Unraveling an Intriguing AWS Lambda Quirk: A Deep Dive into Unusual Behavior


In my recent project leveraging AWS Lambda, I encountered a peculiar behavior that I believe warrants attention and discussion. With nearly five years of hands-on experience with AWS, I can confidently assert that AWS Lambda stands out as one of my favorite services. In this particular project, Lambda played a central role, triggered seamlessly by an SQS queue. However, an unexpected twist emerged during execution that prompted a thorough investigation into its root cause.

Scenario Overview:

The Lambda function, configured with a 15-minute timeout, was seamlessly orchestrated to handle tasks triggered by an SQS queue, with a corresponding visibility time of 16 minutes. To manage the Lambda timeout effectively, a continuation key was embedded in the queue just before the Lambda instance approached its timeout threshold. This approach allowed a new Lambda instance to seamlessly resume the task from the exact point where the previous instance left off.

Issue Unveiled:

Despite the seemingly smooth operation, a perplexing anomaly surfaced when the Lambda function attempted to put a message into the SQS queue for the 16th time during its execution. Scrutinizing each aspect of the configuration, I diligently examined potential parameters set to 16, but to no avail. Even the assistance of ChatGPT proved inconclusive in resolving the mystery.

Discovery and Resolution:

The breakthrough in understanding this behavior came from this AWS blog post, revealing that AWS Lambda incorporates a safeguard mechanism to prevent unintentional recursion. After 16 invocations, the message is automatically redirected to a Dead Letter Queue (DLQ). This unique feature aims to mitigate recursive errors and can be disabled by submitting a support ticket.

Additional Insight:

During my investigation into this issue, I stumbled upon another noteworthy aspect of AWS Lambda. It was revealed that Lambda may not scale properly if it detects error logging, even if the logging is intentional. Interestingly, this behavior is designated as a feature, and its impact can be circumvented by disabling it through a support ticket.


This exploration into the nuances of AWS Lambda’s behavior sheds light on the intricacies that can arise even for seasoned AWS practitioners. By unraveling these subtleties, we not only enhance our understanding of AWS services but also equip ourselves to navigate and troubleshoot unexpected challenges effectively.

Leave a Comment