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VLDB2025顶会

Accio: Bolt-on Query Federation

Xiaoying Wang, Jiannan Wang, Tianzheng Wang, Yong Zhang

2025年份

摘要

Data scientists today often need to analyze data from various places. This makes it necessary for corresponding engines to support query federation (i.e., the ability to perform SQL queries over data hosted in different sources). Although many systems come with federation capabilities, their implementations are tightly coupled with the core engine design. This not only increases complexity and reduces portability across engines, but also often leads to performance issues by missing optimization opportunities. This paper proposes Accio, a new "bolt-on" approach to query federation. Accio is a middleware library that decouples query federation from the target system. It enables two key optimizations—join pushdown and query partitioning—via a declarative interface that can be easily leveraged by different engines. Our experience of adapting five popular data science query engines shows that Accio can outperform existing approaches by orders of magnitude in various scenarios without the need for any intrusive changes or extra maintenance.

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