Dead letter queue
A dead-letter queue is a destination that stores messages or records that cannot be delivered or processed normally for later inspection, retry, handling, or disposal.
MongoDB 8.0 in Action, Third Edition
A dead-letter queue is a queue that stores messages or records that cannot be processed so they can be inspected, reprocessed, or discarded later.
- How it is used
- Keeps stream processing moving by separating unprocessable records for later inspection and handling.
- How deep it goes
- Explains processor-level configuration, validation routing, and operational practices; notes that validationAction "dlq" logs without guarantees.
- Examples
- Illustrates failed dailySales validation and a Kafka-backed processor configured to store problematic records in ErrorLogs.TransactionErrors.
- What you can do after
- Operate/configure it with sp.createStreamProcessor, then monitor the MongoDB collection and plan how to handle failed records.
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Amazon Web Services in Action, Third Edition: An in-depth guide to AWS
A dead-letter queue is a queue that receives messages that exceed a configured number of unsuccessful processing attempts.
- How it is used
- Supports fault-tolerant image-processing infrastructure alongside an SQS queue.
- How deep it goes
- Defines the retry-threshold destination but stops short of showing its queue configuration or processing behavior.
- Examples
- Uses the Imagery application and its CloudFormation deployment as the concrete infrastructure example.
- What you can do after
- Operate a deployed SQS-based application whose infrastructure includes a dead-letter queue.
Architecting an Apache Iceberg Lakehouse
A dead-letter queue is a side channel that stores records rejected or unprocessable by a normal data-processing path for later inspection or correction.
- How it is used
- Keeps ingestion operational while isolating rejected records for later inspection or correction.
- How deep it goes
- Explains placement within hybrid error handling but stops short of specifying queue formats, retention, or replay procedures.
- Examples
- Uses problematic rows from semistructured or external sources and unresolvable ingestion errors as representative DLQ contents.
- What you can do after
- Operate resilient ingestion by combining critical-field checks, controlled casting, automated recovery, and DLQ redirection.
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Kafka Streams in Action, Second Edition
A dead letter queue is a Kafka topic that stores records a connector cannot deliver or process successfully.
- How it is used
- Handles sink-connector failures by preserving undeliverable records while the connector continues processing.
- How deep it goes
- Configures tolerance, topic naming, headers, and replication, but does not detail DLQ consumption or replay procedures.
- Examples
- Uses an Elasticsearch sink connector writing failed records to the orientation_student_dlq topic.
- What you can do after
- Operate/configure a Kafka Connect sink connector with continued processing and diagnosable failed records.
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Related concepts
- permanent failureaddressed by · A permanent failure is a failure condition that is not resolved by repeating the same operation without changing the request or relevant system conditions.
- Atlas Stream Processingused by · Atlas Stream Processing is a MongoDB Atlas capability for processing continuously arriving data streams with MongoDB's data model and query operations.
- topicimplemented by · A topic is a named messaging destination or stream to which producers publish messages or records and from which consumers receive or read them.
- try-catch statementuses · A try-catch statement is a control-flow construct that executes code under exception handling and transfers control to a matching handler when an exception is raised.
- retryaddresses · A retry is a subsequent attempt to perform an operation after an earlier attempt fails or does not complete successfully.
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