mvsqlite VS roapi

I’ve been using FDB for toy projects for a while. It’s truly rock solid. That being said, I wish there were more layers.

Ideally someone could implement the firestore or dynamodb api on top.

https://github.com/losfair/mvsqlite

For the rough plan, it's Cloud Backed SQLite meets FoundationDB.

- Oh, and if you're wondering about backup to S3, they have that too: https://github.com/libsql/bottomless

- Uh, sqld can integrated with this https://github.com/losfair/mvsqlite, so now your SQLite is backed by FoundationDB!?

- Meanwhile Litestream exists https://github.com/benbjohnson/litestream/

Shameless plug of my [mvSQLite](https://github.com/losfair/mvsqlite) project here! It's basically another distributed SQLite, but with support for everything expected from a proper distributed database: synchronous replication, strictly serializable transactions, + scalable reads and writes w/ multiple concurrent writers.

This project looks really exciting!

I'm working on mvsqlite [1], a distributed SQLite based on FoundationDB. When doing the VFS integration I have always wanted to patch SQLite itself, but didn't because of uncertainty around correctness of the patched version...

A few features on my wishlist:

1. Asynchronous I/O. mvsqlite is currently doing its own prefetch prediction that is not very accurate. I assume higher layers in SQLite have more information that can help with better prediction.

2. Custom page allocator. SQLite internally uses a linked list to manage database pages - this causes contention on any two transactions that both allocate or free pages.

3. Random ROWID, without the `max(int64)` row trick. Sequentially increasing ROWIDs is a primary source of contention, and causes significant INSERT slowdown in my benchmark [2].

[1] https://github.com/losfair/mvsqlite

[2] https://univalence.me/posts/mvsqlite-bench-20220930

That DynamoDB VFS looks cool! I agree that the VFS api makes one think about plenty of crazy ideas. Someone is working on a VFS based on Foundation DB[0] that looks very promising. It was recently discussed here[1]

[0]: https://github.com/losfair/mvsqlite

[1]: https://news.ycombinator.com/item?id=32269287

If your use case is read-only I suggest taking a look at roapi[1]. It supports multiple read frontends (GraphQL, SQL, REST) and many backends like SQLite, JSON, google sheets, MySQL, etc.

[1] https://github.com/roapi/roapi

For smaller datasets then anywhere up to a few mb which isn't so bad reasonable with an API but in theory for historic data it could be up to several gb. I've not seen datasette go that high (IIRC it's a 1000 row return limit by default).

That's what got me intrigued with Atlassians offering, as data lakes tend to be something internal to a company, not something I've ever seen offered as an interaction point to users.

I've also tested out roapi [1] which is nice if the data is in some structured format already (Parquet/JSON)

[1] https://github.com/roapi/roapi

https://github.com/roapi/roapi/issues/103?

Very cool and well executed project. Love the sprinkle of Rust in all the other companion projects as well :)

The ROAPI(https://github.com/roapi/roapi) project I built also happened to support a similar feature set, i.e. to expose sqlite through a variety of remote query interfaces including pg wire protocols, rest apis and graphqls.

> why not just accept SQL and cut out all the unnecessary mapping?

You might be interested in what we're building: Seafowl, a database designed for running analytical SQL queries straight from the user's browser, with HTTP CDN-friendly caching [0]. It's a second iteration of the Splitgraph DDN [1] which we built on top of PostgreSQL (Seafowl is much faster for this use case, since it's based on Apache DataFusion + Parquet).

The tradeoff for allowing the client to run any SQL vs a limited API is that PostgREST-style queries have a fairly predictable and low overhead, but aren't as powerful as fully-fledged SQL with aggregations, joins, window functions and CTEs, which have their uses in interactive dashboards to reduce the amount of data that has to be processed on the client.

There's also ROAPI [2] which is a read-only SQL API that you can deploy in front of a database / other data source (though in case of using databases as a data source, it's only for tables that fit in memory).

[0] https://seafowl.io/

[1] https://www.splitgraph.com/connect

[2] https://github.com/roapi/roapi

It could be the NDJSON parser (DF source: [0]) or could be a variety of other factors. Looking at the ROAPI release archive [1], it doesn't ship with the definitive `columnq` binary from your comment, so it could also have something to do with compilation-time flags.

FWIW, we use the Parquet format with DataFusion and get very good speeds similar to DuckDB [2], e.g. 1.5s to run a more complex aggregation query `SELECT date_trunc('month', tpep_pickup_datetime) AS month, COUNT(*) AS total_trips, SUM(total_amount) FROM tripdata GROUP BY 1 ORDER BY 1 ASC)` on a 55M row subset of NY Taxi trip data.

[0]: https://github.com/apache/arrow-datafusion/blob/master/dataf...

[1]: https://github.com/roapi/roapi/releases/tag/roapi-v0.8.0

[2]: https://observablehq.com/@seafowl/benchmarks