Category: HAQM Aurora

In this post, we discuss how Heroku migrated their multi-tenant PostgreSQL database fleet from self-managed PostgreSQL on HAQM Elastic Compute Cloud (HAQM EC2) to HAQM 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 HAQM Aurora PostgreSQL-Compatible Edition and HAQM Relational Database Service (HAQM 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 HAQM 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, HAQM Bedrock, and the database.

In this post, we demonstrate how you can implement scheduled scaling for Aurora Serverless using HAQM EventBridge Scheduler. By proactively adjusting minimum Aurora Capacity Units (ACUs), you can achieve faster scaling rates during peak periods while maintaining cost efficiency during low-demand times.

In this post, we show you several long-term data backup strategies and how to effectively implement them in the AWS environment, with a focus on HAQM Relational Database Service (HAQM RDS) and HAQM Aurora.

In this post, we discuss how to migrate MySQL very large databases (VLDBs) from a self-managed MySQL database to HAQM Aurora MySQL-Compatible Edition using the MyDumper and MyLoader tools.

In this post, we explore the end-of-life (EOL) timeline for Aurora PostgreSQL and HAQM RDS for PostgreSQL. We discuss features in PostgreSQL major versions, HAQM RDS Extended Support, and various upgrade strategies, including in-place upgrades, HAQM RDS blue/green deployments, and out-of-place upgrades.