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Open Source pg_vectorsearch Brings More Scalable Vector Search to PostgreSQL
Its PRISM index uses data partitioning and compression instead of the graph-based approach used by pgvector's HNSW index, reducing memory requirements and index build times.
According to the authors, pg_vectorsearch enables faster, more memory-efficient vector search directly in PostgreSQL, reducing the infrastructure required to handle large vector datasets. The extension supports existing pgvector columns, allowing applications to perform vector search without a separate database.
Formerly known as Timescale, Tiger Data is the company behind TimescaleDB, the open source PostgreSQL extension for time-series data and real-time analytics. Erik Nordström, senior staff engineer at Tiger Data, Matvey Arye, principal engineer at Tiger Data, and Noah Hein, staff product marketing manager at Tiger Data, write: PRISM brings a partition-based approach to PostgreSQL, similar to the approach used by newer dedicated vector search systems.
Instead of connecting every vector to its neighbors in a graph, PRISM groups similar vectors into partitions and stores them as a list in a compressed format that is densely packed into database pages. While HNSW is an approximate nearest-neighbor algorithm that quickly finds vectors similar to a query in large datasets, PRISM narrows searches to relevant partitions and scans compressed vectors sequentially.
