Python Script to Fix "Export EXPORT_NAME cannot be updated as it is in use by STACK_NAME" error in AWS CloudFormation

If you use AWS CloudFormation, you might have encountered an issue while trying to update stacks if the stacks are updated. The error looks something like this:

Export EXPORT_NAME cannot be updated as it is in use by STACK_NAME

If you use AWS CDK, you might still face issues while deploying stacks, as AWS CDK generates a CloudFormation template during deployment. While working with AWS CDK, I have faced this issue multiple times, and until now, I have been following the manual steps outlined in this post to update the export values in the stack using the AWS Console.

I recently wrote a Python script that uses boto3 to automate this process, which has worked well for me. The script solves a very specific use case of replacing Fn::ImportValue usages with actual values. This takes care of replacing the EXPORT_NAME usage with the actual value.

Let us go over the script and learn how to use it. First create a main.py file and add the following code snippet to it:

import boto3
import sys
import json

export_name = sys.argv[1]

print(f'export_name: {export_name}')

client = boto3.client('cloudformation')

def get_export_value(export_name):
    paginator = client.get_paginator('list_exports')

    for page in paginator.paginate():
        for export in page['Exports']:
            if export['Name'] == export_name:
                export_value = export['Value']
                return export_value

    return None

def get_template(stack_name):
    response = client.get_template(StackName=stack_name)

    return response['TemplateBody']

def replace_fn_import_with_value(template, export_name, export_value):
    template_str = json.dumps(template)
    
    import_key = '{' + "\"Fn::ImportValue\": \"{name}\"".format(name = export_name) + '}'
    
    import_value = '"{export_value}"'.format(export_value = export_value)
    
    template_str = template_str.replace(import_key, import_value)
    
    template = json.loads(template_str)

    return template

def get_list_of_stacks_for_export_name(export_name):
    imports = client.list_imports(ExportName=export_name)

    return imports['Imports']

def update_stack(stack_name, template):
    response = client.update_stack(
        StackName=stack_name,
        Capabilities=[
            'CAPABILITY_IAM',
            'CAPABILITY_NAMED_IAM',
            'CAPABILITY_AUTO_EXPAND'
        ],
        TemplateBody=json.dumps(template)
    )

    print("Stack updated:", stack_name)
    return response

def fetch_and_update_stack(stack_name, export_name, export_value):
    template = get_template(stack_name)
    updated_template = replace_fn_import_with_value(template, export_name, export_value)
    update_stack(stack_name, updated_template)

def update_stacks():
    export_value = get_export_value(export_name)
    imported_stacks = get_list_of_stacks_for_export_name(export_name)
    
    print('Export value: ' + export_value)

    for stack in imported_stacks:
        print('Stack name: ' + stack)
        fetch_and_update_stack(stack, export_name, export_value)
    
update_stacks()

The script performs the following operations:

• First, it reads the export_name from command line arguments.
• Next, it fetches the actual export value for the export_name using the get_export_value function
• Next, it fetches a list of all stacks that use export_name using the get_list_of_stacks_for_export_name function.
• Finally, for every stack, the fetch_and_update_stack is invoked to replace export_name with the actual value. Notice that the function uses replace_fn_import_with_value function to manipulate the template JSON for replacing the value.

You can run the script using the following command:

python main.py <YOUR_EXPORT_NAME>

Executing the script will replace your export in all the stacks that use it. After executing the script, you can continue with your AWS CDK or CloudFormation deployment and it should proceed without any issues.

Note: The script assumes that you have configured AWS credentials on your shell before executing it.

That’s it for this post. I hope you find this post useful!