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Optimizing Query Processing in PostgreSQL Through Learned Optimizer Hints

dc.contributor.authorThiessat, Jerome
dc.contributor.authorWoltmann, Lucas
dc.contributor.authorHartmann, Claudio
dc.contributor.authorHabich, Dirk
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T14:00:20Z
dc.date.available2023-02-23T14:00:20Z
dc.date.issued2023
dc.description.abstractQuery optimization in database systems is an important aspect and despite decades of research, it isstill far from being solved. Nowadays, query optimizers usually provide hints to be able to steer theoptimization on a query-by-query basis. However, setting the best-fitting hints is challenging. To tacklethat, we present a learning-based approach to predict the best-fitting hints for each incoming query. Inparticular, our learning approach is based on simple gradient boosting, where we learn one modelper query context for fine-grained predictions rather than a single global context-agnostic model asproposed in related work. We demonstrate the efficiency as well as effectiveness of our learning-basedapproach using the open-source database system PostgreSQL and show that our approach outperformsrelated work in that context.en
dc.identifier.doi10.18420/BTW2023-74
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40384
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.subjectQuery Optimization
dc.subjectHint Set Prediction
dc.subjectGradient Boosting
dc.titleOptimizing Query Processing in PostgreSQL Through Learned Optimizer Hintsen
dc.typeText/Conference Paper
gi.citation.endPage1081
gi.citation.publisherPlaceBonn
gi.citation.startPage1075
gi.conference.date06.-10. März 2023
gi.conference.locationDresden, Germany

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