How We Made PostgreSQL as Fast as Pinecone for Vector Data
The open-sourcing of pgvectorscale, a new PostgreSQL extension, provides advanced indexing techniques for vector data, significantly improving the search performance of approximate nearest neighbor (ANN) queries. This enables applications like retrieval-augmented generation (RAG), summarization, clustering, or general search. The DiskANN algorithm allows the index to be stored on SSDs instead of RAM, and supporting streaming post-filtering ensures accurate retrieval even when secondary filters are applied. A new vector quantization algorithm called SBQ provides a better accuracy vs. performance trade-off compared to existing ones like BQ (binary quantization) and PQ (product quantization). These improvements make PostgreSQL a strong competitor for bespoke databases created for vector data, such as Pinecone.
Company
Timescale
Date published
June 11, 2024
Author(s)
Matvey Arye
Word count
2018
Hacker News points
None found.
Language
English