datasette.io VS dqlite

Opened an issue exploring alternatives here: https://github.com/simonw/datasette.io/issues/109

I decided to just drop "any size" but keep "any shape".

Here's an example from one of my projects: https://github.com/simonw/datasette.io/blob/main/.github/workflows/deploy.yml

I've come around to almost the opposite approach.

I pull all of the data I can get my hands on (from Twitter, GitHub, Swarm, Apple Health, Pocket, Apple Photos and more) into SQLite database tables that match the schema of the system that they are imported from.

For my own personal Dogsheep (https://simonwillison.net/2020/Nov/14/personal-data-warehous...) that's 119 tables right now.

Then I use SQL queries against those tables to extract and combine data in ways that are useful to me.

If the schema of the systems I am importing from changes, I can update my queries to compensate for the change.

This protects me from having to solve for a standard schema up front - I take whatever those systems give me. But it lets me combine and search across all of the data from disparate systems essentially at runtime.

I even have a search engine for this, which is populated by SQL queries against the different source tables. You can see an example of how that works at https://github.com/simonw/datasette.io/blob/main/templates/d... - which powers the search interface at https://datasette.io/-/beta

I've been exploring this technique more over the past year and I really like it - https://datasette.io (code at https://github.com/simonw/datasette.io ) is a more recent and much more complicated example.

Extracting links from markdown and using them to populate some additional columns or tables at build time would be pretty straight forward.

I run SQLite in serverless environments (Cloud Run, Vercel, Heroku) for dozens of projects... but the trick is that they all treat the database as a read-only asset.

If I want to deploy updated data, I build a brand new image and deploy the application bundled with the data. I tend to run the deploys for these (including the database build) in GitHub Actions workflows.

This works really well, but only for applications that don't need to apply constant updates more than a few times an hour! If you have a constant stream of updates I still think you're better off using a hosted database like Heroku PostgreSQL or Google Cloud SQL.

One example of a site I deploy like that is https://datasette.io/ - it's built and deployed by this GitHub Actions workflow here: https://github.com/simonw/datasette.io/blob/main/.github/wor...

If you're interested in this, here are some related projects that all take slightly different approaches:

- LiteSync directly competes with Marmot and supports DDL sync, but is closed source commercial (similar to SQLite EE): https://litesync.io

- dqlite is Canonical's distributed SQLite that depends on c-raft and kernel-level async I/O: https://dqlite.io

- cr-sqlite is a Rust-based loadable extension that adds CRDT changeset generation and reconciliation to SQLite: https://github.com/vlcn-io/cr-sqlite

Slightly related but not really (no multi writer, no C-level SQLite API or other restrictions):

- comdb2 (Bloombergs multi-homed RDMS using SQLite as the frontend)

- rqlite: RDMS with HTTP API and SQLite as the storage engine, used for replication and strong consistency (does not scale writes)

- litestream/LiteFS: disaster recovery replication

- liteserver: active read-only replication (predecessor of LiteSync)

I'd be curious for a similar tuning with Dqlite: https://github.com/canonical/dqlite

Anyone knows how the DB this is about, https://rqlite.io/, compares with https://dqlite.io/ by Canonical (both seem to be distributed versions of sqlite)?

For folks' context, the new tool that's being discussed in the thread mentioned by the parent here is litefs [0], as well as which you can also look at rqlite [1] and dqlite [2], which all provide different trade-offs (e.g. rqlite is 'more strongly consistent' than litefs).

[0]: https://github.com/superfly/litefs

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

[2]: https://github.com/canonical/dqlite

I presume you're familiar with https://github.com/canonical/dqlite (made by my employer) and https://github.com/rqlite/rqlite (unrelated)? How will mvsqlite compare to those?

Check out dqlite, it's sqlite but with a raft consensus to distribute changes through a log: https://dqlite.io/ You can link it in as a library too, it sounds like exactly what you want.