Firestore charges you for every document read, write, and delete. So, if you optimize your indexes, you can cut down on those reads. Firestore does a good job of indexing your data automatically, but if your app has some complex queries, you might want to create composite indexes yourself.

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A great example of when to use a composite index is if you need to handle complex SQL-like queries, like WHERE this AND that ORDER BY something.

In Firestore, it's crucial to model your data to fit how your app reads and writes. Denormalizing data means spreading one piece of data across multiple documents. This might make your data bigger, but it makes read operations faster and cheaper because reading one document with all the needed data is better than reading a whole collection.

But, keep in mind, denormalization can bump up your write costs. If you change a value, you'll need to update it in every document where it's used.

Firestore has some pretty nifty offline capabilities that cache data for offline use. Always set up Firestore to enable caching. This can really boost performance and cut down on read operations from Firestore.

The Firebase SDKs take care of all the caching, and any active listener to Firestore data will get updates from the cache if the client goes offline.

Firestore read operations can get pricey, so always aim to target the exact documents you need for a query. Avoid scanning the whole Firestore database. Firestore's design suggests you store data with the UI in mind, which directly impacts the efficiency of your read operations.

Also, use Firestore's powerful query capabilities to fetch just the data you need, instead of pulling a large data set and filtering it on the client side.

Firestore charges per document read, no matter the data size. To cut down on the number of document reads, try grouping related data into fewer documents.

Firestore also lets you retrieve several specific documents in a collection at once. Using this batch-read feature can mean fewer read operations because you'll get all the needed data in one go.

Firestore bills you per document read, regardless of the document size. Storing a ton of data in a single document can slow down your app because every time you read the document, the client downloads all the data.

So, keep your documents lean, containing only the essential data that's frequently used together. The rest of the data can go into different documents or sub-collections.

Remember, spreading data means more document reads but fewer data downloads. It's all about finding the right balance between the size of data you get in each document read and the number of documents you read.

Always keep an eye on Firestore's usage dashboard to monitor your usage history, including the number of reads, writes, and deletes. This helps you understand and ensure that your Firestore database is fully optimized.

The dashboard can also suggest which indexes to create based on your query history. Check these recommendations regularly to further optimize your Firestore database.