Performance testing is critical to the software development lifecycle, ensuring that applications can handle expected load and stress conditions. By integrating JMeter with AWS CodePipeline and CodeBuild, you can automate performance testing, making it more efficient and reliable. This blog post will cover the process, from requirements and setup to in-depth phase analysis and artifact management.

Requirements and Setup

Before diving into the setup, ensure you have the following prerequisites:

  • AWS Account: An active AWS account with appropriate permissions to create and manage AWS services.
  • JMeter: Install Apache JMeter on your local machine for initial test creation.
  • AWS CLI: Install and configure the AWS Command Line Interface (CLI).
  • AWS CodePipeline and CodeBuild: Familiarity with these AWS services.
  • IAM Roles and Policies: Appropriate IAM roles and policies allow CodePipeline and CodeBuild to access necessary resources.

Setting Up AWS Services

  1. Create an S3 Bucket: This bucket will store JMeter test plans and results.

    aws s3 mb s3://your-jmeter-bucket
  1. Configure IAM Roles: Create and attach policies to IAM roles for CodePipeline and CodeBuild.

    {

  “Version”: “2012-10-17”,

  “Statement”: [

    {

      “Effect”: “Allow”,

      “Action”: [

        “s3:*”,

        “codebuild:*”,

        “codepipeline:*”

      ],

      “Resource”: “*”

    }

  ]

}

  1. Set Up CodePipeline: Create a new pipeline using the AWS Management Console.
  • Source Stage: Configure the source stage to pull the latest code from your repository.
  • Build Stage: Add a build stage using CodeBuild with a build spec file to execute JMeter tests.
  1. Set Up CodeBuild: Create a new CodeBuild project with the following buildspec.yml:

    version: 0.2

phases:

  install:

    runtime-versions:

      java: corretto11

    commands:

      – echo Installing JMeter…

      – wget https://archive.apache.org/dist/jmeter/binaries/apache-jmeter-5.4.1.tgz

      – tar -xzf apache-jmeter-5.4.1.tgz

  pre_build:

    commands:

      – echo Preparing JMeter…

  build:

    commands:

      – echo Running JMeter…

      – apache-jmeter-5.4.1/bin/jmeter -n -t test-plan.jmx -l result.jtl

  post_build:

    commands:

      – echo Uploading results to S3…

      – aws s3 cp result.jtl s3://your-jmeter-bucket/results/result.jtl

Complete Walkthrough

  1. Create JMeter Test Plan: Develop and save your JMeter test plan locally.
  1. Upload Test Plan to S3: Upload the JMeter test plan to the S3 bucket.

    aws s3 cp test-plan.jmx s3://your-jmeter-bucket/test-plans/test-plan.jmx
  1. Configure CodePipeline and CodeBuild: Follow the setup instructions above to configure the pipeline and build the project.
  2. Run Pipeline: Trigger the pipeline to execute the JMeter test. Monitor the build logs in CodeBuild for progress and results.

In-Depth Phase Analysis

Each phase in the CodeBuild project plays a crucial role in ensuring successful execution of JMeter tests:

  • Install Phase: Downloads and installs JMeter on the build server.
  • Pre-Build Phase: Prepares any necessary configurations or scripts for JMeter.
  • Build Phase: Executes the JMeter test plan and generates results.
  • Post-Build Phase: Uploads the test results to S3 for storage and analysis.

Artifact Management and Storage

Storing and managing test artifacts efficiently is essential for maintaining a robust performance testing strategy. By leveraging AWS S3, you can securely store JMeter test plans and results and access them whenever needed.

  1. Organize S3 Bucket: Create a structured directory in your S3 bucket for different test plans and results.

    s3://your-jmeter-bucket/

    ├── test-plans/

    │   ├── test-plan1.jmx

    │   └── test-plan2.jmx

    └── results/

        ├── result1.jtl

        └── result2.jtl

  1. Version Control: Enable versioning on the S3 bucket to keep track of changes to test plans and results.

    aws s3api put-bucket-versioning –bucket your-jmeter-bucket –versioning-configuration Status=Enabled
  1. Lifecycle Policies: Implement lifecycle policies to manage the retention and deletion of old test artifacts.

    {

  “Rules”: [

    {

      “ID”: “DeleteOldResults”,

      “Prefix”: “results/”,

      “Status”: “Enabled”,

      “Expiration”: {

        “Days”: 30

      }

    }

  ]

}

Conclusion

Integrating JMeter with AWS CodePipeline and CodeBuild streamlines the performance testing process, making it more efficient and scalable. Following the steps outlined in this guide, you can automate your performance testing workflow and ensure your applications meet performance expectations.

References

Ensure Optimal Application Performance with Distributed Load Testing on AWS

Performance Testing in Continuous Delivery Using AWS CodePipeline and BlazeMeter