TY - GEN
T1 - A Data-Driven Index Recommendation System for Slow Queries
AU - Peng, Gan
AU - Cai, Peng
AU - Ye, Kaikai
AU - Li, Kai
AU - Cai, Jinlong
AU - Shen, Yufeng
AU - Su, Han
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023/10/21
Y1 - 2023/10/21
N2 - The Database Autonomy Service (DAS) is a platform designed to assist database administrators in managing a large number of database instances within major internet companies. One of the key tasks in DAS is to find missing indexes to improve the slow query execution. In Meituan, a vast array of business lines deploy tens of thousands of MySQL database instances. Consequently, a great number of human-generated index cases are accumulated in the DAS platform. This motivates us to build a data-driven index recommendation system, referred to as idxLearner, which can learn index creation knowledge from human-generated index cases. In this demonstration, users can interact with idxLearner by choosing source databases to construct the training data, training the recommendation model, inputting slow queries for various target databases, and observing the recommended indexes and their evaluation results.
AB - The Database Autonomy Service (DAS) is a platform designed to assist database administrators in managing a large number of database instances within major internet companies. One of the key tasks in DAS is to find missing indexes to improve the slow query execution. In Meituan, a vast array of business lines deploy tens of thousands of MySQL database instances. Consequently, a great number of human-generated index cases are accumulated in the DAS platform. This motivates us to build a data-driven index recommendation system, referred to as idxLearner, which can learn index creation knowledge from human-generated index cases. In this demonstration, users can interact with idxLearner by choosing source databases to construct the training data, training the recommendation model, inputting slow queries for various target databases, and observing the recommended indexes and their evaluation results.
KW - databases
KW - index recommendation
KW - machine learning
UR - https://www.scopus.com/pages/publications/85178096021
U2 - 10.1145/3583780.3614731
DO - 10.1145/3583780.3614731
M3 - 会议稿件
AN - SCOPUS:85178096021
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 5086
EP - 5090
BT - CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Y2 - 21 October 2023 through 25 October 2023
ER -