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.