# Vector Search Glossary This glossary defines key terms related to Vector Search in ScyllaDB. It covers core concepts essential to understanding how vectors are stored, indexed, and queried. * **ANN (Approximate Nearest Neighbor) Search** — A search technique that efficiently finds data points in a large dataset that are most similar to a given query vector. Instead of looking for an exact match, ANN speeds up the search by accepting results that are close enough — making it ideal for working with large datasets and high-dimensional vector spaces in applications like semantic search, recommendations, and generative AI. * **CDC Reader** — A Change Data Capture (CDC) consumer that propagates base table mutations to the vector index. ScyllaDB uses two readers: a *fine-grained reader* with sub-second intervals for low-latency updates, and a *wide-framed reader* with a 30-second safety interval that ensures consistency. * **Embedding** — A vector generated by a machine learning model to represent raw data in a numerical form. ScyllaDB can store and query embeddings generated by an external tool and inserted into the database. * **Filtering** — The ability to combine an ANN similarity search with predicate constraints on primary key columns. ScyllaDB supports filtering via [global vector indexes](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-filtering.md) (with `ALLOW FILTERING`) and [local vector indexes](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-filtering.md) (with partition key restrictions). See [Filtering](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-filtering.md). * **Global Vector Index** — A vector index that spans all partitions and enables cluster-wide similarity search. Global indexes support filtering on the base table’s primary key columns with `ALLOW FILTERING`. See [Global Vector Indexes](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-filtering.md#vs-filtering-global). * **HNSW (Hierarchical Navigable Small World)** — A graph-based algorithm for Approximate Nearest Neighbor search. It organizes vectors into a multi-layered graph, allowing efficient navigation from coarse to fine resolution. ScyllaDB’s vector index uses the HNSW algorithm implemented by the USearch library. * **Local Vector Index** — A per-partition vector index that is co-located with the data it indexes. Local indexes require the full partition key in the `WHERE` clause and support efficient filtered similarity search within a single partition. See [Local Vector Indexes](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-filtering.md#vs-filtering-local). * **Oversampling** — An index option (range 1.0-100.0) that controls how many candidate vectors the index evaluates internally before returning the top-k results. Higher oversampling improves recall at the cost of higher latency. See [Quantization and Rescoring](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-quantization.md). * **Quantization** — A technique that reduces index memory usage by storing vectors in lower-precision formats (e.g., `f16`, `i8`, `b1`) instead of the default `f32`. Lower precision reduces memory but may decrease accuracy; combine with rescoring to recover precision. See [Quantization and Rescoring](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-quantization.md). * **Rescoring** — A post-processing step where the index re-ranks candidate results using the original full-precision vectors from disk, improving accuracy after quantized search. Enabled by setting `'rescoring': 'true'` in the index options. See [Quantization and Rescoring](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-quantization.md). * **Semantic Search** — A type of similarity search that compares the meaning of a query and data items using vector embeddings. It enables context-aware retrieval by focusing on semantic relevance rather than exact terms. * **Similarity Function (Distance Metric)** — A mathematical function that measures how close two vectors are. In ScyllaDB, three similarity functions are supported: `COSINE` (default), `DOT_PRODUCT`, and `EUCLIDEAN`. See [Choosing a Similarity Function](https://cloud.docs.scylladb.com/stable/vector-search/work-with-vector-search.md#vs-similarity-functions). * **Similarity Search** — A technique for finding items in a dataset that are most similar to a query vector, using a distance or similarity measure. It is commonly used in high-dimensional vector spaces to retrieve approximate matches efficiently. * **USearch Index** — A high-performance, in-memory vector index library developed by Unum, designed for fast approximate nearest-neighbor (ANN) search. ScyllaDB uses USearch as the underlying engine for its Vector Search Index to deliver low-latency similarity queries and efficient memory utilization. * **Vector** — An ordered list of numbers (floats) representing data, such as text, images, or audio, in a way that captures its meaning or features. * **Vector Search Index** — In ScyllaDB, a USearch index built on a vector column that accelerates similarity queries. Unlike traditional indexes (for exact matches or ranges), a Vector Search index is optimized for approximate nearest neighbor (ANN) lookups over high-dimensional data. * **Vector Type** — A native ScyllaDB column type used to store fixed-length numeric vectors directly in a table for similarity search. See [Data Types - Vectors](https://docs.scylladb.com/manual/branch-2026.1/cql/types#vectors) in the ScyllaDB documentation.