Datalog Unchained

Vianu, Victor

doi:10.1145/3452021.3458815

Cited by 6 publications

(1 citation statement)

References 135 publications

(137 reference statements)

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“…Datalog is a language designed specifically for recursive queries, and it is gaining in popularity [3,12,14,22,36,38,39,49,50]. But the optimization problem for recursive queries is much less studied.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Recursive Queries with Program Synthesis

Wang¹,

Khamis²,

Ngo³

et al. 2022

Preprint

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Most work on query optimization has concentrated on loop-free queries. However, data science and machine learning workloads today typically involve recursive or iterative computation. In this work, we propose a novel framework for optimizing recursive queries using methods from program synthesis. In particular, we introduce a simple yet powerful optimization rule called the "FGHrule" which aims to find a faster way to evaluate a recursive program. The solution is found by making use of powerful tools, such as a program synthesizer, an SMT-solver, and an equality saturation system. We demonstrate the strength of the optimization by showing that the FGH-rule can lead to speedups up to 4 orders of magnitude on three, already optimized Datalog systems.

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Section: Introductionmentioning

confidence: 99%

Optimizing Recursive Queries with Program Synthesis

Wang¹,

Khamis²,

Ngo³

et al. 2022

Preprint

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Optimizing Recursive Queries with Progam Synthesis

Wang

Khamis²,

Ngo³

et al. 2022

Proceedings of the 2022 International Conference on Management of Data

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Datalog in Wonderland

et al. 2022

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Modern data analytics applications, such as knowledge graph reasoning and machine learning, typically involve recursion through aggregation. Such computations pose great challenges to both system builders and theoreticians: first, to derive simple yet powerful abstractions for these computations; second, to define and study the semantics for the abstractions; third, to devise optimization techniques for these computations. In recent work we presented a generalization of Datalog called Datalog, which addresses these challenges. Datalog is a simple abstraction, which allows aggregates to be interleaved with recursion, and retains much of the simplicity and elegance of Datalog. We define its formal semantics based on an algebraic structure called Partially Ordered Pre-Semirings, and illustrate through several examples how Datalog can be used for a variety of applications. Finally, we describe a new optimization rule for Datalog, called the FGH-rule, then illustrate the FGH-rule on several examples, including a simple magic-set rewriting, generalized semi-naïve evaluation, and a bill-of-material example, and briefly discuss the implementation of the FGH-rule and present some experimental validation of its effectiveness.

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Datalog Unchained

Cited by 6 publications

References 135 publications

Optimizing Recursive Queries with Program Synthesis

Optimizing Recursive Queries with Program Synthesis

Optimizing Recursive Queries with Progam Synthesis

Datalog in Wonderland

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