SQLite VS mentat

If you are into alternative storage engines for SQLite, there is also an LSM (Log-Structured Merge-tree) extension in the main repository that is not announced nor documented but seems to work. It’s based on the SQLite 4 project.

https://github.com/sqlite/sqlite/tree/master/ext/lsm1

https://www.charlesleifer.com/blog/lsm-key-value-storage-in-...

The sqlite code base is really well done. Lots of documentation.

https://github.com/sqlite/sqlite

Especially the VM part: https://github.com/spandanb/learndb-py/blob/master/learndb/v...

Compare it with this: https://github.com/sqlite/sqlite/blob/master/src/vdbe.c

That's said, I'm curious how complete this LearnDB is. SQLite is hard to read not only it's old but also it covers a lot of SQL and following SQL spec makes hings complicated. SQLite has great test suite so it's nice if you run the suit against this implementation.

Correct, see the github mirror[1]. I don't know how well supported that feature is compared to main branch. If it was completely stable, then it would have already landed in the main stable branch. Clarity about the roadmap of that branch would be nice.

1. https://github.com/sqlite/sqlite/tree/begin-concurrent

Including comprehensive comments, documentation and tests in a codebase takes time and effort.

Failing to do so creates code that is very difficult to maintain or for someone new to the codebase to understand.

However, time and effort may not be what the organization wants to pay for, and individuals may view their own incomprehensible code as something like job security, as they can't be replaced by someone else easily.

As an example of complicated code that's still well-documented, the open-source sqlite code is a good example, about 1/4 of the B-tree file is comments, every time a variable is defined there's a short note explaining what it's used for, every function has a comment header that's comprehensive, such that someone new to the codebase could construct a map of how it all works fairly quickly. It's a good model for how to avoid the problem:

https://github.com/sqlite/sqlite/blob/master/src/btree.c

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