Design APIs for Facebook live commenting.

To design APIs for Facebook live commenting, we need to consider the following core functionalities:

1. Posting a Comment:

   • POST /v1/live_videos/{video_id}/comments
   • Request Body: { "user_id": "...", "text": "..." }
   • Response: { "comment_id": "...", "timestamp": "..." }

2. Fetching Comments:

   • GET /v1/live_videos/{video_id}/comments?since_id={comment_id}&limit={limit}
   • This API allows fetching comments chronologically, starting from a specific comment ID (since_id) to handle real-time updates efficiently. limit controls the number of comments returned.
   • Response: [ { "comment_id": "...", "user_id": "...", "text": "...", "timestamp": "..." }, ... ]

3. Real-time Updates (WebSockets/Server-Sent Events):

   • Instead of constant polling, a WebSocket connection or SSE endpoint would be ideal for pushing new comments to clients as they are posted.
   • Endpoint Example: ws://graph.facebook.com/v1/live_videos/{video_id}/comments/stream
   • Message Format: { "type": "new_comment", "data": { ... comment object ... } }

4. Liking a Comment:

   • POST /v1/comments/{comment_id}/likes
   • Request Body: { "user_id": "..." }
   • Response: 200 OK or 201 Created

5. Unliking a Comment:

   • DELETE /v1/comments/{comment_id}/likes
   • Request Body: { "user_id": "..." }
   • Response: 200 OK

6. Fetching Likes for a Comment:

   • GET /v1/comments/{comment_id}/likes?limit={limit}
   • Response: [ { "user_id": "..." }, ... ]

Considerations for Scalability and Performance:

• Database: Use a NoSQL database (like Cassandra or DynamoDB) optimized for high write throughput and time-series data. Sharding by video_id would be crucial.
• Caching: Cache recent comments and like counts to reduce database load.
• Rate Limiting: Implement rate limiting per user and per video to prevent abuse.
• Content Moderation: Integrate mechanisms for filtering or flagging inappropriate comments (e.g., keyword blacklists, AI-based moderation).
• Fan-out: For very popular live streams, consider a fan-out approach where comments are pushed to a large number of connected clients efficiently.