Alright, let's start with the basics. Firestore has a bit of a quirk: it only allows one write per second on any single document. So, if you're planning to hammer away at the same document with a ton of writes, Firestore might not be your best buddy for that task.

Batch writes are like a magic trick. They let you bundle multiple write operations into a single API call. You can pack up to 500 operations in one go! This not only cuts down the number of writes you need to do but also boosts your overall write performance. Use them whenever you can. Seriously, they’re a lifesaver.

Given Firestore's one-write-per-second limit on a single document, a smart move is to spread your writes across multiple documents. This way, you can handle heavy write workloads without hitting that pesky limit.

Denormalization might sound fancy, but it's just about duplicating data in multiple documents. This lets you write to different documents at the same time, sidestepping the one-write-per-second rule. Handy, right?

Need to keep a counter? Try sharding. Spread the increment operations across several documents. This technique can really speed things up when you need to update a counter frequently.

Transactions are awesome for keeping your data consistent, but they can be a bit slow if you pack too many write operations into one. Think about breaking them up into smaller transactions to keep things zippy.

Firestore automatically indexes each document, which is cool, but too many unnecessary indexes can slow down your writes. Plan your indexing strategy carefully. Only keep the indexes you really need.

The Firestore client SDK has a neat feature for caching writes when the network is down. It syncs them up once you're back online. This can make things feel faster for the user, especially if the network is being a bit flaky.

Remember, always tailor your optimizations to your specific workload. And don't forget to measure performance before and after making changes. Happy coding!