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arXiv · 2202.10390

Optimizing Recursive Queries with Program Synthesis

Yisu Remy Wang·Mahmoud Abo Khamis·Hung Q. Ngo·Reinhard Pichler·Dan Suciu
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Abstract

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 "FGH-rule" 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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Yisu Remy Wang, Mahmoud Abo Khamis, Hung Q. Ngo, Reinhard Pichler, Dan Suciu. 2022-02-21. Optimizing Recursive Queries with Program Synthesis. https://arxiv.org/abs/2202.10390

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