I really enjoyed reading this well-written paper that introduces the Rel query language implemented at RelationalAI [1].
One main goal of Rel is to give users the ability to grow the language using libraries [2],
say for linear algebra or graph processing. And I'm really impressed by the result.
With ability to defines (possibly recursive) relations over relation variables and arbitrary-length tuples,
Rel is a nice successor of Datalog.
I also like the oppiniated approach which is to enforce strong data modeling constraints on all relations
(6th normal form and identifier uniqueness across entity types).
This removes the need for nulls and beyond helps to remove complicated corner cases.
I really enjoyed reading this well-written paper that introduces the Rel query language implemented at RelationalAI [1].
One main goal of Rel is to give users the ability to grow the language using libraries [2], say for linear algebra or graph processing. And I'm really impressed by the result. With ability to defines (possibly recursive) relations over relation variables and arbitrary-length tuples, Rel is a nice successor of Datalog.
I also like the oppiniated approach which is to enforce strong data modeling constraints on all relations (6th normal form and identifier uniqueness across entity types). This removes the need for nulls and beyond helps to remove complicated corner cases.
[1] https://relational.ai/ [2] https://docs.relational.ai/getting-started/walkthrough/rel-l...