# One Model to Rule them All: Towards Zero-Shot Learning for Databases

arXiv: https://arxiv.org/abs/2105.00642 · HTML: https://mlsanity.com/paper/2105.00642/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Value Iteration Networks](https://mlsanity.com/paper/1602.02867/) (2016)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [ALEX: An Updatable Adaptive Learned Index](https://mlsanity.com/paper/1905.08898/) (2019)
- [An End-to-End Learning-based Cost Estimator](https://mlsanity.com/paper/1906.02560/) (2019)
- [Deep Unsupervised Cardinality Estimation](https://mlsanity.com/paper/1905.04278/) (2019)
- [Learning to Optimize Join Queries With Deep Reinforcement Learning](https://mlsanity.com/paper/1808.03196/) (2018)
- [FITing-Tree: A Data-aware Index Structure](https://mlsanity.com/paper/1801.10207/) (2018)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads](https://mlsanity.com/paper/2006.13282/) (2020)
- [Plan-Structured Deep Neural Network Models for Query Performance Prediction](https://mlsanity.com/paper/1902.00132/) (2019)
- [NeuroCard: One Cardinality Estimator for All Tables](https://mlsanity.com/paper/2006.08109/) (2020)
- [DeepDB: Learn from Data, not from Queries!](https://mlsanity.com/paper/1909.00607/) (2019)
- [Opportunistic View Materialization with Deep Reinforcement Learning](https://mlsanity.com/paper/1903.01363/) (2019)
- [Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01016/) (2018)
- [High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB](https://mlsanity.com/paper/2103.16267/) (2021)
- [Towards a General Framework for ML-based Self-tuning Databases](https://mlsanity.com/paper/2011.07921/) (2020)

## Built on by (most cited first, 9 of 9)

- [FactorJoin: A New Cardinality Estimation Framework for Join Queries](https://mlsanity.com/paper/2212.05526/) (2022)
- [E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model](https://mlsanity.com/paper/2404.11581/) (2024)
- [PRICE: A Pretrained Model for Cross-Database Cardinality Estimation](https://mlsanity.com/paper/2406.01027/) (2024)
- [Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data](https://mlsanity.com/paper/2210.05508/) (2022)
- [Tailoring Machine Learning for Process Mining](https://mlsanity.com/paper/2306.10341/) (2023)
- [CardBench: A Benchmark for Learned Cardinality Estimation in Relational Databases](https://mlsanity.com/paper/2408.16170/) (2024)
- [In-Context Adaptation to Concept Drift for Learned Database Operations](https://mlsanity.com/paper/2505.04404/) (2025)
- [Robust Plan Evaluation based on Approximate Probabilistic Machine Learning](https://mlsanity.com/paper/2401.15210/) (2024)
- [Learning-Based Data Storage [Vision] (Technical Report)](https://mlsanity.com/paper/2206.05778/) (2022)

## Abstract (arXiv)

In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components. Zero-shot learning for databases is inspired by recent advances in transfer learning of models such as GPT-3 and can support a new database out-of-the box without the need to train a new model. Furthermore, it can easily be extended to few-shot learning by further retraining the model on the unseen database. As a first concrete contribution in this paper, we show the feasibility of zero-shot learning for the task of physical cost estimation and present very promising initial results. Moreover, as a second contribution we discuss the core challenges related to zero-shot learning for databases and present a roadmap to extend zero-shot learning towards many other tasks beyond cost estimation or even beyond classical database systems and workloads.