Your company decided to improve on the current batch processing model and start using the AWS Batch instead of the currently running SQS and EC2 with Auto Scaling configurations.
Your processing jobs are using Docker containers.
You also expect the jobs to grow in future.
These jobs pull messages from the AWS SQS queue and write the data into DynamoDB tables once the processing is completed.
You have created a small Proof-of-concept application to try out the AWS Batch before you actually migrate your job containers.
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answers: B, C, and D.
Option Ais INCORRECT because the CloudWatch Log group does not impact the Batch job's running status.
Please refer to the following link:
https://aws.amazon.com/premiumsupport/knowledge-center/batch-job-stuck-runnable-status/Option B is CORRECT because no internet access for the compute resources may result in this issue.
Refer to the link: https://docs.aws.amazon.com/batch/latest/userguide/troubleshooting.html.
Option C is CORRECT because if the job has asked for more resources than the attached compute environment has, the Batch will not schedule the job to execute, and the job will stay in the RUNNABLE state forever.
Option D is CORRECT because the job may require starting new instances to run the additional containers.
If the AWS account has reached its limit, the service will not start new containers, and the job will remain in the RUNNABLE state.
Option E is INCORRECT because the question has not mentioned connecting to the SQS privately from the VPC.
VPC Endpoint is not required for AWS Batch to work.
Sure, I can provide a detailed explanation of the answer options for this scenario.
Option A: Make sure a CloudWatch Log group is set up for the Batch job. Explanation: AWS Batch logs can be sent to Amazon CloudWatch Logs. It's important to set up a CloudWatch Log group for the Batch job, as it enables logging, monitoring, and debugging of the Batch job. You can also set up alarms and notifications based on the log data to help with troubleshooting.
Option B: Make sure Internet Access is available to your container. Explanation: Docker containers running on AWS Batch require internet access to pull images from the container registry. If internet access is not available, the containers cannot be created. Hence, it's essential to ensure internet access is available for the Batch job to run.
Option C: Make sure suitable CPU and RAM are allocated for the Batch job. Explanation: AWS Batch allows specifying CPU and memory resources for each job. Proper allocation of CPU and RAM is necessary to ensure that the job runs efficiently and within the specified time. Oversizing or undersizing can cause performance issues or excessive costs.
Option D: Make sure your AWS account has not reached the EC2 limit. Explanation: AWS Batch uses EC2 instances to run jobs, and it's important to ensure that your AWS account has enough EC2 instances available to run your Batch jobs. You can check your EC2 instance limits using the AWS Service Quotas console.
Option E: Make sure the VPC Endpoint is set to access the SQS queue from the containers inside the VPC. Explanation: If your Batch job is running within a VPC, it's essential to set up a VPC endpoint to access the SQS queue from within the VPC. This eliminates the need to route traffic outside the VPC, which improves security and network performance. Without this, the containers would not be able to access the SQS queue.
In summary, all the answer options are essential considerations when migrating batch processing from SQS and EC2 to AWS Batch with Docker containers. You should ensure that a CloudWatch Log group is set up, internet access is available to containers, suitable CPU and RAM are allocated, EC2 instance limits are not reached, and the VPC endpoint is set up to access the SQS queue from within the VPC.