Draft:Milvus (vector database)
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| Developer(s) | Zilliz |
|---|---|
| Initial release | October 19, 2019 |
| Stable release | v2.4.12
/ September 26, 2024.[1] |
| Repository | github |
| Written in | C++, Go |
| 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.
Milvus is an open-source project under LF AI & Data Foundation [2] distributed under the Apache License 2.0.
History
Milvus has been developed by Zilliz since 2017.
Milvus has 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 on ACM SIGMOD Conference in 2021[3]
Features
Similarity search
Major similarity search related features that are available in the active 2.4.x Milvus branch[4]:
- 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,
- Support of HNSW index, Inverted-lists bases indices and a brute-force search.
- Re-ranking.
Milvus similarity search engine relies on heavily-modified forks of third-party open-source similarity search libraries, such as Faiss[5][6], DiskANN[7][8] and hnswlib[9].
Database
As a database, Milvus has the following features:[4]:
- Column-oriented database
- Supported data consistency levels[10]:
- Strong consistency ensures that users can read the latest version of data,
- Bounded staleness allows data inconsistency during a certain period of time,
- Session ensures that all data writes can be immediately perceived in reads during the same session,
- Eventual consistency ensures that replicas eventually converge to the same state given that no further write operations are done.
- Data sharding
- Streaming data ingestion, which allows to process and ingest data in real-time as it arrives,
- Dynamic schema, which allows inserting the data without a predefined schema,
- Storage/computing disaggregation, which splits the database system into several mutually independent layers,
- Multi-tenancy scenarios (database-oriented, collection-oriented, partition-oriented)[11]
- Memory-mapped data storage,
- Role-based access control,
- Multi-vector and hybrid search [12]
Deployment options
Milvus supports working in the following modes[13]:
- Embedded, which is achieved via a Python-based wrapper pymilvus[14]
- Standalone, which is designed for operating on a single machine. Docker-based images are preferred.
- Distributed, which can be deployed on a Kubernetes cluster.
GPU support
Milvus provides GPU accelerated index building and search using Nvidia CUDA technology[15][16] via Nvidia RAFT library[17], including a recent GPU-based graph indexing algorithm Nvidia CAGRA[18]
Integration
Milvus provides official SDK clients for Java, NodeJS, Python and Go[19]. An additional C# SDK client was contributed by Microsoft[4][20].
Milvus support integration with Prometheus and Grafana for monitoring and alerts.
Milvus provides connectors[4] for OpenAI models[21][22], HayStack[23], LangChain[24]
Milvus supports integration with IBM Watsonx.[25]
See also
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References
- ^ "Release notes for Milvus v2.4.12". GitHub.
- ^ 2.0 2.1 "LF AI & Data Foundation Announces Graduation of Milvus Project". June 23, 2021.
- ^ "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.
- ^ 4.0 4.1 4.2 4.3 "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.
- ^ "DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node" (PDF). Retrieved September 23, 2024.
- ^ "Hnswlib - fast approximate nearest neighbor search". GitHub. Retrieved September 23, 2024.
- ^ "Consistency levels in Milvus". Retrieved September 29, 2024.
- ^ "Multi-tenancy strategies". Retrieved September 29, 2024.
- ^ "Hybrid Search". Retrieved September 23, 2024.
- ^ "Deployment options".
- ^ "Python SDK for Milvus". GitHub.
- ^ "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 RAFT 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.
- ^ "Getting started with Milvus and OpenAI". Mar 28, 2023. Retrieved September 23, 2024.
- ^ "OpenAI and Milvus simple app". GitHub. Retrieved September 23, 2024.
- ^ "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.