# PostgreSQL compatible Published articles for PostgreSQL compatible. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector DevFeed: [CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector]() Original publisher: [Read original article]() Author: Anisa Dean Published: 2026-08-26T16:40:00Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [AI search](), [tenant data protection]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [architecture](), [compliance](), [customer-solutions](), [embedding](), [postgresql](), [postgresql-compatible](), [production](), [scale](), [search]() ### AI overview CORTO describes scaling legal semantic search on Amazon Aurora PostgreSQL with pgvector for billions of documents and vectors. The article focuses on embedding choices, multi-tenant isolation, cost efficiency, and sub-second query performance. ### Source excerpt How CORTO scaled Amazon Aurora PostgreSQL with pgvector to 7.6 billion vectors and 2.5 billion documents in production, delivering sub-second legal search for 10,000+ law firms at 75% lower storage cost. ## Migrate multilingual full-text search from SQL Server to PostgreSQL DevFeed: [Migrate multilingual full-text search from SQL Server to PostgreSQL]() Original publisher: [Read original article]() Author: Ken Zhang Published: 2026-08-20T20:34:44Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [sql-server](), [SQL](), [Amazon Aurora](), [Amazon RDS](), [tokenization]() Tags: [advanced-300](), [amazon](), [amazon-aurora](), [amazon-rds](), [aws](), [blog](), [how-to](), [postgresql](), [postgresql-compatible](), [rds-for-postgresql](), [sql](), [technical-how-to](), [tokenization]() ### AI overview This tutorial explains how to migrate multilingual full-text search from SQL Server to PostgreSQL while preserving comparable behavior. It covers collations, text search configurations, language-specific tokenization, accent-insensitive search, synonym expansion, and validation testing on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL. ### Source excerpt Migrating full-text search from SQL Server to PostgreSQL can silently change results because the engines handle text, linguistics, and accents differently. This post shows how to reproduce SQL Server full-text search on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL, covering collation, tokenization, accent-insensitive search, and synonyms. ## Understand memory management in Amazon RDS for PostgreSQL to avoid out of memory DevFeed: [Understand memory management in Amazon RDS for PostgreSQL to avoid out of memory]() Original publisher: [Read original article]() Author: Ankita Singh Published: 2026-08-19T22:21:10Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [Memory Leaks](), [Processes](), [Linux](), [Monitoring](), [Amazon Web Services](), [Availability]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [availability](), [aws](), [memory](), [monitoring](), [postgresql](), [postgresql-compatible](), [processes](), [production](), [rds-for-postgresql](), [technical-how-to]() ### AI overview A practical guide to PostgreSQL memory management on Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL. It explains how memory is allocated, how memory-intensive queries and diagnostic signals can be identified, and how to diagnose, prevent, and recover from out-of-memory events and excessive disk spilling. ### Source excerpt PostgreSQL out-of-memory (OOM) events and excessive disk spilling are among the most common production incidents on Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL. Learn how PostgreSQL allocates and consumes memory, how to identify memory-intensive queries, and how to diagnose, prevent, and recover from OOM events on both engines. ## Scale pgvector with binary quantization on Amazon Aurora PostgreSQL DevFeed: [Scale pgvector with binary quantization on Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Steve Dille Published: 2026-08-18T16:37:22Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [quantization](), [AI search](), [Compression](), [Embeddings](), [Cache](), [Latency](), [datasets](), [configuration](), [postgresql clusters](), [Database]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [cache](), [compression](), [datasets](), [embeddings](), [latency](), [postgresql](), [postgresql-compatible](), [quantization](), [rds-for-postgresql](), [search](), [technical-how-to](), [validation](), [vector]() ### AI overview This practical guide explains how to use binary quantization with reranking in pgvector to scale HNSW vector search on Amazon Aurora PostgreSQL. It covers index-size reduction, performance and recall tradeoffs, sizing, validation, and suitable operating conditions for datasets ranging from 5 million to 100 million vectors. ### Source excerpt Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best. ## PostgreSQL-Compatible Databases for AI at Scale: What to Evaluate from Day One DevFeed: [PostgreSQL-Compatible Databases for AI at Scale: What to Evaluate from Day One]() Original publisher: [Read original article]() Author: David Weiss Published: 2026-06-29T00:00:00Z Content type: opinion Language: en Sources: [Cockroach Labs]() Topics: [Databases](), [PostgreSQL](), [Artificial Intelligence](), [Concurrency](), [consistency](), [migration]() Tags: [ai](), [concurrency](), [consistency](), [databases](), [migration](), [postgresql](), [postgresql-compatible]() ### AI overview The article explains why teams building AI applications should evaluate database concurrency, consistency, and scaling characteristics at the greenfield stage. It argues that PostgreSQL compatibility does not necessarily provide horizontal scaling, while AI workloads can create unusually high concurrency through fan-out across tools, APIs, and databases. ### Source excerpt The database you choose at the start of an AI project is the one you'll be living with, or paying to escape, for years. ## DSQL Vignette: Aurora DSQL, and A Personal Story DevFeed: [DSQL Vignette: Aurora DSQL, and A Personal Story]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2024-12-03T00:00:00Z Content type: opinion Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [DSQL](), [Databases](), [Serverless](), [Amazon Web Services](), [PostgreSQL](), [Scalability](), [Transactions](), [Availability]() Tags: [availability](), [aws](), [database](), [dsql](), [postgresql](), [postgresql-compatible](), [scalability](), [serverless](), [sql](), [transactions]() ### AI overview This article introduces Aurora DSQL, a serverless, PostgreSQL-compatible SQL database designed for cloud transaction processing. It describes scaling across workloads, strong consistency and isolation, active-active multi-writer deployments, local reads and writes, and durable atomic transaction commits across availability zones or regions. ### Source excerpt DSQL Vignette: Aurora DSQL, and A Personal Story It's happening. In this morning's re:Invent keynote, Matt Garman announced Aurora DSQL. We're all excited, and some extremely excited, to have this preview release in customers' hands. Over the next few days, I'm going to be writing a few posts about what DSQL is, how it works, and how to make the best use of it. This post is going to look at the product itself, and a little bit of a personal story. The official AWS documentation for Aurora DSQL is a great place to start to understand what DSQL is and how to use it. What is Aurora DSQL? Aurora DSQL is a new serverless SQL database, optimized for transaction processing, and designed for the cloud. DSQL is designed to scale up and down to serve workloads of nearly any size, from your hobby project to your largest enterprise application. All the SQL stuff you expect is there: transactions, schemas, indexes, joins, and so on, all with strong consistency and isolation5. DSQL offers active-active multi-writer capabilities in multiple availability zones (AZs) in a single region, or across multiple regions. Reads and writes, even in read-write transactions, are fast and local, requiring no cross-region communication (or cross-AZ communication in single region setups). Transaction commit goes across regions (for multi-region setups) or AZs (for single-regions setups), ensuring that your transactions are durable, isolated, and atomic. DSQL is PostgreSQL compatible, offering a subset of PostgreSQL's (huge) SQL feature set. You can connect with your favorite PostgreSQL client (even the psql cli), use your favorite ORMs and frameworks, etc. We'll be adding more PostgreSQL-compatible features over time, making it easy to bring your existing code to DSQL. DSQL is serverless. Here, we mean that you create a cluster in the AWS console (or API or CLI), and that cluster will include an endpoint. You connect your PostgreSQL client to that endpoint. That's all you have to do: management,