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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Adaptive factorised data processing via reinforcement learning |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Query optimisation remains an open problem in the field of database research. Inspired by the recent successes of reinforcement learning in various domains, adaptive approaches have emerged for addressing the problem. This thesis introduces a novel system called FRANTIC, which builds on recent advances and extends the adaptive approach to encompass factorised databases. By combining adaptivity and factorisation, FRANTIC outperforms competitors. Unlike previous research on factorised databases, which often assumed knowledge of a good factorised query plan, FRANTIC leverages reinforcement learning to efficiently explore the vast space of potential query plans, seeking effective execution strategies for queries. In addition to providing a performant implementation of FRANTIC, this thesis explores the system's inner workings in detail. Experiments reveal that design choices around the data partitioning, which is required for parallel processing, significantly impact the system's performance and even influence the effectiveness of different execution strategies. By shedding light on the interplay between the used join algorithm and data partitioning, robust heuristics are derived, enabling the reduction of the optimisation problem's search space. Consequently, this approach reduces the number of potential execution plans, making the optimisation problem more tractable. |
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