ML-based cross-platform query optimization

Zoi Kaoudi, Jorge Arnulfo Quiane-Ruiz, Bertty Contreras-Rojas, Rodrigo Pardo-Meza, Anis Troudi, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Cost-based optimization is widely known to suffer from a major weakness: administrators spend a significant amount of time to tune the associated cost models. This problem only gets exacerbated in cross-platform settings as there are many more parameters that need to be tuned. In the era of machine learning (ML), the first step to remedy this problem is to replace the cost model of the optimizer with an ML model. However, such a solution brings in two major challenges. First, the optimizer has to transform a query plan to a vector million times during plan enumeration incurring a very high overhead. Second, a lot of training data is required to effectively train the ML model. We overcome these challenges in Robopt, a novel vector-based optimizer we have built for Rheem, a cross-platform system. Robopt not only uses an ML model to prune the search space but also bases the entire plan enumeration on a set of algebraic operations that operate on vectors, which are a natural fit to the ML model. This leads to both speed-up and scale-up of the enumeration process by exploiting modern CPUs via vectorization. We also accompany Robopt with a scalable training data generator for building its ML model. Our evaluation shows that (i) the vector-based approach is more efficient and scalable than simply using an ML model and (ii) Robopt matches and, in some cases, improves Rheem's cost-based optimizer in choosing good plans without requiring any tuning effort.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Pages1489-1500
Number of pages12
ISBN (Electronic)9781728129037
DOIs
StatePublished - Apr 2020
Externally publishedYes
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: Apr 20 2020Apr 24 2020

Publication series

NameProceedings - International Conference on Data Engineering
Volume2020-April
ISSN (Print)1084-4627

Conference

Conference36th IEEE International Conference on Data Engineering, ICDE 2020
Country/TerritoryUnited States
CityDallas
Period04/20/2004/24/20

Keywords

  • Cross-platform data processing
  • Machine learning
  • Polystores
  • Query optimization

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