Year: 2024
Type: preprint
Abstract: View materialization, index selection, and plan caching are well-known techniques for optimization of query processing in database systems. The essence of these tasks is to select and save a subset of the most useful candidates (views/indexes/plans) for reuse within given space/time budget constraints. In this paper, we propose a unified view on these selection problems. We make a detailed analysis of the root causes of their complexity and summarize techniques to address them. Our survey provides a modern classification of selection algorithms known in the literature, including the latest ones based on Machine Learning. We provide a ground for reuse of the selection techniques between different optimization scenarios and highlight challenges and promising directions in the field. Based on our analysis we derive a method to exponentially accelerate some of the state-of-the-art selection algorithms.
Source: arXiv (Cornell University)
Authors: Zinchenko, Sergey, Ponomaryov, Denis
Institution: Novosibirsk State University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405470476