Introduction to Amazon ECR and Its Importance

Amazon Elastic Container Registry (ECR) is a fully managed container registry service that makes it easy for developers to store, manage, and deploy container images. With the growing use of containerized applications, ECR has become an essential tool for managing Docker images, ensuring that teams can easily pull and deploy containers without worrying about the complexities of registry management.

However, as development teams regularly push new versions of their images, managing these container images becomes increasingly challenging. ECR repositories can become cluttered without proper image cleanup strategies, leading to increased storage costs and a less efficient workflow. This is where lifecycle policies and automation come in to streamline repository management.

Scenario: Managing Multiple Container Images

Consider a scenario where your development team deploys updates multiple times a week, producing many image versions. Over time, these older, unused images build up, increasing costs and making it harder to manage the ECR repository. Instead of manually deleting older photos, you can use AWS Lambda to automate the cleanup process, ensuring that only the relevant images are retained while older versions are systematically purged.

Implementing AWS Lambda for ECR Cleanup

To optimize ECR image management, AWS Lambda can be integrated with Amazon ECR lifecycle policies to automatically remove outdated or unused container images. By combining lifecycle policies with custom Lambda functions, you can enforce rules governing container image retention, such as keeping only the latest ten versions of an image or deleting images older than a specified timeframe.

Understanding the Lambda Function Code

The AWS Lambda function acts as a scheduled job that runs periodically to check your ECR repository and remove images based on your custom logic. Here’s a basic breakdown of what the Lambda function code might include:

  1. List Images: The function first lists all the images in the ECR repository.
  2. Filter Images: It filters out the ones that do not meet the retention criteria, such as tags that indicate they should be kept or images created within a specific timeframe.
  3. Delete Images: After identifying the images that are no longer required, the Lambda function deletes them from the repository.

Here’s a sample Lambda code snippet for ECR image cleanup:

import boto3

def lambda_handler(event, context):

    ecr_client = boto3.client(‘ecr’)

    repository_name = ‘your-repo-name’

    images = ecr_client.list_images(repositoryName=repository_name, filter={‘tagStatus’: ‘UNTAGGED’})

    image_ids = [{‘imageDigest’: image[‘imageDigest’]} for image in images[‘imageIds’]]

    

    if image_ids:

        response = ecr_client.batch_delete_image(repositoryName=repository_name, imageIds=image_ids)

        print(f”Deleted images: {response}”)

    else:

        print(“No images to delete”)

This script focuses on removing untagged images, but it can be easily customized for more advanced scenarios.

Steps to Deploy the Lambda Function

To deploy the Lambda function for automated ECR cleanup, follow these steps:

  1. Create the Lambda Function:
    • Go to the AWS Management Console and navigate to AWS Lambda.
    • Choose Create Function, select “Author from scratch,” and set the necessary configurations, such as the function name and runtime (e.g., Python 3.x).
  2. Assign Necessary Permissions:
    • Ensure the Lambda function has the necessary IAM role with ecr:ListImages and ecr:BatchDeleteImage permissions.
  3. Add the Function Code:
    • Add the Python code to list and delete images from your ECR repository.
  4. Set Up CloudWatch Event Trigger:
    • Use CloudWatch Events to schedule the Lambda function to run periodically (e.g., once per day or week).
    • Go to CloudWatch and create a rule to trigger the Lambda function regularly.
  5. Test the Function:
    • Run a test to ensure the Lambda function correctly identifies and deletes unnecessary images from your ECR repository.

Customizing the Lambda Function for Specific Needs

While the essential Lambda function is designed to delete untagged images, you may want to customize it further to meet specific business needs. For example:

  • Retaining Tagged Images: You can modify the script to maintain images with particular tags (e.g., production or latest).
  • Deleting by Age: Set up the function to delete images older than a specific period (e.g., images older than 30 days).
  • Limiting to Recent Versions: Only retain the last N versions of an image while deleting older ones.

Each customization ensures you control which images are deleted, optimizing storage while retaining essential artifacts.

Conclusion: Streamlining ECR Repository Management

By implementing automated lifecycle policies and leveraging AWS Lambda for ECR image cleanup, you can significantly streamline the management of your container repositories. This approach reduces manual intervention, optimizes storage costs, and helps you maintain a clean and organized ECR repository.

With AWS services like Lambda, CloudWatch, and ECR lifecycle policies, automating image management becomes a simple and scalable solution for growing development teams.

References

Automate the cleanup of images by using lifecycle policies in Amazon ECR

Clean up Your Container Images with Amazon ECR Lifecycle Policies