What is the MOST operationally efficient solution that meets these requirements?
Create a table in Amazon Athena for AWS CloudTrail logs. Create a query for the relevant information.
Enable ALB access logging to Amazon S3. Create a table in Amazon Athena, and query the logs.
Enable ALB access logging to Amazon S3. Open each file in a text editor, and search each line for the relevant information.
Use Amazon EMR on a dedicated Amazon EC2 instance to directly query the ALB to acquire traffic access log information.
Explanations:
While creating a table in Amazon Athena for AWS CloudTrail logs can provide some insights, it does not specifically address the traffic access patterns related to the Application Load Balancer. CloudTrail primarily logs API calls and does not capture detailed request-level data from the ALB itself.
Enabling ALB access logging to Amazon S3 captures detailed request logs that include information about the traffic patterns. Creating a table in Amazon Athena allows for efficient querying of this data without the need to manually sift through log files. This solution provides the best operational efficiency for analyzing traffic abnormalities.
While enabling ALB access logging to Amazon S3 is correct, manually opening each log file in a text editor to search for relevant information is highly inefficient. This approach does not leverage the power of automated querying and analysis tools like Athena, making it unsuitable for operational efficiency.
Using Amazon EMR on a dedicated EC2 instance to query the ALB directly is unnecessary for log access since the ALB already provides access logs. This solution is more complex and requires additional management of the EMR infrastructure, making it less operationally efficient compared to querying S3 logs with Athena.
From my perspective, the answer is:
Enable ALB access logging to Amazon S3. Create a table in Amazon Athena, and query the logs.