# Deep Reinforcement Learning for Join Order Enumeration

arXiv: https://arxiv.org/abs/1803.00055 · HTML: https://mlsanity.com/paper/1803.00055/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 25 of 25)

- [Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction](https://mlsanity.com/paper/2201.00561/) (2022)
- [A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration](https://mlsanity.com/paper/2101.01507/) (2021)
- [FactorJoin: A New Cardinality Estimation Framework for Join Queries](https://mlsanity.com/paper/2212.05526/) (2022)
- [Cortex AISQL: A Production SQL Engine for Unstructured Data](https://mlsanity.com/paper/2511.07663/) (2025)
- [A Unified Transferable Model for ML-Enhanced DBMS](https://mlsanity.com/paper/2105.02418/) (2021)
- [A Survey on Deep Reinforcement Learning for Data Processing and Analytics](https://mlsanity.com/paper/2108.04526/) (2021)
- [One Model to Rule them All: Towards Zero-Shot Learning for Databases](https://mlsanity.com/paper/2105.00642/) (2021)
- [A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning](https://mlsanity.com/paper/2303.05710/) (2023)
- [RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems](https://mlsanity.com/paper/2503.04252/) (2025)
- [Kepler: Robust Learning for Faster Parametric Query Optimization](https://mlsanity.com/paper/2306.06798/) (2023)
- [InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models](https://mlsanity.com/paper/2308.08500/) (2023)
- [Deploying a Steered Query Optimizer in Production at Microsoft](https://mlsanity.com/paper/2210.13625/) (2022)
- [Learned-Database Systems Security](https://mlsanity.com/paper/2212.10318/) (2022)
- [JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning](https://mlsanity.com/paper/2307.11704/) (2023)
- [FOSS: A Self-Learned Doctor for Query Optimizer](https://mlsanity.com/paper/2312.06357/) (2023)
- [Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation](https://mlsanity.com/paper/2405.07770/) (2024)
- [GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints](https://mlsanity.com/paper/2411.04525/) (2024)
- [Baihe: SysML Framework for AI-driven Databases](https://mlsanity.com/paper/2112.14460/) (2021)
- [Multi-agent Databases via Independent Learning](https://mlsanity.com/paper/2205.14323/) (2022)
- [Learning-Based Data Storage [Vision] (Technical Report)](https://mlsanity.com/paper/2206.05778/) (2022)
- [Low Rank Learning for Offline Query Optimization](https://mlsanity.com/paper/2504.06399/) (2025)
- [AutoIndexer: A Reinforcement Learning-Enhanced Index Advisor Towards Scaling Workloads](https://mlsanity.com/paper/2507.23084/) (2025)
- [Gradient-Based Join Ordering](https://mlsanity.com/paper/2511.14482/) (2025)
- [MetaJoin: Adaptive Join Selections for Heterogeneous Tables](https://mlsanity.com/paper/2512.02444/) (2025)
- [RELOAD: A Robust and Efficient Learned Query Optimizer for Database Systems](https://mlsanity.com/paper/2604.14725/) (2026)

## Abstract (arXiv)

Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they do not have a mechanism for "learning from their mistakes". In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve decision making by incorporating feedback from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, and present preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency.