What distinguishes the HNSW from the IVF vector indexes in Oracle Database 23ai?

The distinction between HNSW (Hierarchical Navigable Small World) and IVF (Inverted File) vector indexes in Oracle Database 23ai primarily lies in their underlying mechanisms for search processing and data structure organization.

HNSW utilizes an in-memory neighbor graph, which enables it to conduct approximate nearest neighbor searches efficiently. This graph structure allows HNSW to traverse connections between points quickly, providing a higher likelihood of finding relevant matches in less time. This structure contributes to its speed and efficiency in performing searches, as it essentially maps out the relationships between data points in a way that facilitates rapid exploration of the nearest neighbors.

In contrast, IVF relies on partitioning the dataset into multiple clusters and using the buffer cache to manage these partitions. With IVF, the initial search phase involves locating appropriate partitions based on a query vector, and then performing a more thorough search within those partitions. While IVF can be effective, this approach may not achieve the same level of performance and speed during the nearest neighbor search compared to the dynamic graph utilized in HNSW.

Therefore, the correct choice highlights how HNSW's innovative use of an in-memory neighbor graph results in faster approximate searches, contrasting with IVF's strategy of leveraging buffered partitions for indexing and retrieval.