Low Rank Learning for Offline Query Optimization
This work addresses the issue of high computational overhead in query optimization for database systems, offering a low-overhead solution with a no-regressions guarantee, though it appears incremental as it builds on existing learned optimizer concepts.
The paper tackles the problem of expensive neural networks and ad-hoc search policies in learned query optimizers by introducing LimeQO, a framework using low-rank learning for offline query optimization, which reduces a 3-hour workload to 1.5 hours after 1.5 hours of exploration with linear methods and achieves the same reduction with only 0.5 hours using a transductive TCNN.
Recent deployments of learned query optimizers use expensive neural networks and ad-hoc search policies. To address these issues, we introduce \textsc{LimeQO}, a framework for offline query optimization leveraging low-rank learning to efficiently explore alternative query plans with minimal resource usage. By modeling the workload as a partially observed, low-rank matrix, we predict unobserved query plan latencies using purely linear methods, significantly reducing computational overhead compared to neural networks. We formalize offline exploration as an active learning problem, and present simple heuristics that reduces a 3-hour workload to 1.5 hours after just 1.5 hours of exploration. Additionally, we propose a transductive Tree Convolutional Neural Network (TCNN) that, despite higher computational costs, achieves the same workload reduction with only 0.5 hours of exploration. Unlike previous approaches that place expensive neural networks directly in the query processing ``hot'' path, our approach offers a low-overhead solution and a no-regressions guarantee, all without making assumptions about the underlying DBMS. The code is available in \href{https://github.com/zixy17/LimeQO}{https://github.com/zixy17/LimeQO}.