Message Queue Consumer Crashes and Loses Message — No Dead Letter Queue
Symptom
- Tasks submitted to queue never complete and leave no trace
- Queue depth goes to 0 but expected output never appears
- Consumer crashes → message disappears → no error visible
- After restart, consumer processes new messages but missed ones are gone
- Work items lost under load when multiple consumers compete
Root Cause
Most queues require explicit acknowledgment after processing. If a consumer crashes before ack-ing, the message can be lost (depending on queue config). Without a dead letter queue (DLQ), failed messages have nowhere to go — they’re silently dropped after max retry attempts.
Fix
Option 1: Acknowledge only after successful processing
import redis
r = redis.Redis()
def process_with_ack(queue_name: str):
"""Use BRPOPLPUSH for reliable dequeue — message stays in processing list"""
processing_queue = f"{queue_name}:processing"
while True:
# Atomically move from queue to processing list
message = r.brpoplpush(queue_name, processing_queue, timeout=5)
if not message:
continue
try:
process_message(message)
# Only remove from processing after success
r.lrem(processing_queue, 1, message)
except Exception as e:
print(f"Processing failed: {e}")
# Move to dead letter queue instead of losing it
r.rpush(f"{queue_name}:dead_letter", message)
r.lrem(processing_queue, 1, message)
Option 2: AWS SQS with visibility timeout and DLQ
import boto3, json
sqs = boto3.client("sqs", region_name="us-east-1")
QUEUE_URL = "https://sqs.us-east-1.amazonaws.com/123/agent-tasks"
def consume_with_dlq():
while True:
response = sqs.receive_message(
QueueUrl=QUEUE_URL,
MaxNumberOfMessages=1,
VisibilityTimeout=300, # Message invisible to other consumers for 5min
WaitTimeSeconds=20 # Long polling
)
messages = response.get("Messages", [])
if not messages:
continue
for msg in messages:
receipt_handle = msg["ReceiptHandle"]
body = json.loads(msg["Body"])
try:
process_task(body)
# Success: delete from queue
sqs.delete_message(
QueueUrl=QUEUE_URL,
ReceiptHandle=receipt_handle
)
except Exception as e:
print(f"Task failed: {e}")
# Don't delete — SQS will retry (up to maxReceiveCount)
# After maxReceiveCount, SQS automatically moves to DLQ
# SQS DLQ config (set via AWS console or Terraform):
# RedrivePolicy: {"deadLetterTargetArn": "arn:aws:sqs:...:agent-tasks-dlq", "maxReceiveCount": 3}
Option 3: RabbitMQ with acknowledgments and DLQ
import pika, json
def consume_rabbitmq():
connection = pika.BlockingConnection(pika.ConnectionParameters("localhost"))
channel = connection.channel()
# Declare main queue with DLQ routing
channel.queue_declare(
queue="agent_tasks",
durable=True,
arguments={
"x-dead-letter-exchange": "agent_tasks_dlx",
"x-message-ttl": 3600000, # 1 hour TTL
"x-max-length": 10000
}
)
# Declare dead letter exchange and queue
channel.exchange_declare(exchange="agent_tasks_dlx", exchange_type="direct")
channel.queue_declare(queue="agent_tasks_dead", durable=True)
channel.queue_bind("agent_tasks_dead", "agent_tasks_dlx", routing_key="agent_tasks")
def callback(ch, method, properties, body):
try:
task = json.loads(body)
process_task(task)
# Explicit ack — message removed from queue
ch.basic_ack(delivery_tag=method.delivery_tag)
except Exception as e:
print(f"Task failed: {e}")
# Nack without requeue — sends to DLQ
ch.basic_nack(delivery_tag=method.delivery_tag, requeue=False)
channel.basic_qos(prefetch_count=1) # One message at a time per consumer
channel.basic_consume("agent_tasks", callback)
channel.start_consuming()
Option 4: Recover orphaned messages on startup
import redis, time
def recover_orphaned_messages(queue_name: str, processing_queue: str, max_age_seconds: int = 300):
"""Re-queue messages stuck in processing (consumer crashed mid-process)"""
r = redis.Redis()
processing_messages = r.lrange(processing_queue, 0, -1)
for msg in processing_messages:
# Check if message has been in processing too long
# (requires storing timestamp with message)
try:
data = json.loads(msg)
started_at = data.get("processing_started_at", 0)
if time.time() - started_at > max_age_seconds:
print(f"Recovering orphaned message: {data.get('id')}")
r.lrem(processing_queue, 1, msg)
r.rpush(queue_name, msg) # Re-queue
except Exception as e:
print(f"Could not recover message: {e}")
# Call at consumer startup
recover_orphaned_messages("agent_tasks", "agent_tasks:processing")
Option 5: Monitor DLQ for alerting
async def monitor_dead_letter_queue(dlq_name: str):
"""Alert when messages land in DLQ"""
r = redis.Redis()
while True:
dlq_length = r.llen(dlq_name)
if dlq_length > 0:
# Alert and attempt to inspect
print(f"ALERT: {dlq_length} messages in dead letter queue '{dlq_name}'")
# Sample the oldest failure
sample = r.lindex(dlq_name, 0)
print(f"Sample failed message: {sample}")
await asyncio.sleep(60) # Check every minute
Queue Reliability Comparison
| Queue | Auto-retry | DLQ support | Ack required | Best for |
|---|---|---|---|---|
| Redis LPOP | No | Manual | No | Simple queues, low criticality |
| Redis BRPOPLPUSH | Manual | Manual | Manual | Reliable Redis queuing |
| AWS SQS | Yes (up to maxReceiveCount) | Yes (built-in) | Yes (delete) | AWS-native, high scale |
| RabbitMQ | Yes (nack+requeue) | Yes (DLX) | Yes (ack/nack) | Enterprise messaging |
| Celery | Yes (configurable) | Yes | Yes | Python task queues |
| BullMQ | Yes | Yes | Yes | Node.js |
Expected Token Savings
Debugging lost messages after crash: ~10,000 tokens DLQ + ack pattern prevents loss: messages are always recoverable
Environment
- Any agent system using message queues for task distribution
- Source: direct experience with production agent deployments
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