Thiessat, JeromeWoltmann, LucasHartmann, ClaudioHabich, DirkKönig-Ries, BirgittaScherzinger, StefanieLehner, WolfgangVossen, Gottfried2023-02-232023-02-232023978-3-88579-725-8https://dl.gi.de/handle/20.500.12116/40384Query 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.enQuery OptimizationHint Set PredictionGradient BoostingText/Conference Paper10.18420/BTW2023-74