Demonstrating UDO: A Unified Approach for ...
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning
Auteur(s) :
Wang, Junxiong [Auteur]
Cornell University [New York]
Trummer, Immanuel [Auteur]
Cornell University [New York]
Basu, Debabrota [Auteur]
Scool [Scool]
Cornell University [New York]
Trummer, Immanuel [Auteur]
Cornell University [New York]
Basu, Debabrota [Auteur]
Scool [Scool]
Titre de la manifestation scientifique :
SIGMOD/PODS '21: International Conference on Management of Data
Ville :
Virtual Event
Pays :
Chine
Date de début de la manifestation scientifique :
2021-06
Titre de la revue :
Proceedings of the 2021 International Conference on Management of Data (SIGMOD ’21)
Éditeur :
ACM
Mot(s)-clé(s) en anglais :
Query Optimization
Database Management Systems
Reinforcement learning RL
Database Management Systems
Reinforcement learning RL
Discipline(s) HAL :
Informatique [cs]/Base de données [cs.DB]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Intelligence artificielle [cs.AI]
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 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 configuration switching overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We demonstrate 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 >
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 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 configuration switching overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We demonstrate 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 :
Source :
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