Titre :

UDO: Universal Database Optimization using Reinforcement Learning

Auteur(s) :

Wang, Junxiong [Auteur]
Cornell University [New York]
Trummer, Immanuel [Auteur]
Cornell University [New York]
Basu, Debabrota [Auteur]
Scool [Scool]

Titre de la manifestation scientifique :

Proceedings of the VLDB Endowment

Ville :

Sydney

Pays :

Australie

Date de début de la manifestation scientifique :

2022-09

Titre de la revue :

Proceedings of the VLDB Endowment (PVLDB)
Proceedings of the VLDB Endowment

Éditeur :

VLDB Endowment

Date de publication :

2021-09

Mot(s)-clé(s) en anglais :

Reinforcement learning RL
Monte carlo tree search
Database management system
Query optimization

Discipline(s) HAL :

Informatique [cs]/Base de données [cs.DB]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL