Hello!

I completely understand the frustration you’re dealing with while working on AWS CodePipeline. There are a few strategies you can consider to bypass or skip a specific middle stage in your pipeline for this instance:

1. Manual Execution: When you start a new execution of your pipeline, you can choose to skip the specific action by creating a manual execution and not including that stage in the execution process.
2. Use Conditions: If you’re using AWS CodeBuild in that stage, consider adding a specific environment variable that enables or disables its execution based on your needs. This way, you can control the process without affecting future executions.
3. Clone the Pipeline: Create a duplicate of your pipeline temporarily. In this duplicate, modify or remove the stage that is causing delays. You can run your changes through this temporary pipeline.
4. Pipeline Parameters: If your pipeline setup includes parameters, you can define a parameter that governs the execution of the troublesome stage, effectively allowing you to skip it during specific runs.

Be sure to monitor your changes and plan a review of the skipped processes afterward to ensure everything is running smoothly. Good luck with your project!

Troubleshooting Role Assumption from a Kubernetes Docker Container

Hi there!

I’ve encountered a similar issue when trying to assume a service account role from a Docker container in a Kubernetes cluster. Here are some steps you can take to troubleshoot this issue:

1. Check IAM Role Trust Policy:

   Make sure the IAM role’s trust policy allows the service account from your Kubernetes cluster to assume the role. It should have a statement like:

   {
       "Effect": "Allow",
       "Principal": {
           "Federated": "arn:aws:iam::YOUR_ACCOUNT_ID:oidc-provider/PROVIDER_URL"
       },
       "Action": "sts:AssumeRole"
   }
   

2. Verify Kubernetes Service Account:

   Ensure that your Kubernetes service account is annotated properly to link with the IAM role. The annotation should look something like this:

   apiVersion: v1
   kind: ServiceAccount
   metadata:
     name: your-service-account
     annotations:
       eks.amazonaws.com/role-arn: arn:aws:iam::YOUR_ACCOUNT_ID:role/YourRoleName
   

3. Inspect Pod’s IAM Role:

   Log into your pod and run the command:

   curl 169.254.169.254/latest/meta-data/iam/security-credentials/

   This should return the role name that the pod is using. Ensure it’s the correct one.

4. Check Logs for Errors:

   Inspect the logs of your application and look specifically for any errors related to AWS SDK or assumption of roles. Implement verbose logging if possible.

5. Test AWS CLI Inside the Pod:

   If you have the AWS CLI installed in your container, try assuming the role directly with:

   aws sts assume-role --role-arn arn:aws:iam::YOUR_ACCOUNT_ID:role/YourRoleName --role-session-name testSession

   This can help you understand if the issue is within your application or with the IAM setup.

6. Review Network Policies and Security Groups:

   Ensure that there are no network policies or security groups blocking access to the AWS endpoints from your Kubernetes cluster.

If you follow these steps, you should be able to trace where the problem lies. Good luck, and let us know how it goes!

Migrating from Amazon RDS Serverless v1 to Provisioned Engine Mode

Transitioning your Amazon RDS cluster from serverless to a provisioned setup involves several key steps and considerations. Follow the guide below to ensure a smooth migration process:

Steps for Migration:

1. Backup Your Database:

   Before making any changes, create a backup of your current RDS database. Use the AWS Management Console or CLI to create a snapshot.

2. Review Instance Types:

   Determine the right instance type for your workload based on your performance requirements and expected traffic.

3. Create a New Provisioned Cluster:

   Using the AWS Management Console, create a new RDS cluster with the desired instance type. Make sure to configure all necessary parameters (VPC, Subnet Group, Security Groups, etc.) to match your needs.

4. Data Migration:

   Once the provisioned cluster is set up, you can migrate your data. Use the following methods:

   • AWS Database Migration Service (DMS)
   • Export/Import using SQL dump files
   • Using tools like pg_dump and pg_restore for PostgreSQL
5. Update Application Configuration:

   Modify your application’s database connection settings to point to the new provisioned cluster. Update any necessary environment variables.

6. Testing:

   Thoroughly test your application against the new provisioned RDS instance to ensure functionality and performance meet expectations.

7. Monitoring and Scaling:

   After migration, keep a close eye on performance metrics and adjust instance size or storage as needed.

8. Decommission the Old Cluster:

   Once you have verified that everything is working correctly on the new provisioned cluster, you can safely delete the old serverless cluster.

Key Considerations and Potential Pitfalls:

• Ensure compatibility of your database and applications; especially check for any differences in configurations or features.
• Provisioned RDS may incur higher costs compared to serverless, especially during low-traffic periods.
• Downtime can occur during migration; plan this according to your usage patterns.
• Consider the need for scaling; provisioned instances require manual scaling, keep an eye on usage patterns.
• Security settings (IAM roles, VPC settings) must be correctly configured for the new instance.

Following these steps and considerations will help facilitate a smooth transition from an Amazon RDS Serverless v1 cluster to a provisioned setup. Good luck!