DOP-C02 Leak Questions

The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:

Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.

Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.

Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.

The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.

1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda

2: AWS Lambda event source mappings - AWS Lambda

3: Managing concurrency for a Lambda function - AWS Lambda

4: AWS Lambda function scaling - AWS Lambda

AWS Lambda event source mappings - AWS Lambda

Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

The startup delay occurs because large container images must be pulled from Amazon ECR when pods are scheduled, especially on newly added nodes that do not have cached images. To consistently reduce pod startup time to seconds, container images must be prefetched directly onto the worker nodes that run the pods, not the EKS control plane.

Amazon EKS control plane nodes are fully managed by AWS and cannot be accessed or modified using AWS Systems Manager. Therefore, any solution that attempts to run Systems Manager commands on control plane nodes is invalid. Prefetching must target the EKS worker nodes (EC2 instances in managed node groups) where container images are actually stored and used.

Option C correctly creates an IAM role that allows Amazon EventBridge to invoke AWS Systems Manager to run commands on the EKS worker nodes. By using Systems Manager State Manager associations with node tags, the automation dynamically targets both existing nodes and newly added nodes that inherit the same tags. This ensures that container images are pulled immediately when a new image is pushed to ECR or when new nodes join the cluster.

Option B incorrectly targets nodes based on machine size, which is not reliable for lifecycle automation. Options A and D incorrectly reference control plane nodes, which cannot be managed with Systems Manager.

Therefore, Option C is the correct and AWS-aligned solution to ensure fast pod startup times across all nodes.