Neo: a learned query optimizer
Name
3342263.3342644.pdf
Description
Published version
Size
2.79 MB
Format
Adobe PDF
Checksum (MD5)
d429f3d26b6f84e83a1a6d95f9785d19
Author(s)
Marcus, Ryan
Negi, Parimarjan
Mao, Hongzi
Zhang, Chi
Alizadeh, Mohammad
Kraska, Tim
Papaemmanouil, Olga
Tatbul, Nesime
Date Issued
2019
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Version
Final published version
Abstract
© 2019, is held by the owner/author(s). Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3342263.3342644
