Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue

TL;DR

This paper introduces a machine learning approach to decide when Yannakakis' query evaluation algorithm should be applied, significantly improving database query performance across various systems.

Contribution

It formulates the decision of applying Yannakakis' algorithm as an algorithm selection problem and employs machine learning to optimize this choice.

Findings

Statistically significant performance improvements achieved

Effective decision procedure for applying Yannakakis' algorithm

Validated across multiple database systems and benchmarks

Abstract

Query optimization has played a central role in database research for decades. However, more often than not, the proposed optimization techniques lead to a performance improvement in some, but not in all, situations. Therefore, we urgently need a methodology for designing a decision procedure that decides for a given query whether the optimization technique should be applied or not. In this work, we propose such a methodology with a focus on Yannakakis-style query evaluation as our optimization technique of interest. More specifically, we formulate this decision problem as an algorithm selection problem and we present a Machine Learning based approach for its solution. Empirical results with several benchmarks on a variety of database systems show that our approach indeed leads to a statistically significant performance improvement.