Abstract
Modern database systems are increasingly expected to support hybrid queries that combine traditional relational operators with vector-based similarity search enabling applications such as semantic search, contextual recommendations, and multimodal analytics. However, existing benchmarks target either purely relational workloads or isolated similarity search, leaving a gap in evaluating database systems that aim to integrate both. In this paper, we present RVBench, a benchmarking framework for evaluating hybrid relational–vector workloads. RVBench comes with a relational data model that adapts the real-world MediaWiki schema to include vector columns and provides a suite of parameterized SQL templates that interleave similarity search with traditional SQL operations. The framework supports multiple similarity semantics (neighbors based on top-k, rank intervals, and sampled ranks) and allows generating hybrid workloads to evaluate database systems on both performance and retrieval quality. We demonstrate RVBench by benchmarking and comparing PostgreSQL and other commercial databases with a reference implementation of the workloads to highlight key trade-offs in query execution strategies and accuracy.
