SmartIX: A database indexing agent based on reinforcement learning

Gabriel Paludo Licks*, Julia Colleoni Couto, Priscilla de Fátima Miehe, Renata de Paris, Duncan Dubugras Ruiz, Felipe Meneguzzi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2575-2588
Number of pages14
JournalApplied Intelligence
Issue number8
Early online date14 Mar 2020
Publication statusPublished - 1 Aug 2020


  • Artificial intelligence
  • Database
  • Indexing
  • Reinforcement learning


Dive into the research topics of 'SmartIX: A database indexing agent based on reinforcement learning'. Together they form a unique fingerprint.

Cite this