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

arXiv: https://arxiv.org/abs/2005.08439 · HTML: https://mlsanity.com/paper/2005.08439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 1 of 1)

- [Optimal Resource Allocation for Serverless Queries](https://mlsanity.com/paper/2107.08594/) (2021)

## Abstract (arXiv)

There is a large body of recent work applying machine learning (ML) techniques to query optimization and query performance prediction in relational database management systems (RDBMSs). However, these works typically ignore the effect of textitintra-parallelism -- a key component used to boost the performance of OLAP queries in practice -- on query performance prediction. In this paper, we take a first step towards filling this gap by studying the problem of textittuning the degree of parallelism (DOP) via ML techniques in Microsoft SQL Server, a popular commercial RDBMS that allows an individual query 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 performance prediction in a multi-core setting. We explore the design space and perform an extensive experimental study comparing different models against a list of performance 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 and queries. Our experimental results show that a simple featurization of the input query plan that ignores cost model estimations can accurately predict query performance, capture the speedup trend with respect to the available parallelism, as well as help with automatically choosing an optimal per-query DOP.