prolog VS logica

Other resources for logic programming and Go:

ichiban/prolog - ISO Prolog interpreter in pure Go, getting close to v1: https://github.com/ichiban/prolog

trealla-prolog/go - ISO Prolog interpreter embedded via WASM: https://github.com/trealla-prolog/go

guregu/pengine - library for interfacing with Pengines (SWI-Prolog's RPC protocol): https://github.com/guregu/pengine

biscuit-auth/biscuit-go - Biscuits are a fancy auth token with a little Datalog engine: https://github.com/biscuit-auth/biscuit-go

I'm a big fan of logic programming. We've been seeing a small resurgence of interest in it (for example Yarn using Prolog made some waves) and I have some optimism for its future.

fully native Prolog, or embedded? I am very familiar with Go, so I was intrigued to see ichiban/prolog. This might enable me to leverage some existing libraries (GitHub, git, etc) and save Prolog for the logic that drives them.

You may also be interested in this Prolog interpreter in Go: https://github.com/ichiban/prolog

There have been several Prolog interpreters written in Golang. This one appears to be most active: https://github.com/ichiban/prolog .

For Go, you have prolog as a scripting engine https://github.com/ichiban/prolog

An embeddable Prolog scripting language for Go\ (2 comments)

If you're interested in this I would also recommend you check out Logica[0], which is a datalog-like language that is explicitly made to compile to SQL queries.

0: https://logica.dev/

> I guess the intention is to be better than SQL but then I was left with "under which circumstances?"

Excellent question.

Two of the most common use cases for databases are "transactional processing" (manipulating small numbers of rows in real time) and "analytical processing" (querying enormous numbers of rows, typically in a read-only fashion).

SQL is generally fine for transactional workloads.

But analytical queries sometimes involve multi-page queries, with lots of JOINs and CTEs. And these queries are often automatically generated.

And once you start writing actual multi-page "programs" in SQL, you may decide that it's a fairly clunky and miserable programming language. What Datalog typically buys you is a way to cleanly decompose large queries into "subroutines." And it offers a simpler syntax for many kinds of complex JOINs.

Unfortunately, there isn't really a standard dialect of Datalog, or even a particular dialect with mainstream traction. So choosing Datalog is a bit of a tradeoff: does it buy you enough, for your use case, that it's worth being a bit outside the mainstream? Maybe! But I'd love to see something like Logica gain more traction: https://logica.dev/

Interesting; a Google engineer previously published a Datalog variant for BigQuery: https://logica.dev/

This new language seems similar to differential-Datalog (which is sadly in maintenance mode): https://news.ycombinator.com/item?id=33521561

Looks pretty cool. I'd be interested if the README had a comparison with Google's Logica (https://github.com/EvgSkv/logica)

Oh wow that is neat!

And yes, this kind of thing is why datalog is a lot more amenable to fast query plans & runtimes than prolog. This part is especially cool: https://github.com/EvgSkv/logica/blob/main/compiler/dialects...

Google new programming Language that compiles to SQL (Support BigQuery and Postgres) feels very exciting. Blog: https://opensource.googleblog.com/2021/04/logica-organizing-your-data-queries.html Github: https://github.com/EvgSkv/logica

Going to be? It already is. In fact, one thing the article misses is right there at the bottom of the project page: