Storing Multi-model Data in RDBMSs based on Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method to optimize relational schemas for multi-model data storage in RDBMSs, improving query efficiency and space usage without replacing existing systems.

Contribution

It proposes a novel Double Q-tables reinforcement learning algorithm to learn effective relational schemas for multi-model data in RDBMSs, addressing the complexity mismatch.

Findings

Outperforms existing schemas in query time

Reduces space consumption

Enhances learning efficiency

Abstract

How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant in the current market, it is expensive to replace them with others. Besides, due to the theories and technologies of RDBMSs having been enhanced over decades, it is hard to use few years to develop a multi-model database that can be compared with existing RDBMSs in handling security, query optimization, transaction management, etc. In this paper, we reconsider employing relational databases to store and query multi-model data. Unfortunately, the mismatch between the complexity of multi-model data structure and the simplicity of flat relational tables makes this difficult. Against this challenge, we utilize the reinforcement learning (RL) method to learn a relational schema by interacting with an RDBMS. Instead of using the classic Q-learning algorithm, we propose a variant Q-learning algorithm, called \textit{Double Q-tables}, to reduce the dimension of the original Q-table and improve learning efficiency. Experimental results show that our approach could learn a relational schema outperforming the existing multi-model storage schema in terms of query time and space consumption.