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Milvus (vector database)
View on Wikipedia | A major contributor to this article appears to have a close connection with its subject. (November 2024) |
| Milvus | |
|---|---|
 | |
| Developer | Zilliz |
| Initial release | October 19, 2019 |
| Stable release | v2.6.2
/ September 19, 2025.:[1] |
| Repository | github |
| Written in | Go, C++ |
| Operating system | Linux, macOS |
| Platform | x86, ARM |
| Type | Vector database |
| License | Apache License 2.0 |
| Website | milvus |
Milvus is a distributed vector database developed by Zilliz. It is available as both open-source software and a cloud service called Zilliz Cloud.
Milvus is an open-source project under the LF AI & Data Foundation[2] and is distributed under the Apache License 2.0.
History
[edit]Milvus has been developed by Zilliz since 2017.[3]
Milvus joined Linux Foundation as an incubation project in January 2020 and became a graduate in June 2021.[2] The details about its architecture and possible applications were presented at ACM SIGMOD Conference in 2021.[4]
Milvus 2.0, a major redesign of the whole product with a new architecture,[5] was released in January 2022.
Features
[edit]Similarity search
[edit]Various similarity search-related features are available in Milvus:[6]
- In-memory, on-disk and GPU indices,
- Single query, batch query and range query search,
- Support of sparse vectors, binary vectors, JSON and arrays,
- FP32, FP16 and BF16 data types,
- Euclidean distance, inner product distance and cosine distance support for floating-point data,
- Hamming distance and jaccard distance for binary data,
- Support of graph indices (including HNSW), Inverted-lists based indices and a brute-force search.
- Support of vector quantization for lossy input data compression, including product quantization (PQ) and scalar quantization (SQ), that trades stored data size for accuracy,
- Re-ranking.
Milvus' similarity search engine relies on heavily-modified forks of third-party open-source similarity search libraries, such as Faiss,[7][8] DiskANN[9][10] and hnswlib.[11]
Milvus includes optimizations for I/O data layout, specific to graph search indices.[12]
Database
[edit]As a database, Milvus provides the following features:[6]
- Support for column-oriented databases
- Four supported data consistency levels, including strong consistency and eventual consistency[13]
- Data sharding
- Streaming data ingestion, which allows processing and ingestion of data in real-time as it arrives
- A dynamic schema, which allows insertion of data without a predefined schema
- Independent storage and compute layers
- Support for multi-tenancy scenarios (database-oriented, collection-oriented, partition-oriented)[14]
- Memory-mapped data storage
- Role-based access control
- Multi-vector and hybrid search[15]
Deployment options
[edit]Milvus can be deployed as an embedded database, standalone server, or distributed cluster. Zilliz Cloud offers a fully managed version.[16]
GPU support
[edit]Milvus provides GPU accelerated index building and search using Nvidia CUDA technology[17][18] via the Nvidia cuVS library,[19] including a recent GPU-based graph indexing algorithm known as CAGRA.[20]
Integration
[edit]Milvus provides official SDK clients for Java, NodeJS, Python and Go.[21] An additional C# SDK client was contributed by Microsoft.[6][22] The database can integrate with DataDog,[23] Prometheus and Grafana for monitoring and alerts, as well as generative AI frameworks Haystack,[24] LangChain,[25] IBM Watsonx,[26] and those provided by OpenAI.[27][28]
Several storage providers have built integrations with Milvus to support AI workloads and large-scale vector search. These integrations aim to optimize performance, simplify inferencing workflows, and enhance data management capabilities:
See also
[edit]
References
[edit]- ^ "Release notes for Milvus v2.6.2". GitHub.
- ^ a b "LF AI & Data Foundation Announces Graduation of Milvus Project". June 23, 2021.
- ^ Liao, Ingrid Lunden and Rita (2022-08-24). "Zilliz raises $60M, relocates to SF". TechCrunch. Retrieved 2024-10-21.
- ^ "Milvus: A Purpose-Built Vector Data Management System". SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data. June 18, 2021. pp. 2614–2627. doi:10.1145/3448016.3457550. ISBN 978-1-4503-8343-1.
- ^ Guo, Rentong; Luan, Xiaofan; Xiang, Long; Yan, Xiao; Yi, Xiaomeng; Luo, Jigao; Cheng, Qianya; Xu, Weizhi; Luo, Jiarui; Liu, Frank; Cao, Zhenshan; Qiao, Yanliang; Wang, Ting; Tang, Bo; Xie, Charles (2022). "Manu: A Cloud Native Vector Database Management System". arXiv:2206.13843 [cs.DB].
- ^ a b c "Milvus overview". Retrieved September 23, 2024.
- ^ "Faiss". GitHub. Retrieved September 23, 2024.
- ^ Douze, Matthijs; Guzhva, Alexandr; Deng, Chengqi; Johnson, Jeff; Szilvasy, Gergely; Mazaré, Pierre-Emmanuel; Lomeli, Maria; Hosseini, Lucas; Jégou, Hervé (2024). "The Faiss library". arXiv:2401.08281 [cs.LG].
- ^ "DiskANN library". GitHub. Retrieved September 23, 2024.
- ^ Subramanya, Suhas Jayaram; Kadekodi, Rohan; Krishaswamy, Ravishankar; Simhadri, Harsha Vardhan (8 December 2019). "DiskANN: fast accurate billion-point nearest neighbor search on a single node". Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc.: 13766–13776.
- ^ "Hnswlib - fast approximate nearest neighbor search". GitHub. Retrieved September 23, 2024.
- ^ Wang, Mengzhao; Xu, Weizhi; Yi, Xiaomeng; Wu, Songlin; Peng, Zhangyang; Ke, Xiangyu; Gao, Yunjun; Xu, Xiaoliang; Guo, Rentong; Xie, Charles (2024). "Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment". Proceedings of the ACM on Management of Data. 2: 1–27. arXiv:2401.02116. doi:10.1145/3639269.
- ^ "Consistency levels in Milvus". Retrieved September 29, 2024.
- ^ "Multi-tenancy strategies". Retrieved September 29, 2024.
- ^ "Hybrid Search". Retrieved September 23, 2024.
- ^ "Zilliz cloud". Retrieved October 10, 2024.
- ^ "What's New In Milvus 2.3 Beta - 10X faster with GPUs". Retrieved September 29, 2024.
- ^ "Milvus 2.3 Launches with Support for Nvidia GPUs". 23 March 2023. Retrieved September 29, 2024.
- ^ "NVIDIA cuVS library". GitHub.
- ^ Ootomo, Hiroyuki; Naruse, Akira; Nolet, Corey; Wang, Ray; Feher, Tamas; Wang, Yong (August 2023). "CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs". arXiv:2308.15136 [cs.DS].
- ^ "Install Milvus Go SDK". Retrieved September 29, 2024.
- ^ "Get Started with Milvus Vector DB in .NET". March 6, 2024. Retrieved September 29, 2024.
- ^ "Integration roundup: Monitoring your modern database platforms". 26 February 2025. Retrieved February 26, 2025.
- ^ "Integration HayStack + Milvus". Retrieved September 23, 2024.
- ^ "Milvus connector for LangChain". Retrieved September 23, 2024.
- ^ "IBM watsonx.data's integrated vector database: unify, prepare, and deliver your data for AI". IBM. April 9, 2024. Retrieved September 29, 2024.
- ^ "Getting started with Milvus and OpenAI". Mar 28, 2023. Retrieved September 23, 2024.
- ^ "OpenAI and Milvus simple app". GitHub. Retrieved September 23, 2024.
- ^ "Pure Storage Introduces New GenAI Infrastructure with NVIDIA and Run:ai". Pure Storage. 2024-06-25.
- ^ "Cloudian AI Inferencing Platform". Cloudian. 2024-05-07.
- ^ "Weka Debuts New Solution Blueprint to Simplify AI Inferencing at Scale". Weka. 2024-04-23.
- ^ "Revolutionizing Biomedical GenAI with Hyperscale RAG: DDN Infinia, Milvus, and the Full PubMed Dataset". DDN. 2024-06-03.