Optimizing Social Network Timeline with Firestore

In designing a social network with feed and follow functionality, database scalability is crucial to handle potential issues with large datasets. Firebase's Realtime Database presents scalability challenges, particularly with the approach of storing user timelines. To resolve these issues, consider transitioning to Firestore.

Optimized Database Structure

Firestore's schema addresses the scalability concerns with a hierarchical data structure:

• Users collection: Stores user information (uid, name, email)
• Following collection: Tracks the users a user follows. Each document represents the user being followed, containing a sub-collection of other user IDs that are following them.
• Posts collection: Stores individual posts. Each post document exists within a sub-collection associated with the posting user's uid.

Eliminating Scalability Issues

With this structure, the database addresses the initial concerns:

• Issue 1 (10,000 follower notifications): Firestore collections handle large numbers of documents effectively, eliminating the performance bottlenecks of Realtime Database.
• Issue 2 (Followers receiving all posts): By storing posts in sub-collections within the posting user's document, new followers only retrieve the latest posts, eliminating the need to load an entire post history.

Querying for Timelines

To retrieve a user's timeline, follow these steps:

• Get the current user's uid and create a reference to the Following collection.
• Query the sub-collection of user IDs the current user follows.
• For each user being followed, create a query for their latest posts within the Posts collection.

Additional Optimizations

Consider storing the user feed in a separate document for each user to further enhance performance. If the feed exceeds 1 MiB, it can be stored in a collection instead.

Conclusion

By employing this optimized database structure, Firestore effectively eliminates the scalability issues encountered in Firebase's Realtime Database, providing a robust foundation for handling large volumes of data in a social network application.