Fixpoints for the masses: programming with first-class Datalog constraints
Magnus Madsen, Ondřej Lhoták
Abstract
Datalog is a declarative logic programming language that has been used in a variety of applications, including big-data analytics, language processing, networking and distributed systems, and program analysis. In this paper, we propose first-class Datalog constraints as a mechanism to construct, compose, and solve Datalog programs at run time. The benefits are twofold: We gain the full power of a functional programming language to operate on Datalog constraints-as-values, while simultaneously we can use Datalog where it really shines: to declaratively express and solve fixpoint problems. We present an extension of the lambda calculus with first-class Datalog constraints, including its semantics and a type system with row polymorphism based on Hindley-Milner. We prove soundness of the type system and implement it as an extension of the Flix programming language.