Deep Reinforcement Learning for Join Order Enumeration

TL;DR

This paper introduces ReJOIN, a deep reinforcement learning-based join enumerator that learns from feedback to improve query plan quality and efficiency, outperforming traditional static methods.

Contribution

The paper presents ReJOIN, a novel reinforcement learning approach for join order enumeration, demonstrating its potential to enhance query optimization beyond static algorithms.

Findings

ReJOIN matches or outperforms PostgreSQL in plan quality.

ReJOIN improves join enumeration efficiency.

Preliminary results show reinforcement learning benefits in query optimization.

Abstract

Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they do not have a mechanism for "learning from their mistakes". In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve decision making by incorporating feedback from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, and present preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency.