Which action will be MOST effective in accomplishing this?
Replace the SQS queue with Amazon Kinesis Data Firehose.
Use Amazon ElastiCache for Redis in front of the DynamoDB backend tier.
Add an Amazon CloudFront distribution to cache the responses for the web tier.
Use Amazon EC2 Auto Scaling to scale out the middle tier instances based on the SQS queue depth.
Explanations:
Amazon Kinesis Data Firehose is primarily used for real-time data streaming and delivery, not for queue management or order processing. Replacing SQS with Kinesis does not directly address the issue of lengthy processing times for order confirmations.
While using Amazon ElastiCache for Redis can improve read performance by caching frequently accessed data, it does not directly reduce processing times related to order submissions, as the bottleneck is likely in the processing layer rather than in data retrieval.
Adding an Amazon CloudFront distribution would help with static content delivery and reduce latency for web requests, but it does not affect the backend processing times of order submissions and confirmations.
Using Amazon EC2 Auto Scaling to scale out the middle tier instances based on the SQS queue depth allows the application to dynamically adjust its processing capacity in response to the volume of incoming orders, thereby reducing processing times during peak loads.
I chart that the answer is:
Use Amazon EC2 Auto Scaling to scale out the middle tier instances based on the SQS queue depth.
If I’m not mistaken, the answer is:
Use Amazon EC2 Auto Scaling to scale out the middle tier instances based on the SQS queue depth.