clingo VS mangle

One of the easiest to get started on Datalog in my opinion is really clingo https://potassco.org/clingo/ , which can be pip installed and has python bindings. Answer Set Programming goes beyond datalog, but it holds datalog semantics as a sublanguage. It is unfortunate this is not well advertised.

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Love this article and the push to build awareness of what modern SAT solvers can do.

The thing it misses, though, is that there are higher level abstractions that are far more accessible than SAT. If I were teaching a course on this, I would start with either Answer Set Programming or Satisfiability Modulo Theories (SMT). The most widely used solvers for those are clingo [0] and Z3 [1]:

With ASP, you write in a much clearer Prolog-like syntax that does not require nearly as much encoding effort as your typical SAT problem. Z3 is similar -- you can code up problems in a simple Python API, or write them in the smtlib language.

Both of these make it easy to add various types of optimization, constraints, etc. to your problem, and they're much better as modeling languages than straight SAT. Underneath, they have solvers that leverage all the modern CDCL tricks.

We wrote up a paper [2] on how to formulate a modern dependency solver in ASP; it's helped tremendously for adding new types of features like options, variants, and complex compiler/arch dependencies to Spack [3]. You could not get good solutions to some of these problems without a capable and expressive solver.

[0] https://github.com/potassco/clingo

Answer Set Programming is an incredibly powerful tool to declaratively solve combinatorial problems. Clingo is one of the best open source implementations in my opinion: https://github.com/potassco/clingo

Mangle https://github.com/google/mangle is an open-source implementation in golang, it was an explicit goal to make it easy to learn. Meaning: it is easy to recognize the pure datalog part, the syntax is following the good old course material.

It was discussed here: https://news.ycombinator.com/item?id=33756800

Logic programming offers a good foundation for anything that people call "rule engines." Within logic programming, there is some variation on the degree of declarativeness.

Datalog is arguably the minimal core logic programming, similar to what the lambda calculus achieves for functional programming. Unfortunately, it has been forgotten outside of database and query processing realm. A resurgence has happened in recent years, as PL researchers and also industry have discovered the virtues of datalog (e.g. Flix, DataFun). My own attempt at making this more widely known is here https://github.com/google/mangle, a language from the datalog family and its implementation as a go library.

As the example shows: plain "rules" (or: plain datalog) is rarely enough to capture everything that one wants to express: the question then is, how to combine a pure declarative "kernel" with more general purpose programming (e.g. mapping a list).

PROLOG offered one answer, already in the 1980s, but I fully reject it: the fact that the writing a program in the wrong order with negation and recursion makes it non-terminating is not something we'd want everyone to deal with. Datalog with stratified recursion is somewhat better, as "layers of rules" is a concept that is easy to understand.

In mainstream programming languages, the possibility of writing non-terminating programs also exists, but is rarely an issue. That is why I believe a good combination of declarative and general-purpose has to make it really easy to recognize which parts of a program are in the declarative, terminating, safe kernel and which parts require more attention from the programmer.