Ask HN: What's Prolog like in 2024?

Hacker News Discussion ↗

Prolog in 2024 is seen as a powerful but niche logic programming language, thriving in areas like constraint solving, configuration, scheduling, expert systems, and knowledge representation rather than as a general-purpose workhorse. Contributors highlight mature implementations such as SWI-Prolog and newer ISO-focused systems like Scryer and Trealla, plus integrations with Python, JavaScript, and databases, and related tools like Datalog engines, Answer Set Programming, and CLP(FD/Z). While many praise its declarative expressiveness and bidirectional reasoning, they also point to steep learning curves, sensitivity to rule ordering, weaker tooling, and limited type systems, so Prolog is often recommended today as an embedded DSL or specialized solver rather than the core of large applications.

State of Prolog in 2024

  • Still actively developed and used, especially via SWI‑Prolog; other modern engines include Scryer and Trealla aiming at ISO conformance.
  • Very much a niche language: strong academic presence and some industrial pockets, but not a mainstream general‑purpose choice.
  • Some see Prolog as “dead” or obsolete, others say usage has been steady for decades in specialized domains.

Strengths and Appealing Ideas

  • Declarative “describe the problem, not the algorithm” style; same predicate can answer multiple related queries (e.g., parent/child/ancestor in one definition).
  • Built‑in search, unification and backtracking; particularly powerful with constraint logic programming (CLP(FD/Z)) for combinatorial and optimization problems.
  • Good fit for knowledge representation, ontologies, and reasoning over complex relationships; compared favorably to OOP for that.
  • DCGs and parsing: very concise, elegant parsing and state‑machine descriptions.
  • Logic code can often be reused in different “directions,” giving a relational feel that many find conceptually beautiful.

Limitations and Criticisms

  • Performance and robustness can be brittle; small changes in rule order or search strategy can drastically affect speed or termination.
  • Depth‑first backtracking makes certain infinite loops surprisingly easy to write; learners often struggle with unbound variables and search space explosion.
  • Lacks modern module/package ecosystem in many implementations; large codebases can become hard to manage, especially with extra‑logical features like “cut”.
  • Static typing is absent in classic Prolog; some argue this hurts robustness and maintainability.
  • Several commenters argue that for many industrial problems it’s better to use mainstream languages plus dedicated solvers (MIP, OR‑Tools, SMT, etc.).

Ecosystem and Related Technologies

  • Rich ecosystem around SWI (CLP libraries, Python and Java bridges, Janus, MQI).
  • Alternative or successor ideas: Mercury, miniKanren/core.logic, Answer Set Programming, Datalog systems (Soufflé, Logica, TypeDB, Cozo), CP solvers, and probabilistic programming.
  • Logtalk brings OO‑style structuring on top of Prolog; ErgoAI extends Prolog for advanced KR.
  • Logic/datalog‑style querying shows up in databases (Datomic, XTDB, DataScript, Cozo) and policy engines (Rego).

Use Cases and Adoption Patterns

  • Successful niches: configuration/CPQ systems, complex product configuration, scheduling and planning, expert systems, RDF/OWL reasoning, static analysis, constraint‑based search, some medical and industrial safety applications.
  • Often recommended today as:
    • A prototyping or modeling language before re‑implementation.
    • An embedded DSL or separate service for the “hard logic” part of a system, rather than the entire stack.
  • Widely valued pedagogically: learning Prolog (and CLP/Datalog) changes how people think about programming, even if they don’t use it daily.