Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning
| Autor: | Debabrota Basu, Junxiong Wang, Immanuel Trummer |
|---|---|
| Přispěvatelé: | Cornell University [New York], Scool (Scool), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), NSF grant IIS-1910830 ('Regret-Bounded Query Evaluation via Reinforcement Learning'), Cornell Computer Science Electronic Arts Fellowship |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Computer science Reinforcement learning RL Sample (statistics) Workload 02 engineering and technology Benchmarking Data structure Query optimization Query Optimization [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Computer engineering [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 020204 information systems 0202 electrical engineering electronic engineering information engineering Reinforcement learning Database Management Systems 020201 artificial intelligence & image processing Point (geometry) Physical design |
| Zdroj: | SIGMOD/PODS '21: International Conference on Management of Data SIGMOD/PODS '21: International Conference on Management of Data, Jun 2021, Virtual Event, China. pp.2794-2797, ⟨10.1145/3448016.3452754⟩ SIGMOD Conference |
| DOI: | 10.1145/3448016.3452754⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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