Common Questions

Common System Design Questions

1. Basic System Design

1.1 Design a URL Shortener (e.g., TinyURL)

Background: A URL shortener converts long URLs into short, unique aliases. It must handle high traffic, ensure low latency, and be highly available.

Key Components:

1. URL Shortening: Use a hash function (e.g., Base62) to generate short codes.
2. Redirection: Look up the original URL and redirect users.
3. Database: Store URL mappings in a distributed database (e.g., Cassandra).
4. Caching: Use Redis to cache frequently accessed URLs.

Interview Response Script:

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

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

1. Shortening: Convert long URLs into short, unique codes.
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 my approach:

1. URL Shortening:

   • When a user submits a long URL, the system generates a unique short code using a hash function like Base62.
   • The short code and original URL are stored in a distributed database like Cassandra for scalability.

2. Redirection:

   • When a user visits the short URL, 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.

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.”

1.2 Design a Rate Limiter

Background: A rate limiter controls the number of requests a user or service can make within a specific time period to prevent abuse and ensure fair usage.

Key Components:

1. Algorithm: Use token bucket or leaky bucket algorithms.
2. Implementation: Use Redis for distributed rate limiting or NGINX for centralized rate limiting.
3. Throttling: Return 429 (Too Many Requests) or delay requests when limits are exceeded.

Interview Response Script:

Interviewer: “How would you design a rate limiter for a high-traffic API?”

You:
“To design a rate limiter, I’d start by defining the limits (e.g., 100 requests per minute per user).

Here’s my approach:

1. Algorithm:

   • I’d use the token bucket algorithm, where each user gets a fixed number of tokens per time interval.
   • Each request consumes a token, and requests are rejected when tokens are exhausted.

2. Implementation:

   • For a distributed system, I’d use Redis to store and manage token counts across multiple nodes.
   • For a centralized system, I’d use an API gateway like NGINX or AWS API Gateway to enforce rate limits.

3. Throttling:

   • If a user exceeds the limit, I’d throttle their requests by delaying responses or returning a 429 (Too Many Requests) status code.

Trade-offs:

• Strict rate limiting prevents abuse but may block legitimate users.
• Throttling ensures fair usage but increases latency.

Tools:

• Redis for distributed rate limiting.
• NGINX for centralized rate limiting.

This design ensures the API is protected from abuse while maintaining fair usage.”

1.3 Design a Key-Value Store (e.g., Redis)

Background: A key-value store is a NoSQL database that stores data as key-value pairs. It must be highly performant, scalable, and fault-tolerant.

Key Components:

1. Data Model: Store data as key-value pairs.
2. Scalability: Use sharding to distribute data across multiple nodes.
3. Consistency: Choose between strong consistency (e.g., Redis) or eventual consistency (e.g., DynamoDB).

Interview Response Script:

Interviewer: “How would you design a key-value store like Redis?”

You:
“To design a key-value store, I’d start by defining the core requirements:

1. Performance: Ensure low-latency reads and writes.
2. Scalability: Handle large datasets and high traffic.
3. Fault Tolerance: Ensure data is not lost in case of failures.

Here’s my approach:

1. Data Model:

   • Data would be stored as key-value pairs, where keys are unique identifiers and values can be strings, lists, or other data structures.

2. Scalability:

   • I’d use sharding to distribute data across multiple nodes.
   • A consistent hashing algorithm would ensure even distribution of data.

3. Consistency:

   • For strong consistency, I’d use synchronous replication, where data is written to multiple nodes before acknowledging the write.
   • For eventual consistency, I’d use asynchronous replication, where data is propagated to other nodes over time.

4. Fault Tolerance:

   • Data would be replicated across multiple nodes to ensure redundancy.
   • Automatic failover would ensure the system remains available in case of node failures.

Trade-offs:

• Strong consistency ensures data accuracy but increases latency.
• Eventual consistency improves performance but may return stale data.

Tools:

• Redis for in-memory key-value storage.
• DynamoDB for distributed key-value storage.

This design ensures the key-value store is performant, scalable, and fault-tolerant.”

1.4 Design a Notification System

Background: A notification system sends real-time alerts to users via email, SMS, or push notifications. It must be scalable, reliable, and low-latency.

Key Components:

1. Message Queues: Use Kafka or RabbitMQ to handle notifications asynchronously.
2. Delivery Channels: Integrate with email, SMS, and push notification services.
3. Scalability: Use distributed systems to handle high traffic.

Interview Response Script:

Interviewer: “How would you design a notification system?”

You:
“To design a notification system, I’d start by defining the core requirements:

1. Real-Time Delivery: Ensure notifications are delivered instantly.
2. Scalability: Handle millions of notifications per day.
3. Reliability: Ensure no notifications are lost.

Here’s my approach:

1. Message Queues:

   • Notifications would be published to a message queue like Kafka or RabbitMQ.
   • Consumers would process notifications and send them via the appropriate channels (e.g., email, SMS, push).

2. Delivery Channels:

   • I’d integrate with third-party services like Twilio for SMS, SendGrid for email, and Firebase for push notifications.

3. Scalability:

   • The system would be distributed across multiple nodes to handle high traffic.
   • Load balancers would distribute incoming requests to multiple servers.

4. Reliability:

   • Notifications would be retried in case of delivery failures.
   • A dead-letter queue would store failed notifications for manual intervention.

Trade-offs:

• Using message queues ensures reliability but adds complexity.
• Third-party services simplify delivery but introduce external dependencies.

Tools:

• Kafka for message queuing.
• Twilio for SMS, SendGrid for email, Firebase for push notifications.

This design ensures the notification system is scalable, reliable, and low-latency.”

2. Social Media and Communication

2.1 Design a Social Media Feed (e.g., Twitter, Instagram)

Background: A social media feed displays a personalized list of posts for each user. It must handle high read and write throughput with low latency.

Key Components:

1. Feed Generation: Use a push or pull model to generate feeds.
2. Database: Store posts and feeds in a distributed database (e.g., Cassandra).
3. Caching: Use Redis to cache frequently accessed feeds.

Interview Response Script:

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

You:
“To design a social media feed, I’d start by defining the core requirements:

1. Personalization: Display a personalized feed for each user.
2. Scalability: Handle high read and write throughput.
3. Low Latency: Ensure feeds are generated quickly.

Here’s my approach:

1. Feed Generation:

   • I’d use a hybrid model:
     • For active users, precompute and store feeds (push model).
     • For less active users, fetch posts on-demand (pull model).

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 Redis to cache precomputed feeds for active users.

4. Real-Time Updates:

   • A message queue like Kafka would handle real-time updates (e.g., new posts).

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.”

2.2 Design a Chat Application (e.g., WhatsApp, Slack)

Background: A chat application enables real-time messaging between users. It must handle high concurrency, ensure message delivery, and be highly available.

Key Components:

1. Messaging: Use WebSockets for real-time communication.
2. Database: Store messages in a distributed database (e.g., Cassandra).
3. Caching: Use Redis to cache recent messages and active sessions.

Interview Response Script:

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

You:
“To design a chat application, I’d start by defining the core 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 Redis to cache recent messages and active sessions.

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.”

2.3 Design a Newsfeed Ranking System (e.g., Facebook)

Background: A newsfeed ranking system prioritizes and displays posts based on relevance to the user. It must handle high traffic and ensure low latency.

Key Components:

1. Ranking Algorithm: Use machine learning models to score posts.
2. Database: Store posts and user interactions in a distributed database (e.g., Cassandra).
3. Caching: Use Redis to cache ranked feeds.

Interview Response Script:

Interviewer: “How would you design a newsfeed ranking system like Facebook?”

You:
“To design a newsfeed ranking system, I’d start by defining the core requirements:

1. Relevance: Display posts that are most relevant to the user.
2. Scalability: Handle high traffic and large datasets.
3. Low Latency: Ensure feeds are generated quickly.

Here’s my approach:

1. Ranking Algorithm:

   • I’d use machine learning models to score posts based on factors like user interactions, post freshness, and content type.
   • The scores would be used to rank posts in the feed.

2. Database:

   • Posts and user interactions would be stored in a distributed database like Cassandra.
   • Indexes would be used to optimize query performance.

3. Caching:

   • I’d use Redis to cache ranked feeds for active users.

4. Real-Time Updates:

   • A message queue like Kafka would handle real-time updates (e.g., new posts, likes).

Trade-offs:

• Using machine learning improves relevance but increases computational complexity.
• Caching improves performance but introduces eventual consistency.

Tools:

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

This design ensures the newsfeed is relevant, scalable, and low-latency.”

3. E-commerce and Marketplaces

3.1 Design an E-commerce Platform (e.g., Amazon)

Background: An e-commerce platform allows users to browse, search, and purchase products. It must handle high traffic, ensure low latency, and be highly available.

Key Components:

1. Product Catalog: Store product details in a distributed database (e.g., Cassandra).
2. Search: Use Elasticsearch for fast and relevant search results.
3. Caching: Use Redis to cache frequently accessed product details.

Interview Response Script:

Interviewer: “How would you design an e-commerce platform like Amazon?”

You:
“To design an e-commerce platform, I’d start by defining the core requirements:

1. Product Catalog: Display detailed product information.
2. Search: Enable fast and relevant search results.
3. Scalability: Handle high traffic and large datasets.

Here’s my approach:

1. Product Catalog:

   • Product details would be stored in a distributed database like Cassandra.
   • Indexes would be used to optimize query performance.

2. Search:

   • I’d use Elasticsearch to index and search products.
   • Machine learning models could improve search relevance.

3. Caching:

   • I’d use Redis to cache frequently accessed product details.

4. Scalability:

   • The system would be distributed across multiple nodes to handle high traffic.
   • Load balancers would distribute incoming requests to multiple servers.

Trade-offs:

• Using Elasticsearch improves search performance but increases storage requirements.
• Caching improves performance but introduces eventual consistency.

Tools:

• Database: Cassandra.
• Search: Elasticsearch.
• Cache: Redis.

This design ensures the e-commerce platform is scalable, performant, and user-friendly.”

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

Background: A ride-sharing service matches riders with nearby drivers in real-time. It must handle high concurrency, ensure low latency, and be highly available.

Key Components:

1. Matching Algorithm: Use a geospatial index (e.g., R-tree) to find nearby drivers.
2. Real-Time Tracking: Use WebSockets or Kafka to track driver locations.
3. Database: Store ride and user data in a distributed database (e.g., Cassandra).

Interview Response Script:

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

You:
“To design a ride-sharing service, I’d start by defining the core 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 Kafka.

2. Database:

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

3. Caching:

   • I’d use Redis to cache frequently accessed data (e.g., driver locations).

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.”

3.3 Design a Food Delivery App (e.g., DoorDash, Uber Eats)

Background: A food delivery app allows users to order food from restaurants and have it delivered. It must handle high traffic, ensure low latency, and be highly available.

Key Components:

1. Order Management: Store orders in a distributed database (e.g., Cassandra).
2. Real-Time Tracking: Use WebSockets or Kafka to track delivery status.
3. Caching: Use Redis to cache frequently accessed data (e.g., restaurant menus).

Interview Response Script:

Interviewer: “How would you design a food delivery app like DoorDash?”

You:
“To design a food delivery app, I’d start by defining the core requirements:

1. Order Management: Handle order placement, tracking, and delivery.
2. Scalability: Handle high traffic and large datasets.
3. Low Latency: Ensure real-time updates for users and drivers.

Here’s my approach:

1. Order Management:

   • Orders would be stored in a distributed database like Cassandra.
   • Indexes would be used to optimize query performance.

2. Real-Time Tracking:

   • I’d use WebSockets or Kafka to track delivery status in real-time.

3. Caching:

   • I’d use Redis to cache frequently accessed data (e.g., restaurant menus).

4. Notifications:

   • A push notification service like Firebase would notify users and drivers of order updates.

Trade-offs:

• Using WebSockets ensures real-time updates 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 food delivery app is scalable, performant, and user-friendly.”

4. Streaming and Content Delivery

4.1 Design a Video Streaming Platform (e.g., Netflix, YouTube)

Background: A video streaming platform delivers high-quality video to millions of users. It must handle large-scale data storage, ensure low latency, and be highly available.

Key Components:

1. Content Delivery: Use a CDN to cache and deliver video content.
2. Video Encoding: Encode videos into multiple formats for adaptive streaming.
3. Storage: Use distributed file storage (e.g., HDFS) for video files.

Interview Response Script:

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

You:
“To design a video streaming platform, I’d start by defining the core requirements:

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 CDN (e.g., Cloudflare, Akamai) 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.”

4.2 Design a Music Streaming Service (e.g., Spotify)

Background: A music streaming service allows users to stream music on-demand. It must handle high traffic, ensure low latency, and be highly available.

Key Components:

1. Content Delivery: Use a CDN to cache and deliver audio content.
2. Metadata Storage: Store song metadata in a distributed database (e.g., Cassandra).
3. Caching: Use Redis to cache frequently accessed songs and playlists.

Interview Response Script:

Interviewer: “How would you design a music streaming service like Spotify?”

You:
“To design a music streaming service, I’d start by defining the core requirements:

1. Content Delivery: Stream high-quality audio to millions of users.
2. Scalability: Handle high traffic and large datasets.
3. Low Latency: Ensure smooth playback with minimal buffering.

Here’s my approach:

1. Content Delivery:

   • I’d use a CDN (e.g., Cloudflare, Akamai) to cache and deliver audio content.
   • CDN servers would be distributed globally to reduce latency.

2. Metadata Storage:

   • Song metadata (e.g., title, artist, album) would be stored in a distributed database like Cassandra.
   • Indexes would be used to optimize query performance.

3. Caching:

   • I’d use Redis to cache frequently accessed songs and playlists.

4. Streaming Servers:

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

Trade-offs:

• Using a CDN improves latency but increases costs.
• Caching improves performance but introduces eventual consistency.

Tools:

• CDN: Cloudflare, Akamai.
• Database: Cassandra.
• Cache: Redis.

This design ensures the music streaming service is scalable, performant, and user-friendly.”

4.3 Design a Content Delivery Network (CDN)

Background: A CDN caches and delivers content (e.g., images, videos) to users from servers located closer to them. It must reduce latency, handle high traffic, and be highly available.

Key Components:

1. Edge Servers: Distribute content across multiple servers globally.
2. Caching: Cache content on edge servers to reduce latency.
3. Load Balancing: Distribute requests across edge servers.

Interview Response Script:

Interviewer: “How would you design a content delivery network (CDN)?”

You:
“To design a CDN, I’d start by defining the core requirements:

1. Low Latency: Deliver content quickly to users.
2. Scalability: Handle high traffic and large datasets.
3. High Availability: Ensure the system is always accessible.

Here’s my approach:

1. Edge Servers:

   • Content would be distributed across multiple edge servers located globally.
   • Users would be routed to the nearest edge server to reduce latency.

2. Caching:

   • Content would be cached on edge servers to reduce the load on origin servers.
   • Cache expiration policies (e.g., TTL) would ensure content is up-to-date.

3. Load Balancing:

   • Load balancers would distribute requests across edge servers to ensure even load distribution.

4. Monitoring:

   • Monitoring tools (e.g., Prometheus, Grafana) would track server performance and cache hit rates.

Trade-offs:

• Using edge servers reduces latency but increases infrastructure costs.
• Caching improves performance but requires careful cache invalidation.

Tools:

• Edge Servers: Cloudflare, Akamai.
• Load Balancer: NGINX.
• Monitoring: Prometheus, Grafana.

This design ensures the CDN is scalable, low-latency, and highly available.”

5. Search and Recommendation Systems

5.1 Design a Search Engine (e.g., Google)

Background: A search engine indexes and searches billions of web pages. It must handle large-scale data, ensure fast and relevant search results, and be highly available.

Key Components:

1. Web Crawler: Crawl and index web pages.
2. Indexing: Use an inverted index to map keywords to web pages.
3. Search Algorithm: Use algorithms like PageRank to rank search results.

Interview Response Script:

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

You:
“To design a search engine, I’d start by defining the core requirements:

1. Indexing: 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.”

5.2 Design a Recommendation System (e.g., Netflix, Amazon)

Background: A recommendation system suggests personalized content to users based on their preferences and behavior. It must handle large-scale data, ensure low latency, and be highly available.

Key Components:

1. Data Collection: Collect user interactions (e.g., clicks, views).
2. Machine Learning Models: Use collaborative filtering or content-based filtering to generate recommendations.
3. Caching: Use Redis to cache frequently accessed recommendations.

Interview Response Script:

Interviewer: “How would you design a recommendation system like Netflix?”

You:
“To design a recommendation system, I’d start by defining the core requirements:

1. Personalization: Suggest content tailored to each user.
2. Scalability: Handle large-scale data and high traffic.
3. Low Latency: Ensure recommendations are generated quickly.

Here’s my approach:

1. Data Collection:

   • User interactions (e.g., clicks, views) would be collected and stored in a distributed database like Cassandra.

2. Machine Learning Models:

   • I’d use collaborative filtering to recommend content based on similar users’ preferences.
   • Content-based filtering could also be used to recommend similar items.

3. Caching:

   • I’d use Redis to cache frequently accessed recommendations.

4. Real-Time Updates:

   • A message queue like Kafka would handle real-time updates (e.g., new interactions).

Trade-offs:

• Using machine learning improves relevance but increases computational complexity.
• Caching improves performance but introduces eventual consistency.

Tools:

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

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

5.3 Design an Autocomplete System (e.g., Google Search)

Background: An autocomplete system suggests search queries as users type. It must handle high traffic, ensure low latency, and provide relevant suggestions.

Key Components:

1. Trie Data Structure: Store and retrieve prefixes efficiently.
2. Ranking: Use frequency or relevance to rank suggestions.
3. Caching: Use Redis to cache frequently searched prefixes.

Interview Response Script:

Interviewer: “How would you design an autocomplete system like Google Search?”

You:
“To design an autocomplete system, I’d start by defining the core requirements:

1. Low Latency: Provide suggestions as users type.
2. Relevance: Ensure suggestions are relevant to the user’s query.
3. Scalability: Handle high traffic and large datasets.

Here’s my approach:

1. Trie Data Structure:

   • I’d use a trie (prefix tree) to store and retrieve prefixes efficiently.
   • Each node in the trie would represent a character, and leaf nodes would represent complete queries.

2. Ranking:

   • Suggestions would be ranked based on frequency or relevance.
   • Machine learning models could improve ranking by considering user behavior.

3. Caching:

   • I’d use Redis to cache frequently searched prefixes and their suggestions.

4. Scalability:

   • The trie would be distributed across multiple nodes to handle high traffic.
   • Load balancers would distribute incoming requests to multiple servers.

Trade-offs:

• Using a trie improves prefix retrieval efficiency but increases memory usage.
• Caching improves performance but introduces eventual consistency.

Tools:

• Trie: Custom implementation or libraries like Apache Lucene.
• Cache: Redis.
• Load Balancer: NGINX.

This design ensures the autocomplete system is fast, relevant, and scalable.”

6. Storage and Databases

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

Background: A distributed file storage system allows users to store and retrieve files from anywhere. It must handle large-scale data, ensure high availability, and be fault-tolerant.

Key Components:

1. File Storage: Use distributed file systems like HDFS or S3.
2. Metadata Storage: Store file metadata in a distributed database (e.g., Cassandra).
3. Replication: Replicate files across multiple nodes for fault tolerance.

Interview Response Script:

Interviewer: “How would you design a distributed file storage system like Dropbox?”

You:
“To design a distributed file storage system, I’d start by defining the core requirements:

1. File Storage: Store and retrieve files efficiently.
2. Scalability: Handle large-scale data and high traffic.
3. Fault Tolerance: Ensure files are not lost in case of failures.

Here’s my approach:

1. File Storage:

   • Files would be stored in a distributed file system like HDFS or S3.
   • Files would be split into chunks for efficient storage and retrieval.

2. Metadata Storage:

   • File metadata (e.g., name, size, location) would be stored in a distributed database like Cassandra.
   • Indexes would be used to optimize query performance.

3. Replication:

   • Files would be replicated across multiple nodes to ensure fault tolerance.
   • Automatic failover would ensure the system remains available in case of node failures.

4. Caching:

   • I’d use Redis to cache frequently accessed files and metadata.

Trade-offs:

• Using replication ensures fault tolerance but increases storage requirements.
• Caching improves performance but introduces eventual consistency.

Tools:

• File Storage: HDFS, S3.
• Database: Cassandra.
• Cache: Redis.

This design ensures the file storage system is scalable, fault-tolerant, and highly available.”

6.2 Design a Distributed Database (e.g., Cassandra, DynamoDB)

Background: A distributed database stores and retrieves data across multiple nodes. It must handle large-scale data, ensure high availability, and be fault-tolerant.

Key Components:

1. Data Partitioning: Use sharding to distribute data across nodes.
2. Replication: Replicate data across multiple nodes for fault tolerance.
3. Consistency: Choose between strong consistency or eventual consistency.

Interview Response Script:

Interviewer: “How would you design a distributed database like Cassandra?”

You:
“To design a distributed database, I’d start by defining the core requirements:

1. Scalability: Handle large-scale data and high traffic.
2. High Availability: Ensure the system is always accessible.
3. Fault Tolerance: Ensure data is not lost in case of failures.

Here’s my approach:

1. Data Partitioning:

   • Data would be partitioned across multiple nodes using sharding.
   • A consistent hashing algorithm would ensure even distribution of data.

2. Replication:

   • Data would be replicated across multiple nodes to ensure fault tolerance.
   • Automatic failover would ensure the system remains available in case of node failures.

3. Consistency:

   • For strong consistency, I’d use synchronous replication, where data is written to multiple nodes before acknowledging the write.
   • For eventual consistency, I’d use asynchronous replication, where data is propagated to other nodes over time.

4. Query Processing:

   • Query coordinators would handle user queries and fetch data from the appropriate nodes.

Trade-offs:

• Strong consistency ensures data accuracy but increases latency.
• Eventual consistency improves performance but may return stale data.

Tools:

• Database: Cassandra, DynamoDB.

This design ensures the distributed database is scalable, highly available, and fault-tolerant.”

6.3 Design a Logging System (e.g., Splunk, ELK Stack)

Background: A logging system collects, stores, and analyzes log data from applications and systems. It must handle large-scale data, ensure low latency, and be highly available.

Key Components:

1. Log Collection: Use agents or APIs to collect logs.
2. Log Storage: Store logs in a distributed file system (e.g., HDFS) or database (e.g., Elasticsearch).
3. Log Analysis: Use tools like Elasticsearch and Kibana for analysis and visualization.

Interview Response Script:

Interviewer: “How would you design a logging system like Splunk?”

You:
“To design a logging system, I’d start by defining the core requirements:

1. Log Collection: Collect logs from multiple sources.
2. Scalability: Handle large-scale data and high traffic.
3. Low Latency: Ensure logs are processed and analyzed quickly.

Here’s my approach:

1. Log Collection:

   • Logs would be collected using agents or APIs and sent to a central logging server.
   • A message queue like Kafka would handle log ingestion.

2. Log Storage:

   • Logs would be stored in a distributed file system like HDFS or a database like Elasticsearch.
   • Indexes would be used to optimize query performance.

3. Log Analysis:

   • Tools like Elasticsearch and Kibana would be used for log analysis and visualization.
   • Machine learning models could detect anomalies or patterns in the logs.

4. Caching:

   • I’d use Redis to cache frequently accessed log data.

Trade-offs:

• Using a distributed file system ensures scalability but increases storage requirements.
• Caching improves performance but introduces eventual consistency.

Tools:

• Log Storage: HDFS, Elasticsearch.
• Analysis: Kibana.
• Cache: Redis.

This design ensures the logging system is scalable, performant, and provides actionable insights.”

7. Scalability and Performance

7.1 Design a System to Handle Millions of Concurrent Users

Background: A system handling millions of concurrent users must be highly scalable, ensure low latency, and be fault-tolerant.

Key Components:

1. Load Balancing: Use load balancers to distribute traffic.
2. Caching: Use Redis or CDNs to cache frequently accessed data.
3. Database Sharding: Shard the database to distribute the load.

Interview Response Script:

Interviewer: “How would you design a system to handle millions of concurrent users?”

You:
“To design a system for millions of concurrent users, I’d start by defining the core requirements:

1. Scalability: Handle high traffic and large datasets.
2. Low Latency: Ensure fast response times.
3. Fault Tolerance: Ensure the system remains available in case of failures.

Here’s my approach:

1. Load Balancing:

   • Load balancers like NGINX would distribute incoming traffic across multiple servers.

2. Caching:

   • I’d use Redis to cache frequently accessed data (e.g., user sessions, product details).
   • A CDN would cache static content (e.g., images, videos).

3. Database Sharding:

   • The database would be sharded to distribute the load across multiple nodes.
   • A consistent hashing algorithm would ensure even distribution of data.

4. Monitoring:

   • Monitoring tools like Prometheus and Grafana would track system performance and health.

Trade-offs:

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

Tools:

• Load Balancer: NGINX.
• Cache: Redis.
• CDN: Cloudflare.
• Monitoring: Prometheus, Grafana.

This design ensures the system is scalable, performant, and fault-tolerant.”

7.2 Design a System for Real-Time Analytics

Background: A real-time analytics system processes and analyzes data streams in real-time. It must handle high throughput, ensure low latency, and be highly available.

Key Components:

1. Stream Processing: Use tools like Apache Flink or Kafka Streams.
2. Data Storage: Store processed data in a distributed database (e.g., Cassandra).
3. Visualization: Use tools like Grafana or Kibana for visualization.

Interview Response Script:

Interviewer: “How would you design a system for real-time analytics?”

You:
“To design a real-time analytics system, I’d start by defining the core requirements:

1. Real-Time Processing: Process data streams in real-time.
2. Scalability: Handle high throughput and large datasets.
3. Low Latency: Ensure insights are generated quickly.

Here’s my approach:

1. Stream Processing:

   • I’d use Apache Flink or Kafka Streams to process data streams in real-time.
   • Streams would be divided into windows for aggregation and analysis.

2. Data Storage:

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

3. Visualization:

   • Tools like Grafana or Kibana would be used for real-time visualization of insights.

4. Caching:

   • I’d use Redis to cache frequently accessed insights.

Trade-offs:

• Using stream processing ensures real-time insights but increases computational complexity.
• Caching improves performance but introduces eventual consistency.

Tools:

• Stream Processing: Apache Flink, Kafka Streams.
• Database: Cassandra.
• Visualization: Grafana, Kibana.
• Cache: Redis.

This design ensures the real-time analytics system is scalable, performant, and provides actionable insights.”

7.3 Design a System for Handling Large-Scale Data Processing (e.g., MapReduce)

Background: A system for large-scale data processing must handle massive datasets, ensure fault tolerance, and be highly scalable.

Key Components:

1. Batch Processing: Use MapReduce for parallel processing.
2. Distributed Storage: Use HDFS for storing large datasets.
3. Fault Tolerance: Replicate data and tasks across nodes.

Interview Response Script:

Interviewer: “How would you design a system for large-scale data processing like MapReduce?”

You:
“To design a system for large-scale data processing, I’d start by defining the core requirements:

1. Scalability: Handle massive datasets and high computational load.
2. Fault Tolerance: Ensure tasks are completed even in case of failures.
3. Efficiency: Process data in parallel to reduce processing time.

Here’s my approach:

1. Batch Processing:

   • I’d use the MapReduce model for parallel processing.
   • The Map phase processes data in parallel, and the Reduce phase aggregates the results.

2. Distributed Storage:

   • Data would be stored in a distributed file system like HDFS for scalability.
   • Data would be split into chunks for parallel processing.

3. Fault Tolerance:

   • Tasks would be replicated across multiple nodes to ensure fault tolerance.
   • Failed tasks would be retried automatically.

4. Monitoring:

   • Monitoring tools like Prometheus and Grafana would track job progress and system health.

Trade-offs:

• Using MapReduce ensures fault tolerance but increases computational overhead.
• Distributed storage improves scalability but increases infrastructure costs.

Tools:

• Batch Processing: Hadoop MapReduce.
• Storage: HDFS.
• Monitoring: Prometheus, Grafana.

This design ensures the system is scalable, fault-tolerant, and efficient for large-scale data processing.”

8. Advanced System Design

8.1 Design a Distributed Cache (e.g., Memcached, Redis)

Background: A distributed cache stores frequently accessed data in memory across multiple nodes. It must be highly performant, scalable, and fault-tolerant.

Key Components:

1. Data Partitioning: Use consistent hashing to distribute data.
2. Replication: Replicate data across nodes for fault tolerance.
3. Eviction Policies: Use LRU or LFU to manage cache size.

Interview Response Script:

Interviewer: “How would you design a distributed cache like Redis?”

You:
“To design a distributed cache, I’d start by defining the core requirements:

1. Performance: Ensure low-latency reads and writes.
2. Scalability: Handle large datasets and high traffic.
3. Fault Tolerance: Ensure data is not lost in case of failures.

Here’s my approach:

1. Data Partitioning:

   • Data would be partitioned across multiple nodes using consistent hashing.
   • This ensures even distribution of data and minimizes rehashing when nodes are added or removed.

2. Replication:

   • Data would be replicated across multiple nodes to ensure fault tolerance.
   • Automatic failover would ensure the cache remains available in case of node failures.

3. Eviction Policies:

   • I’d use an LRU (Least Recently Used) policy to evict the least accessed data when the cache is full.

4. Monitoring:

   • Monitoring tools like Prometheus and Grafana would track cache performance and health.

Trade-offs:

• Using replication ensures fault tolerance but increases memory usage.
• Eviction policies improve cache efficiency but may evict frequently accessed data.

Tools:

• Distributed Cache: Redis, Memcached.
• Monitoring: Prometheus, Grafana.

This design ensures the distributed cache is performant, scalable, and fault-tolerant.”

8.2 Design a Distributed Locking Mechanism

Background: A distributed locking mechanism ensures that only one process can access a shared resource at a time in a distributed system. It must be highly available, fault-tolerant, and efficient.

Key Components:

1. Lock Acquisition: Use a distributed coordination service like ZooKeeper or Redis.
2. Lock Release: Ensure locks are released properly, even in case of failures.
3. Deadlock Prevention: Use timeouts to automatically release locks.

Interview Response Script:

Interviewer: “How would you design a distributed locking mechanism?”

You:
“To design a distributed locking mechanism, I’d start by defining the core requirements:

1. Exclusive Access: Ensure only one process can access a shared resource at a time.
2. Fault Tolerance: Ensure locks are released even in case of failures.
3. Efficiency: Minimize the overhead of acquiring and releasing locks.

Here’s my approach:

1. Lock Acquisition:

   • A process would request a lock by creating an ephemeral node in ZooKeeper or setting a key in Redis.
   • If the lock is available, the process acquires it; otherwise, it waits.

2. Lock Release:

   • The process would release the lock by deleting the node or key.
   • To handle process failures, I’d use timeouts to automatically release locks.

3. Deadlock Prevention:

   • I’d ensure locks are always released, even in case of failures, by using timeouts and monitoring.

Trade-offs:

• Using ZooKeeper ensures strong consistency but adds complexity.
• Using Redis is simpler but may have weaker consistency guarantees.

Tools:

• Distributed Coordination: ZooKeeper, Redis.

This design ensures exclusive access to shared resources in a distributed system.”

8.3 Design a System for Leader Election in a Distributed System

Background: A leader election mechanism ensures that one node is designated as the leader in a distributed system. It must be fault-tolerant, efficient, and ensure consistency.

Key Components:

1. Election Algorithm: Use algorithms like Paxos or Raft.
2. Fault Tolerance: Ensure a new leader is elected if the current leader fails.
3. Consistency: Ensure all nodes agree on the leader.

Interview Response Script:

Interviewer: “How would you design a system for leader election in a distributed system?”

You:
“To design a leader election system, I’d start by defining the core requirements:

1. Fault Tolerance: Ensure a new leader is elected if the current leader fails.
2. Consistency: Ensure all nodes agree on the leader.
3. Efficiency: Minimize the overhead of leader election.

Here’s my approach:

1. Election Algorithm:

   • I’d use the Raft algorithm for leader election, as it’s simpler to implement than Paxos.
   • Nodes would communicate with each other to elect a leader based on their logs and terms.

2. Fault Tolerance:

   • If the leader fails, the remaining nodes would initiate a new election.
   • Automatic failover would ensure the system remains available.

3. Consistency:

   • All nodes would agree on the leader through a consensus mechanism.
   • Log replication would ensure consistency across nodes.

Trade-offs:

• Using Raft ensures fault tolerance and consistency but adds communication overhead.
• Simpler algorithms like Bully may be less reliable.

Tools:

• Consensus Algorithm: Raft, Paxos.

This design ensures the leader election system is fault-tolerant, consistent, and efficient.”