AWS services generate events that invoke Lambda functions, and Lambda functions can send messages to AWS services. Generally, the service or resource that invokes a Lambda function should be different to the service or resource that the function outputs to. Failure to manage this can result in infinite loops.

For example, a Lambda function writes an object to an S3 object, which in turn invokes the same Lambda function via a put event. The invocation causes a second object to be written to the bucket, which invokes the same Lambda function:

While the potential for infinite loops exists in most programming languages, this anti-pattern has the potential to consume more resources in serverless applications. Both Lambda and S3 automatically scale based upon traffic, so the loop may cause Lambda to scale to consume all available concurrency and S3 will continue to write objects and generate more events for Lambda. In this event, you can press the “Throttle” button in the Lambda console to scale the function concurrency down to zero and break the recursion cycle.

This example uses S3 but the risk of recursive loops also exists in SNS, SQS, DynamoDB, and other services. In most cases, it is safer to separate the resources that produce and consume events from Lambda. However, if you need a Lambda function to write data back to the same resource that invoked the function, ensure that you:

• Use a positive trigger: for example, an S3 object trigger may use a naming convention or meta tag that is only triggered on the first invocation. This prevents objects written from the Lambda function from invoking the same Lambda function again. See the S3-to-Lambda translation application for an example of this mechanism.
• Use reserved concurrency: setting the function’s reserved concurrency to a lower limit prevents the function from scaling concurrently beyond that limit. It does not prevent the recursion, but limits the resources consumed as a safety mechanism. This can be useful during the development and test phases.
• Use CloudWatch monitoring and alarming: by setting an alarm on a function’s concurrency metric, you can receive alerts if the concurrency suddenly spikes and take appropriate action. See monitoring and observability in Monitoring and observability for more information.