Troubleshooting And Fixing AWS Resource Naming Issues In AsyncWorkerStack

Jul 18, 2025 by Jeany 74 views

Fix AWS Resource Naming Issues in AsyncWorkerStack

In the realm of cloud infrastructure, adhering to naming conventions and limitations is crucial for successful deployments. This article addresses and provides solutions for common AWS resource naming issues encountered within the AsyncWorkerStack, specifically focusing on S3 bucket names and DynamoDB attribute names. These issues can prevent infrastructure deployment and proper functioning of applications. Understanding these constraints and implementing the suggested fixes will ensure a smoother, more reliable cloud infrastructure.

Issue 1: Invalid S3 Bucket Name (Uppercase Letters)

One of the most frequent problems encountered when provisioning S3 buckets is violating the naming rules. S3 bucket names must adhere to specific guidelines: they must be all lowercase, can only contain lowercase letters, numbers, periods (.), and hyphens (-), and must start and end with a lowercase letter or number.

In the given scenario, the bucket name 'My-Company-User-Uploads-BUCKET' includes uppercase letters, thus violating the AWS S3 bucket naming conventions. This leads to a deployment failure, as highlighted in the error log:

Error: Invalid S3 bucket name (value: My-Company-User-Uploads-BUCKET)\nBucket name must only contain lowercase characters and the symbols, period (.) and dash (-) (offset: 0)\nBucket name must start and end with a lowercase character or number (offset: 0)\nBucket name must start and end with a lowercase character or number (offset: 29)\n    at Function.validateBucketName (/home/runner/work/backend-infra-cdk/backend-infra-cdk/node_modules/aws-cdk-lib/aws-s3/lib/bucket.js:1:19533)\n    at new Bucket (/home/runner/work/backend-infra-cdk/backend-infra-cdk/node_modules/aws-cdk-lib/aws-s3/lib/bucket.js:1:20135)\n    at new AsyncWorkerStack (/home/runner/work/backend-infra-cdk/backend-infra-cdk/lib/async-worker-stack.ts:12:5)

The problematic code snippet is located in lib/async-worker-stack.ts:

// User uploads bucket
new s3.Bucket(this, 'UserUploadsBucket', {
  bucketName: 'My-Company-User-Uploads-BUCKET',
  removalPolicy: cdk.RemovalPolicy.DESTROY,
});

To rectify this, the bucket name needs to be updated to comply with S3 naming rules. The solution involves converting the bucket name to lowercase while still maintaining readability and clarity. Here’s the corrected code:

// User uploads bucket
new s3.Bucket(this, 'UserUploadsBucket', {
  bucketName: 'my-company-user-uploads-bucket',
  removalPolicy: cdk.RemovalPolicy.DESTROY,
});

This simple change ensures that the bucket name adheres to AWS S3 naming conventions. After modifying the file, commit and push the changes to the repository and re-run the GitHub workflow to deploy the infrastructure. This adjustment is critical for ensuring that your S3 bucket is created successfully and your application can store and retrieve data as intended.

Issue 2: Exceeding Maximum S3 Bucket Name Length

Another common pitfall in S3 bucket naming is exceeding the maximum allowed length. AWS S3 bucket names are limited to 63 characters. When a bucket name surpasses this limit, the deployment will fail. In this particular case, the bucket name 'application-data-storage-bucket-for-our-microservices-architecture-system' significantly exceeds the 63-character limit.

The error log does not explicitly show this error because the first bucket name issue (uppercase letters) caused the deployment to fail prematurely. However, if the first issue were resolved, this length violation would trigger a similar validation error.

The relevant code snippet in lib/async-worker-stack.ts is:

// Application data storage bucket
new s3.Bucket(this, 'AppDataBucket', {
  bucketName: 'application-data-storage-bucket-for-our-microservices-architecture-system',
  removalPolicy: cdk.RemovalPolicy.DESTROY,
});

To resolve this issue, the bucket name must be shortened to 63 characters or less. There are a couple of approaches to achieve this:

Shorten the Bucket Name: The most direct solution is to abbreviate the bucket name while preserving its meaning. For instance, the name could be shortened to 'app-data-storage-microservices'. This revised name is concise and clearly conveys the bucket's purpose.
```
// Application data storage bucket
new s3.Bucket(this, 'AppDataBucket', {
  bucketName: 'app-data-storage-microservices',
  removalPolicy: cdk.RemovalPolicy.DESTROY,
});
```
Use a Logical ID: Alternatively, you can omit the explicit bucket name and rely on AWS CloudFormation to generate a unique name based on the logical ID. This approach can be beneficial as it ensures uniqueness without requiring manual name management.
```
// Application data storage bucket
new s3.Bucket(this, 'AppDataBucket', {
  removalPolicy: cdk.RemovalPolicy.DESTROY,
});
```

Choosing between these approaches depends on your specific requirements. If you need a specific, human-readable name, shortening the bucket name is the way to go. If you prioritize simplicity and automatic name generation, using a logical ID is a viable option.

After implementing the chosen solution, commit and push the changes to the repository, and then re-run the GitHub workflow to deploy the infrastructure. Ensuring that bucket names comply with the length restrictions is essential for avoiding deployment failures and maintaining a well-organized cloud environment.

Issue 3: Invalid DynamoDB Time-To-Live Attribute Name

When working with DynamoDB, specifying the Time-To-Live (TTL) attribute is a common practice for automatically removing expired items. However, DynamoDB attribute names have restrictions: they should not contain special characters like exclamation marks (!). In the AsyncWorkerStack, the timeToLiveAttribute name includes an exclamation mark, which violates this rule.

Similar to the previous issue, the error log doesn't explicitly display this error because the S3 bucket name issues caused the deployment to fail first. Nevertheless, if the earlier issues were resolved, this DynamoDB attribute name issue would surface as a validation error.

The problematic code snippet in lib/async-worker-stack.ts is:

// Session data table
new dynamodb.Table(this, 'SessionTable', {
  partitionKey: { name: 'sessionId', type: dynamodb.AttributeType.STRING },
  billingMode: dynamodb.BillingMode.PAY_PER_REQUEST,
  removalPolicy: cdk.RemovalPolicy.DESTROY,
  pointInTimeRecovery: true,
  stream: dynamodb.StreamViewType.KEYS_ONLY,
  timeToLiveAttribute: 'expires-at!',
});

To fix this, the exclamation mark must be removed from the timeToLiveAttribute name. A common and appropriate name for this attribute is 'expiresAt'. The corrected code is as follows:

// Session data table
new dynamodb.Table(this, 'SessionTable', {
  partitionKey: { name: 'sessionId', type: dynamodb.AttributeType.STRING },
  billingMode: dynamodb.BillingMode.PAY_PER_REQUEST,
  removalPolicy: cdk.RemovalPolicy.DESTROY,
  pointInTimeRecovery: true,
  stream: dynamodb.StreamViewType.KEYS_ONLY,
  timeToLiveAttribute: 'expiresAt',
});

This adjustment ensures that the DynamoDB table complies with attribute naming rules, preventing deployment failures and ensuring the TTL functionality works correctly. After making this change, commit and push the updates to the repository, and re-run the GitHub workflow to deploy the infrastructure.

Conclusion

Addressing AWS resource naming issues is critical for successful cloud infrastructure deployments. This article has covered common pitfalls related to S3 bucket names and DynamoDB attribute names within the AsyncWorkerStack. By adhering to AWS naming conventions—such as using lowercase letters for S3 bucket names, staying within the 63-character limit, and avoiding special characters in DynamoDB attribute names—developers can prevent deployment failures and ensure their applications function smoothly.

Remember, attention to detail in naming conventions can significantly impact the reliability and maintainability of your cloud infrastructure. Regularly reviewing and validating resource names as part of your development process will help you avoid these issues and maintain a robust cloud environment. Commit these changes, push them to your repository, and re-run your workflow to deploy your corrected infrastructure, ensuring a stable and efficient cloud setup.