Graph neural networks (GNNs) are powerful deep learning models for graph-structured data, demonstrating remarkable success across diverse domains. Recently, the database (DB) community has increasingly recognized the potentiality of GNNs, prompting a surge of researches focusing on improving database systems through GNN-based approaches. However, despite notable advances, There is a lack of a comprehensive review and understanding of how GNNs could improve DB systems. Therefore, this survey aims to bridge this gap by providing a structured and in-depth overview of GNNs for DB systems. Specifically, we propose a new taxonomy that classifies existing methods into two key categories: (1) Relational Databases, which includes tasks like performance prediction, query optimization, and text-to-SQL, and (2) Graph Databases, addressing challenges like efficient graph query processing and graph similarity computation. We systematically review key methods in each category, highlighting their contributions and practical implications. Finally, we suggest promising avenues for integrating GNNs into Database systems.

翻译：图神经网络（GNNs）是针对图结构数据的强大深度学习模型，已在众多领域展现出卓越的成功。近年来，数据库（DB）领域日益认识到GNNs的潜力，引发了大量研究关注如何通过基于GNN的方法改进数据库系统。然而，尽管取得了显著进展，目前仍缺乏关于GNNs如何改进DB系统的全面综述与深入理解。因此，本综述旨在弥合这一差距，为面向DB系统的GNNs提供一个结构化且深入的概览。具体而言，我们提出了一种新的分类法，将现有方法划分为两个关键类别：（1）关系数据库，涵盖性能预测、查询优化和文本到SQL等任务；（2）图数据库，应对高效图查询处理和图相似性计算等挑战。我们系统地回顾了每个类别中的关键方法，并强调了它们的贡献与实际意义。最后，我们提出了将GNNs集成到数据库系统中的若干有前景的研究方向。