Refactoring Index Tuning Process with Benefit Estimation

Abstract

Index tuning is a challenging task aiming to improve query performance by selecting the most effective indexes for a database and a workload. Existing automatic index tuning methods typically rely on "what-if tools" to evaluate the benefit of an index configuration, which is costly and sometimes inaccurate. In this paper, we propose RIBE, a novel method that effectively eliminates redundant queries from the workload and harnesses statistical information of query plans to enable fast and accurate estimation of the benefit of an index configuration. With RIBE, a considerable portion of what-if calls can be skipped, thereby reducing index tuning time and increasing estimation accuracy. At the heart of RIBE is a deep learning model based on attention mechanism that predicts the impact of indexes on queries. A practical advantage of RIBE is that it achieves both improved accuracy of benefit estimation and time savings without making any changes to DBMS implementation and index configuration enumeration algorithms. Our evaluation shows that RIBE can achieve competitive tuning results and 1--2 orders of magnitude faster performance compared with the tuning method based on the full workload, and RIBE also attains higher tuning quality and comparable efficiency against the tuning methods based on the state-of-the-art workload compression methods.