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SmartIX: A database indexing agent based on reinforcement learning
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DOI：10.1007/s10489-020-01674-8.png)
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Abstract
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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 theSmartIXarchitecture 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 evaluateSmartIXperformance using TPC-H, a standard, and scalable database benchmark. Our empirical evaluation shows thatSmartIXconverges to indexing configurations with superior performance compared to standard baselines we define and other reinforcement learning methods used in related work.
Keywords:
Artificial intelligence
Reinforcement learning
Database
Indexing
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