# Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning

arXiv: https://arxiv.org/abs/1904.11121 · HTML: https://mlsanity.com/paper/1904.11121/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [GeCo: Quality Counterfactual Explanations in Real Time](https://mlsanity.com/paper/2101.01292/) (2021)
- [BAGUA: Scaling up Distributed Learning with System Relaxations](https://mlsanity.com/paper/2107.01499/) (2021)
- [Query Processing on Tensor Computation Runtimes](https://mlsanity.com/paper/2203.01877/) (2022)
- [JoinBoost: Grow Trees Over Normalized Data Using Only SQL](https://mlsanity.com/paper/2307.00422/) (2023)
- [The Tensor Data Platform: Towards an AI-centric Database System](https://mlsanity.com/paper/2211.02753/) (2022)
- [A Comparison of Decision Forest Inference Platforms from A Database Perspective](https://mlsanity.com/paper/2302.04430/) (2023)
- [TranSQL+: Serving Large Language Models with SQL on Low-Resource Hardware](https://mlsanity.com/paper/2502.02818/) (2025)
- [Serving Deep Learning Model in Relational Databases](https://mlsanity.com/paper/2310.04696/) (2023)
- [The Duck's Brain: Training and Inference of Neural Networks in Modern Database Engines](https://mlsanity.com/paper/2312.17355/) (2023)
- [Evaluating Deep Learning in SystemML using Layer-wise Adaptive Rate Scaling(LARS) Optimizer](https://mlsanity.com/paper/2102.03018/) (2021)
- [AMULET: Adaptive Matrix-Multiplication-Like Tasks](https://mlsanity.com/paper/2305.08872/) (2023)
- [InferF: Declarative Factorization of AI/ML Inferences over Joins](https://mlsanity.com/paper/2511.20489/) (2025)

## Abstract (arXiv)

A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system (RDBMS) to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, learning based on a database management system allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.