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There is a large body of recent work applying machine learning (ML)techniques to query optimization and query performance prediction in relationaldatabase management systems (RDBMSs). However, these works typically ignore theeffect of \textit{intra-parallelism} -- a key component used to boost theperformance of OLAP queries in practice -- on query performance prediction. Inthis paper, we take a first step towards filling this gap by studying theproblem of \textit{tuning the degree of parallelism (DOP) via ML techniques} inMicrosoft SQL Server, a popular commercial RDBMS that allows an individualquery to execute using multiple cores.  In our study, we cast the problem of DOP tuning as a {\em regression} task,and examine how several popular ML models can help with query performanceprediction in a multi-core setting. We explore the design space and perform anextensive experimental study comparing different models against a list ofperformance metrics, testing how well they generalize in different settings:$(i)$ to queries from the same template, $(ii)$ to queries from a new template,$(iii)$ to instances of different scale, and $(iv)$ to different instances andqueries. Our experimental results show that a simple featurization of the inputquery plan that ignores cost model estimations can accurately predict queryperformance, capture the speedup trend with respect to the availableparallelism, as well as help with automatically choosing an optimal per-queryDOP.

A Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism