Practical Resources for System Design and ML interview Questions (repo+website)
Category: ml_system_design
Date: 2026-03-23
Practical Resources for System Design and ML interview Questions (repo+website)
Introduction
As a principal FAANG system design interviewer, I will guide you through a structured system design discussion for a practical resource repository and website.
Requirements (Functional + Non-Functional)
- Functional Requirements
- Users can create and manage accounts.
- Users can contribute and manage resources (e.g., articles, videos, code snippets).
- Users can search and browse resources.
- Users can rate and comment on resources.
- Admins can manage user accounts, resources, and comments.
- Admins can monitor website analytics.
- Non-Functional Requirements
- High availability: 99.99% uptime.
- Scalability: handle 10,000 concurrent users.
- Performance: respond within 200ms for search queries.
- Security: protect user data and prevent SQL injection attacks.
High-Level Architecture
To meet the requirements, we will design a microservices-based architecture with the following components:
- Frontend (React or Angular): Handles user interactions, search queries, and resource rendering.
- Backend (Node.js, Express, or Django): Handles resource management, user authentication, and API calls.
- Database (PostgreSQL or MongoDB): Stores user data, resource metadata, and comments.
- Search Index (Elasticsearch or Algolia): Optimizes search queries for high performance.
- Load Balancer (NGINX or HAProxy): Distributes incoming traffic across multiple backend servers.
- Caching Layer (Redis or Memcached): Caches frequently accessed resources and search results.
Database Design
We will use a relational database (PostgreSQL) with the following schema:
- Users: stores user information (e.g., username, email, password).
- Resources: stores resource metadata (e.g., title, description, tags).
- Comments: stores user comments on resources.
- Votes: stores user ratings on resources.
Scaling Strategy
To handle 10,000 concurrent users, we will:
- Horizontal Scaling: Add more backend servers behind the load balancer.
- Auto Scaling: Use a cloud provider’s auto-scaling feature to add/remove servers based on traffic.
- Caching: Cache frequently accessed resources and search results to reduce database queries.
- CDN: Use a content delivery network (CDN) to distribute static assets.
Bottlenecks
Potential bottlenecks include:
- Database Queries: High traffic may lead to slow database queries.
- Search Index: Large search indexes may lead to slow search queries.
- Caching: Cache expiration may lead to cache misses.
Trade-offs
Trade-offs include:
- Database Schema: Simplify the database schema to reduce complexity.
- Search Index: Choose a search index that balances performance and scalability.
- Caching: Choose a caching layer that balances cache hit ratio and cache expiration.
Solution using the first principle of system design
The first principle of system design is “simpllicity”: avoid unnecessary complexity.
In this solution, we avoided unnecessary complexity by:
- Using a microservices architecture: Each service has a single responsibility, making it easier to maintain and scale.
- Choosing a simple database schema: The schema is easy to understand and maintain.
- Selecting a caching layer: We chose a caching layer that balances cache hit ratio and cache expiration.
By applying the first principle of system design, we created a scalable, performant, and maintainable system.
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