Dead Letter Queue Analyzer

Analyze dead letter queue (DLQ) messages to identify failure patterns, root causes, and remediation strategies. Supports AWS SQS, RabbitMQ, Kafka, Azure Service Bus, and generic message queues.

Stop ignoring your dead letter queue. Analyze DLQ messages to find failure patterns, identify root causes, determine which messages are replayable, and generate remediation plans — turning your DLQ from a black hole into an actionable error stream.

Use when: "analyze DLQ", "dead letter queue growing", "why are messages failing", "replay failed messages", "DLQ backlog", "message processing failures", or when unprocessed messages accumulate.

Commands

1. analyze — Categorize DLQ Messages

Step 1: Read DLQ Messages

AWS SQS:

bash

aws sqs receive-message \
  --queue-url "$DLQ_URL" \
  --max-number-of-messages 10 \
  --attribute-names All \
  --message-attribute-names All | python3 -c "
import json, sys
msgs = json.load(sys.stdin).get('Messages', [])
for m in msgs:
    body = json.loads(m['Body']) if m['Body'].startswith('{') else m['Body']
    attrs = m.get('Attributes', {})
    print(f'ID: {m[\"MessageId\"]}')
    print(f'  Received count: {attrs.get(\"ApproximateReceiveCount\", \"?\")}')
    print(f'  First received: {attrs.get(\"ApproximateFirstReceiveTimestamp\", \"?\")}')
    print(f'  Body preview: {str(body)[:200]}')
    print()
"

# Count total DLQ depth
aws sqs get-queue-attributes --queue-url "$DLQ_URL" \
  --attribute-names ApproximateNumberOfMessages | python3 -c "
import json, sys
attrs = json.load(sys.stdin)['Attributes']
print(f'DLQ depth: {attrs[\"ApproximateNumberOfMessages\"]} messages')
"

RabbitMQ:

bash

# List DLQ queues
rabbitmqctl list_queues name messages | grep -i "dead\|dlq\|error"

# Peek at messages
rabbitmqadmin get queue="dead_letter_queue" count=10 2>/dev/null

Kafka:

bash

# Read from DLT (dead letter topic)
kafka-console-consumer --bootstrap-server $KAFKA_BROKER \
  --topic "$DLT_TOPIC" --from-beginning --max-messages 20 \
  --property print.headers=true --property print.timestamp=true

Step 2: Classify Failure Causes

Group DLQ messages by failure reason:

Category	Signal	Replayable?	Action
Schema error	Validation failure, missing field	After fix	Fix producer or consumer schema
Timeout	Processing exceeded deadline	Yes	Increase timeout or optimize processing
Dependency down	Connection refused, 503	Yes	Wait for recovery, then replay
Poison message	Crash/exception on processing	No	Fix handler, then replay
Data integrity	FK violation, duplicate key	Maybe	Fix data, then replay
Permission	Auth error, access denied	After fix	Fix credentials, then replay
Deserialization	Invalid JSON/Protobuf/Avro	No	Discard or fix producer

python

# Group messages by error pattern
from collections import Counter
errors = Counter()
for msg in dlq_messages:
    # Extract error reason from message attributes or headers
    error = msg.get('error_reason', msg.get('x-death-reason', 'unknown'))
    errors[error] += 1

for error, count in errors.most_common(10):
    print(f'{count:>5}x  {error}')

Step 3: Generate Report

markdown

# DLQ Analysis Report

## Summary
- Queue: orders-processing-dlq
- Total messages: 1,247
- Oldest message: 3 days ago
- Growth rate: ~400/day (increasing)

## Failure Categories
| Category | Count | % | Replayable | Root Cause |
|----------|-------|---|------------|------------|
| Timeout | 823 | 66% | ✅ | DB slow queries since Tuesday deploy |
| Schema error | 312 | 25% | ✅ (after fix) | New field `currency` not in consumer schema |
| Poison message | 67 | 5% | ❌ | NullPointer in price calculation |
| Permission | 45 | 4% | ✅ (after fix) | Expired service account token |

## Root Cause
Primary: DB slow queries causing processing timeouts (66% of failures)
- Started: Tuesday 14:30 UTC (correlates with deploy)
- Impact: 823 orders stuck in DLQ

## Remediation Plan
1. **Fix DB performance** — add missing index on orders.status (immediate)
2. **Replay timeout messages** (823) — safe, operations are idempotent
3. **Update consumer schema** to accept `currency` field (312 messages)
4. **Rotate service account token** (45 messages)
5. **Fix NullPointer** in OrderPriceCalculator.java:67 (67 messages — investigate first)
6. Set up DLQ depth alerting (threshold: 50 messages)

2. replay — Generate Replay Script

bash

# SQS: move messages from DLQ back to main queue
aws sqs start-message-move-task \
  --source-arn "$DLQ_ARN" \
  --destination-arn "$MAIN_QUEUE_ARN" \
  --max-number-of-messages-per-second 10

# Or selective replay (only timeout errors)
# Read, filter, re-send

3. monitor — Set Up DLQ Alerting

Generate CloudWatch alarm / Prometheus alert for DLQ depth:

• Alert when DLQ depth > 0 (any message is a signal)
• Alert when growth rate > N/hour (active problem)
• Alert when oldest message > 24h (messages going stale)
• Dashboard showing DLQ depth over time + categorization

4. prevent — Improve Message Handling

Recommend changes to prevent future DLQ accumulation:

• Add retry with backoff before sending to DLQ
• Add idempotency keys for safe replay
• Add dead letter reason headers for faster triage
• Add message TTL to prevent infinite accumulation
• Add schema validation before publishing (catch at source)