Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. In this paper, we develop the SMARTIX architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. We train and evaluate SMARTIX performance using TPC-H, a standard, and scalable database benchmark. Our empirical evaluation shows that SMARTIX converges to indexing configurations with superior performance compared to standard baselines we define and other reinforcement learning methods used in related work.

SmartIX: A database indexing agent based on reinforcement learning / Paludo Licks, G.; Colleoni Couto, J.; de Fátima Miehe, P.; de Paris, R.; Dubugras Ruiz, D.; Meneguzzi, F.. - In: APPLIED INTELLIGENCE. - ISSN 1573-7497. - 50:8(2020), pp. 2575-2588. [10.1007/s10489-020-01674-8]

SmartIX: A database indexing agent based on reinforcement learning

Paludo Licks, G.
;
2020

Abstract

Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. In this paper, we develop the SMARTIX architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. We train and evaluate SMARTIX performance using TPC-H, a standard, and scalable database benchmark. Our empirical evaluation shows that SMARTIX converges to indexing configurations with superior performance compared to standard baselines we define and other reinforcement learning methods used in related work.
2020
artificial intelligence; reinforcement learning; database; indexing
01 Pubblicazione su rivista::01a Articolo in rivista
SmartIX: A database indexing agent based on reinforcement learning / Paludo Licks, G.; Colleoni Couto, J.; de Fátima Miehe, P.; de Paris, R.; Dubugras Ruiz, D.; Meneguzzi, F.. - In: APPLIED INTELLIGENCE. - ISSN 1573-7497. - 50:8(2020), pp. 2575-2588. [10.1007/s10489-020-01674-8]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1670710
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