Skip to main content
Version: Next

Cumulus Dead Letter Archive

This documentation explains the Cumulus dead letter archive and associated functionality.

DB Records DLQ Archive

The Cumulus system contains a number of dead letter queues. Perhaps the most important system lambda function supported by a DLQ is the sfEventSqsToDbRecords lambda function which parses Cumulus messages from workflow executions to generate and write database records to the Cumulus database.

As of Cumulus v9+, the dead letter queue for this lambda (named sfEventSqsToDbRecordsDeadLetterQueue) has been updated with a consumer lambda that will automatically write any incoming records to the S3 system bucket, under the path <stackName>/dead-letter-archive/sqs/. This will allow integrators and operators engaged in debugging missing records to inspect any Cumulus messages which failed to process and did not result in the successful creation of database records.

Dead Letter Archive recovery

In addition to the above, as of Cumulus v9+, the Cumulus API also contains a new endpoint at /deadLetterArchive/recoverCumulusMessages.

Sending a POST request to this endpoint will trigger a Cumulus AsyncOperation that will attempt to reprocess (and if successful delete) all Cumulus messages in the dead letter archive, using the same underlying logic as the existing sfEventSqsToDbRecords. Otherwise, all Cumulus messages that fail to be reprocessed will be moved to a new archive location under the path <stackName>/dead-letter-archive/failed-sqs/<YYYY-MM-DD>.

This endpoint may prove particularly useful when recovering from extended or unexpected database outage, where messages failed to process due to external outage and there is no essential malformation of each Cumulus message.

Dead Letter Archive Message structure

The Messages yielded to the dead letter archive have some inherent uncertainty to their structure due to their nature as failed messages that may have failed due to structural issues. However there is a standard format that they will overwhelmingly conform to. This follows, but adds attributes to the format documented at SQSMessage

body: [string], // parseable as EventBridge
error: [string | null], // error that caused the message to be shunted to the DLQ
execution: [string | null], // execution ARN for the execution which created the originating sf event
time: [string | null], // Zulu timestamp of of the originating sf event
collection: [string | null], // collection the granule belongs to
granules: [Array[string | null] | null], // granules
stateMachine: [string | null], // ARN of the triggering workflow
status: [string | null], status of triggering execution
/* these following are standard, not built by cumulus */
md5OfBody: [string], // checksum of message body
eventSource: [string], // aws:sqs
awsRegion: [string], // aws region that this is happening in
messageId: [string], // uniqueID of the DLQ message
receiptHandle: [string], // An identifier associated with the act of receiving the message. A new receipt handle is returned every time you receive a message.
attributes: [Object], // A map of the attributes requested in ReceiveMessage to their respective values.
messageAttributes: [Object], // Each message attribute consists of a Name, Type, and Value.

note that each of these fields except for 'body' can be null if no data was found, usually due to a parsing error for further details on body contents: [see below]

Dead Letter Archive Body contents

The body attribute should be a JSON string containing an event bridge event

Note that

  • Because this message body arrived in the Dead Letter Archive because of issues in processing it, there is no strict guarantee that it is a valid json object, or conforms to expected structure. the expected structure follows.
  • Automated processing of these messages must be prepared for attributes to be missing.
version: [string | null], // versionString
id: [string | null], // unique ID of the triggering event
'detail-type': 'Step Functions Execution Status Change', // defines the below 'detail' spec
source: 'aws.states',
account: [string], // account ID
time: [string], // Zulu timestamp of the originating sf event
region: [string], //aws region
resources: [Array[string]], //ARNs of involved resources
detail: [string], //parses as Step Function Execution Status Change object, see below

Step Function Execution Status Change (detail) here:

executionArn: [string], // ARN of the triggering execution
stateMachineArn: [string], // ARN of the triggering workflow
name: [string], // Execution name of triggering execution
status: [string], // status of triggering execution
startDate: [int], // timestamp of
stopDate: [int | null], // timestamp of
input: [string], //parses as the cumulus message input
output: [string | null], //parses as the cumulus message output if execution succeeded
stateMachineVersionArn: [string | null], // The version ARN is a combination of state machine ARN and the version number separated by a colon (:)
stateMachineAliasArn: [string | null], // a combination of state machine ARN and the alias name separated by a colon (:)
inputDetails: [CloudWatchEventsExecutionDataDetails], // Details about execution input
outputDetails: [CloudWatchEventsExecutionDataDetails | null], // Details about execution output
error: [string | null], // The cause string if the state machine execution failed (most errors that send to the DLA will not have a *caught* failure that does not arrive here)
cause: [string | null], // the cause string if the state machine execution failed
/* note that these redrive statistics can be misleading, as they are not referring to the execution that failed if the triggering execution was sfEventSqsToDbRecords*/
redriveCount: [int],
redriveDate: [string | null],
redriveStatus: [string],
redriveStatusReason: [string],

Search and View Dead Letter Archive Messages

Amazon Athena is a powerful serverless query service that allows us to analyze data directly from Amazon S3 using standard SQL. One of the key features of Athena is its support for partition projection. Partition projection allows us to define a virtual partitioning scheme for our data stored in Amazon S3 without physically partitioning the data.

We have provided an AWS Glue Catalog database, an AWS Glue Catalog table and an example query for querying S3 DLA messages. Our AWS Glue Catalog table <prefix>_dla_glue_table defines partition projection for eventdate key which corresponds to date folder under Dead Letter Archive S3 storage location.

Note: <prefix> is your stack name with dash replaced by underscore


  1. Navigate to AWS Athena Console:

    Launch query editor to Query your data with Trino SQL.

    Choose Workgroup <prefix>_athena_workgroup from the workgroup drop down menu and acknowledge Workgroup <prefix>_athena_workgroup settings.

    The Saved queries tab should have an example query <prefix>_athena_test_query, click it to open.

    Select the appropriate database <prefix>_glue_database from the Database dropdown menu and run the query.

  2. Write and Run the Query:

    When the query includes the partition key eventdate, the query on the table will be executed using partition projection settings and would result in faster results by directly scanning the folder and files based on the partition information provided in the query.

    In the following query, the data is filtered based on the eventdate partition key and a specific value in the granules column. $path returns the S3 file location for the data in a table row.

    select "$path",
    from <prefix>_dla_glue_table
    where eventdate between '2024-03-10' and '2024-03-15'
    and contains(

    See SQL reference for Athena for the complete SQL guide.