Google system design interview experience

To excel in a system design interview at Google India, you’ll need a structured, methodical approach while demonstrating clarity and confidence. Here’s how you can handle system design questions effectively:

1. Understand the Problem Statement

• Before diving in, clarify the requirements:

  • Ask questions to understand functional requirements (e.g., "What features does the system need?").

  • Explore non-functional requirements like scalability, performance, reliability, and security.

• Example: If asked to design a URL shortener, clarify if analytics tracking or expiration for URLs is required.

2. Start with a High-Level Approach

• Begin by breaking the problem into logical components. Use simple terms initially:

  • For example: "For a URL shortener, we need to generate short URLs, store mappings, and support quick redirections."

• Draw a rough block diagram:

  • Show user interaction, application servers, caching layers, databases, etc.

  • Use terms like "user sends request," "application generates short URL," and "database stores mapping."

3. Dive Deeper into Core Components

• Now, drill down into the architecture:

  • Database: What type of database fits the use case? Relational vs. NoSQL?

  • Caching: When and where to add caching for performance optimization.

  • Load Balancing: How to distribute requests across servers.

  • Scalability: Vertical (adding more resources to a server) and horizontal scaling (adding more servers).

4. Capacity Planning

• Show your ability to handle real-world use cases by estimating resource needs:

  • Storage: How much data will the system store? Estimate based on user base and data size.

  • Traffic: How many requests per second must the system handle during peak load?

  • Throughput: Calculate bandwidth requirements.

5. Address Edge Cases

• Always include these discussions:

  • How will the system behave under high traffic?

  • What happens if a component fails? (e.g., database failure).

  • How will data integrity and consistency be maintained in distributed systems?

6. Incorporate Non-Functional Requirements

• Discuss how your design meets:

  • Reliability: Use replication and backups.

  • Fault Tolerance: Explain failure recovery mechanisms.

  • Security: Include encryption for sensitive data and authentication for user actions.

7. Trade-Offs and Justifications

• Google interviewers love to see pragmatic thinking:

  • Explain why you chose one database over another (e.g., "NoSQL for scalability, as this system doesn't require complex joins").

  • Discuss trade-offs like cost vs. performance or consistency vs. availability (CAP theorem).

8. Be Collaborative and Communicative

• Keep your thought process transparent:

  • Think out loud and explain your reasoning for every step.

  • If an interviewer questions your approach, handle it constructively and adapt your design if necessary.

• Use Google’s "smart generalist" mindset—balance depth with breadth.

9. Final Review and Summary

• Summarize your solution briefly:

  • Reiterate key design choices and how they align with the requirements.

• Example: "In summary, I designed a scalable URL shortener with a distributed database for storage, Redis for caching popular URLs, and load balancers for handling traffic peaks."

10. Practice Mock Interviews

• Prepare for common system design scenarios:

  • Design a scalable chat application.

  • Build a global video streaming service.

  • Create a recommendation system for an e-commerce platform.

• Practice with peers or mentors to refine your communication and problem-solving skills.

I'll approach these system design questions as a Google engineer, incorporating edge cases, design diagrams, capacity planning, and non-functional requirements. Let's dive in:

1. Design a URL Shortener (e.g., bit.ly)

Requirements

• Functional: Shorten URLs, redirect to original URLs, track usage statistics.

• Non-functional: Scalability, low latency, fault tolerance, high availability.

Design

1. Architecture:

   • Use a hashing algorithm (e.g., Base62 encoding) to generate unique short URLs.

   • Store mappings in a distributed NoSQL database (e.g., DynamoDB or Cassandra).

   • Implement caching (e.g., Redis) for frequently accessed URLs.

   • Use load balancers to distribute traffic across servers.

2. Capacity Planning:

   • Storage:

     • Assume 1 billion URLs with an average of 100 bytes per URL (short + original URLs combined).

     • Total storage: 100 GB for URL mappings.

     • If we store analytics (e.g., click counts), assume an additional 50 GB for statistics.

   • Traffic:

     • Peak load: 10,000 requests per second (short URL redirection).

     • Use Redis cache to handle the most frequently accessed URLs. Cache size: 20 GB.

     • Throughput: Each server can process 1,000 requests/sec. At least 10 servers needed for peak traffic.

3. Edge Cases:

   • Collision: Handle hash collisions by appending random characters.

   • Expired URLs: Implement TTL (Time-to-Live) for temporary URLs.

   • Invalid URLs: Validate URLs before shortening.

Diagram

Client -> Load Balancer -> Application Server -> Database
       -> Cache (Redis) -> Database

2. Design a Scalable Chat Application

Requirements

• Functional: Real-time messaging, group chats, message history.

• Non-functional: Scalability, low latency, fault tolerance.

Design

1. Architecture:

   • Use WebSocket for real-time communication.

   • Store messages in a distributed database (e.g., Cassandra).

   • Implement sharding based on user IDs.

   • Use message queues (e.g., Kafka) for asynchronous processing.

2. Capacity Planning:

   • Storage:

     • Assume 10 million users, with each user sending 100 messages/day.

     • Average message size: 200 bytes.

     • Total storage per day: 200 GB.

     • For 1 year of history: 73 TB.

   • Traffic:

     • Peak load: 100,000 concurrent connections.

     • WebSocket servers: Each server handles 5,000 connections. At least 20 servers required during peak hours.

     • Use Kafka for asynchronous processing; throughput: 1 million messages/sec.

3. Edge Cases:

   • Offline Users: Queue messages for delivery when users reconnect.

   • Message Ordering: Use sequence numbers to ensure correct ordering.

   • Spam: Implement rate limiting and spam detection.

Diagram

Client -> WebSocket Server -> Message Queue -> Database

3. Design a Ride-Sharing Service (e.g., Uber)

Requirements

• Functional: Match riders with drivers, calculate fares, track rides.

• Non-functional: Scalability, real-time updates, fault tolerance.

Design

1. Architecture:

   • Use GPS-based tracking for real-time updates.

   • Implement a matching algorithm to pair riders with nearby drivers.

   • Store ride data in a relational database (e.g., PostgreSQL).

2. Capacity Planning:

   • Storage:

     • Assume 1 million rides/day, with each ride generating 10 updates (e.g., location, fare, etc.).

     • Average update size: 500 bytes.

     • Total storage per day: 5 GB.

     • For 1 year: 1.8 TB (for historical data storage).

   • Traffic:

     • Peak load: 10,000 ride matching requests/sec.

     • Use 10 application servers, each handling 1,000 requests/sec.

     • GPS tracking: Real-time updates require 50 MB/sec bandwidth.

3. Edge Cases:

   • Surge Pricing: Implement dynamic pricing based on demand.

   • Driver Cancellations: Reassign rides to other drivers.

   • Network Failures: Use retries and fallback mechanisms.

Diagram

Client -> Load Balancer -> Application Server -> Database
       -> GPS Tracking -> Matching Algorithm

4. Design a Distributed File Storage System (e.g., Google Drive)

Requirements

• Functional: Upload/download files, share files, version control.

• Non-functional: Scalability, fault tolerance, high availability.

Design

1. Architecture:

   • Use distributed storage (e.g., HDFS) for file storage.

   • Implement replication for fault tolerance.

   • Use metadata servers to track file locations.

2. Capacity Planning:

   • Storage:

     • Assume 1 billion files, with an average size of 1 MB.

     • Total storage: 1 PB.

     • For replication (3 copies): 3 PB.

   • Traffic:

     • Peak load: 10,000 uploads/downloads/sec.

     • Each server handles 1,000 requests/sec. At least 10 servers required.

     • Metadata size for tracking files: 100 TB.

3. Edge Cases:

   • Large Files: Split files into chunks for efficient uploads/downloads.

   • Conflicts: Implement version control for concurrent edits.

   • Data Loss: Use replication and backups.

Diagram

Client -> Metadata Server -> Distributed Storage

5. Design a Search Engine

Requirements

• Functional: Index web pages, return relevant results, handle queries.

• Non-functional: Scalability, low latency, fault tolerance.

Design

1. Architecture:

   • Use web crawlers to index pages.

   • Store indexed data in a distributed database.

   • Implement ranking algorithms (e.g., PageRank).

2. Capacity Planning:

   • Storage:

     • Assume 1 billion web pages, with an average size of 10 KB per page.

     • Total storage: 10 TB.

     • For additional metadata (e.g., indexing terms), storage increases to 20 TB.

   • Traffic:

     • Peak load: 100,000 search queries/sec.

     • Each server handles 1,000 queries/sec. At least 100 servers required.

     • Cache size for popular queries: 5 TB.

3. Edge Cases:

   • Spam Pages: Filter out low-quality pages using heuristics.

   • Query Failures: Provide fallback results.

   • High Traffic: Use caching for popular queries.

Diagram

Client -> Load Balancer -> Search Server -> Database
       -> Web Crawler -> Indexing

These answers incorporate edge cases, design diagrams, capacity planning, and non-functional requirements. Let me know if you'd like to explore any of these designs further or add more details! 😊