Open source USearch library jumpstarts ScyllaDB vector search
Summary
ScyllaDB has enhanced its open-source columnar database with vector search capabilities powered by USearch, enabling organizations to store vector embeddings alongside structured data. This innovation supports real-time applications, ensuring low latency and efficient data management.
Key Insights
What is USearch and how does it enable ScyllaDB's vector search?
USearch is a high-performance, open-source, in-memory vector index library developed by Unum for fast approximate nearest-neighbor (ANN) search. ScyllaDB uses USearch as the core engine in its dedicated Vector Store service, built with Rust, to power low-latency similarity queries on vector embeddings stored alongside structured data.
What is Approximate Nearest Neighbor (ANN) search in the context of ScyllaDB vector search?
ANN search is a technique that efficiently finds the most similar data points in large, high-dimensional vector datasets by accepting approximate rather than exact matches, ideal for AI applications like semantic search. In ScyllaDB, ANN queries use the 'ANN OF' syntax on vector columns with indexes built via USearch, supporting similarity functions like cosine, dot product, and Euclidean distance.