Curious if the advanced indexing strategies cover vector workloads. I've been trying to consolidate my GenAI stack to avoid maintaining a separate vector database, but performance has been a bit of a bottleneck. Would be great to hear if 18 makes it viable to keep everything in Postgres for heavier workloads.
pgedge_postgres•1w ago
Hi! From the presenter,
> The PG 18 features that i am presenting don't directly address vector search; however asynchronous I/O can impact large vector index scan, RETURNING enhancements are useful for tracking vector insertion and updates, and generated column replication could replicate calculated embedding across distributed nodes. PostgreSQL 18 itself don't have native vector search improvements but the pgvector extension has been making significant strides in PostgreSQL 18 performance improvements which can indirectly benefit vector workloads....
storystarling•1w ago
pgedge_postgres•1w ago
> The PG 18 features that i am presenting don't directly address vector search; however asynchronous I/O can impact large vector index scan, RETURNING enhancements are useful for tracking vector insertion and updates, and generated column replication could replicate calculated embedding across distributed nodes. PostgreSQL 18 itself don't have native vector search improvements but the pgvector extension has been making significant strides in PostgreSQL 18 performance improvements which can indirectly benefit vector workloads....