Top System Design Interview Questions: Solved Examples & Explanations

System design interviews, they can feel like a puzzle with a million pieces, right? I remember my first one, I was sweating, trying to piece together a coherent architecture while the interviewer stared. It's not just about knowing the tech, it's about showing you can think big and connect the dots.

I want to walk through some of the most common system design questions. I'll give you solved examples and clear explanations. By the end, you'll be ready to tackle your next interview with confidence.

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Why System Design Matters

Why do companies even bother with these interviews? It's simple: they need engineers who can build scalable, reliable, and efficient systems. It's not enough to just code, you need to understand how all the parts work together.

I've seen projects fail because the architecture couldn't handle the load or because the team didn't consider future growth. System design is about planning for success, not just writing code.

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Question 1: Design a URL Shortener (Like TinyURL)

This is a classic, and it tests a range of skills. Here’s how I'd approach it:

1. Requirements: What are we building?
   • Shorten a long URL into a shorter one.
   • Redirect the short URL to the original URL.
   • Handle a large number of requests.
   • Ensure uniqueness of short URLs.
2. High-Level Design: How do the pieces fit?
   • A service that accepts long URLs and generates short URLs.
   • A database to store the mapping between short and long URLs.
   • A redirection service to handle requests for short URLs.
3. Components: What are the key parts?
   • API Server: Accepts long URLs and returns short URLs.
   • Database: Stores URL mappings (e.g., using a relational database like MySQL or a NoSQL database like Cassandra).
   • Key Generation Service: Generates unique short URL keys (e.g., using a counter or a UUID).
4. Scaling: How do we handle more users?
   • Load Balancing: Distribute traffic across multiple API servers.
   • Caching: Cache frequently accessed URL mappings to reduce database load.
   • Database Sharding: Split the database across multiple servers.

Example Explanation

Imagine you type a long URL into the system. The API server receives it, the Key Generation Service creates a unique short key, and the URL mapping is stored in the database. When someone clicks the short URL, the redirection service looks up the original URL in the cache or database and redirects the user.

Key Considerations

• Uniqueness: How do you guarantee short URLs are unique?
• Collision Handling: What happens if two users submit the same long URL?
• Storage: How do you efficiently store and retrieve URL mappings?

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Question 2: Design a Rate Limiter

Rate limiters are essential for protecting APIs from abuse. Here's how I'd tackle this one:

1. Requirements: What are we trying to achieve?
   • Limit the number of requests a user can make in a given time period.
   • Prevent abuse and protect the system from overload.
   • Support different rate limits for different users or API endpoints.
2. Algorithms: Which approach is best?
   • Token Bucket: Each user gets a "bucket" of tokens that refill at a certain rate. Each request consumes a token. If the bucket is empty, the request is rejected.
   • Leaky Bucket: Similar to the Token Bucket, but requests are processed at a constant rate.
   • Fixed Window Counter: Track the number of requests in a fixed time window. Reset the counter at the end of the window.
   • Sliding Window Log: Keep a log of request timestamps within a sliding time window. Calculate the rate based on the log.
3. Components: What do we need to build?
   • Rate Limiter Middleware: Intercepts incoming requests and applies the rate limiting logic.
   • Storage: Stores the state of each user's rate limit (e.g., tokens remaining, request timestamps). This could be an in-memory cache like Redis or a database.
4. Scaling: How do we handle high traffic?
   • Distributed Rate Limiting: Use a distributed cache to share rate limit state across multiple servers.
   • Consistent Hashing: Distribute users across different rate limiter instances to avoid hotspots.

Example Explanation

With the Token Bucket algorithm, each user starts with a certain number of tokens. Every time they make a request, a token is removed. Tokens are added back at a set rate. If they run out of tokens, their requests are blocked until more tokens are available.

Key Considerations

• Accuracy: How precise does the rate limiting need to be?
• Performance: How much latency does the rate limiter add to requests?
• Configuration: How flexible is the rate limiter to support different rate limits?

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Question 3: Design a Message Queue (Like RabbitMQ or Amazon MQ)

Message queues decouple services and enable asynchronous communication. Let's see how to design one:

1. Requirements: What should it do?
   • Allow services to send messages to a queue.
   • Allow services to consume messages from the queue.
   • Ensure messages are reliably delivered.
   • Support multiple producers and consumers.
2. Architecture: What's the overall structure?
   • Producers: Services that send messages to the queue.
   • Queue: A buffer that stores messages.
   • Consumers: Services that receive and process messages.
   • Broker: Manages the queue and routes messages between producers and consumers.
3. Components: What are the building blocks?
   • Message Broker: The core component that manages queues, exchanges, and routing.
   • Queues: Named channels where messages are stored.
   • Exchanges: Route messages from producers to queues based on rules.
   • Routing Keys: Used by producers to specify the destination of messages.
   • Bindings: Define the relationship between exchanges and queues.
4. Reliability: How do we ensure messages aren't lost?
   • Persistence: Store messages on disk to survive broker restarts.
   • Acknowledgements: Consumers send acknowledgements to confirm they've processed a message.
   • Redelivery: If a consumer fails to acknowledge a message, it's redelivered to another consumer.
5. Scaling: How do we handle more messages?
   • Clustering: Run multiple message brokers in a cluster to increase capacity.
   • Sharding: Split queues across multiple brokers.
   • Replication: Replicate queues for fault tolerance.

Example Explanation

A producer sends a message to an exchange with a routing key. The exchange routes the message to one or more queues based on bindings. Consumers subscribe to queues and receive messages. Once a consumer processes a message, it sends an acknowledgement to the broker.

Key Considerations

• Message Ordering: Does the order of messages need to be preserved?
• Message Delivery Guarantees: At least once, at most once, or exactly once?
• Throughput: How many messages can the queue handle per second?

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Common Mistakes to Avoid

• Not Clarifying Requirements: Always ask questions to understand the scope and constraints.
• Ignoring Scalability: Think about how your design will handle growth.
• Overcomplicating Things: Keep your design as simple as possible.
• Poor Communication: Explain your thought process clearly.
• Lack of Real-World Practice: Practice with sample problems and case studies.

For more practice, check out Coudo AI’s interview questions for hands-on experience.

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FAQs

Q: How important is it to know specific technologies for system design interviews?

While knowing specific technologies can be helpful, it's more important to understand the underlying concepts and trade-offs. Focus on understanding the principles of scalability, reliability, and performance.

Q: How do I prepare for system design interviews?

Practice with sample problems, read system design case studies, and understand the fundamentals of distributed systems. Consider using Coudo AI problems for interactive practice and feedback.

Q: What if I don't know the answer to a question?

Be honest and explain your thought process. It's better to show how you approach the problem than to try to bluff your way through it.

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Wrapping Up

System design interviews are challenging, but with the right preparation, you can ace them. Remember to clarify requirements, think about scalability, keep it simple, and practice, practice, practice. And if you want to really level up your skills, check out the Coudo AI learning platform, there are some really cool problems to solve. Happy designing, and I'll see you at the top!