Prolog Coding Horror

I've just rolled out an internal SWI Prolog app that is similar to one linked to elsewhere in the thread [1]. We have a large Cloud estate with 10s of thousands of resources in it. Detecting unused or misconfigured resources manually isn't practical, and there are significant cost savings to be had by cleaning things up. The Prolog app reads in JSON resource snapshots, creates an in-memory database of facts from it and then applies rules to detect issues. Most of the rules are simple and the ones for detecting unused resources (3 LOC) or resources that reference other non-existent resources (7 LOC) are entirely generic. There's also link metadata that models the possibility of links existing between resource types, even if they aren't always there in practice.

There's TUI that allows querying of issues and the resource hierarchy. Issues can also be output as JSON which is fed into a LLM to produce cleanup actions and management reporting.

The Prolog app is very fast, considering what it's doing, largely because it makes heavy use of tabling so once an issue has been detected it's not recomputed when queries are made.

[1] https://web.archive.org/web/20190525163234/https://dev.to/da...

My last use case: Testing Scenario Generator.

I have a application that has actions and actions can happen pretty much in any order. By default all scenarios should be an error except for ones that are in the boundary of logical steps, e.g.

   login  -> ...[!logout]... -> logout
   login  -> ...[!logout]... -> dashboard
   upload -> ...             -> view_file

Of course you can immediately see problem with this scope - what happens when non-logged-in user tries to upload or user logouts after upload etc.

So I have ~50 actions, ~30 constraints which generate >200 scenarios which then are transformed into test suite.

Yet in short: Prolog is useful everywhere it's simple to express a rule but not that easy to implement it.

A few years ago I wrote a workforce scheduling program designed to be used by non-programmers. I worked in a restricted environment so couldn't install anything. The whole thing ran on SWIPL's web offering.

Users simply had to change the basic "facts" (who was available on what days, how many people were needed), and the program solved for the various constraints and offered solutions.

It was maybe about 300 lines of Prolog, no complex dependencies. It replaced a pile of Python scripts that required a lot of state, didn't really work, and could only run on a few specific computers.

For regular users, it was relatively easy to understand and change the facts. SWIPL for web also offers a nice "notebook" interface that lets you mix data, code, and markdown / output blocks so the documentation was inline.

20+ years ago, it was the backend for the business rules engine that processed various logging and monitoring events. The concept was interesting, the performance was terrible, and businesses mostly didn't want to touch it. After I setup clients with a generic set of rules that worked on Prolog facts, most all of my clients were happy to limit their changes to only those fact files.

"Planning, optimization, diagnostics, and complex configuration" [1]

Prolog also works extremely well as a target language for code generation by LLMs for these domains due to it being "higher up in the food chain" compared to procedural languages so to speak, and because Prolog was originally envisioned for classic NLP and hence has a corpus of one-to-one mappings from natural language to logic (as in the example in [3]). So well in fact that even with last-gen models textual descriptions for suitable problems become the bottleneck and you can in many cases just go straight to Prolog code instead ([2]).

[1]: https://quantumprolog.sgml.net

[2]: https://quantumprolog.sgml.net/llm-demo/part1.html

[3]: https://news.ycombinator.com/item?id=48080201

Many years ago Maemo (the mobile OS from Nokia) had the profile manager (day mode/night mode etc) written in Prolog. To me it seems like it's a very appropriate application for it.

Some applications were discussed in https://news.ycombinator.com/item?id=40994552

Dunno about Prolog, but Datomic uses datalog for its query language, and it’s excellent. Datalog is a subset of Prolog.

The JS package manager Yarn had an experimental feature to define dependency constraints using prolog, it made for very concise way to represent the logic.

https://v3.yarnpkg.com/features/constraints

It never got released for good though. I actually had need of such feature for a project but I thought that using an exoteric programming language and an experimental feature was a bit much. I ended up setting up those constraints as a CI check hand-made script and the code was surprisingly large (~300 lines), but not that hard to understand.

I used it working on automated tests for telecoms protocols, the practical problem reduces to solving instances of the Pressburger arithmetic, triple exponential time in general (ouch) but we got some results anyway using heuristics. Prolog's a nice space to do high-level heuristics.

> What do people use Prolog for in the real world?

Here[0] is an example of using Ruby and Prolog to solve a real-world AWS management problem.

0 - https://web.archive.org/web/20190525163234/https://dev.to/da...

Prolog is very succinct and interactive so it is quite nice for modeling and analysing problem spaces, similar to what you might do with Z3 or some other solver library.

Prolog also has exceptionally good string management through DCG:s. If you have a bunch of structured text and want to query it, Prolog allows you to do that. The syntax is very simple so you can even just sed your data into Prolog code and load that sometimes.

Supposedly SICSTUS Prolog is used in flight booking, and NASA did something with it sometime, but that's kind of obscure. If you ever need a rule engine, consider poking it with logic programming for a bit, perhaps it'll turn out both pragmatic and elegant.

Windows NT Networking configuration, which may have been removed in the post Win2K versions - see https://web.archive.org/web/20030218034509/http://www.resear... and https://news.ycombinator.com/item?id=36821871

Not used. Quote about prolog: The elegant solution is not efficient. The efficient solution is not elegant.

Everything, you heard the joke about those who don't know Lisp end up reinventing it, well, the same can be said for Prolog.

I used Prolog twice in production, but none of them were what I'd expect to be the "typical" uses.

1. I used GoLog (a Prolog interpreted in Go) for defining some functional tests in the project where the testing infrastructure was otherwise written in Go.

2. I used Prolog (SWI) to write a parser for Thrift (both the definition and binary format) simply because I needed one, and Prolog was convenient.

What I expect people who use Prolog for the stuff it's really good at is databases that encode some complicated business or legal processes. I.e. databases with many complex constraints that have to all somehow come together to produce a solution set. Prolog would also be a good language to encode / query graph databases. So, whatever you can think of being a good match for a graph database would also work well with Prolog.

There are also (even more niche) Prolog derivatives, eg. Ciao or Mercury, that are... well, decent all-purpose languages. You can just use them in the same context where you'd use Python or Haskell respectively. The implementations are pretty solid in terms of performance and correctness... so, if you like the approach, then why not?

https://www.openpolicyagent.org/docs/policy-language Not quite Prolog as they teach in universities, but its close descendant. Used for policy evaluation, e.g. to validate tree-structured datasets against arbitrary permission rule sets.

Prolog is for logic what inverse kinematics is for robotics.

Where in imperative programming languages you supply arguments to a function and get a result in Prolog you can give the result of a function call and get all argument sets that lead to that result.

But you are right that in production systems you would seldom see Prolog, for reason that you can easily LLM.

Many years ago I used Prolog (BIM Prolog, I believe ) since it was used as an event correlation engine in Tivoli Enterprise Console (IBM). Fun to assert predicates for complex correlation logic.

There was a neat helicopter landing game written in prolog way back in the day

Gerrit had an embedded prolog engine, and I recall branch protection rules / PR workflows were configured using progolog.

TerminusDb

And to understand the four-port model is to understand solution-space navigation and pruning.

I've actually run into this in the wild, with regards to sales forecasting. A program we were using returned zero if the error bars on a forecast were over 100%. For example, selling somewhere between 1 and 7 units, but averaging 3.

Returning 3 was "wrong", but infinitely more correct than retuning 0.

Sometimes the Biorhythm program on my Apple ][ failed to produce correct answers. But it sure was great for impressing cool hippie chicks.

https://www.youtube.com/watch?v=jYoY1cwAd90

iirc, shor's algorithm for factoring relies on this.

The article server is offline, but I assume they found out that prolog rule evaluation depends on the order the rules are presented in the program.

If so, the language they thought they were using (and that they should actually use) is datalog, not prolog.

Datalog has declarative semantics: All facts that are derivable from the base database and the rules will be derived by the interpreter, and it will not add extra hallucinated facts. If that's not true, it's a bug in the runtime, not in the language.

Super simple example

    % Prolog
    next_light(green, yellow).
    next_light(yellow, red).
    next_light(red, green).

    % Erlang
    next_light(green)  -> yellow;
    next_light(yellow) -> red;
    next_light(red)    -> green.

Notable differences:

- `;` in Erlang indicates multi clause function

- In Prolog next_light is database, in Erlang it's just a function

Question: What's the light before green?

    % Erlang
    % Returns actual value
    prev_light_search() -> 
    [State || State <- [green, yellow, red], next_light(State) == green].

    % Prolog (?- means that it's in query mode)
    ?- next_light(Light, green).

So syntax IS similar though the thing is that Prolog is more like binding and quering database and Erlang is executing function.

In a nutshell Erlang is more like: "when I have X, then I can calculate Y" and Prolog like "If I want Y, what's the X".

Erlang has very little in common with Prolog, which is a language in an entirely different paradigm (logic programming).

Early versions of Erlang were implemented in Prolog, which is why Erlang's syntax looks a whole lot like Prolog's, but beyond that they're not very similar.

Not familiar with Erlang that much, but it's pretty clear Erlang was prototyped on Prolog because of its convenient facilities for DSLs using op/3 to define new tokens for its built-in bottom-up expression parser (using operator precedence parsing) and its Definite Clause Grammar recursive descent parser as trivial specialization of core SLD resolution (Prolog was created for NLP and planning apps in the first place after all).

I guess what may also have contributed is that there are a number of concurrent logics implemented in Prolog for prototyping Erlang's scheduler such as Concurrent Transaction Logic ([1]).

[1]: https://www.cs.toronto.edu/~bonner/ctr/Home.html

What Joe Armstrong et al took from Prolog is mainly the syntax.

These two languages are completely different paradigms.

Prolog operators like `4 > 5` are a syntax sugar which desugars into a normal function call `>(4, 5)`. This part of the language is programmable, so you can add your own function `#>(X, Y)` and then declare it as an operator and use it like `4 #> 5`. See [1]. Nitpickingly, this means we can't be sure what #> is without looking, but it's common in Scryer and SWI Prolog (at least) that the #> #< versions of numeric comparison operators are used by constraint solver libraries. In imaginary Python it might be this code:

    import solver

    solver.add_variable(x)
    solver.variable_range(x, 0, 100)
    solver.add_constraint(x, greater_than, 50)

    solver.solve_for(x)

in pseudo-Prolog it can be:

    :- using constraint solver library
    
    X in 0..100,
    X #> 50,

    label(X)

where "in" and "#>" were added into the language at runtime by the import of the constraint library. That is, it calls out to a custom 'function' which tells the constraint solver to restrict possible values for for X from 0..100 down to 51..100.

> "and what ! does"

This is a concept which doesn't translate easily to other languages, but analogously it's like the performance difference between this code which always searches the entire haystack:

    found = false
    for item in haystack:
        if item == 'needle':
            found = true

    return found

and this which stops searching the haystack if the needle is found, but still searches the entire haystack in the worst case:

    for item in haystack:
        if item == 'needle':
            return true

    return false

The catch being that ! is not exactly a performance thing, it's an instruction to the Prolog runtime to skip some of the code, which can speed up performance but if you throw it in carelessly, your code no longer gives the right answers.

[1] They aren't Prolog "functions", they are predicates, functions are different, but it will do for this explanation.

!/0 is the cut. It prunes the search space. Useful to say "do not look at the other alternatives since I know they will fail" (when mutually exclusivity is hard) but also necessary to do negation in Prolog (when negated information cannot be easily or efficiently propagated).

is/2 is arithmetic evaluator. It runs only in one direction and it does not solve equations.

#>, #=, etc. are constraints, like (in)equalities over linear arithmetic. When constraints have the form of some known theory (like in SMT solvers), they can be solved (incrementally). That is called "constraint logic programming" (CLP). Modern Prolog systems are indeed CLP systems.

Prolog is older than CLP. CLP is older than SMT. Prolog+CLP systems are turing complete, can be used as programming languages. SMT is powerful but not a programming language.

Can "impure" features be avoided? Not in all cases. Think of them as 'unsafe' in Rust, but less dangerous.

Markus pushes for more purity in Prolog (using CLPFD), but sometimes some impurity (or imperative-like code with side-effects) is the best solution. Sometimes the pure solution is also the better. In other cases, it is not. Better compilers and static analyzers can reduce the friction between these worlds.

Take away: do pure code if you can afford it and it looks like a natural solution to your problem, use impure features later if you really need them.

You could always read the docs?

https://www.swi-prolog.org/pldoc/man?section=clpfd-integer-a...

https://www.swi-prolog.org/pldoc/doc_for?object=!/0

>> The version without ! looks identical to the version with ! except only the ! is removed - is this a joke?

That's just showing getting rid of the cut in two stages. The line that makes it possible to remove it is this one:

N #> 0,

Markus Triskas' argument is that if you use the #> etc versions of declarative arithmetic operators, instead of the > ones, you can then call the factorial predicate with both arguments as variables, i.e. without inputs, just outputs, to enumerate the entire factorial relation. Like this:

  ?- n_factorial(N, F).
     N = 0, F = 1
  ;  N = 1, F = 1
  ;  N = 2, F = 2
  ;  N = 3, F = 6
  ;  ... .

If you use > instead of #> the line N > 0 will raise an exception if N is a variable, which will be the case if you call it as above. This stops you from enumerating the relation, which declarative arithmetic allows.

Of course there are other ways to write a factorial predicate (or any predicate) so that it always enumerates a relation but they are more verbose. Then again, you do need a special library to use declarative arithmetic anyway, so.

It reminds me of an old joke:

Radio Yerevan: A listener asks: "Is it true that in Moscow, on Red Square, they are giving away cars?"

Our answer: "Yes, it is true. Except it isn't in Moscow, but in Leningrad. And it isn't on Red Square, but on Palace Square. And they aren't cars, but bicycles. And they aren't giving them away, they are stealing them."

I kind of agree with both of you. I would have become better at Prolog faster if I had stuck to the discipline Triska promotes early on, but I also think the guidelines you cite are reasonable once one is starting to do somewhat productive work where compromises and speed factors in.

Over the years I've come to a similar position in other languages as well. If a functional-ish solution fits performance constraints and is maintainable, don't mutate or reach for global state, things like that.