# Kepler: Robust Learning for Faster Parametric Query Optimization

arXiv: https://arxiv.org/abs/2306.06798 · HTML: https://mlsanity.com/paper/2306.06798/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [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)
- [Bao: Learning to Steer Query Optimizers](https://mlsanity.com/paper/2004.03814/) (2020)
- [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)
- [Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation](https://mlsanity.com/paper/2109.05877/) (2021)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [Flow-Loss: Learning Cardinality Estimates That Matter](https://mlsanity.com/paper/2101.04964/) (2021)
- [Are We Ready For Learned Cardinality Estimation?](https://mlsanity.com/paper/2012.06743/) (2020)
- [Balsa: Learning a Query Optimizer Without Expert Demonstrations](https://mlsanity.com/paper/2201.01441/) (2022)
- [Towards a Hands-Free Query Optimizer through Deep Learning](https://mlsanity.com/paper/1809.10212/) (2018)
- [Sampling-Based Query Re-Optimization](https://mlsanity.com/paper/1601.05748/) (2016)

## Built on by (most cited first, 3 of 3)

- [NeurDB: An AI-powered Autonomous Data System](https://mlsanity.com/paper/2405.03924/) (2024)
- [Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization](https://mlsanity.com/paper/2501.17414/) (2025)
- [Exqutor: Extended Query Optimizer for Vector-augmented Analytical Queries](https://mlsanity.com/paper/2512.09695/) (2025)

## Abstract (arXiv)

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training an ML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.