The pgvector extension transforms PostgreSQL into a capable vector database, allowing organizations to add semantic search without introducing new infrastructure. For teams already running Postgres, this approach reduces operational complexity. However, production deployments require understanding pgvector's performance characteristics and tuning database configuration for vector workloads.
Index Selection and Configuration
pgvector supports two main index types: IVFFlat and HNSW. IVFFlat offers faster index creation and lower memory usage but slower queries. HNSW provides better query performance at the cost of longer build times and higher memory requirements. The right choice depends on your data volume, query patterns, and infrastructure constraints.
- HNSW indexes excel for datasets under 10 million vectors with frequent queries
- IVFFlat works better for larger datasets where index build time matters
- Configure lists parameter for IVFFlat based on dataset size—start with sqrt(rows)
- Set ef_construction and m parameters for HNSW to balance accuracy versus speed
- Monitor query plans to ensure the database uses vector indexes appropriately
Performance Optimization
Vector operations are memory-intensive, requiring database configuration adjustments. Increasing shared_buffers allows Postgres to cache more vector data in memory. Setting maintenance_work_mem higher speeds up index creation. For hybrid search combining vectors with traditional filters, proper indexing on filter columns prevents sequential scans that kill performance.
Scaling Strategies
As vector datasets grow, scaling requires strategic approaches. Partitioning tables by time or category distributes data across partitions that can be searched independently. Read replicas handle query load for read-heavy applications. For datasets exceeding hundreds of millions of vectors, specialized vector databases may provide better performance, but pgvector serves most use cases effectively when properly tuned.