Hydro: Adaptive Query Processing of ML Queries

arXiv:2403.14902v1 Announce Type: cross
Abstract: Query optimization in relational database management systems (DBMSs) is critical for fast query processing. The query optimizer relies on precise selectivity and cost estimates to effectively optimize queries prior to execution. While this strategy is effective for relational DBMSs, it is not sufficient for DBMSs tailored for processing machine learning (ML) queries. In ML-centric DBMSs, query optimization is challenging for two reasons. First, the performance bottleneck of the queries shifts to user-defined functions (UDFs) that …

abstract arxiv cost cs.db cs.lg database database management management optimization prior processing queries query query processing relational relational database strategy systems type