I built a SQLite VFS in Rust that serves cold queries directly from S3 with sub-second performance, and often much faster.

It’s called turbolite. It is experimental, buggy, and may corrupt data. I would not trust it with anything important yet.

I wanted to explore whether object storage has gotten fast enough to support embedded databases over cloud storage. Filesystems reward tiny random reads and in-place mutation. S3 rewards fewer requests, bigger transfers, immutable objects, and aggressively parallel operations where bandwidth is often the real constraint. This was explicitly inspired by turbopuffer’s ground-up S3-native design. https://turbopuffer.com/blog/turbopuffer

The use case I had in mind is lots of mostly-cold SQLite databases (database-per-tenant, database-per-session, or database-per-user architectures) where keeping a separate attached volume for inactive database feels wasteful. turbolite assumes a single write source and is aimed much more at “many databases with bursty cold reads” than “one hot database.”

Instead of doing naive page-at-a-time reads from a raw SQLite file, turbolite introspects SQLite B-trees, stores related pages together in compressed page groups, and keeps a manifest that is the source of truth for where every page lives. Cache misses use seekable zstd frames and S3 range GETs for search queries, so fetching one needed page does not require downloading an entire object.

At query time, turbolite can also pass storage operations from the query plan down to the VFS to frontrun downloads for indexes and large scans in the order they will be accessed.

You can tune how aggressively turbolite prefetches. For point queries and small joins, it can stay conservative and avoid prefetching whole tables. For scans, it can get much more aggressive.

It also groups pages by page type in S3. Interior B-tree pages are bundled separately and loaded eagerly. Index pages prefetch aggressively. Data pages are stored by table. The goal is to make cold point queries and joins decent, while making scans less awful than naive remote paging would.

On a 1M-row / 1.5GB benchmark on EC2 + S3 Express, I’m seeing results like sub-100ms cold point lookups, sub-200ms cold 5-join profile queries, and sub-600ms scans from an empty cache with a 1.5GB database. It’s somewhat slower on normal S3/Tigris.

Current limitations are pretty straightforward: it’s single-writer only, and it is still very much a systems experiment rather than production infrastructure.

I’d love feedback from people who’ve worked on SQLite-over-network, storage engines, VFSes, or object-storage-backed databases. I’m especially interested in whether the B-tree-aware grouping / manifest / seekable-range-GET direction feels like the right one to keep pushing.

You might be interested in taking a look at Graft (https://graft.rs/). I have been iterating in this space for the last year, and have learned a lot about it. Graft has a slightly different set of goals, one of which is to keep writes fast and small and optimize for partial replication. That said, Graft shares several design decisions, including the use of framed ZStd compression to store pages.

I do like the B-tree aware grouping idea. This seems like a useful optimization for larger scan-style workloads. It helps eliminate the need to vacuum as much.

Have you considered doing other kinds of optimizations? Empty pages, free pages, etc.

This is awesome! With all of the projects/teams working on improving sqlite, it feels like it's just a matter of time before it becomes a better default than postgres for serious projects.

I do wonder - for projects that do ultimately enforce single writer sqlite setups - it still feels to me as if it would always be better to keep the sqlite db local (and then rsync/stream backups to whatever S3 storage one prefers).

The nut I've yet to see anyone crack on such setup is to figure out a way to achieve zero downtime deploys. For instance, adding a persistent disk to VMs on Render prevents zero downtime deploys (see https://render.com/docs/disks#disk-limitations-and-considera...) which is a real unfortunate side effect. I understand that the reason for this is because a VM instance is attached to the volume and needs to be swapped with the new version of said instance...

There are so many applications where merely scaling up a single VM as your product grows simplifies devops / product maintenance so much that it's a very compelling choice vs managing a cluster/separate db server. But getting forced downtime between releases to achieve that isn't acceptable in a lot of cases.

Not sure if it's truly a cheaply solvable problem. One potential option is to use a tool like turbolite as a parallel data store and, only during deployments, use it to keep the application running for the 10 to 60 seconds during a release swap. During this time, writes to the db are slower than usual but entirely online. And then, when your new release is live, it can sync the difference of data written to s3 back to the local db. In this way, during regular operation, we get the performance of local IO and fallback onto s3 backed sqlite during upgrades for persistent uptime.

Sounds like a fraught thing to build. But man it really is hard/impossible to beat the speed of local reads!

A bit more color on what I found interesting building this:

The motivating question for me was less “can SQLite read over the network?” and more “what assumptions break once the storage layer is object storage instead of a filesystem?”

The biggest conceptual shift was around *layout*.

What felt most wrong in naive designs was that SQLite page numbers are not laid out in a way that matches how you want to fetch data remotely. If an index is scattered across many unrelated page ranges, then “prefetch nearby pages” is kind of a fake optimization. Nearby in the file is not the same thing as relevant to the query.

That pushed me toward B-tree-aware grouping. Once the storage layer starts understanding which table or index a page belongs to, a lot of other things get cleaner: more targeted prefetch, better scan behavior, less random fetching, and much saner request economics.

Another thing that became much more important than I expected is that *different page types matter a lot*. Interior B-tree pages are tiny in footprint but disproportionately important, because basically every query traverses them. That changed how I thought about the system: much less as “a database file” and much more as “different classes of pages with very different value on the critical path.”

The query-plan-aware “frontrun” part came from the same instinct. Reactive prefetch is fine, but SQLite often already knows a lot about what it is about to touch. If the storage layer can see enough of that early, it can start warming the right structures before the first miss fully cascades. That’s still pretty experimental, but it was one of the more fun parts of the project.

A few things I learned building this:

1. *Cold point reads and small joins seem more plausible than I expected.* Not local-disk fast, obviously, but plausible for the “many mostly-cold DBs” niche.

2. *The real enemy is request count more than raw bytes.* Once I leaned harder into grouping and prefetch by tree, the design got much more coherent.

3. *Scans are still where reality bites.* They got much less bad, but they are still the place where remote object storage most clearly reminds you that it is not a local SSD.

4. *The storage backend is super important.* Different storage backends (S3, S3 Express, Tigris) have verg different round trip latencies and it's the single most important thing in determining how to tune prefetching.

Anyway, happy to talk about the architecture, the benchmark setup, what broke, or why I chose this shape instead of raw-file range GETs / replication-first approaches / etc.

Have you considered using techniques like conditional PUT to enable multiple writers?

https://aws.amazon.com/about-aws/whats-new/2024/08/amazon-s3...

https://docs.aws.amazon.com/AmazonS3/latest/userguide/condit...

Also I want to acknowledge the other projects in adjacent parts of this space — raw SQLite range-request VFSes, Litestream/LiteFS-style replication approaches, libSQL/Turso, Neon, mvsqlite, etc. I took a lot of inspiration from them, thanks!

What are your thoughts on eviction, re how easy to add some basic policy?

i wonder how much that costs per hour to run any normal load? what benefit does this have versuss using mysql (or any similar rdbms) for the queries? mysql/pgsql/etc is free remember, so using S3 obviously charges by the request, or am i wrong?

very cool!

super sick

Really cool