Category: Amazon Aurora

In this post, we demonstrate how upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 on Aurora I/O-Optimized cluster configuration can deliver significant price-performance gains – delivering up to 1.7 times higher write throughput, 1.38 times better price-performance and reducing commit latency by up to 46% on r8g.16xlarge instances and 38% on r8g.2xlarge instances as compared to Graviton2-based R6g instances.

pgTAP (PostgreSQL Test Anything Protocol) is a unit testing framework that empowers developers to write and run tests directly within the database. In this post, we explore how to leverage the pgTAP extension for unit testing on Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL-Compatible Edition database, helping you build robust and reliable database applications.

In this post, we show how you can use Amazon CloudWatch Database Insights for troubleshooting your Amazon RDS and Amazon Aurora resources. CloudWatch Database Insights serves as a database observability solution offering a tailored experience for DevOps engineers, application developers, and database administrators. This tool is designed to accelerate database troubleshooting processes and address issues across entire database fleets, enhancing overall operational efficiency.

On April 29, 2025, Jepsen published a report about transaction visibility behavior in Amazon RDS for PostgreSQL Multi-AZ clusters. We appreciate Jepsen’s thorough analysis and would like to provide additional context about this behavior, which exists both in Amazon RDS and community PostgreSQL. In this post, we dive into the specifics of the issue to provide further clarity, discuss what classes of architectures it might affect, share workarounds, and highlight our ongoing commitment to improving community PostgreSQL in all areas, including correctness.

In this post, we discuss how Heroku migrated their multi-tenant PostgreSQL database fleet from self-managed PostgreSQL on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Aurora PostgreSQL-Compatible Edition. Heroku completed this migration with no customer impact, increasing platform reliability while simultaneously reducing operational burden. We dive into Heroku and their previous self-managed architecture, the new architecture, how the migration of hundreds of thousands of databases was performed, and the enhancements to the customer experience since its completion.

In this post, we explore the pgstattuple extension in depth; what insights it offers, how to use it to diagnose issues in Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service (Amazon RDS) for PostgreSQL, and best practices for harnessing its capabilities.

In this post, we highlight often overlooked architectural designs and the inherent features of Aurora that optimize costs when deploying an open source database. The following sections examine various use cases, contrasting typical self-managed database configurations and their associated costs with the equivalent solution on Aurora, highlighting potential cost savings and operational efficiencies.

In this post, we show you a worked example of taking an existing application that works on PostgreSQL databases and adapting it to work with an Aurora DSQL database. In addition to adapting for the previously mentioned aspects, we also address some data type incompatibilities and work around some limits that currently exist in Aurora DSQL.

When assisting customers with migrating their workloads from SQL Server to PostgreSQL, we often encounter a scenario where the PIVOT function is used extensively for generating dynamic reports. In this post, we show you how to use the crosstab function, provided by PostgreSQL’s tablefunc extension, to implement functionality similar to SQL Server’s PIVOT function, offering greater flexibility.

In this post, we present an approach to using natural language processing (NLP) to query an Amazon Aurora PostgreSQL-Compatible Edition database. The solution presented in this post assumes that an organization has an Aurora PostgreSQL database. We create a web application framework using Flask for the user to interact with the database. JavaScript and Python code act as the interface between the web framework, Amazon Bedrock, and the database.