ghostwheel VS datascript

big fan of fulcro logic, but isn’t this almost identical to https://github.com/gnl/ghostwheel ?

if all you care about is information / documentation, and maybe validation, then spec or malli are fine. using spec during runtime to simulate a type system is a poor choice in most cases, because you are doing runtime type checking (something akin to dependent types or even esoteric contracts that are hard/impossible to express statically). There are some libs that help bolt a typed facade (using spec) onto the familiar defn / fn, https://github.com/gnl/ghostwheel https://github.com/Provisdom/defn-spec .

Hi pests, I don't think the criticism in the comments gives a full picture.

I wrote about a particular flavor of datalog, in common use today. [1] [2]. The earliest representation I know, which matches the syntax of my essay, was in SICP [3]

There's another, more academic form of datalog, which looks a lot more like prolog. Both have lots of similarities: both systems have a set of facts and rules. Both systems have can take a partially filled fact or rule, and find all matching facts. The more academic flavors of Datalog are useful for general logic, and particularly powerful for recursive questions. The variant I showed is more tailed for database queries.

[1] https://github.com/tonsky/datascript

There is also datascript as a similar option.

What's the use case for relic? Sounds similar to https://github.com/tonsky/datascript ?

This look nice !

Datascript seems to be another Datalog engine (in memory only)

https://github.com/tonsky/datascript

Inspired by past work: LINQ, inline-python, crepe, DataScript, Riffle.

An in-memory database like Datascript may be worth looking into. Otherwise you could take an indexing approach: put all the data into one big map indexed by some unique key, and have a bunch of supplementary indexes that are updated on insertion.