A Gateway VPC endpoint to privately access Amazon S3.An Amazon Simple Storage Service (Amazon S3) bucket.psql client with connectivity to your Aurora cluster.An AWS Identity and Access Management (IAM) instance profile for Systems Manager.An Aurora PostgreSQL DB cluster version 12.4 or later.
Amazon Virtual Private Cloud (Amazon VPC) with three private subnets.Run benchmark tests using Systems Manager Run Command.įor this walkthrough, you should have the following prerequisites:.Launch two groups of EC2 instances: one for write activity and another for read activity.Create an EC2 Amazon Machine Image (AMI).To implement this solution, we complete the following high-level steps: To create the automated solution, we configure pgbench as the benchmark tool, an EC2 launch template scales the number of instances, and AWS Systems Manager Run Command performs the benchmark test on each instance. 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 test across Amazon Elastic Compute Cloud (Amazon EC2) instances, and multiple benchmarks may run concurrently. The solution supports running multiple workloads utilizing multiple client instances, which gives you the ability to create realistic benchmarks. In this post, we provide a solution to automate and scale benchmark tests. With Amazon Aurora PostgreSQL-Compatible Edition, you can run multiple benchmark tests with different transaction characteristics matching your data access patterns. Conducting benchmark tests help with these considerations. You need to take cost, operations, performance, security, and reliability into consideration. Optimizing a database is an important activity for new and existing application workloads.
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