System design interview questions and answers tips

System design interview questions assess a candidate's ability to design and understand complex systems. Here’s a list of top 10 system design interview questions along with a brief overview of how one might approach the answers. For the sake of brevity, I'll provide high-level guidelines for answering these questions, as detailed answers would require a more in-depth discussion.

1. Design a URL Shortening Service (like Bitly)

• Key Points: Generate short URLs, redirect to original URLs, and handle high traffic with minimal latency. Use a hash function for shortening, handle collisions, and consider a distributed database for scalability.

2. Design Twitter

• Key Points: Focus on core features: tweeting, following, timelines, and notifications. Use a microservices architecture for scalability, with separate services for user management, tweet storage, and timeline generation. Consider eventual consistency for timelines and use caching for high-read operations.

3. Design a Chat Application (like WhatsApp)

• Key Points: Real-time message delivery, group chats, and media sharing. Use WebSockets for real-time communication, a distributed database for storing messages, and ensure end-to-end encryption for privacy.

4. Design an E-commerce System (like Amazon)

• Key Points: Product listings, search functionality, user accounts, shopping carts, and order management. Use a microservices approach, with services for user management, product management, inventory, and orders. Implement a reliable payment processing system with third-party integrations.

5. Design a Video Streaming Service (like Netflix)

• Key Points: Support for streaming high-quality video content to various devices, content recommendation, and account management. Use CDN for content delivery, microservices for user and content management, and machine learning algorithms for personalized recommendations.

6. Design a Ride-Sharing Service (like Uber)

• Key Points: Matching riders to nearby drivers, pricing calculations, and route optimization. Implement geospatial indexing for efficient matching, a pricing engine based on demand and supply, and integrate with mapping APIs for routing.

7. Design a Social Media Feed (like Facebook's News Feed)

• Key Points: Generate and display a personalized feed for each user, including posts from friends and pages. Use a combination of push and pull models to update feeds, apply ranking algorithms to prioritize content, and cache feeds for quick access.

8. Design a Distributed File System (like HDFS)

• Key Points: Store large files across multiple machines, ensure data reliability and fault tolerance, and support high throughput. Implement data chunking and replication, master-slave architecture for metadata management, and consistency mechanisms.

9. Design a Web Crawler

• Key Points: Efficiently crawl websites to index content, respect robots.txt, handle dynamic content, and manage URL deduplication. Use a distributed architecture with a queue for URLs to be crawled, a set of worker nodes for fetching pages, and a storage solution for the crawled data.

10. Design a Key-Value Store (like Redis)

• Key Points: High-performance storage and retrieval of key-value pairs, support for various data types, and operations like set, get, delete, and data expiration. Implement in-memory storage for fast access, with optional persistence to disk, and use hashing for key distribution in a distributed environment.

Answering Approach:

For each question, consider following a structured approach:

1. Clarify Requirements: Understand both functional and non-functional requirements.
2. High-Level Design: Propose a high-level architecture, including major components and how they interact.
3. Deep Dive: Discuss key components in detail, considering scalability, reliability, and relevant technologies.
4. Data Model: Describe the data model or schema if applicable.
5. Trade-offs: Discuss trade-offs and justify your design choices.
6. Scaling: Explain how your design scales and handles failure scenarios. With message queues, you can make your system higly scalable so that it can handle peak traffic.