SQLVec: SQL-Based Vector Similarity Search

Vector data is ubiquitous in many applications, primarily due to the advances in deep learning that enable the representation of diverse data types as vectors. A number of vector databases have been developed to support efficient vector similarity search, and they are generally categorized into two types: specialized and generalized. The latter are gaining increasing interest as they can eliminate data silos and support complex query processing for both vector and non-vector data. However, these systems, built on specific databases, must construct and access vector indices via low-level, database-specific APIs. This requires substantial engineering effort and hinders their migration to alternative databases. In this paper, for the first time, we study whether vector similarity search can be efficiently mapped into SQL, which is independent of the underlying databases and naturally portable across relational databases. We propose a general framework SQLVec, which decomposes the vector search process into a set of atomic operators to support different variants of search tasks flexibly. We map these operators to efficient SQL statements and then further optimize them on search strategies and distance computations to accelerate vector search across various scenarios. We deployed our framework SQLVec on two representative relational databases, and conducted extensive evaluation to compare it with existing vector databases extended from them. Our experimental results demonstrate the portability of SQLVec across relational databases and show that it matches or outperforms existing systems on diverse vector search variants.