Before you dive into setting up Firebase Realtime Database for high write throughput, it's crucial to get a grip on its limitations. The database can handle around 1000 write and read operations per second per connection. Keep in mind, this is about individual operations, not events. If you're dealing with a ton of data or complex writes, things might slow down a bit.

When structuring data in Firebase, think differently than you would with a traditional SQL database. To boost scalability, you should denormalize your data. This means duplicating the same piece of data in multiple places so it's easier to access. It might feel a bit odd at first, but trust me, having some data redundancy is way better for performance than trying to minimize data usage with complex joins or similar queries.

Want to crank up your write throughput? Try batching your write operations. Firebase SDKs and REST APIs let you perform multiple operations in a single atomic action. This cuts down on connection overhead and helps boost throughput. With Firebase Realtime Database, you can update some keys for a specific path without messing with other keys in the same path.

If your app's write operations are pushing the limits of a single database, think about sharding. Sharding is all about splitting your data across multiple databases to balance the load. You can split data based on function, user location, or any other logical factor that makes sense for your app.

Always aim to fetch only the data you need. When your app reads data, it uses both read and write capacity. By cutting down on the amount of data fetched, you reduce the number of operations the database has to handle, freeing up more capacity for write operations.

Lastly, give your database a good workout under high load using Firebase Test Lab. It offers infrastructure for testing Android apps, including load tests. This can help you spot any performance bottlenecks and fine-tune your database for better performance.