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https://doi.org/10.21256/zhaw-30586| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Is your learned query optimizer behaving as you expect? : a machine learning perspective |
| Autor/-in: | Lehmann, Claude Sulimov, Pavel Stockinger, Kurt |
| et. al: | No |
| DOI: | 10.14778/3654621.3654625 10.21256/zhaw-30586 |
| Erschienen in: | Proceedings of the VLDB Endowment |
| Band(Heft): | 17 |
| Heft: | 7 |
| Seite(n): | 1565 |
| Seiten bis: | 1577 |
| Erscheinungsdatum: | Mär-2024 |
| Verlag / Hrsg. Institution: | Association for Computing Machinery |
| ISSN: | 2150-8097 |
| Sprache: | Englisch |
| Schlagwörter: | Database; Query optimization; Machine learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | The current boom of learned query optimizers (LQO) can be explained not only by the general continuous improvement of deep learning (DL) methods but also by the straightforward formulation of a query optimization problem (QOP) as a machine learning (ML) one. The idea is often to replace dynamic programming approaches, widespread for solving QOP, with more powerful methods such as reinforcement learning. However, such a rapid "game change" in the field of QOP could not pass without consequences - other parts of the ML pipeline, except for predictive model development, have large improvement potential. For instance, different LQOs introduce their own restrictions on training data generation from queries, use an arbitrary train/validation approach, and evaluate on a voluntary split of benchmark queries. In this paper, we attempt to standardize the ML pipeline for evaluating LQOs by introducing a new end-to-end benchmarking framework. Additionally, we guide the reader through each data science stage in the ML pipeline and provide novel insights from the machine learning perspective, considering the specifics of QOP. Finally, we perform a rigorous evaluation of existing LQOs, showing that PostgreSQL outperforms these LQOs in almost all experiments depending on the train/test splits. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30586 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Publiziert im Rahmen des ZHAW-Projekts: | GraphQueryML – Verwendung von maschinellem Lernen zur Optimierung von Abfragen in Graphdatenbanken (SNF/DFG) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2024_Lehmann-etal_Learned-query-optimizers_VLDB.pdf | 693.26 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Lehmann, C., Sulimov, P., & Stockinger, K. (2024). Is your learned query optimizer behaving as you expect? : a machine learning perspective. Proceedings of the VLDB Endowment, 17(7), 1565–1577. https://doi.org/10.14778/3654621.3654625
Lehmann, C., Sulimov, P. and Stockinger, K. (2024) ‘Is your learned query optimizer behaving as you expect? : a machine learning perspective’, Proceedings of the VLDB Endowment, 17(7), pp. 1565–1577. Available at: https://doi.org/10.14778/3654621.3654625.
C. Lehmann, P. Sulimov, and K. Stockinger, “Is your learned query optimizer behaving as you expect? : a machine learning perspective,” Proceedings of the VLDB Endowment, vol. 17, no. 7, pp. 1565–1577, Mar. 2024, doi: 10.14778/3654621.3654625.
LEHMANN, Claude, Pavel SULIMOV und Kurt STOCKINGER, 2024. Is your learned query optimizer behaving as you expect? : a machine learning perspective. Proceedings of the VLDB Endowment. März 2024. Bd. 17, Nr. 7, S. 1565–1577. DOI 10.14778/3654621.3654625
Lehmann, Claude, Pavel Sulimov, and Kurt Stockinger. 2024. “Is Your Learned Query Optimizer Behaving as You Expect? : A Machine Learning Perspective.” Proceedings of the VLDB Endowment 17 (7): 1565–77. https://doi.org/10.14778/3654621.3654625.
Lehmann, Claude, et al. “Is Your Learned Query Optimizer Behaving as You Expect? : A Machine Learning Perspective.” Proceedings of the VLDB Endowment, vol. 17, no. 7, Mar. 2024, pp. 1565–77, https://doi.org/10.14778/3654621.3654625.
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