How Good Are Query Optimizers, Really?
By Viktor Leis, Andrey Gubichev, Atanas Mirchev, Peter A. Boncz, Alfons Kemper, Thomas Neumann. Proceedings of the VLDB Endowment, 2015
832 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/888764f05a60d770cfc0b49944308fd92ed45ee5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Sanjay Krishnan
- Ion Stoica
- Ken Goldberg
- Mohammad Alizadeh
- Tim Kraska
- Hongzi Mao
- Viktor Leis
- Joseph M Hellerstein
In Signal Processing
- Sanjay Krishnan
- Ken Goldberg
- Tim Kraska
- Ion Stoica
- Alfons Kemper
- Peter A. Boncz
- Joseph M Hellerstein
- Zongheng Yang
Citations in the corpus
- Learned Cardinalities: Estimating Correlated Joins with Deep Learning (2018, methodology): “ry’s features, which results in smaller models and better predictions. The join enumeration and cost model are purposely left to the query optimizer. Weevaluateourapproachusingthereal-worldIMDbdataset[16] and show that our technique is more robust than sampling-based techniques and even is competiti”
- Learning to Optimize Join Queries With Deep Reinforcement Learning (2018, methodology): “We present results on all 113 JOB queries.”
- Neo: A Learned Query Optimizer (2019, methodology): “JOB: the join order benchmark [25], with a set of queries over the Internet Movie Data Base (IMDB) consisting of complex predicates, designed to test query optimizers.”