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 (Brandeis University)
- 2. Parimarjan Negi (Massachusetts Institute of Technology)
- 3. Hongzi Mao (Massachusetts Institute of Technology)
- 4. Chi Zhang (Brandeis University)
- 5. Mohammad Alizadeh (Massachusetts Institute of Technology)
- 6. Tim Kraska (Massachusetts Institute of Technology)
- 7. Olga Papaemmanouil (Brandeis University)
- 8. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
BibTeX Citation
@article{marcus_vldb19,
title = {{Neo: A Learned Query Optimizer}},
author = {Marcus, Ryan and Negi, Parimarjan and Mao, Hongzi and Zhang, Chi and Alizadeh, Mohammad and Kraska, Tim and Papaemmanouil, Olga and Tatbul, Nesime},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {11},
pages = {1705--1718},
doi = {10.14778/3342263.3342644},
url = {https://doi.org/10.14778/3342263.3342644},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 24 of 174 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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