The target use cases include:

1- Session stores (can be reconstructed from auth service) 2- leaderboards/counters (recent scores/counters can be recalculated) 3- Real-time analytics/metrics (losing ~100ms of metrics is acceptable) 4- Caching layers with upstream persistence 5- High-frequency systems where latency > everything

I generally think that for KV stores, there are more use cases that can accept this _slightly_ relaxed durability model than not. of course this isn't the case for a main DB. KV stores often handle derived data, caches, or state that can be rebuilt.

That said, for cases needing stronger durability, you can call flush_all() after critical operations - gives you fsync-level guarantees. Also considering adding a "sync" or "Full ACID" mode that auto-flushes on every write for users who want strong durability.

The philosophy is: make the fast path really fast for those who need it, but provide escape hatches for stronger guarantees when needed.

I don't see many single user/desktop application use cases where that kind of time range would be risky.

Maybe if you used this as the main production database with thousands of concurrent sessions. But that doesn't seem to be their main use case, is it?

A related questions is if the code base is mature enough when configured for higher durability to work as intended. Even with Rust, there needs to be some hard systems testing and it's often not just a matter of sprinkling flushes around. Further optimization can try to close the window tighter - maybe with a transaction log, but then you obviously trade some speed for it.