An Aurora PostgreSQL Serverless v2 DB cluster version 13.7 or later.Amazon Virtual Private Cloud (Amazon VPC) with three private subnets.Amazon CloudWatch and Performance Insights capture the database activity.įor this walkthrough, you should have the following prerequisites:.The Amazon EC2 instances receive the command, retrieve the database secret, and run on the user’s database for a defined duration.User initiates benchmark by selecting the Systems Manager document, using Systems Manager Run Command, and specifying tags.Amazon EC2 instances are provisioned using the launch template and Amazon EC2 Auto Scaling groups.Instances are grouped by tags into a “read” or “write” category. An Amazon EC2 launch template is created defining an Amazon Linux 2 image and configuration settings.The CloudFormation template deploys an AWS Systems Manager document (SSM document).The following diagram illustrates this architecture. At the same time, another benchmark performs select statements on an Aurora reader node. For instance, one benchmark performs select, update, insert, and delete on an Aurora writer node. The solution provides the ability to run a benchmark across Amazon Elastic Compute Cloud (Amazon EC2) instances, and multiple benchmarks may run concurrently. In this post, we provide you with an AWS CloudFormation template to automate the solution, demonstrate on Amazon Aurora PostgreSQL Serverless v2, and show you how to monitor performance while the benchmark is running. With Amazon Aurora PostgreSQL-Compatible Edition, you can run multiple benchmarks with different transaction characteristics matching your data access patterns. Conducting benchmarks help with these considerations. You need to take cost, operations, performance, security, and reliability into consideration. To recap, optimizing a database is an important activity for new and existing application workloads. This post is a continuation of Automate benchmark tests for Amazon Aurora PostgreSQL.
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