Let's design a "stories" feature, similar to Instagram or WhatsApp. These stories should disappear after a friend views them and only be available for 24 hours after publishing. Consider the following aspects:

1. Functionality:

   • How would users create and upload stories (photos and videos)?
   • How would users view stories from their friends?
   • How would you implement the 24-hour expiration?
   • How would you handle the "disappearing" aspect after viewing?

2. Scalability:

   • How would you handle a large number of users uploading and viewing stories simultaneously?
   • How would you efficiently store and retrieve stories?
   • How would you manage the storage as stories expire?

3. Data Model:

   • What database schema would you use to store story information (user, timestamp, media URL, view status)?
   • How would you track which users have viewed a story?

4. Technical Challenges:

   • What are the potential challenges in implementing this feature?
   • How would you address issues like latency, storage limitations, and data consistency?

5. Edge Cases:

   • How would you handle users with slow internet connections?
   • What happens if a user uploads a story right before the 24-hour mark?
   • How would you handle deleted user accounts and their associated stories?

Stories Feature Design

Let's design a "stories" feature, similar to Instagram or WhatsApp. These stories should disappear after a friend views them and only be available for 24 hours after publishing. Here's a breakdown of the design considerations:

1. Functionality

• Creating and Uploading Stories:
  • Users can upload photos and videos from their device's gallery or capture them directly through the app.
  • The app should provide editing tools to add text, drawings, and filters to the stories.
  • Uploaded media files should be compressed and optimized for different network conditions to reduce upload times.
• Viewing Stories:
  • Stories from friends are displayed in a horizontal carousel at the top of the app screen.
  • Tapping on a friend's profile picture starts playing their story.
  • Stories play automatically one after another, allowing seamless viewing.
• 24-Hour Expiration:
  • Each story is associated with a timestamp indicating when it was published.
  • A background job runs periodically to identify stories older than 24 hours and mark them for deletion.
  • Deleted stories are removed from storage asynchronously to prevent performance impact.
• Disappearing After Viewing:
  • When a user views a story, the system records that the story has been viewed by that user.
  • Subsequent attempts to view the same story by the same user will result in the story being hidden or marked as unavailable.

2. Scalability

• Handling High Volume of Uploads and Views:
  • Utilize a Content Delivery Network (CDN) to store and serve story media files efficiently.
  • Implement load balancing to distribute incoming traffic across multiple servers.
  • Cache frequently accessed stories in memory to reduce database load.
• Efficient Storage and Retrieval:
  • Store media files in a distributed object storage system like Amazon S3 or Google Cloud Storage.
  • Partition the database tables based on user ID or timestamp to improve query performance.
  • Use indexing strategies to optimize story retrieval queries.
• Storage Management:
  • Implement a data retention policy to automatically delete expired stories.
  • Regularly archive older stories to a cheaper storage tier for compliance or auditing purposes.
  • Monitor storage usage and scale the storage infrastructure as needed.

3. Data Model

We can use a relational database like PostgreSQL or MySQL to store story information.

Tables:

• users
  • user_id (INT, Primary Key)
  • username (VARCHAR)
  • ...
• stories
  • story_id (INT, Primary Key)
  • user_id (INT, Foreign Key to users)
  • media_url (VARCHAR)
  • timestamp (TIMESTAMP)
  • ...
• story_views
  • story_id (INT, Foreign Key to stories)
  • user_id (INT, Foreign Key to users)
  • view_timestamp (TIMESTAMP)
  • PRIMARY KEY (story_id, user_id)

Relationships:

• One-to-many relationship between users and stories (one user can have multiple stories).
• Many-to-many relationship between users and stories via the story_views table (users can view many stories, and a story can be viewed by many users).

4. Technical Challenges

• Latency:
  • Optimize media file sizes and compression algorithms to reduce upload and download times.
  • Use CDNs to serve media files from geographically distributed locations.
  • Implement caching strategies to reduce database latency.
• Storage Limitations:
  • Use a scalable storage system like Amazon S3 or Google Cloud Storage.
  • Implement a data retention policy to automatically delete expired stories.
  • Monitor storage usage and scale the storage infrastructure as needed.
• Data Consistency:
  • Use transactions to ensure that story views are recorded accurately.
  • Implement data replication to ensure that data is available even if one server fails.
  • Use a distributed consensus algorithm like Raft or Paxos to ensure that data is consistent across all replicas.

5. Edge Cases

• Slow Internet Connections:
  • Implement adaptive bitrate streaming to adjust media quality based on network conditions.
  • Allow users to download stories for offline viewing.
  • Provide feedback to the user about the status of the upload or download.
• Stories Uploaded Right Before 24-Hour Mark:
  • The story should still be available for 24 hours from the time it was uploaded, even if that goes past the regular "purge" time.
  • The background job should check each story's timestamp individually rather than using a fixed time window.
• Deleted User Accounts:
  • When a user account is deleted, all associated stories should also be deleted.
  • Implement cascading deletes in the database to automatically remove stories when a user account is deleted.

Other Approaches

• NoSQL Database: We could use a NoSQL database like MongoDB to store story information. This would allow us to store more flexible data structures and scale more easily. However, it would also require us to manage data consistency ourselves.
• Message Queue: We could use a message queue like Kafka to process story uploads and views asynchronously. This would allow us to handle a large volume of traffic without impacting the performance of the main application.

Future Considerations

• Add support for live stories.
• Add support for interactive stories (e.g. polls, quizzes).
• Add support for sharing stories to other platforms.
• Improve the recommendation algorithm to show users more relevant stories.