In the past platforms have integrated ML algorithms into relational databases and SQL through extensions (both commercial and open source). A famous open source one was MADlib [1], which has an implementation of neural networks. Even the commercial ones were similar, I used ML algorithms in SQL Server many years ago around 2005 I think.

I am wondering about.. SQL as a declarative structured query language that can be optimized into essentially any kind of distributed, directed acyclic graph of processing - vs the special characteristics of relational databases (relational algebra, relvars, etc. etc.) is an important distinction as - of yet, I see the author linking both together so I'm trying to understand what it is about relational structures that specifically helped here (just not seeing it yet, not that it isn't there).

Also, wondering if ISO/IEC 9075-15:2023 SQL standard for multidimensional arrays (MDA) is of any use here? Paper describing SQL/MDA here [2].

[1] https://madlib.apache.org/documentation.html

[2] https://www.ifis.uni-luebeck.de/~moeller/Lectures/WS-19-20/N...

I've been working on something similar, implementing a relational language on top of a tensor library[0].

Mathematically, einsum and database joins are the same thing, just over different semirings (real numbers for einsum, booleans for databases). A lot of papers about datalog explore this sort of thing in more depth. In particular, Dyna[1] might be interesting.

[0]: https://arxiv.org/abs/2509.22614 [1]: https://dyna.org/

Somewhat more reliable than implementing SQL in neural networks.

I'm just going to go back to making my CRUD endpoints...

Jokes aside, sounds really impressive, though I only understood about 10% :D

initially rolled my eyes at "neural networks in SQL," but after reading the code I came away impressed

basically it comes down to using relational algebra as the IR, letting a database optimizer reason about tensor programs

Neat! Feels analogous to "X runs Doom" demos (but NN).

Why? lol

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