Workload acceleration by optimizing materialized view selection using local search

2026-06-02

AI Summary

This work addresses the challenge of materialized view selection under increasingly complex database workloads by explicitly modeling incremental maintenance cost within the optimization objective and integrating it into an integer linear programming framework. The approach innovatively leverages subquery containment analysis to devise strategies for generating high-quality initial solutions and constructing effective neighborhoods, guided by metrics such as subquery frequency, utility, or utility per unit of storage. These strategies efficiently steer local search toward superior solutions. Experimental evaluation on the Redbench benchmark demonstrates that the proposed method significantly outperforms BIGSUBS in both optimization utility and the quality of selected views.

Abstract

The growing size of database workloads has made view selection a key performance challenge. Materializing frequent sub-queries in workloads improves query efficiency, but it incurs significant view maintenance costs due to updates. Although existing methods such as BIGSUBS address this trade-off between the benefit of using materialized views and the overhead of view maintenance, they have two drawbacks: insufficient maintenance cost modeling and ineffective view selection due to probabilistic techniques. We propose a novel view selection method that incorporates incremental view maintenance cost directly into the optimization objective of an integer linear program and applies local search to efficiently explore the solution space. In order to apply local search to the view selection problem, we develop neighboring solutions using sub-query containment, and select initial solutions based on sub-query frequency, utility, or utility per storage unit. Experiments using Redbench, a benchmark simulating real-world query workloads on Amazon Redshift, show that our approach outperforms BIGSUBS in both optimization utility and the quality of selected views.

Problem

• materialized view selection
• view maintenance cost
• workload acceleration
• query optimization
• database performance

Innovation

• materialized view selection
• local search
• incremental view maintenance
• integer linear programming
• query workload optimization