Neo: A Learned Query Optimizer
Summary: Neo (Neural Optimizer) uses deep neural networks to generate query execution plans, offering a learning-based alternative to hand-tuned optimizers. Bootstrapped from traditional optimizers, it learns from live queries, adapts to data patterns, is robust to estimation errors, and can match or surpass state-of-the-art engines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ryan Marcus
- 2. Parimarjan Negi
- 3. Hongzi Mao
- 4. Chi Zhang
- 5. Mohammad Alizadeh
- 6. Tim Kraska
- 7. Olga Papaemmanouil
- 8. Nesime Tatbul
Incoming Citations (Sorted by Pagerank)
Showing 20 of 170 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,423 | Kepler: Robust Learning for Faster Parametric Query Optimization | 2023 | SIGMOD | 5.5130233e-05 |
| 8,659 | Learned Offline Query Planning via Bayesian Optimization | 2025 | SIGMOD | 4.4722928e-05 |
| 884 | Plan-Structured Deep Neural Network Models for Query Performance Prediction | 2019 | VLDB | 0.00015654004 |
| 3,348 | Lero: A Learning-to-Rank Query Optimizer | 2023 | VLDB | 7.1904529e-05 |
| 9,345 | LIMAO: A Framework for Lifelong Modular Learned Query Optimization | 2025 | VLDB | 4.3536343e-05 |
| 7,008 | Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective | 2024 | VLDB | 4.8643538e-05 |
| 5,334 | LEON: A New Framework for ML-Aided Query Optimization | 2023 | VLDB | 5.5649836e-05 |
| 3,658 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR | 6.8704209e-05 |
| 10,096 | NeuSO: Neural Optimizer for Subgraph Queries | 2026 | SIGMOD | 4.1945683e-05 |
| 11,350 | DeepO: A Learned Query Optimizer | 2022 | SIGMOD | 4.1945683e-05 |