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Introduction:

Amazon Elastic Container Registry (ECR) provides a scalable and secure solution for storing, managing, and deploying Docker container images. Over time, however, repositories may accumulate images and grow in size, leading to increased storage costs and potential performance issues. In this blog post, we will explore how to use AWS Lambda and Boto3 to identify ECR repositories exceeding a specified storage limit and discuss strategies for managing their sizes effectively.

Prerequisites:

Before diving into the implementation, make sure you have the following prerequisites in place:

1. An AWS account with sufficient permissions to access ECR and Lambda services.
2. Basic knowledge of Python programming.

Implementation:

Let's break down the provided Python script and discuss its key components:

import boto3

def lambda_handler(event, context):
 # Initialize ECR client
 ecr_client = boto3.client('ecr')

 # Retrieve a list of repositories in the account
 repositories = ecr_client.describe_repositories()['repositories']

 # Iterate through each repository
 for repository in repositories:
 repository_name = repository['repositoryName']

 # Retrieve image details for the repository
 images = ecr_client.describe_images(repositoryName=repository_name).get('imageDetails', [])

 # Check if the repository has images
 if images:
 # Calculate the repository size in gigabytes
 repository_size = images[0].get('imageSizeInBytes', 0)
 repository_size_gb = repository_size / (1024 ** 3)

 # Check if the repository size exceeds the specified limit (5GB in this case)
 if repository_size_gb > 5:
 print(f"Repository {repository_name} has a size of {repository_size_gb:.2f} GB, exceeding 5GB.")

 else:
 print(f"Repository {repository_name} has no images.")

 return {
 'statusCode': 200,
 'body': 'Function execution completed successfully.'
 }

Explanation:

1. ECR Client Initialization: The script starts by initializing the ECR client using the boto3 library.
2. List Repositories: It retrieves a list of ECR repositories associated with the AWS account using the describe_repositories method.
3. Iterate Through Repositories: The script then iterates through each repository, extracting its name.
4. Retrieve Image Details: For each repository, it retrieves image details using the describe_images method.
5. Check Repository Size: If the repository has images, it calculates the total size of the repository in gigabytes and checks whether it exceeds the specified limit (5GB in this case).
6. Print Results: The script prints information about repositories exceeding the size limit or having no images.
7. Lambda Function Return: Finally, the Lambda function returns a response indicating the successful execution.

Lambda Setup Steps:

• Create a Lambda Function:
  1. Go to the AWS Lambda console.
  2. Click on "Create function."
  3. Choose "Author from scratch."
  4. Provide a name for your function, choose the runtime as "Python 3.x," and set up an execution role with necessary permissions for ECR access.

Once Lambda function is set up, it will automatically run based on the defined trigger. You can monitor its execution and view the results in the CloudWatch Logs.
Conclusion:

By deploying this AWS Lambda function, you can automate the process of identifying ECR repositories with sizes exceeding a predefined limit. You can use environment variables to customize the behavior of your Lambda function. For example, you might set a specific size limit as an environment variable.This proactive approach allows you to manage storage costs more effectively and ensures optimal performance for your containerized applications.