Survey of vector database management systems

Abstract

There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. Driving this shift from algorithms to systems are new data intensive applications, notably large language models, that demand vast stores of unstructured data coupled with reliable, secure, fast, and scalable query processing capability. A variety of new data management techniques now exist for addressing these needs, however there is no comprehensive survey to thoroughly review these techniques and systems. We start by identifying five main obstacles to vector data management, namely the ambiguity of semantic similarity, large size of vectors, high cost of similarity comparison, lack of structural properties that can be used for indexing, and difficulty of efficiently answering “hybrid” queries that jointly search both attributes and vectors. Overcoming these obstacles has led to new approaches to query processing, storage and indexing, and query optimization and execution. For query processing, a variety of similarity scores and query types are now well understood; for storage and indexing, techniques include vector compression, namely quantization, and partitioning techniques based on randomization, learned partitioning, and “navigable” partitioning; for query optimization and execution, we describe new operators for hybrid queries, as well as techniques for plan enumeration, plan selection, distributed query processing, data manipulation queries, and hardware accelerated query execution. These techniques lead to a variety of VDBMSs across a spectrum of design and runtime characteristics, including “native” systems that are specialized for vectors and “extended” systems that incorporate vector capabilities into existing systems. We then discuss benchmarks, and finally outline research challenges and point the direction for future work.