Understanding Pgvector's HNSW Index Storage in Postgres

The article provides an in-depth analysis of the HNSW (Hierarchical Navigable Small World) index storage mechanism used by pgvector in PostgreSQL. It begins with a brief overview of how PostgreSQL organizes data on disk, emphasizing the structure of pages and the role of ItemIDs for efficient data management. The HNSW index is divided into a metadata page and subsequent index pages, with the metadata page containing essential configuration details such as dimensions, connection limits, and entry points for the HNSW graph. The article details the structures used for element tuples and neighbor info tuples, explaining how they store information about graph nodes and their connections. It highlights optimizations in the storage process, such as sharing element tuples for duplicate entries to save space. The article also discusses the visualization of index pages and the mapping of hex dumps to the corresponding data structures, providing insights into the internal workings of the pgvector index. Additionally, a parser developed in C is mentioned, which converts the HNSW index into JSON format for better understanding and visualization. The article concludes with a demonstration of the index's behavior during insertions and deletions, showcasing how the system manages space and maintains efficiency.

- The HNSW index in pgvector is structured into metadata and index pages.

- Metadata includes configuration parameters essential for managing the HNSW graph.

- Element and neighbor info tuples store data about graph nodes and their connections.

- Optimizations allow for shared storage of duplicate entries to save space.

- A C-based parser converts the index into JSON for visualization and understanding.