History
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
Similarity search
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
As a database, Milvus provides the following features: [6]
Deployment options
Milvus can be deployed as an embedded database, standalone server, or distributed cluster. Zilliz Cloud offers a fully managed version. [16]
GPU support
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
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:
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