Automated Database Indexing using Model-free Reinforcement Learning

TL;DR

This paper presents a reinforcement learning-based architecture for automatic database indexing, effectively optimizing query performance and scaling to large databases without requiring extensive domain knowledge.

Contribution

It introduces a model-free reinforcement learning approach for automatic database indexing, outperforming existing methods like genetic algorithms.

Findings

Superior performance over related reinforcement learning and genetic algorithm methods

Maintains near-optimal index configurations

Efficiently scales to large databases

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. We develop an 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. In our experimental evaluation, our architecture shows superior performance compared to related work on reinforcement learning and genetic algorithms, maintaining near-optimal index configurations and efficiently scaling to large databases.