Bring Your Own Data Structures to Datalog

The restricted logic programming language Datalog has become a popular implementation target for deductive-analytic workloads including social-media analytics and program analysis. Modern Datalog engines compile Datalog rules to joins over explicit representations of relations—often B-trees or hash...

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Veröffentlicht in:Proceedings of ACM on programming languages Jg. 7; H. OOPSLA2; S. 1198 - 1223
Hauptverfasser: Sahebolamri, Arash, Barrett, Langston, Moore, Scott, Micinski, Kristopher
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 16.10.2023
Schlagworte:
Constraint and logic programming
Domain specific languages
Software and its engineering
Theory of computation
Logic Programming
Datalog
Program Analysis
Static Analysis
ISSN:2475-1421, 2475-1421
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Zusammenfassung:The restricted logic programming language Datalog has become a popular implementation target for deductive-analytic workloads including social-media analytics and program analysis. Modern Datalog engines compile Datalog rules to joins over explicit representations of relations—often B-trees or hash maps. While these modern engines have enabled high scalability in many application domains, they have a crucial weakness: achieving the desired algorithmic complexity may be impossible due to representation-imposed overhead of the engine’s data structures. In this paper, we present the "Bring Your Own Data Structures" (Byods) approach, in the form of a DSL embedded in Rust. Using Byods, an engineer writes logical rules which are implicitly parametric on the concrete data structure representation; our implementation provides an interface to enable "bringing their own" data structures to represent relations, which harmoniously interact with code generated by our compiler (implemented as Rust procedural macros). We formalize the semantics of Byods as an extension of Datalog’s; our formalization captures the key properties demanded of data structures compatible with Byods, including properties required for incrementalized (semi-naïve) evaluation. We detail many applications of the Byods approach, implementing analyses requiring specialized data structures for transitive and equivalence relations to scale, including an optimized version of the Rust borrow checker Polonius; highly-parallel PageRank made possible by lattices; and a large-scale analysis of LLVM utilizing index-sharing to scale. Our results show that Byods offers both improved algorithmic scalability (reduced time and/or space complexity) and runtimes competitive with state-of-the-art parallelizing Datalog solvers.
ISSN:2475-1421
2475-1421
DOI:10.1145/3622840