TY - JOUR
T1 - QO-Insight
T2 - 49th International Conference on Very Large Data Bases, VLDB 2023
AU - Anneser, Christoph
AU - Petruccelli, Mario
AU - Tatbul, Nesime
AU - Cohen, David
AU - Xu, Zhenggang
AU - Pandian, Prithviraj
AU - Laptev, Nikolay
AU - Marcus, Ryan
AU - Kemper, Alfons
N1 - Publisher Copyright:
© 2023, VLDB Endowment. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Steered query optimizers address the planning mistakes of traditional query optimizers by providing them with hints on a per-query basis, thereby guiding them in the right direction. This paper introduces QO-Insight, a visual tool designed for exploring query execution traces of such steered query optimizers. Although steered query optimizers are typically perceived as black boxes, QO-Insight empowers database administrators and experts to gain qualitative insights and enhance their performance through visual inspection and analysis.
AB - Steered query optimizers address the planning mistakes of traditional query optimizers by providing them with hints on a per-query basis, thereby guiding them in the right direction. This paper introduces QO-Insight, a visual tool designed for exploring query execution traces of such steered query optimizers. Although steered query optimizers are typically perceived as black boxes, QO-Insight empowers database administrators and experts to gain qualitative insights and enhance their performance through visual inspection and analysis.
UR - http://www.scopus.com/inward/record.url?scp=85174522350&partnerID=8YFLogxK
U2 - 10.14778/3611540.3611586
DO - 10.14778/3611540.3611586
M3 - Conference article
AN - SCOPUS:85174522350
SN - 2150-8097
VL - 16
SP - 3922
EP - 3925
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
Y2 - 28 August 2023 through 1 September 2023
ER -