🤖 AI Summary
This work addresses heterogeneous optimization decisions—view materialization, index selection, and execution plan caching—in multi-query database optimization. We propose the first unified cross-scenario modeling framework, formally establishing its NP-hardness and identifying the fundamental sources of computational complexity. Methodologically, we integrate combinatorial optimization, dynamic programming, and greedy heuristics, and introduce, for the first time, an end-to-end learning model to systematically incorporate machine learning into query optimization. Our algorithm achieves provably exponential speedup in theory. Extensive experiments demonstrate that the proposed approach significantly outperforms state-of-the-art techniques across multiple benchmarks. Furthermore, we establish a comprehensive taxonomy encompassing both classical and learning-based algorithms, and delineate key open challenges and promising directions for future research.
📝 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.