Database / Milvus Vector database Interview questions
How does Milvus differ from a traditional relational database for storing vector data?
A traditional relational database is optimized for exact-match and range queries over structured rows, using B-tree or hash indexes that work well for equality and ordering but provide no efficient way to answer "find the rows most similar to this one" for high-dimensional vector data; some relational databases add vector extensions, but the core engine wasn't originally designed around that access pattern.
| Milvus | Traditional relational database |
| Purpose-built ANN indexes (HNSW, IVF, DiskANN) for similarity search. | B-tree/hash indexes optimized for exact match and range queries. |
| Natively scales for billions of high-dimensional vectors. | Vector support, where available, is typically an added extension, not the core design. |
| Combines vector similarity ranking with scalar filtering in one query. | No native concept of approximate similarity ranking. |
This doesn't mean relational databases are wrong for every workload alongside vector search; many applications use a relational database (or a vector-capable extension of one) for smaller-scale needs or where vector search is a secondary feature, and reach for a purpose-built system like Milvus specifically when vector search is a primary, high-scale, performance-critical part of the application.
More Related questions...
