A Comprehensive Survey on Vector Database: Storage and Retrieval Techniques

doi:10.11871/jfdc.issn.2096-742X.2026.04.004

Abstract

Abstract:

[Objective] As high-dimensional vector data increasingly surpass the processing capabilities of traditional database management systems, Vector Databases (VDBs) have emerged and become tightly integrated with large language models, being widely applied in modern artificial intelligence systems. However, existing research has primarily focused on underlying technologies such as approximate nearest neighbor search, with relatively few studies providing a systematic architectural-level review of VDBs or analyzing how these core technologies collectively support the overall capacity of VDBs. This survey aims to offer a comprehensive overview of the core designs and algorithms of VDBs, establishing a holistic understanding of this rapidly evolving field. [Methods] First, we systematically review the key technologies and design principles of VDBs from the two core dimensions of storage and retrieval, tracing their technological evolution. Next, we conduct an in-depth comparison of several mainstream VDB architectures, summarizing their strengths, limitations, and typical application scenarios. Finally, we explore emerging directions for integrating VDBs with large language models, including open research challenges and trends such as novel indexing strategies. [Conclusions] This survey serves as a systematic reference guide for researchers and practitioners, helping readers quickly grasp the technological landscape and development trends in the field of vector databases, and promoting further innovation in both theoretical and applied aspects.

Key words: vector database, similarity search, indexing techniques, machine learning embeddings, large language models, AI infrastructure

MA Le, ZHANG Ran, HAN Yikun, YU Shirui, WANG Zaitian, NING Zhiyuan, ZHANG Jinghan, XU Ping, LI Pengjiang, QIAO Ziyue, JU Wei, CHEN Chong, WANG Dongjie, LIU Kunpeng, WANG Pengyang, WANG Pengfei, FU Yanjie, LIU Chunjiang, LYU Changtian. A Comprehensive Survey on Vector Database: Storage and Retrieval Techniques[J]. Frontiers of Data and Computing, 2026, 8(4): 42-71, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.04.004.

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Database	Query Types		Indexing Methods					NSD	Scalability		Replica tion	Sharding	Partition ing	Maximum Dimension
Database	ANNS	NNS	Brute Force	Tree Based	Hash Based	Graph Based	Quantiza tion Based		Horizon tal Scaling	Vertical Scaling
PgVector	√	√	√	√	√	√	√	7	√	√	√	√	√	16,000
QdrantCloud	√	√	√	×	×	√	√	4	√	√	√	√	√	65,535
Weaviate Cloud	√	√	√	×	×	√	×	6	√	×	√	√	√	65,535
ZillizCloud	√	√	√	×	√	√	√	4	×	√	√	√	√	32,768
Milvus	√	√	√	×	×	√	√	6	√	×	√	√	√	32,768
ElasticCloud	√	√	√	×	×	√	×	4	√	×	√	√	√	N/A
Pinecone	√	√	N/A	N/A	N/A	N/A	N/A	3	√	√	√	√	√	N/A

	PgVector	QdrantCloud	WeaviateCloud	ZillizCloud	Milvus	ElasticCloud	Pinecone
Inner Product	√	√	√	√	√	√	√
Cosine Similarity	√	√	√	√	√	×	√
Manhattan Distance	√	√	√	×	×	×	×
Hamming Distance	√	×	√	√	√	×	×
Jaccard Distance	√	×	×	√	√	×	×
Taxicab Distance	√	×	×	×	×	×	×
Euclidean Distance	√	√	√	×	√	√	√
Structural Similarity	×	×	×	×	√	×	×
Maximum Inner Product	×	×	×	×	×	√	×

	PgVector	QdrantCloud	WeaviateCloud	ZillizCloud	Milvus	ElasticCloud	Pinecone
HNSW	√	√	√	√	√	√	N/A
Flat	×	×	√	×	√	×	N/A
BINFlat	×	×	×	×	√	×	N/A
IVF_Flat	×	×	×	×	√	×	N/A
BIN_IVF_Flat	×	×	×	×	√	×	N/A
IVF_SQ8	×	×	×	×	√	×	N/A
IVF_PQ	×	×	×	×	√	×	N/A
B-tree	√	×	×	×	×	×	N/A
LSH	×	×	×	√	×	×	N/A
BRIN	√	×	×	×	×	×	N/A
Inverted_File_Index	√	×	×	×	√	√	N/A
SPARSE Inverted Index	×	×	×	×	×	×	N/A
SPARSE WAND	×	×	×	×	√	×	N/A
GIST	√	×	×	×	×	×	N/A
GIN	√	×	×	×	×	×	N/A
DiskANN	×	√	×	√	×	×	N/A
SCANN	×	×	×	√	×	×	N/A
Sparse Vector Index	×	√	×	×	×	×	N/A
Parameterized index	×	√	×	×	×	×	N/A

Case NO.	Case Type	Dataset	Dataset Size	Vector Dimensions	Filtering Rate	Test Metrics
1	Capacity	SIFT	500K	128	N/A	NIV
2	Capacity	GIST	100K	960	N/A	NIV
3	Search Performance	Google C4	500K	1,536	N/A	IBT, R, L, MQPS

Vector Database	Total Rank	QPS (more is better)	Recall (more is better)	Latency (less is better)	Load Duration (less is better)	960 Dim Max Load Count (more is better)	128 Dim Max Load Count (more is better)
Milvus	1	380	0.982	12.4	1586	1000K	9100K
Pinecone	2	67.63	0.8064	36	1409	700K	4100K
WeaviateCloud	3	48.68	0.9957	123	2973	1800K	5500K
Zilliz Cloud	4	180.3	0.9942	6	3268	350(P)	2000(P)
Qdrant Cloud	5	78.72	0.9203	49.4	1818	900K	4000K
PgVector	6	0.8836	0.8528	2523	1381	350(P)	2000(P)
Elastic Cloud	7	11.29	0.996	3611	8671	350(P)	2000(P)