Optimizing Query Processing in PostgreSQL Through Learned Optimizer Hints

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review



Query optimization in database systems is a crucial issue and despite decades of research, it is still far from being solved. Nowadays, query optimizers usually provide hints to be able to steer the optimization on a query-by-query basis. However, setting the best-fitting optimizer hints is challenging. To tackle that, we present a learning-based approach to predict the best-fitting hints for each incoming query. In particular, our learning approach is based on simple gradient boosting, where we learn one model per query context for fine-grained predictions rather than a single global context-agnostic model as proposed in related work. We demonstrate the efficiency as well as effectiveness of our learning-based approach using the open-source database system PostgreSQL and show that our approach outperforms related work in that context.


Original languageEnglish
Title of host publicationDatenbanksysteme fur Business, Technologie und Web, BTW 2023
EditorsBirgitta Konig-Ries, Stefanie Scherzinger, Wolfgang Lehner, Gottfried Vossen
PublisherGesellschaft fur Informatik (GI)
Number of pages7
ISBN (electronic)978-3-88579-725-8
Publication statusPublished - 6 Mar 2023

External IDs

Scopus 85149934725
dblp conf/btw/ThiessatWHH23
Mendeley 8eafd0cd-d506-30cb-a1d9-894ef8ffe6ab


ASJC Scopus subject areas


  • Gradient Boosting, Hint Set Prediction, Query Optimization