4 papers
Tailwind: A Practical Framework for Query Accelerators
Geoffrey X. Yu, Ryan Marcus, Tim Kraska
Relational database management systems (RDBMSes) can process general-purpose queries, but often have lower performance compared to custom-built solutions for specific queries. For…
Data-Agnostic Cardinality Learning from Imperfect Workloads
Peizhi Wu, Rong Kang, Tieying Zhang +3
Cardinality estimation (CardEst) is a critical aspect of query optimization. Traditionally, it leverages statistics built directly over the data. However, organizational policies (…
A Practical Theory of Generalization in Selectivity Learning
Peizhi Wu, Haoshu Xu, Ryan Marcus +1
Query-driven machine learning models have emerged as a promising estimation technique for query selectivities. Yet, surprisingly little is known about the efficacy of these techniq…
Low Rank Learning for Offline Query Optimization
Zixuan Yi, Yao Tian, Zachary G. Ives +1
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 offl…