Milvus - Unlocking Your Data’s Hidden Relationships
An introductory overview of Milvus as a vector database for scalable similarity search and AI applications. Covers what Milvus is, its core advantages, how to embed plain data, and how to perform similarity search using those embeddings.
What Is Milvus?
Milvus is an open-source vector database designed to empower similarity searches and AI applications based on embeddings. Built on top of Facebook FAISS (C++ library for vector similarity search), it adds:
- Dynamic data support (insertions and deletions via LSM-based storage)
- Scalable distributed architecture
- Heterogeneous computing (CPUs and GPUs)
- Multiple storage backends (local, S3, HDFS)
- Accessible APIs (SDKs, RESTful)
Advantages as a Vector Database
- Efficient Similarity Search — retrieve similar vectors from massive collections (news, reports, books, media)
- Dynamic Data Management — handles insertions and deletions at scale via LSM structure; real-time search with snapshot isolation
- Multiple Index Types — quantization-based and graph-based (HNSW); extensible interface
- Heterogeneous Computing — CPUs + GPUs; SIMD optimizations
- Distributed System — multi-node for scalability and availability
- Versatile Query Types — vector similarity, attribute filtering, multi-vector queries
Key Use Cases
- Security video frame identification (image similarity)
- Virtual library document retrieval (text similarity)
- Recommendation systems
- Any AI application requiring “find things similar to this”
Related Concepts
- Embeddings — the data representation Milvus stores and searches
- Dense Vector Retrieval — primary search mode
- HNSW — graph-based ANN index supported by Milvus
- Vector Quantization — quantization-based indexes supported by Milvus
Related Articles
- Exploring Vector Databases with Milvus — deep technical architecture dive
- Choosing a Vector Database for ANN Search at Reddit — production evaluation
Tools
People
- Freddy Domínguez — author