Category: PostgreSQL compatible

In this post, we share Ring’s billion-scale semantic video search on Amazon RDS for PostgreSQL with pgvector architectural decisions vs alternatives, cost-performance-scale challenges, key lessons, and future directions. The Ring team designed for global scale their vector search architecture to support millions of customers with vector embeddings, the key technology for numerical representations of visual content generated by an AI model. By converting video frames into vectors-arrays of numbers that capture what’s happening (visual content) in each frame – Ring can store these representations in a database and search them using similarity search. When you type “package delivery,” the system converts that text into a vector and finds the video frames whose vectors are most similar-delivering relevant results in under 2 seconds.

In this post, I show you how to connect Lambda functions to Aurora PostgreSQL using Amazon RDS Proxy. We cover how to configure AWS Secrets Manager, set up RDS Proxy, and create a C# Lambda function with secure credential caching. I provide a GitHub repository which contains a YAML-format AWS CloudFormation template to provision the key components demonstrated, a C# sample function. I also walk through the Lambda function deployment step by step.

Using a movie streaming reference architecture, this post shows how to implement and sync operational, analytical, and search JSON workloads across AWS services. This pattern provides a scalable blueprint for any use case requiring multi-modal JSON data capabilities.

In this post, we show how logical replication with fine-grained filtering works in PostgreSQL, when to use it, and how to implement it using a realistic healthcare compliance scenario. Whether you’re running Amazon RDS for PostgreSQL, Amazon Aurora PostgreSQL, or a self-managed PostgreSQL database on an Amazon EC2 instance, the approach is the same.

In this post, we show you how to migrate spatial (geospatial) data from self-managed PostgreSQL, Amazon RDS for PostgreSQL, or Amazon Aurora PostgreSQL-Compatible Edition to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL using AWS DMS. Spatial data is useful for applications such as mapping, routing, asset tracking, and geographic visualization. We walk through setting up your environment, configuring AWS DMS, and validating the successful migration of spatial datasets.

In this post, we discuss configuring AWS DMS tasks to migrate HierarchyID columns from SQL Server to Aurora PostgreSQL-Compatible efficiently.

In this post, we help you plan your upgrade from PostgreSQL version 13 before standard support ends on February 28, 2026. We discuss the key benefits of upgrading, breaking changes to consider, and multiple upgrade strategies to choose from.

In this post, we discuss how the Shared Plan Cache feature of the Amazon Aurora PostgreSQL-Compatible Edition can significantly reduce memory consumption of generic SQL plans in high-concurrency environments.

Correlated subqueries can cause performance challenges in Amazon Aurora PostgreSQL which can cause applications to experience reduced performance as data volumes grow. In this post, we explore the advanced optimization configurations available in Aurora PostgreSQL that can transform these performance challenges into efficient operations without requiring you to modify a single line of SQL code.

In this post, we demonstrate how your workloads can benefit from upgrading Graviton2-based R6g and R6gd instances to Graviton4-based R8gd instances with Aurora PostgreSQL 17.5 on Aurora I/O-Optimized using an Optimized Reads-enabled tiered cache.