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Version: v18.2.0

Dead Letter Queues

startSF SQS queue

The workflow-trigger for the startSF queue has a Redrive Policy set up that directs any failed attempts to pull from the workflow start queue to a SQS queue Dead Letter Queue.

This queue can then be monitored for failures to initiate a workflow. Please note that workflow failures will not show up in this queue, only repeated failure to trigger a workflow.

Named Lambda Dead Letter Queues

Cumulus provides configured Dead Letter Queues (DLQ) for non-workflow Lambdas (such as ScheduleSF) to capture Lambda failures for further processing.

These DLQs are setup with the following configuration:

  receive_wait_time_seconds  = 20
message_retention_seconds = 1209600
visibility_timeout_seconds = 60

Default Lambda Configuration

The following built-in Cumulus Lambdas are setup with DLQs to allow handling of process failures:

  • dbIndexer (Updates Elasticsearch)
  • JobsLambda (writes logs outputs to Elasticsearch)
  • ScheduleSF (the SF Scheduler Lambda that places messages on the queue that is used to start workflows, see Workflow Triggers)
  • publishReports (Lambda that publishes messages to the SNS topics for execution, granule and PDR reporting)
  • reportGranules, reportExecutions, reportPdrs (Lambdas responsible for updating records based on messages in the queues published by publishReports)

Troubleshooting/Utilizing messages in a Dead Letter Queue

Ideally an automated process should be configured to poll the queue and process messages off a dead letter queue.

For aid in manually troubleshooting, you can utilize the SQS Management console to view/messages available in the queues setup for a particular stack. The dead letter queues will have a Message Body containing the Lambda payload, as well as Message Attributes that reference both the error returned and a RequestID which can be cross referenced to the associated Lambda's CloudWatch logs for more information:

Screenshot of the AWS SQS console showing how to view SQS message attributes