Top System Design Interview Questions: Solved Examples & Explanations

System design interviews. They’re the gatekeepers to some of the most exciting roles in tech, but also a source of major stress for many engineers. I remember my first system design interview—I walked in feeling confident, only to be completely stumped by the open-ended nature of the questions. It felt like I was trying to build a skyscraper without a blueprint.

I've been on both sides of the table, both as a candidate and an interviewer. I've seen firsthand what makes a system design candidate shine, and where people often stumble. I’m going to arm you with solved examples and explanations for some of the most frequently asked system design interview questions. Let’s jump in!

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Why are System Design Questions So Important?

System design questions assess your ability to think holistically about building scalable, robust, and efficient systems. Interviewers want to see if you can:

• Understand requirements and constraints.
• Break down complex problems into manageable components.
• Choose appropriate technologies and architectures.
• Communicate your design decisions clearly.
• Consider trade-offs and potential bottlenecks.

These questions aren't about knowing the "right" answer. They're about demonstrating your problem-solving process and your understanding of fundamental design principles.

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Top System Design Interview Questions (with Solutions)

Let's dive into some common system design interview questions, along with example solutions and explanations.

1. Design a URL Shortener (like TinyURL or Bitly)

This is a classic system design question that tests your understanding of:

• Hashing and data storage.
• Scalability and load balancing.
• Cache management.

High-Level Design:

1. User Input: The user enters a long URL.
2. Short URL Generation: The system generates a unique short URL (e.g., http://tinyurl.com/xyz123). This typically involves hashing the long URL.
3. Storage: The long URL and its corresponding short URL are stored in a database.
4. Redirection: When a user accesses the short URL, the system retrieves the long URL from the database and redirects the user.

Key Considerations:

• Hash Function: Choose a hash function that minimizes collisions and generates short, unique URLs.
• Database: Select a database that can handle a large number of reads and writes (e.g., Cassandra, DynamoDB).
• Caching: Implement a caching layer (e.g., Redis, Memcached) to store frequently accessed URL mappings and reduce database load.
• Scalability: Use load balancers to distribute traffic across multiple servers.

Example Code (Conceptual):

java
// Simplified example (not production-ready)

public class URLShortener {
    private Map<String, String> urlMap = new HashMap<>();
    private int counter = 0;

    public String shortenURL(String longURL) {
        String shortURL = "http://tinyurl.com/" + Integer.toHexString(counter++);
        urlMap.put(shortURL, longURL);
        return shortURL;
    }

    public String getLongURL(String shortURL) {
        return urlMap.get(shortURL);
    }
}

2. Design a Rate Limiter

Rate limiters protect your system from abuse by limiting the number of requests a user or service can make within a given time period. This question tests your understanding of:

• Concurrency and distributed systems.
• Data structures for tracking requests.
• Different rate limiting algorithms.

High-Level Design:

1. Request Interception: Incoming requests are intercepted by the rate limiter.
2. Counter Tracking: The rate limiter tracks the number of requests made by each user or service.
3. Limit Enforcement: If the number of requests exceeds the defined limit, the request is rejected.
4. Storage: Request counts are stored in a fast, persistent storage (e.g., Redis).

Key Considerations:

• Rate Limiting Algorithms: Common algorithms include token bucket, leaky bucket, and fixed window counters.
• Concurrency: Handle concurrent requests from multiple users efficiently.
• Distributed Rate Limiting: Coordinate rate limiting across multiple servers.
• Storage: Choose a storage solution that provides low latency and high throughput.

3. Design a Chat Application (like WhatsApp or Slack)

This question explores your ability to design real-time communication systems and tests your knowledge of:

• WebSockets and real-time protocols.
• Message queues and asynchronous processing.
• Data storage and retrieval.

High-Level Design:

1. Client Connection: Clients connect to the server using WebSockets.
2. Message Handling: When a client sends a message, the server receives it and broadcasts it to the intended recipients.
3. Message Persistence: Messages are stored in a database for future retrieval.
4. Presence: The system tracks the online status of users.

Key Considerations:

• Real-Time Communication: Use WebSockets for bidirectional, real-time communication between clients and the server.
• Message Queues: Implement a message queue (e.g., RabbitMQ, Kafka) for asynchronous message processing and delivery.
• Scalability: Scale the chat servers horizontally to handle a large number of concurrent users.
• Data Storage: Choose a database that can handle high write throughput and complex queries (e.g., Cassandra).

4. Design a Social Media Feed (like Twitter or Facebook)

This question assesses your ability to design systems that handle large amounts of data and complex relationships. It tests your understanding of:

• Data modeling and database design.
• Caching strategies.
• Fan-out and distribution.

High-Level Design:

1. User Posts: Users create and post content (e.g., text, images, videos).
2. Feed Generation: The system generates a personalized feed for each user, consisting of posts from the users they follow.
3. Data Storage: Posts and user relationships are stored in a database.
4. Delivery: Feed is delivered to the user when they open their social media application.

Key Considerations:

• Data Model: Design a data model that efficiently represents users, posts, and relationships.
• Fan-Out: Implement a fan-out strategy to efficiently distribute posts to followers (push vs. pull).
• Caching: Cache frequently accessed feeds and user data to reduce database load.
• Scalability: Use sharding and replication to scale the database horizontally.

5. Design a Search Autocomplete System

Autocompleting search queries as users type is a common feature. This question tests your knowledge of:

• Trie data structure.
• Ranking and relevance.
• Caching.

High-Level Design:

1. User Input: The user types a search query.
2. Suggestion Retrieval: The system retrieves a list of suggested queries based on the user's input.
3. Ranking: The suggestions are ranked based on relevance and popularity.
4. Display: The top suggestions are displayed to the user.

Key Considerations:

• Trie Data Structure: Use a Trie (prefix tree) to efficiently store and retrieve search queries based on prefixes.
• Ranking Algorithms: Implement ranking algorithms that consider factors like query frequency, user history, and trending topics.
• Caching: Cache popular suggestions to reduce latency.
• Real-Time Updates: Update the Trie and cache in real-time as new queries are made.

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Tips for Cracking System Design Interviews

• Clarify Requirements: Start by asking clarifying questions to fully understand the problem.
• Think Out Loud: Explain your thought process and design decisions clearly.
• Consider Trade-Offs: Discuss the pros and cons of different approaches.
• Focus on Scalability: Design systems that can handle a large number of users and requests.
• Practice Regularly: The more you practice, the more comfortable you'll become with system design concepts.
• Use Diagrams: Use diagrams to illustrate your design and communication.
• Stay Up-to-Date: Keep up with the latest trends and technologies in system design.

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Coudo AI: Your System Design Interview Partner

Want to level up your system design skills? Coudo AI offers a range of resources to help you prepare for your interviews:

• Machine Coding Problems: Solve real-world design challenges in a hands-on coding environment. Problems like Movie Ticket API are available.
• AI-Powered Feedback: Get instant feedback on your code and design.
• Community-Based PR Reviews: Collaborate with other engineers and get expert feedback on your solutions.

Try solving design patterns problem on Coudo AI

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FAQs

Q: What are the key areas to focus on for system design interviews?

• Scalability, reliability, availability, and performance are crucial.

Q: How important is it to know specific technologies?

• Understanding the principles behind technologies is more important than memorizing specific details.

Q: How can Coudo AI help me prepare?

• Coudo AI provides hands-on practice and AI-driven feedback to help you master system design concepts. Also look at System Design Interview Preparation for more structured prep.

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Wrapping Up

System design interviews can be challenging, but with the right preparation, you can ace them. By understanding fundamental design principles, practicing regularly, and using resources like Coudo AI, you can confidently tackle any system design question that comes your way. Good luck, and keep building!