# HERO: Hint-Based Efficient and Reliable Query Optimizer

arXiv: https://arxiv.org/abs/2412.02372 · HTML: https://mlsanity.com/paper/2412.02372/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [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)
- [Flow-Loss: Learning Cardinality Estimates That Matter](https://mlsanity.com/paper/2101.04964/) (2021)
- [An Empirical Analysis of Deep Learning for Cardinality Estimation](https://mlsanity.com/paper/1905.06425/) (2019)

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

- [Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization](https://mlsanity.com/paper/2501.17414/) (2025)
- [Training-Free Query Optimization via LLM-Based Plan Similarity](https://mlsanity.com/paper/2506.05853/) (2025)
- [Piece of CAKE: Adaptive Execution Engines via Microsecond-Scale Learning](https://mlsanity.com/paper/2602.04181/) (2026)

## Abstract (arXiv)

We propose a novel model for learned query optimization which provides query hints leading to better execution plans. The model addresses the three key challenges in learned hint-based query optimization: reliable hint recommendation (ensuring non-degradation of query latency), efficient hint exploration, and fast inference. We provide an in-depth analysis of existing NN-based approaches to hint-based optimization and experimentally confirm the named challenges for them. Our alternative solution consists of a new inference schema based on an ensemble of context-aware models and a graph storage for reliable hint suggestion and fast inference, and a budget-controlled training procedure with a local search algorithm that solves the issue of exponential search space exploration. In experiments on standard benchmarks, our model demonstrates optimization capability close to the best achievable with coarse-grained hints. Controlling the degree of parallelism (query dop) in addition to operator-related hints enables our model to achieve 3x latency improvement on JOB benchmark which sets a new standard for optimization. Our model is interpretable and easy to debug, which is particularly important for deployment in production.