Integrating AWS Services with the Local Application

Image Processing Application Using S3 and DynamoDB

Prerequisites

Step 1: Install the AWS CLI

Copy
aws --version

Step 2: Configure the AWS CLI

1. Open a terminal or Command Prompt and run the following command:

Copy
aws configure

2. You will be prompted to enter the following information:

• AWS Access Key ID: This is a key that allows you to access AWS services. You can obtain it by logging into the AWS Management Console, navigating to Identity and Access Management (IAM), and creating a new user with programmatic access. Once created, the Access Key ID will be provided to you.

  
• AWS Secret Access Key: This is the secret part of the Access Key pair. It's provided along with the Access Key ID when you create the user with programmatic access. Ensure you store this key securely, as you won't be able to view it again.
• Default region name: This is the AWS region where you want your AWS services to operate. Examples include:

  • eu-west-1 (Ireland)
  • eu-west-2 (London)
  • eu-west-3 (Paris)
• Default output format: Choose how you want the CLI to display the results. Options include json, text, and table.

  Example input:

  Copy
  AWS Access Key ID [None]: AAKIEXAMPLE
  AWS Secret Access Key [None]: wJalFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
  Default region name [None]: eu-west-2
  Default output format [None]: json

Step 3: Test the Configuration

Copy
aws s3 ls

Setting Up AWS Resources

1. Creating an S3 Bucket:

1. Open the AWS Management Console and navigate to the S3 service. Please make sure you are in the correct AWS region. You can select the region by clicking on the region dropdown in the top right corner of the console. Refer to the image below, where the region selection is highlighted with a red box for guidance on changing the region.

   
2. Click on Create bucket. Refer to the image below if you need guidance, where the "Create bucket" option is highlighted with a red box:

   
3. Provide a unique bucket name (e.g., ar-image-processing-bucket). It's recommended to include your initials or another unique identifier in the bucket name to avoid naming conflicts. Refer to the image below, where the field for entering the bucket name is highlighted with a red box:

   
4. Leave the other settings as default unless you have specific requirements. However, it's important to understand and consider the following options:

   • Object Ownership defines the access control behavior for objects uploaded to the bucket. By default, Amazon disables Access Control Lists (ACLs), which is the recommended approach for most use cases as it simplifies permissions management. Disabling ACLs ensures that the bucket owner automatically owns all objects, even if they are uploaded by different AWS accounts.
   • Block Public Access settings are critical for securing your bucket. By default, Block all public access is selected, which is highly recommended unless you need to expose objects publicly. This setting prevents any accidental exposure of sensitive data stored in the bucket.
   • Bucket Versioning is disabled by default. Enabling versioning allows you to retain multiple versions of an object, which is useful for backup and recovery scenarios. If versioning is important to your use case, you can enable it after creating the bucket.
   • Server-Side Encryption (SSE) is automatically applied to new objects stored in the bucket. By default, SSE-S3 (Server-Side Encryption with Amazon S3 managed keys) is selected. This ensures that your data is encrypted at rest without requiring you to manage encryption keys.

   You also have the option to choose:

   • SSE-KMS: Server-side encryption with AWS Key Management Service (KMS) keys, which allows you to have more control over encryption keys.
   • DSSE-KMS: Dual-layer encryption using AWS KMS keys for even greater security. This option applies two separate layers of encryption to your data, although it might incur additional costs.
   • Bucket Key

     If you select SSE-KMS, you will also have the option to enable the S3 Bucket Key, which reduces the cost of encryption by minimizing calls to AWS KMS. Enabling S3 Bucket Keys can optimize costs when encrypting large volumes of data. This option is automatically enabled by default but can be disabled if necessary. Note that it is not supported for DSSE-KMS.
5. Click Create bucket once you are comfortable with these settings. Refer to the image below for guidance, where the "Create bucket" button is highlighted with a red box:

   

2. Creating a DynamoDB Table:

1. Open the AWS Management Console and navigate to the DynamoDB service. Please make sure you are in the correct AWS region. You can select the region by clicking on the region dropdown in the top right corner of the console. Refer to the image below, where the region selection is highlighted with a red box for guidance on changing the region.

   
2. In the left panel, click on Tables. This will display the tables panel on the right-hand side, where you will see a Create table button. Click on Create table to begin setting up your new table. Refer to the images below for guidance, with each relevant section highlighted in a red box:

   • 
   • 
3. Provide a table name (e.g., ar-ImageMetadata) and set the Partition key to id (String). It's recommended to include your initials or another unique identifier in the table name to avoid naming conflicts. Refer to the image below for guidance, where the table name and partition key fields are highlighted with a red box:

   
4. You can leave the other settings as default unless you need advanced configuration.
5. Click Create table to finalize the setup. Refer to the image below for guidance, where the "Create table" button is highlighted with a red box:

   

Cloning the Application from GitHub

1. Clone the repository from GitHub:

Copy
git clone https://github.com/Oxford-Research-Cloud-Competency-Centre/image-processing-hybrid-app

2. Navigate to the project directory:

Copy
cd image-processing-hybrid-app

Copy
REGION_NAME = 'YOUR_AWS_REGION'
S3_BUCKET = 'YOUR_AWS_S3_BUCKET_NAME'
IMAGE_METADATA_TABLE = 'YOUR_AWS_DYNAMODB_TABLE_NAME'

Note: Please replace the values above with those specific to your AWS resources. Ensure you use the correct region, S3 bucket name, and DynamoDB table name that you created.

Running the Application

1. Running Locally with Python:

1. Make sure the repository is cloned and you are inside the home directory of the application.
2. Install the necessary Python dependencies:

   Copy
   pip install -r requirements.txt

3. Run the Flask application:

   Copy
   python -m app.app

2. Running with Docker:

1. Build the Docker image:

   Copy
   docker build -t image-processing-hybrid-app .

2. Run the Docker container:

   Copy
   docker run -p 5000:5000 -e AWS_ACCESS_KEY_ID=your_access_key -e AWS_SECRET_ACCESS_KEY=your_secret_key -e AWS_DEFAULT_REGION=your_aws_region image_processing_cloud_app_local

Resource Cleanup

1. Delete S3 Bucket:

   • Go to the S3 console and locate the bucket you created for this application.
   • Select the bucket, then click on Empty to delete all objects within the bucket. Refer to the image below, where the Empty button is highlighted in red for guidance:

     
   • After clicking Empty, a new page will open. To confirm deletion, type "permanently delete" in the text input field, then click on Empty again to finalize. Refer to the image below for guidance, where the confirmation field and Empty button are highlighted in red:

     
   • After emptying the bucket, return to the bucket's main page and click on Delete to remove the bucket itself. Refer to the image below, where the Delete button is highlighted in red:

     
   • A new window will open. To confirm the bucket deletion, enter the bucket name in the text input field, then click on Delete Bucket. Refer to the image below, where the confirmation field and Delete Bucket button are highlighted in red:

     
   • Please Note: It may take a few moments for AWS to fully delete the bucket. Wait until you see a confirmation message indicating that the bucket has been deleted to ensure the resource is fully removed.
2. Delete DynamoDB Table:

   • Navigate to the DynamoDB console.
   • In the Tables section, locate the table you created (e.g., ar-ImageMetadata).
   • Select the table, then click on Delete table to remove it. Refer to the image below, where the Delete table button is highlighted in red:

     
   • After clicking Delete table, a new window will open asking for confirmation. Type confirm in the text input field, then click on the Delete button to finalize the deletion. Refer to the image below, where the confirmation field and Delete button are highlighted in red:

     
   • Please Note: It may take a few moments for AWS to fully delete the table. Wait until you see a confirmation message indicating that the table has been deleted to ensure the resource is completely removed.

Note: Ensure you have backed up any necessary data from these resources before deleting them, as this action is irreversible.