Examples

Real-World Systems for System Design

1. Design a URL Shortener

Interview Response Script:

Interviewer: “How would you design a URL shortener like TinyURL?”

You:
“To design a URL shortener, I’d start by understanding the core requirements:

1. Shortening: Convert long URLs into compact, unique short URLs.
2. Redirection: Redirect users from the short URL to the original URL.
3. Scalability: Handle millions of URLs and high traffic.
4. Availability: Ensure the system is always accessible.

Here’s how I’d approach it:

1. URL Shortening:

   • When a user submits a long URL, the system generates a unique short code (e.g., abc123).
   • I’d use a hash function like Base62 to encode the URL into a short string.
   • The short code and original URL are stored in a distributed database like Cassandra for scalability and fault tolerance.

2. Redirection:

   • When a user visits the short URL (e.g., tinyurl.com/abc123), the web server looks up the original URL in the database.
   • If the URL is found, the user is redirected (HTTP 301).
   • To improve performance, I’d use an in-memory cache like Redis to store frequently accessed URLs.

3. Scalability:

   • The database would be sharded to distribute the load across multiple servers.
   • A load balancer would distribute incoming traffic to multiple web servers.
   • Caching would reduce the load on the database and improve response times.

4. Availability:

   • The system would be deployed across multiple regions to ensure high availability.
   • Database replication would ensure data redundancy.

Trade-offs:

• Using a cache improves performance but introduces eventual consistency.
• Sharding the database improves scalability but adds complexity.

Tools:

• Database: Cassandra.
• Cache: Redis.
• Load Balancer: NGINX.

This design ensures the system is scalable, performant, and highly available.”

2. Design a Social Media Feed

Interview Response Script:

Interviewer: “How would you design a social media feed like Twitter?”

You:
“Designing a social media feed involves handling two main challenges:

1. Feed Generation: Displaying a personalized feed of posts for each user.
2. Scalability: Handling high read and write throughput with low latency.

Here’s my approach:

1. Feed Generation:

   • I’d consider two models:
     • Pull Model: Fetch posts from followed users on each request. This is simple but can lead to high latency for users with many followers.
     • Push Model: Precompute and store feeds for each user. This reduces latency but increases storage and write complexity.
   • For a system like Twitter, I’d use a hybrid approach:
     • Precompute feeds for active users (push).
     • Fetch posts on-demand for less active users (pull).

2. Database:

   • Posts and feeds would be stored in a distributed database like Cassandra for scalability.
   • Indexes would be used to optimize query performance.

3. Caching:

   • I’d use an in-memory cache like Redis to store precomputed feeds for active users.
   • This reduces database load and improves response times.

4. Real-Time Updates:

   • A message queue like Kafka would handle real-time updates (e.g., new posts).
   • Subscribers (e.g., feed services) would update feeds as new posts arrive.

Trade-offs:

• The push model improves latency but increases storage and write complexity.
• Caching improves performance but introduces eventual consistency.

Tools:

• Database: Cassandra.
• Cache: Redis.
• Message Queue: Kafka.

This design ensures the feed is personalized, scalable, and low-latency.”

3. Design a Chat Application

Interview Response Script:

Interviewer: “How would you design a chat application like WhatsApp?”

You:
“Designing a chat application involves addressing several key requirements:

1. Real-Time Messaging: Ensure messages are delivered instantly.
2. Message Persistence: Store messages for future retrieval.
3. Scalability: Handle millions of concurrent users.

Here’s my approach:

1. Messaging:

   • I’d use WebSockets for real-time communication between clients and servers.
   • Messages would be stored in a distributed database like Cassandra for persistence.

2. Message Queue:

   • A message queue like Kafka would handle message delivery.
   • Producers (e.g., chat servers) would publish messages to topics.
   • Consumers (e.g., recipient clients) would subscribe to topics to receive messages.

3. Caching:

   • I’d use an in-memory cache like Redis to store recent messages and active sessions.
   • This reduces database load and improves performance.

4. Notifications:

   • A push notification service like Firebase would notify users of new messages when they’re offline.

Trade-offs:

• Using WebSockets ensures real-time communication but requires maintaining persistent connections.
• Caching improves performance but introduces eventual consistency.

Tools:

• Database: Cassandra.
• Cache: Redis.
• Message Queue: Kafka.
• Push Notifications: Firebase.

This design ensures the chat application is real-time, scalable, and reliable.”

4. Design a Video Streaming Platform

Interview Response Script:

Interviewer: “How would you design a video streaming platform like Netflix?”

You:
“Designing a video streaming platform involves addressing several challenges:

1. Content Delivery: Stream high-quality video to millions of users.
2. Scalability: Handle large-scale data storage and delivery.
3. Low Latency: Ensure smooth playback with minimal buffering.

Here’s my approach:

1. Content Delivery:

   • I’d use a Content Delivery Network (CDN) to cache and deliver video content.
   • CDN servers would be distributed globally to reduce latency.

2. Video Encoding:

   • Videos would be encoded into multiple formats and resolutions for adaptive streaming.
   • Protocols like HLS or DASH would be used to switch between resolutions based on network conditions.

3. Storage:

   • Video files would be stored in a distributed file system like HDFS for scalability.
   • Metadata (e.g., video titles, descriptions) would be stored in a distributed database like Cassandra.

4. Streaming Servers:

   • Streaming servers would handle requests from clients and fetch video chunks from the CDN or storage.

Trade-offs:

• Using a CDN improves latency but increases costs.
• Adaptive streaming improves user experience but requires additional encoding.

Tools:

• CDN: Cloudflare, Akamai.
• Storage: HDFS.
• Database: Cassandra.

This design ensures the platform is scalable, low-latency, and provides a high-quality user experience.”

5. Design a Ride-Sharing Servic

Interview Response Script:

Interviewer: “How would you design a ride-sharing service like Uber?”

You:
“Designing a ride-sharing service involves addressing several key requirements:

1. Real-Time Matching: Match riders with nearby drivers.
2. Scalability: Handle high concurrency and low latency.
3. Reliability: Ensure the system is fault-tolerant.

Here’s my approach:

1. Matching Algorithm:

   • I’d use a geospatial index (e.g., R-tree) to find nearby drivers.
   • Drivers’ locations would be updated in real-time using WebSockets or a message queue like Kafka.

2. Database:

   • Ride and user data would be stored in a distributed database like Cassandra for scalability.
   • Indexes would be used to optimize query performance.

3. Caching:

   • I’d use an in-memory cache like Redis to store frequently accessed data (e.g., driver locations).
   • This reduces database load and improves response times.

4. Notifications:

   • A push notification service like Firebase would notify drivers of ride requests.

Trade-offs:

• Using a geospatial index improves matching efficiency but increases complexity.
• Caching improves performance but introduces eventual consistency.

Tools:

• Database: Cassandra.
• Cache: Redis.
• Message Queue: Kafka.
• Push Notifications: Firebase.

This design ensures the ride-sharing service is real-time, scalable, and reliable.”

6. Design a Search Engine

Interview Response Script:

Interviewer: “How would you design a search engine like Google?”

You:
“Designing a search engine involves addressing several challenges:

1. Web Crawling: Index billions of web pages.
2. Search Relevance: Ensure fast and relevant search results.
3. Scalability: Handle high query throughput.

Here’s my approach:

1. Web Crawler:

   • A distributed crawler would fetch web pages and extract content.
   • Crawled data would be stored in a distributed file system like HDFS.

2. Indexing:

   • An inverted index would map keywords to web pages.
   • The index would be stored in a distributed database like Bigtable for scalability.

3. Search Algorithm:

   • Algorithms like PageRank would rank search results based on relevance.
   • Machine learning models could further improve result quality.

4. Query Processing:

   • A query server would handle user queries, fetch results from the index, and rank them.
   • Caching would be used to store frequently searched queries.

Trade-offs:

• Using an inverted index improves search efficiency but increases storage requirements.
• Ranking algorithms improve relevance but add computational complexity.

Tools:

• Storage: HDFS.
• Database: Bigtable.
• Cache: Redis.

This design ensures the search engine is scalable, fast, and provides relevant results.”