Scholars@Duke

Hint-QPT: Hints for Robust Query Performance Tuning

Publication ,  Conference
Xiu, H; Li, Y; Yang, Q; Guo, W; Liu, Y; Agarwal, PK; Roy, S; Yang, J
Published in: Proceedings of the VLDB Endowment
January 1, 2025
Published version (DOI)

Query optimizers rely heavily on selectivity estimates to choose efficient execution plans, but inaccuracies in these estimates often result in poor query performance. We introduce Hint-QPT (Hints for Robust Query Performance Tuning), an interactive tool designed to help users diagnose and improve query performance. Hint-QPT proactively recommends robust plans that are resilient to uncertainty in selectivity estimates, identifies sensitive subqueries for which selectivity estimation errors greatly affect plan quality, and provides intuitive interfaces for targeted selectivity adjustments. Users can either choose the recommended robust plans for execution, or acquire additional statistics on the identified sensitive subqueries to tune query performance. Moreover, Hint-QPT visualizes the alternative execution plans and their costs under uncertainty, helping users to better understand their robustness.

Duke Scholars

Pankaj K. Agarwal
Author Pankaj K. Agarwal Computer Science
Sudeepa Roy
Author Sudeepa Roy Computer Science
Haibo Xiu
Author Haibo Xiu  
Jun Yang
Author Jun Yang Computer Science

Published In

Proceedings of the VLDB Endowment

DOI

10.14778/3750601.3750663

EISSN

2150-8097

Publication Date

January 1, 2025

Volume

18

Issue

12

Start / End Page

5327 / 5330

Related Subject Headings

  • 4605 Data management and data science
  • 0807 Library and Information Studies
  • 0806 Information Systems
  • 0802 Computation Theory and Mathematics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Xiu, H., Li, Y., Yang, Q., Guo, W., Liu, Y., Agarwal, P. K., … Yang, J. (2025). Hint-QPT: Hints for Robust Query Performance Tuning. In Proceedings of the VLDB Endowment (Vol. 18, pp. 5327–5330). https://doi.org/10.14778/3750601.3750663
Xiu, H., Y. Li, Q. Yang, W. Guo, Y. Liu, P. K. Agarwal, S. Roy, and J. Yang. “Hint-QPT: Hints for Robust Query Performance Tuning.” In Proceedings of the VLDB Endowment, 18:5327–30, 2025. https://doi.org/10.14778/3750601.3750663.
Xiu H, Li Y, Yang Q, Guo W, Liu Y, Agarwal PK, et al. Hint-QPT: Hints for Robust Query Performance Tuning. In: Proceedings of the VLDB Endowment. 2025. p. 5327–30.
Xiu, H., et al. “Hint-QPT: Hints for Robust Query Performance Tuning.” Proceedings of the VLDB Endowment, vol. 18, no. 12, 2025, pp. 5327–30. Scopus, doi:10.14778/3750601.3750663.
Xiu H, Li Y, Yang Q, Guo W, Liu Y, Agarwal PK, Roy S, Yang J. Hint-QPT: Hints for Robust Query Performance Tuning. Proceedings of the VLDB Endowment. 2025. p. 5327–5330.

Published In

Proceedings of the VLDB Endowment

DOI

10.14778/3750601.3750663

EISSN

2150-8097

Publication Date

January 1, 2025

Volume

18

Issue

12

Start / End Page

5327 / 5330

Related Subject Headings

  • 4605 Data management and data science
  • 0807 Library and Information Studies
  • 0806 Information Systems
  • 0802 Computation Theory and Mathematics