility: Ensuring data security and protection while providing easy access to users.
Solution using the First Principle of System Design:
The first principle of system design states, “Simplicity is a fundamental aspect of system design.” To apply this principle, we can simplify the system design by:
- Reducing the number of microservices: Combine the Prop Service and Search Service into a single service to reduce complexity.
- Using a single database: Instead of using a search index, use a single database with optimized indexing to reduce the number of systems to manage.
- Implementing caching: Use caching mechanisms to reduce database queries and improve performance.
By applying the first principle of system design, we can create a simpler and more efficient system that meets the requirements while minimizing trade-offs.
Learning Links: