# Towards a Hands-Free Query Optimizer through Deep Learning

arXiv: https://arxiv.org/abs/1809.10212 · HTML: https://mlsanity.com/paper/1809.10212/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm](https://mlsanity.com/paper/2103.00858/) (2021)
- [Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload](https://mlsanity.com/paper/2103.12465/) (2021)
- [No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees](https://mlsanity.com/paper/2108.10130/) (2021)
- [Kepler: Robust Learning for Faster Parametric Query Optimization](https://mlsanity.com/paper/2306.06798/) (2023)
- [A Pluggable Learned Index Method via Sampling and Gap Insertion](https://mlsanity.com/paper/2101.00808/) (2021)
- [GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints](https://mlsanity.com/paper/2411.04525/) (2024)
- [Multi-agent Databases via Independent Learning](https://mlsanity.com/paper/2205.14323/) (2022)
- [RELOAD: A Robust and Efficient Learned Query Optimizer for Database Systems](https://mlsanity.com/paper/2604.14725/) (2026)

## Abstract (arXiv)

Query optimization remains one of the most important and well-studied problems in database systems. However, traditional query optimizers are complex heuristically-driven systems, requiring large amounts of time to tune for a particular database and requiring even more time to develop and maintain in the first place. In this vision paper, we argue that a new type of query optimizer, based on deep reinforcement learning, can drastically improve on the state-of-the-art. We identify potential complications for future research that integrates deep learning with query optimization, and we describe three novel deep learning based approaches that can lead the way to end-to-end learning-based query optimizers.