<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Learning From User-Specified Optimizer Hints in Database Systems

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Learning From User-Specified Optimizer Hints in Database Systems"}

Recently, numerous machine learning (ML) techniques have been applied to address database performance management problems, including cardinality estimation,...

Learning From User-Specified Optimizer Hints in Database Systems

Online veröffentlicht: 26. Mai 2024

Seitenbereich: 181 - 197

Eingereicht: 19. Okt. 2023

Akzeptiert: 15. Feb. 2024

DOI: https://doi.org/10.2478/fcds-2024-0011

Schlüsselwörter
database systems, query optimization

© 2024 Maciej Zakrzewicz, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Learning From User-Specified Optimizer Hints in Database Systems

Online veröffentlicht: 26. Mai 2024

Seitenbereich: 181 - 197

Eingereicht: 19. Okt. 2023

Akzeptiert: 15. Feb. 2024

DOI: https://doi.org/10.2478/fcds-2024-0011

Schlüsselwörterdatabase systems, query optimization

© 2024 Maciej Zakrzewicz, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
database systems, query optimization