AWS Compute Blog
Tracking the latest server images in Amazon EC2 Image Builder pipelines
This post courtesy of Anoop Rachamadugu, Cloud Architect at AWS
The Amazon EC2 Image Builder service helps users to build and maintain server images. The images created by EC2 Image Builder can be used with Amazon Elastic Compute Cloud (EC2) and on-premises. Image Builder reduces the effort of keeping images up-to-date and secure by providing a graphical interface, built-in automation, and AWS-provided security settings. Customers have told us that they manage multiple server images and are looking for ways to track the latest server images created by the pipelines.
In this blog post, I walk through a solution that uses AWS Lambda and AWS Systems Manager (SSM) Parameter Store. It tracks and updates the latest Amazon Machine Image (AMI) IDs every time an Image Builder pipeline is run. With Lambda, you pay only for what you use. You are charged based on the number of requests for your functions and the time it takes for your code to run. In this case, the Lambda function is invoked upon the completion of the image builder pipeline. Standard SSM parameters are available at no additional charge.
Users can reference the SSM parameters in automation scripts and AWS CloudFormation templates providing access to the latest AMI ID for your EC2 infrastructure. Consider the use case of updating Amazon Machine Image (AMI) IDs for the EC2 instances in your CloudFormation templates. Normally, you might map AMI IDs to specific instance types and Regions. Then to update these, you would manually change them in each of your templates. With the SSM parameter integration, your code remains untouched and a CloudFormation stack update operation automatically fetches the latest Parameter Store value.
This solution uses a Lambda function written in Python that subscribes to an Amazon Simple Notification Service (SNS) topic. The Lambda function and the SNS topic are deployed using AWS SAM CLI. Once deployed, the SNS topic must be configured in an existing Image Builder pipeline. This results in the Lambda function being invoked at the completion of the Image Builder pipeline.
When a Lambda function subscribes to an SNS topic, it is invoked with the payload of the published messages. The Lambda function receives the message payload as an input parameter. The Lambda function first checks the message payload to see if the image status is available. If the image state is available, it retrieves the AMI ID from the message payload and updates the SSM parameter.
To get started with this solution, the following is required:
- AWS SAM CLI to deploy the solution.
- An existing Amazon EC2 Image Builder pipeline.
- Automate OS Image Build Pipelines with EC2 Image Builder provides a tutorial on how to create an image pipeline with the EC2 Image Builder console. For more information, review Getting started with EC2 Image Builder.
Deploying the solution
The solution consists of two files, which can be downloaded from the amazon-ec2-image-builder GitHub repository.
- The Python file image-builder-lambda-update-ssm.py contains the code for the Lambda function. It first checks the SNS message payload to determine if the image is available. If it’s available, it extracts the AMI ID from the SNS message payload and updates the SSM parameter specified.The ‘ssm_parameter_name’ variable specifies the SSM parameter path where the AMI ID should be stored and updated. The Lambda function finishes by adding tags to the SSM parameter.
- The template.yaml file is an AWS SAM template. It deploys the Lambda function, SNS topic, and IAM role required for the Lambda function. I use Python 3.7 as the runtime and assign a memory of 256 MB for the Lambda function. The IAM policy gives the Lambda function permissions to retrieve and update SSM parameters. Deploy this application using the AWS SAM CLI guided deploy:
sam deploy --guided
After deploying the application, note the ARN of the created SNS topic. Next, update the infrastructure settings of an existing Image Builder pipeline with this newly created SNS topic. This results in the Lambda function being invoked upon the completion of the image builder pipeline.
Verifying the solution
After the completion of the image builder pipeline, use the AWS CLI or check the AWS Management Console to verify the updated SSM parameter. To verify via AWS CLI, run the following commands to retrieve and list the tags attached to the SSM parameter:
aws ssm get-parameter --name ‘/ec2-imagebuilder/latest’ aws ssm list-tags-for-resource --resource-type "Parameter" --resource-id ‘/ec2-imagebuilder/latest’
To verify via the AWS Management Console, navigate to the Parameter Store under AWS Systems Manager. Search for the parameter /ec2-imagebuilder/latest:
Select the Tags tab to view the tags attached to the SSM parameter:
Referencing the SSM Parameter in CloudFormation templates
Users can reference the SSM parameters in automation scripts and AWS CloudFormation templates providing access to the latest AMI ID for your EC2 infrastructure. This sample code shows how to reference the SSM parameter in a CloudFormation template.
Parameters : LatestAmiId : Type : 'AWS::SSM::Parameter::Value<AWS::EC2::Image::Id>' Default: ‘/ec2-imagebuilder/latest’ Resources : Instance : Type : 'AWS::EC2::Instance' Properties : ImageId : !Ref LatestAmiId
In this blog post, I demonstrate a solution that allows users to track and update the latest AMI ID created by the Image Builder pipelines. The Lambda function retrieves the AMI ID of the image created by a pipeline and update an AWS Systems Manager parameter. This Lambda function is triggered via an SNS topic configured in an Image Builder pipeline.
The solution is deployed using AWS SAM CLI. I also note how users can reference Systems Manager parameters in AWS CloudFormation templates providing access to the latest AMI ID for your EC2 infrastructure.
The amazon-ec2-image-builder-samples GitHub repository provides a number of examples for getting started with EC2 Image Builder. Image Builder can make it easier for you to build virtual machine (VM) images.