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Article

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Title

Learning From User-Specified Optimizer Hints in Database Systems

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

Foundations of Computing and Decision Sciences

Journal year: 2024 | Journal volume: vol. 49 | Journal number: no. 2

Article type

scientific article

Publication language

english

Keywords
EN
  • database systems
  • query optimization
Abstract

EN Recently, numerous machine learning (ML) techniques have been applied to address database performance management problems, including cardinality estimation, cost modeling, optimal join order prediction, hint generation, etc. In this paper, we focus on query optimizer hints employed by users in their queries in order to mask some Query Optimizer deficiencies. We treat the query optimizer hints, bound to previous queries, as significant additional query metadata and learn to automatically predict which new queries will pose similar performance challenges and should therefore also be supported by query optimizer hints. To validate our approach, we have performed a number of experiments using real-life SQL workloads and we achieved promising results.

Date of online publication

26.05.2024

Pages (from - to)

181 - 197

DOI

10.2478/fcds-2024-0011

URL

https://sciendo.com/pl/article/10.2478/fcds-2024-0011

License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

40

Impact Factor

1,8 [List 2023]

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