Use CCM and QPM to optimize recovery performance and execution plans in Amazon Aurora PostgreSQL - AWS Prescriptive Guidance

Use CCM and QPM to optimize recovery performance and execution plans in Amazon Aurora PostgreSQL

Raunak Rishabh, Rohit Kapoor, and Sujitha Sasikumaran, Amazon Web Services (AWS)

January 2023 (document history)

As businesses expand, they use more and more data to make critical decisions. With increasing amounts of data, it is important to optimize database performance and stabilize it during system changes. Highly transactional workloads, such as those involving financial transactions or customer orders, require stable, consistent, and fast performance because poor performance can affect customer satisfaction and business revenue. For databases that handle these highly transactional workloads, such as Amazon Aurora PostgreSQL-Compatible Edition database instances, it is critical that you understand and implement the available performance-optimization features.

Amazon Aurora PostgreSQL-Compatible is a fully managed relational database engine that helps you set up, operate, and scale PostgreSQL deployments. It is a widely used database engine because of its self-sustaining storage architecture and its features, which help you optimize performance on real-life workload scenarios with minimal maintenance overhead.

Two of these features are cluster cache management (CCM) and query plan management (QPM). CCM helps recover application and database performance in the event of a failover, and QPM helps you manage the query execution plans generated by the optimizer for your SQL applications. Both of these features can help optimize the performance of SQL queries by providing more control over the database. This guide is intended to help managers, product owners, and database architects (DBAs) understand the benefits and potential business outcomes of implementing CCM and QPM.

Intended audience

The intended audience for this guide is business stakeholders who want to understand the available features for optimizing the performance of Amazon Aurora PostgreSQL-Compatible database instances and understand the use cases for those features.