Data denormalization in Firestore is handled by duplicating data across collections or documents. This approach reduces the need for complex queries and joins, which are not natively supported in Firestore. It also improves read performance as it allows fetching all necessary data in a single operation.
However, this method increases write costs and complexity since updates must be performed on each instance of duplicated data. To manage this, batched writes or transactions can be used to ensure atomicity.
For example, if we have ‘users’ and ‘posts’ collections, instead of storing user details in each post document, we store only the user’s ID. When displaying posts, we fetch user details separately using the stored IDs. If user details change, we update the ‘users’ collection without affecting ‘posts’.
Denormalization is done primarily for performance optimization. Firestore charges based on the number of reads, writes, and deletes. By reducing the number of operations, we can minimize costs and improve efficiency.
