# RDBench: ML Benchmark for Relational Databases

arXiv: https://arxiv.org/abs/2310.16837 · HTML: https://mlsanity.com/paper/2310.16837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 52 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [PDEBENCH: An Extensive Benchmark for Scientific Machine Learning](https://mlsanity.com/paper/2210.07182/) (2022)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022)
- [Do Transformers Really Perform Bad for Graph Representation?](https://mlsanity.com/paper/2106.05234/) (2021)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification](https://mlsanity.com/paper/2306.08385/) (2023)
- [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://mlsanity.com/paper/1909.06312/) (2019)
- [Well-tuned Simple Nets Excel on Tabular Datasets](https://mlsanity.com/paper/2106.11189/) (2021)
- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT](https://mlsanity.com/paper/2307.08674/) (2023)
- [DANets: Deep Abstract Networks for Tabular Data Classification and Regression](https://mlsanity.com/paper/2112.02962/) (2021)
- [Supervised Learning on Relational Databases with Graph Neural Networks](https://mlsanity.com/paper/2002.02046/) (2020)
- [PTab: Using the Pre-trained Language Model for Modeling Tabular Data](https://mlsanity.com/paper/2209.08060/) (2022)
- [Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks](https://mlsanity.com/paper/1906.04580/) (2019)

## Built on by (most cited first, 3 of 3)

- [No Need to Train Your RDB Foundation Model](https://mlsanity.com/paper/2602.13697/) (2026)
- [RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases](https://mlsanity.com/paper/2506.01360/) (2025)
- [InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs](https://mlsanity.com/paper/2607.05478/) (2026)

## Abstract (arXiv)

Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications. However, while applying machine learning to relational databases (RDBs), the absence of a well-established benchmark remains a significant obstacle to the development of ML. To address this issue, we introduce ML Benchmark For Relational Databases (RDBench), a standardized benchmark that aims to promote reproducible ML research on RDBs that include multiple tables. RDBench offers diverse RDB datasets of varying scales, domains, and relational structures, organized into 4 levels. Notably, to simplify the adoption of RDBench for diverse ML domains, for any given database, RDBench exposes three types of interfaces including tabular data, homogeneous graphs, and heterogeneous graphs, sharing the same underlying task definition. For the first time, RDBench enables meaningful comparisons between ML methods from diverse domains, ranging from XGBoost to Graph Neural Networks, under RDB prediction tasks. We design multiple classification and regression tasks for each RDB dataset and report averaged results over the same dataset, further enhancing the robustness of the experimental findings. RDBench is implemented with DBGym, a user-friendly platform for ML research and application on databases, enabling benchmarking new ML methods with RDBench at ease.