A Novel Framework Using Deep Reinforcement Learning for Join Order Selection

TL;DR

This paper introduces GTDD, a novel deep reinforcement learning framework combining GNN, Tree LSTM, and DuelingDQN to improve join order selection in query optimization, outperforming existing methods.

Contribution

The paper presents GTDD, a new DRL-based framework that integrates GNN, Tree LSTM, and DuelingDQN for more effective join order selection in query optimization.

Findings

GTDD outperforms state-of-the-art techniques in experiments.

The integration of GNN, Tree LSTM, and DuelingDQN enhances join order decision quality.

GTDD adapts better to changing query conditions.

Abstract

Join order selection is a sub-field of query optimization that aims to find the optimal join order for an SQL query with the minimum cost. The challenge lies in the exponentially growing search space as the number of tables increases, making exhaustive enumeration impractical. Traditional optimizers use static heuristics to prune the search space, but they often fail to adapt to changes or improve based on feedback from the DBMS. Recent research addresses these limitations with Deep Reinforcement Learning (DRL), allowing models to use feedback to dynamically search for better join orders and enhance performance over time. Existing research primarily focuses on capturing join order sequences and their representations at various levels, with limited comparative analysis of reinforcement learning methods. In this paper, we propose GTDD, a novel framework that integrates Graph Neural Networks (GNN), Treestructured Long Short-Term Memory (Tree LSTM), and DuelingDQN. We conduct a series of experiments that demonstrate a clear advantage of GTDD over state-of the-art techniques.