Best Practices for Monitoring Latency in AWS Lambda and DynamoDB

Correct Answer: B.

Option A is incorrect because this option considers how long it takes Lambda to establish a connection, perform operations in DynamoDB and return a response, in this question we only want to know how long it takes to connect with DynamoDB.

Additionally, more factors can affect the performance of the Lambda, for instance, cold starts, code optimization, or CPU memory.

Option B is CORRECT because AWS X-Ray allows you to record every call to the other AWS service like DynamoDB and trace the latency for AWS services.

Mixing X-Ray with CloudWatch is a very good choice to get alarms if the latency becomes high.

Option C is incorrect because with AWS CloudTrail you can track the API requests made by IAM users or services, Cloudtrail is not used to send alerts or to process logs.

Option D is incorrect because until now, it is not possible to enable AWS X-Ray in DynamoDB.

You can only track the traces generated by calls to DynamoDB in Lambda with the AWS SDK.

The correct answer is B: Enable X-Ray tracing on Lambda, use the AWS SDK inside Lambda code to monitor DynamoDB API calls in X-Ray, send this information to CloudWatch, and create a metric that triggers an SNS alert if the response times get too high.

Explanation: AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. DynamoDB is a NoSQL database service that provides fast and predictable performance with seamless scalability. To monitor the latency of a serverless application, you can use AWS X-Ray, which is a service that helps you debug and analyze distributed applications.

Option A suggests importing the time library in Lambda code and calculating how much time it takes the Lambda to finish operations in DynamoDB, deploying these logs to CloudWatch, and creating an alarm if the time in the logs is high. However, this approach requires manual intervention, and it does not provide a scalable solution.

Option B suggests enabling X-Ray tracing on Lambda, using the AWS SDK inside Lambda code to monitor DynamoDB API calls in X-Ray, sending this information to CloudWatch, and creating a metric that triggers an SNS alert if the response times get too high. This approach provides an automated and scalable solution that leverages X-Ray's tracing capabilities.

Option C suggests enabling X-Ray tracing on Lambda, using the AWS SDK inside Lambda code to monitor DynamoDB API calls in X-Ray, sending this information to CloudTrail, and creating a metric that triggers an SNS alert if the response times get too high. However, CloudTrail is not designed for real-time monitoring and alerting, so this approach is not suitable for latency-sensitive applications.

Option D suggests enabling X-Ray in Lambda and DynamoDB. However, it does not provide a solution for monitoring and alerting on high latency.

Option E suggests creating a Lambda that will retrieve the info from X-Ray every minute, sending this information to CloudWatch, creating a metric that triggers an SNS alert if the response times get too high. This approach requires manual intervention, and it does not provide real-time monitoring and alerting.

In conclusion, option B is the best solution for monitoring and alerting on high latency in a serverless application with Lambda and DynamoDB.