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I don't really know enough about the specifics here. But my main points isn't about checksums, but more something like WAL in Postgres. For an embedded KV store this is probably not the solution, but my understanding is that there are data structures like LSM that would result in similar robustness. But I don't actually understand this topic well enough.

Checksumming detects corruption after it happened. A database like Postgres will simply notice it was not cleanly shut down and put the DB into a consistent state by replaying the write ahead log on startup. So that is kind of my default expectation for any DB that handles data that isn't ephemeral or easily regenerated.

But I also likely have the wrong mental model of what Garage does with the metadata, as I wouldn't have expected that to be ever limited by Sqlite.





So the thing is, different KV stores have different trade-offs, and for now we haven't yet found one that has the best of all worlds.

We do recommend SQLite in our quick-start guide to setup a single-node deployment for small/moderate workloads, and it works fine. The "real world deployment" guide recommends LMDB because it gives much better performance (with the current status of Garage, not to say that this couldn't be improved), and the risk of critical data loss is mitigated by the fact that such a deployment would use multi-node replication, meaning that the data can always be recovered from another replica if one node is corrupted and no snapshot is available. Maybe this should be worded better, I can see that the alarmist wording of the deployment guide is creating quite a debate so we probably need to make these facts clearer.

We are also experimenting Fjall as an alternate KV engine based on LSM, as it theoretically has good speed and crash resilience, which would make it the best option. We are just not recommending it by default yet, as we don't have much data to confirm that it works up to these expectations.




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