Pgvector

Table of Contents Introduction Getting Started with PostgreSQL Core Concepts Every Developer Should Know Data Modeling for Scale Indexing Strategies Scaling Reads: Replication & Read‑Replicas Scaling Writes: Partitioning & Sharding Connection Pooling & Session Management High Availability & Failover Monitoring & Observability Deploying PostgreSQL in the Cloud Vector Search with pgvector Integrating Vector Search into Applications Performance Tuning for Vector Workloads Security & Compliance Best‑Practice Checklist Conclusion Resources Introduction PostgreSQL has evolved from a reliable relational database to a full‑featured data platform capable of powering everything from simple CRUD APIs to massive, globally distributed systems. In the last few years, two trends have reshaped how developers think about PostgreSQL: ...

Vector search in PostgreSQL has gone from “experimental hack” to a serious production option, largely thanks to the pgvector extension. But as teams push from thousands to tens or hundreds of millions of embeddings, a natural question emerges: How do you keep vector search fast and cost‑effective as the dataset grows, without adding yet another external database? This is exactly the problem pgvectorscale is designed to address. In this article, we’ll take a detailed look at pgvectorscale: what it is, how it fits into the Postgres ecosystem, how it scales vector search, and what trade‑offs you should understand before using it. ...