TY - JOUR
T1 - Learned Query Optimizer
T2 - 25th International Conference on Extending Database Technology, EDBT 2022
AU - Zhu, Rong
AU - Wu, Ziniu
AU - Chai, Chengliang
AU - Pfadler, Andreas
AU - Ding, Bolin
AU - Li, Guoliang
AU - Zhou, Jingren
N1 - Publisher Copyright:
© 2022 Copyright held by the owner/author(s).
PY - 2022
Y1 - 2022
N2 - Applying ML-based techniques to optimize traditional databases, or AI4DB, has becoming a hot research spot in recent. Learned techniques for query optimizer(QO) is the forefront in AI4DB. QO provides the most suitable experimental plots for utilizing ML techniques and learned QO has exhibited superiority with enough evidence. In this tutorial, we aim at providing a wide and deep review and analysis on learned QO, ranging from algorithm design, real-world applications and system deployment. For algorithm, we would introduce the advances for learning each individual component in QO, as well as the whole QO module. For system, we would analyze the challenges, as well as some attempts, for deploying ML-based QO into actual DBMS. Based on them, we summarize some design principles and point out several future directions. We hope this tutorial could inspire and guide researchers and engineers working on learned QO, as well as other context in AI4DB.
AB - Applying ML-based techniques to optimize traditional databases, or AI4DB, has becoming a hot research spot in recent. Learned techniques for query optimizer(QO) is the forefront in AI4DB. QO provides the most suitable experimental plots for utilizing ML techniques and learned QO has exhibited superiority with enough evidence. In this tutorial, we aim at providing a wide and deep review and analysis on learned QO, ranging from algorithm design, real-world applications and system deployment. For algorithm, we would introduce the advances for learning each individual component in QO, as well as the whole QO module. For system, we would analyze the challenges, as well as some attempts, for deploying ML-based QO into actual DBMS. Based on them, we summarize some design principles and point out several future directions. We hope this tutorial could inspire and guide researchers and engineers working on learned QO, as well as other context in AI4DB.
UR - http://www.scopus.com/inward/record.url?scp=105003127789&partnerID=8YFLogxK
U2 - 10.48786/edbt.2022.56
DO - 10.48786/edbt.2022.56
M3 - Conference article
AN - SCOPUS:105003127789
SN - 2367-2005
SP - 582
EP - 585
JO - Advances in Database Technology - EDBT
JF - Advances in Database Technology - EDBT
Y2 - 29 March 2022 through 1 April 2022
ER -