Vector databases and relational databases are designed for different types of data and applications. Relational databases store structured data in tables with predefined schemas and are optimized for querying rows using SQL. They work well for transactional systems, where operations like updates, joins, and aggregations are common.
In contrast, vector databases store high-dimensional vectors representing unstructured data like images, text, and audio. These vectors are embeddings generated by machine learning models to capture the semantic meaning of the data. Vector databases are optimized for similarity search, where queries find items most similar to a given vector using distance metrics like cosine similarity or Euclidean distance.
Relational databases are not ideal for similarity search because they lack specialized indexing structures for vectors. Vector databases use techniques like Approximate Nearest Neighbor (ANN) search and HNSW indexing to ensure fast, scalable operations, even with billions of vectors.
The two database types often complement each other: relational databases handle structured data, while vector databases manage unstructured data. For instance, in a recommendation system, a vector database retrieves similar items, while a relational database provides structured metadata for display.
Choosing between them depends on your data type, query needs, and application use case.