用机器学习判断何时用特定查询优化，提升数据库性能。
Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue
- 将优化决策转化为算法选择问题，用机器学习自动判断是否应用
- 在多个数据库系统上实验，性能提升具有统计显著性
- 适合数据库优化研究者和需要高效查询的工程团队
查询优化在数据库研究中已数十年，但多数优化技术仅在部分场景下有效。因此，亟需一种决策机制，以判断给定查询是否应应用特定优化。本文聚焦于Yannakakis风格的查询评估方法，将该决策问题建模为算法选择问题，并提出基于机器学习的解决方案。在多种数据库系统上的多组基准测试表明，该方法能带来统计显著的性能提升。
原文摘要 · Abstract (English)
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.
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