# Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem

arXiv: https://arxiv.org/abs/2209.04579 · HTML: https://mlsanity.com/paper/2209.04579/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [A Tensor Compiler for Unified Machine Learning Prediction Serving](https://mlsanity.com/paper/2010.04804/) (2020)
- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)
- [The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design](https://mlsanity.com/paper/1911.05289/) (2019)
- [Query Processing on Tensor Computation Runtimes](https://mlsanity.com/paper/2203.01877/) (2022)
- [Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML](https://mlsanity.com/paper/1909.00084/) (2019)

## Built on by (most cited first, 7 of 7)

- [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)
- [The Duck's Brain: Training and Inference of Neural Networks in Modern Database Engines](https://mlsanity.com/paper/2312.17355/) (2023)
- [Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning](https://mlsanity.com/paper/2306.00088/) (2023)
- [Bullion: A Column Store for Machine Learning](https://mlsanity.com/paper/2404.08901/) (2024)
- [DataKernelBench: Can LLMs Optimize Database Queries on GPUs?](https://mlsanity.com/paper/2608.25061/) (2026)
- [MaskSearch: Querying Image Masks at Scale](https://mlsanity.com/paper/2305.02375/) (2023)

## Abstract (arXiv)

We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to support the TPC-H benchmark, and it provides performance that is comparable to, and often better than, that of specialized CPU and GPU query processors.