mentat VS differential-datalog

Why did Mozilla abandon the Mentat database?

There is (now unmaintained) project called Mentat [0] from Mozilla.

[0] https://github.com/mozilla/mentat

Mozilla was working on the opposite, a Datalog of SQLite, with Mentat, now abandoned: https://github.com/mozilla/mentat

Strikes me as a basically sound idea and it would be lovely if someone picked up the ball.

mentat was archived by mozilla back in 2017, but there are a bunch of forks. Because github is dumb and has a terrible interface for exploring forks [0], I used the Active GitHub Forks tool [1] that helped to find:

qpdb/mentat [2] seems to be the largest (+131 commits) and most recently modified (May this year) fork of mozilla/mentat.

[0]: https://github.com/mozilla/mentat/network/members - Seriously, how am I supposed to use this? Hundreds of entries, but no counts for stars, contributors, or commits, no details about recent commits. Just click every one?

[1]: https://techgaun.github.io/active-forks/index.html

[2]: https://github.com/qpdb/mentat

There are plenty of open source Datomic Inspired databases. Check out https://github.com/juji-io/datalevin and scroll down all the way down to “Alternatives”. There was even the beginning of a rust one by Mozilla: https://github.com/mozilla/mentat

I think you might be slightly misreading things, from what you quoted it’s about the query language (sql vs Datalog) not the database engine, that would likely be SQLite in any case. Now whether grafting a Datalog query engine onto SQLite is a good idea is a different question (though it’s been done, eg https://github.com/mozilla/mentat), but we should all at least talk about the same thing :)

You might be interested in the now defunct Mentat project from Mozilla. They made an EAV store with syncing on top of sqlite. It ran datalog queries by translating them into sql.

https://github.com/mozilla/mentat

Hi,

> I wonder if it lives up to the hype.

We do think so! (disclaimer: I'm a co-founder at Feldera)

To give some more background: We are co-designing/trialing feldera with several industry/enterprise partners from different domains. Our core team also built differential datalog (https://github.com/vmware/differential-datalog) in the past. And while ddlog is used quite successfully in products today, we believe the many lessons we learned with ddlog will help us to build an even better continuous analytics platform. FYI our code is open-source at https://github.com/feldera/feldera if you'd like to try it out.

Also feel free to join our community slack channel (https://www.feldera.com/slack/) if you have more questions.

The relational model (and generally working at the level of sets/collections, instead of the level of individual values/objects) actually makes it easier to have this kind of incremental computation in a consistent way, I think.

There's a bunch of work being done on making relational systems work this way. Some interesting reading:

- https://www.scattered-thoughts.net/writing/an-opinionated-ma...

- https://materialize.com/ which is built on https://timelydataflow.github.io/differential-dataflow/, which has a lot of research behind it

- Which also can be a compilation target for Datalog: https://github.com/vmware/differential-datalog

- Some prototype work on building UI systems in exactly the way you describe using a relational approach: https://riffle.systems/essays/prelude/ (and HN discussion: https://news.ycombinator.com/item?id=30530120)

(There's a lot more too -- I have a hobby interest in this space, so I have a small collection of links)

Tutorial which I didn’t see linked in the README: https://github.com/vmware/differential-datalog/blob/master/d...

This is amazing!

Have you looked at differential-datalog? It's rust-based, maintained by VMWare, and has a very rich, well-typed Datalog language. differential-datalog is in-memory only right now, but could be ideal to integrate your graph as a datastore or disk spill cache.

https://github.com/vmware/differential-datalog

Sort of related, in my mind at least, is differential dataflow, e.g. https://github.com/vmware/differential-datalog

It’s fascinating to see so many different parties converging on Datalog for reactive apps & UI.

- There are several such talks at https://www.hytradboi.com/ (happening this Friday)

- Roam Research and its clones Athens, Logseq, use Datascript / ClojureScript https://github.com/tonsky/datascript

- differential-datalog isn’t an end-to-end system, but is highly optimized for quick reactivity https://github.com/vmware/differential-datalog

- Datalog UI is a Typescript port of some of differential-datalog’s ideas https://datalogui.dev/

Rust related https://github.com/vmware/differential-datalog

Actually, that makes me wonder whether or not differential datalog falls under that umbrella, and if it could be applied in the same way Compose is.