LanceDB

"LanceDB: Columnar Vector Storage for Fast Local and Cloud Retrieval"
Built for experienced engineers, data infrastructure practitioners, and advanced ML application developers, this book explains LanceDB as more than a vector database: it is a storage-aware retrieval system whose behavior is shaped by columnar design, schema discipline, and execution strategy. If you already know the basics of embeddings and search, this guide helps you reason about why LanceDB performs the way it does, when its architecture is advantageous, and how to use it rigorously in both local and cloud-backed environments.
Across the book, you will learn how LanceDB’s Lance storage core influences table design, indexing, filtering, and multimodal query execution. The coverage moves from retrieval-first schema modeling and embedding management to vector search, full-text search, hybrid retrieval, scalar indexing, ANN tradeoffs, quantization, and benchmark design. It also addresses practical concerns that often decide success in production: metadata-aware query planning, prefilter versus post-filter execution, SDK-specific behavior, version-sensitive features, and the economics of object storage versus local filesystems.
Rather than treating search as an isolated feature, this book presents LanceDB as a coherent retrieval stack. The result is a technical, architecture-driven guide that helps you design systems with better relevance, stronger performance, and cleaner operational paths from prototype to production.