Resolving Common Issues in Python AWS Scripts: A Journey of Troubleshooting and Solutions

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Working with AWS and Python can sometimes lead to challenges, especially when it comes to scripting and automation. I recently encountered several issues while developing a Python script for managing EC2 instances. Here's a look at the key problems I faced and the solutions that helped me get things back on track.

1. Endpoint Connection Errors

Issue: While trying to stop EC2 instances, I encountered the error:

botocore.exceptions.EndpointConnectionError: Could not connect to the endpoint URL: "https://ec2.ap-southeast-2.amazonaws.com/"

This was due to an incorrect endpoint URL being used, caused by a misconfigured AWS region in the CLI.

Solution: I corrected the region setting in the AWS CLI configuration. To fix this:

1. Opened the AWS CLI configuration:

    aws configure
   

2. Set the correct region to ap-southeast-2 (Sydney).

This adjustment resolved the connection issue.

2. Invalid Credentials for EC2 Login

Issue: I faced issues logging into my EC2 instance with the error:

botocore.exceptions.NoCredentialsError: Unable to locate credentials

This error occurred because the credentials used for accessing the EC2 instance were incorrect.

Solution: The problem was resolved by creating a new access key and updating the AWS CLI configuration with the new credentials. Here’s the process:

1. Created a new access key from the AWS Management Console.

2. Updated the AWS CLI configuration with the new access key:

    aws configure
   

By updating the credentials, the script was able to successfully access and manage the EC2 instances.

3. Handling Missing Instance IDs in Output

Issue: The script did not display instance IDs in the output for actions such as stopping or terminating instances. For example:

Stopped instances are:

The instance IDs were missing in the output, making it unclear which instances were affected.

Solution: I updated the print statements in the functions to include instance IDs:

def stop_instance(instance_ids):
    try:
        ec2.instances.filter(InstanceIds=instance_ids).stop()
        print(f"Stopped instances: {', '.join(instance_ids)}")
    except ClientError as e:
        print(f"Error occurred: {e}")

This change ensured that the instance IDs were correctly displayed in the output messages.

4. Adding Error Handling

Issue: Initially, the script lacked proper error handling, which made diagnosing issues difficult. For example, running the script without handling exceptions might have resulted in uninformative error messages.

Solution: I enhanced the script with error handling using try-except blocks to catch exceptions like ClientError. Here’s an updated function with improved error handling:

from botocore.exceptions import ClientError

def start_instance(instance_ids):
    try:
        ec2.instances.filter(InstanceIds=instance_ids).start()
        print(f"Started instances: {', '.join(instance_ids)}")
    except ClientError as e:
        print(f"Error occurred: {e}")

This addition improved the script’s robustness and made it easier to diagnose and handle errors effectively.

Conclusion

Encountering issues while scripting is a part of the learning process, especially with cloud services like AWS. By systematically addressing connection errors, fixing credential issues, enhancing error handling, and ensuring accurate output, I was able to refine my Python AWS script. These solutions not only resolved the problems but also enhanced the overall functionality and reliability of the script.

Feel free to use these solutions as a reference for your own AWS scripting challenges. You can check out the code I used here on GitHub. Happy coding!

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