The Selection Problem in Multi-Query Optimization: a Comprehensive Survey

TL;DR

This paper surveys various selection problems in multi-query optimization, analyzing their complexity, classifying existing algorithms including machine learning approaches, and proposing a method to accelerate these algorithms for better query processing efficiency.

Contribution

It provides a unified view and detailed analysis of selection problems in query optimization, including a new method to exponentially speed up existing algorithms.

Findings

Classified selection algorithms, including machine learning-based methods.

Identified root causes of complexity in selection problems.

Proposed a method to accelerate state-of-the-art algorithms.

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.