UDO: Universal Database Optimization using Reinforcement Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Proceedings of the VLDB Endowment (PVLDB) Year : 2021

UDO: Universal Database Optimization using Reinforcement Learning

Debabrota Basu


UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO uses reinforcement learning to converge to near-optimal configurations, creating and evaluating different configurations via actual query executions (instead of relying on simplifying cost models). To cater to different parameter types, UDO distinguishes heavy parameters (which are expensive to change, e.g. physical design parameters) from light parameters. Specifically for optimizing heavy parameters, UDO uses reinforcement learning algorithms that allow delaying the point at which the reward feedback becomes available. This gives us the freedom to optimize the point in time and the order in which different configurations are created and evaluated (by benchmarking a workload sample). UDO uses a cost-based planner to minimize reconfiguration overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We evaluate UDO on Postgres as well as MySQL and on TPC-H as well as TPC-C, optimizing a variety of light and heavy parameters concurrently.

Dates and versions

hal-03445686 , version 1 (24-11-2021)


Attribution - NonCommercial



Junxiong Wang, Immanuel Trummer, Debabrota Basu. UDO: Universal Database Optimization using Reinforcement Learning. Proceedings of the VLDB Endowment, Sep 2022, Sydney, Australia. pp.3402-3414, ⟨10.14778/3484224.3484236⟩. ⟨hal-03445686⟩
130 View
0 Download



Gmail Facebook X LinkedIn More