System Design Questions: Practice Problems and Solutions

System design interviews can be tricky, right? I remember sweating bullets during my first few, feeling like I was trying to build a skyscraper with LEGOs. The good news is, with practice and the right approach, you can nail these interviews. I'm going to share some practice problems and solutions that have helped me and countless others. Let’s jump right in.

Why Practice System Design Questions?

System design isn't just about knowing the theory; it's about applying it. By practicing, you:

• Sharpen Your Problem-Solving Skills: You learn to break down complex problems into manageable parts.
• Improve Your Communication: You get better at explaining your design choices.
• Gain Confidence: The more you practice, the more confident you become.
• Understand Trade-offs: Every design decision involves trade-offs. Practice helps you weigh them.

I once worked on a project where we had to design a recommendation system. We knew the theory, but putting it into practice was a different beast. We faced challenges like scaling, data consistency, and latency. By working through these problems, we not only built a solid system but also learned valuable lessons that helped us in future projects.

Practice Problems and Solutions

Let's look at some common system design problems and how to approach them.

1. Designing a URL Shortener

Problem: Design a service like TinyURL or Bitly.

Requirements:

• Shorten long URLs.
• Redirect to the original URL when the shortened URL is accessed.
• Handle a large number of requests.

Solution Approach:

1. High-Level Design: Start with the basic components:

   • A service to accept long URLs and generate short URLs.
   • A database to store the mapping between short and long URLs.
   • A redirection service to handle requests for short URLs.

2. Data Model: Decide on the data model. A simple table with columns like id, long_url, and short_url can work.

3. Algorithm for Short URL Generation: Use a base-62 encoding (a-z, A-Z, 0-9) to generate short URLs.

4. Scaling: Consider using a distributed database like Cassandra or DynamoDB to handle large amounts of data. Implement caching to reduce database load.

5. Trade-offs: Discuss trade-offs like consistency vs. availability. For a URL shortener, availability is more critical.

2. Designing a Rate Limiter

Problem: Design a rate limiter to prevent abuse of an API.

Requirements:

• Limit the number of requests a user can make within a certain time window.
• Support different rate limits for different users or API endpoints.
• Be scalable and efficient.

Solution Approach:

1. High-Level Design: Use a token bucket or leaky bucket algorithm.

2. Token Bucket Algorithm: Each user gets a bucket that holds tokens. Each request consumes a token. Tokens are refilled at a certain rate.

3. Data Storage: Use Redis to store the number of tokens for each user. Redis is fast and supports atomic operations.

4. Scaling: Use a distributed rate limiter with multiple Redis instances. Use consistent hashing to distribute users across instances.

5. Trade-offs: Discuss trade-offs like accuracy vs. performance. A distributed rate limiter might not be perfectly accurate but offers better scalability.

3. Designing a Social Media Feed

Problem: Design a system to display a social media feed for users.

Requirements:

• Display posts from users a user follows.
• Order posts by time.
• Support a large number of users and posts.

Solution Approach:

1. High-Level Design: Use a fan-out approach. When a user posts, the post is added to the feeds of all followers.

2. Data Model: Use a database like Cassandra to store posts. Cassandra is good for write-heavy workloads.

3. Fan-Out: Implement fan-out on write or fan-out on read. Fan-out on write adds the post to all followers' feeds when the post is created. Fan-out on read retrieves posts from the user's followees when the feed is requested.

4. Caching: Cache frequently accessed feeds to reduce database load.

5. Trade-offs: Discuss trade-offs like consistency vs. latency. Fan-out on write can lead to higher write latency but faster read times. Fan-out on read can lead to slower read times but faster write times.

4. Designing a Chat System

Problem: Design a real-time chat system.

Requirements:

• Send and receive messages in real-time.
• Support one-on-one chats and group chats.
• Handle a large number of concurrent users.

Solution Approach:

1. High-Level Design: Use WebSockets for real-time communication. WebSockets allow persistent connections between the client and server.

2. Message Handling: Use a message queue like RabbitMQ or Kafka to handle messages asynchronously.

3. Data Storage: Store messages in a database like MongoDB or Cassandra.

4. Scaling: Use multiple chat servers behind a load balancer. Use horizontal scaling to handle more users.

5. Trade-offs: Discuss trade-offs like reliability vs. performance. A message queue ensures messages are delivered even if the server is down but adds latency.

5. Designing a Movie Ticket Booking System

Problem: Design a movie ticket booking system like BookMyShow.

Requirements:

• Allow users to browse movies, showtimes, and theaters.
• Allow users to book tickets.
• Handle concurrent bookings.

Solution Approach:

1. High-Level Design: Break the system into components like movie catalog, theater management, booking service, and payment service.

2. Data Model: Design tables for movies, theaters, showtimes, seats, and bookings.

3. Concurrency Control: Use optimistic locking or pessimistic locking to handle concurrent bookings.

4. Payment Processing: Integrate with a payment gateway like Stripe or PayPal.

5. Scaling: Use caching to reduce database load. Use a distributed database to handle large amounts of data.

6. Trade-offs: Discuss trade-offs like consistency vs. availability. For bookings, consistency is critical.

Tips for System Design Interviews

• Clarify Requirements: Always start by clarifying the requirements. Ask questions to understand the scope and constraints.
• Think Out Loud: Explain your thought process. Don't just jump to a solution. Walk the interviewer through your reasoning.
• Start with a High-Level Design: Begin with a broad overview and then dive into the details.
• Consider Trade-offs: Discuss the trade-offs of different design choices.
• Don't Be Afraid to Say "I Don't Know": If you don't know something, admit it. It's better to be honest than to bluff.
• Practice Regularly: The more you practice, the better you'll become.

How Coudo AI Can Help

Coudo AI is a great platform to practice your system design skills. It offers a variety of problems and solutions, and you can get feedback on your designs. Here are some ways Coudo AI can help:

• Practice Problems: Solve real-world system design problems.

• AI-Powered Feedback: Get feedback on your designs from an AI.
• Community Reviews: Get feedback from other users.

I've found Coudo AI to be incredibly helpful in preparing for system design interviews. The AI-powered feedback is particularly useful, as it helps you identify areas where you can improve. Plus, the community reviews give you a chance to see how other engineers approach the same problems.

FAQs

1. How important is it to know specific technologies?

It's more important to understand the underlying concepts than to know specific technologies. However, familiarity with common technologies like databases, message queues, and caching systems is helpful.

2. What if I get stuck during an interview?

It's okay to get stuck. The interviewer wants to see how you approach problems. Explain your thought process, and ask for help if you need it.

3. How do I handle non-functional requirements like scalability and reliability?

Address non-functional requirements early in the design process. Discuss how your design will handle scalability, reliability, and security.

4. How can I improve my system design skills?

Practice regularly, read system design articles and books, and participate in system design discussions.

5. What are some common system design patterns?

Some common patterns include caching, load balancing, sharding, and message queues. Understanding these patterns is essential for system design interviews.

Wrapping Up

System design interviews are challenging but also rewarding. By practicing with these problems and solutions, you'll be well-prepared for your next interview. Remember to clarify requirements, think out loud, and consider trade-offs. And don't forget to check out Coudo AI for more practice problems and feedback. Keep pushing forward, and you'll become a system design pro in no time! Remember, consistent effort and a strategic approach are key to mastering system design questions. Try Coudo AI to accelerate your learning and confidently tackle any system design challenge that comes your way.