dqlite VS datasette

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.

Simon Willison's github would be a great place to get started imo -

https://github.com/simonw/datasette

I'm a fan on simonw's datasette/dogsheep ecosystem https://datasette.io/

I use Anki the exact same way. After a lifetime of learning I have accepted that I will never read over anything I write for myself voluntarily - so my two options are:

1. Write an article so good I can publish it and look it over myself later on. I did this last year with https://andrew-quinn.me/fzf/, for example.

2. Create Anki cards out of the material. Use the builtin Card Browser or even https://datasette.io/ on the underlying SQLite database in a pinch to search for my notes any time I have to.

Were you aware of, or tempted by https://datasette.io/ for creating your solution?

Browsing HN, GitHub and the like we get to see a huge variety of software products and code bases.

I often see products and think - if this product X, got together with Y, it would be pretty cool - kind of like if they had a kid together.

Not too literally, but more on the conceptual level - my level of programming is low.

E.g. Just some....

- pocketable.io & datasette (+with some more charting) [https://pocketbase.io, https://datasette.io]

You might like the Datasette project: https://datasette.io/

I don't think they are desperate for contributions but it's a welcoming environment and a fun project to hack on. You'll learn a lot just from reading the source and the incredibly informative PRs. The creator is a really talented developer with a great blog which shows up on the HN front page often.

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

> uploads of boring raw excel data and receive a nice UI

https://datasette.io/