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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Published in:	Proceedings of ACM on programming languages Vol. 7; no. OOPSLA2; pp. 1198 - 1223
Main Authors:	Sahebolamri, Arash, Barrett, Langston, Moore, Scott, Micinski, Kristopher
Format:	Journal Article
Language:	English
Published:	New York, NY, USA ACM 16.10.2023
Subjects:	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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Abstract	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.
AbstractList	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.
ArticleNumber	264
Author	Barrett, Langston Moore, Scott Micinski, Kristopher Sahebolamri, Arash
Author_xml	– sequence: 1 givenname: Arash orcidid: 0000-0003-3657-093X surname: Sahebolamri fullname: Sahebolamri, Arash email: asahebol@syr.edu organization: Syracuse University, Syracuse, USA – sequence: 2 givenname: Langston orcidid: 0009-0005-0190-8912 surname: Barrett fullname: Barrett, Langston email: langston@galois.com organization: Galois, Portland, USA – sequence: 3 givenname: Scott orcidid: 0009-0009-0719-1130 surname: Moore fullname: Moore, Scott email: scott@galois.com organization: Galois, Portland, USA – sequence: 4 givenname: Kristopher orcidid: 0000-0002-8650-0991 surname: Micinski fullname: Micinski, Kristopher email: kkmicins@syr.edu organization: Syracuse University, Syracuse, USA
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Snippet	The restricted logic programming language Datalog has become a popular implementation target for deductive-analytic workloads including social-media analytics...
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SubjectTerms	Constraint and logic programming Domain specific languages Software and its engineering Theory of computation
SubjectTermsDisplay	Software and its engineering -- Domain specific languages Theory of computation -- Constraint and logic programming
Title	Bring Your Own Data Structures to Datalog
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