Category: Amazon Aurora

In this post, we give insights into Aurora DSQL Auto Analyze, a probabilistic and de-facto stateless method to automatically compute DSQL optimizer statistics. Users who are familiar with PostgreSQL will appreciate the similarity to autovacuum analyze.

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.

Amazon RDS for MySQL and Amazon Aurora MySQL provide built-in audit logging capabilities, but customers might need to export and store these logs for long-term retention and analysis. Amazon S3 offers an ideal destination, providing durability, cost-effectiveness, and integration with various analytics tools. In this post, we explore two approaches for exporting MySQL audit logs to Amazon S3: either using batching with a native export to Amazon S3 or processing logs in real time with Amazon Data Firehose.

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.

AWS Organizations now supports an upgrade rollout policy, a new capability that provides a streamlined solution for managing automatic minor version upgrades across your database fleet. This feature supports Amazon Aurora MySQL-Compatible Edition and Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS database engines MySQL, PostgreSQL, MariaDB, SQL Server, Oracle, and Db2. It eliminates the operational overhead of coordinating upgrades across hundreds of resources and accounts while validating changes in less critical environments before reaching production. In this post, we explore how upgrade rollout policy works, its key benefits, and how you can use it to implement a systematic approach to database maintenance across your organization.

In this post, we show how you can enhance your Java application with the cloud-based capabilities of Amazon Aurora by using the JDBC Wrapper. Simple code changes shared in this post can transform a standard JDBC application to use fast failover, read/write splitting, IAM authentication, AWS Secrets Manager integration, and federated authentication.

Applications using Aurora DSQL multi-Region clusters should implement a DNS-based routing solution (such as Amazon Route 53) to automatically redirect traffic between AWS Regions. In this post, we show you automated solution for redirecting database traffic to alternate regional endpoints without requiring manual configuration changes, particularly in mixed data store 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.

In this post, we show how you can turn your ideas into full-stack applications with Kiro powers for Aurora. We explore how a new innovation, Kiro powers, can help you use Amazon Aurora best practices built into your development workflow, automatically implementing configurations and optimizations that make sure your database layer is production-ready from day one.