Towards a Hands-Free Query Optimizer through Deep Learning

TL;DR

This paper proposes a novel deep reinforcement learning approach to develop a hands-free, end-to-end query optimizer that can potentially outperform traditional heuristic-based systems in database management.

Contribution

It introduces three new deep learning-based approaches for query optimization, aiming to create a more adaptable and efficient optimizer system.

Findings

Identifies potential challenges in integrating deep learning with query optimization.

Proposes three innovative deep learning approaches for end-to-end query optimization.

Highlights the potential for deep learning to revolutionize query optimizer design.

Abstract

Query optimization remains one of the most important and well-studied problems in database systems. However, traditional query optimizers are complex heuristically-driven systems, requiring large amounts of time to tune for a particular database and requiring even more time to develop and maintain in the first place. In this vision paper, we argue that a new type of query optimizer, based on deep reinforcement learning, can drastically improve on the state-of-the-art. We identify potential complications for future research that integrates deep learning with query optimization, and we describe three novel deep learning based approaches that can lead the way to end-to-end learning-based query optimizers.