Full Text Search (2 blogmarks)

Unconventionally, they transitioned from Elasticsearch to Postgres FTS and Vector Search.

A key insight was to bring compute closer to storage. This is opposed to more recent database patterns, where storage and compute layers are separated by networked I/O. The Postgres based search ended up being twice as fast by pushing logic and computation down to the data layer instead of pulling data up to the application layer for computation. This approach, combined with Postgres on NVMEs, further improved data fetching performance and reducing latency.

turbopuffer is a fast search engine that combines vector and full-text search using object storage, making all your data easily searchable.

Turbopuffer is used by Cursor, Linear, Notion, and others to do improved search across large document sets by combining vector and FTS.

I learned about it from this article on how Cursor uses Merkle Trees for Indexing Your Codebase.