What should a solutions architect do to decouple the architecture and make it scalable?
Use Amazon S3 to serve the front-end application, which sends requests to Amazon EC2 to execute the backend application. The backend application will process and store the data in Amazon RDS.
Use Amazon S3 to serve the front-end application and write requests to an Amazon Simple Notification Service (Amazon SNS) topic. Subscribe Amazon EC2 instances to the HTTP/HTTPS endpoint of the topic, and process and store the data in Amazon RDS.
Use an EC2 instance to serve the front end and write requests to an Amazon SQS queue. Place the backend instance in an Auto Scaling group, and scale based on the queue depth to process and store the data in Amazon RDS.
Use Amazon S3 to serve the static front-end application and send requests to Amazon API Gateway, which writes the requests to an Amazon SQS queue. Place the backend instances in an Auto Scaling group, and scale based on the queue depth to process and store the data in Amazon RDS.
Explanations:
While serving the front-end from Amazon S3 is a good approach for scalability, this option still ties the front end directly to the backend on EC2 without any queuing mechanism or decoupling. It doesn’t leverage any managed services to handle scaling of backend processes.
This option introduces Amazon SNS, but it is not suitable for direct processing of data requests since SNS is primarily used for pub/sub messaging. The architecture does not facilitate decoupling of the frontend and backend properly, as it still requires EC2 instances to be subscribed directly to the topic, which complicates scaling.
While using SQS for decoupling is a good choice, this option does not provide an efficient way to scale the backend instances based on the queue depth since it relies solely on EC2. Additionally, it does not provide the benefits of using managed services like API Gateway for request handling and validation.
This option effectively decouples the front-end from the backend by using Amazon S3 for serving static content and Amazon API Gateway to handle incoming requests. It uses Amazon SQS for queuing requests, allowing the backend instances in an Auto Scaling group to scale based on the queue depth. This architecture is scalable, resilient, and well-suited for handling varying loads.
If I’m not mistaken, the answer is:
Use Amazon S3 to serve the static front-end application and send requests to Amazon API Gateway, which writes the requests to an Amazon SQS queue. Place the backend instances in an Auto Scaling group, and scale based on the queue depth to process and store the data in Amazon RDS.