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 often wrap around deep learning models, making it difficult to accurately estimate UDF statistics without profiling the query. This leads to inaccurate statistics and sub-optimal query plans. Second, the optimal query plan for ML queries is data-dependent, necessitating DBMSs to adapt the query plan on the fly during execution. So, a static query plan is not sufficient for such queries. In this paper, we present Hydro, an ML-centric DBMS that utilizes adaptive query processing (AQP) for efficiently processing ML queries. Hydro is designed to quickly evaluate UDF-based query predicates by ensuring optimal predicate evaluation order and improving the scalability of UDF execution. By integrating AQP, Hydro continuously monitors UDF statistics, routes data to predicates in an optimal order, and dynamically allocates resources for evaluating predicates. We demonstrate Hydro's efficacy through four illustrative use cases, delivering up to 11.52x speedup over a baseline system.

在本研究中，我们介绍了Hydro，它是一种以自适应查询处理（AQP）为特点的面向机器学习查询的数据库管理系统（DBMS），通过确保最佳谓词评估顺序和提高UDF执行的可伸缩性，能够快速评估基于UDF的查询谓词。通过集成AQP，Hydro不断监视UDF统计信息，以最佳顺序将数据路由到谓词，并动态分配资源来评估谓词。通过四个实例用例的演示，我们展示了Hydro相对于基准系统的高效性，提供了高达11.52倍的加速。

Hydro：机器学习查询的自适应查询处理