Solve: Debugging 'Messages in Flight'— A Systematic Guide to Unsticking AWS SQS in Document Processing Pipelines Introduction

Debugging 'Messages in Flight': A Systematic Guide to Unsticking AWS SQS in Document Processing Pipelines
Introduction

The AI boom has transformed document processing from a niche enterprise need into a mainstream requirement. Teams everywhere are building pipelines that ingest PDFs, extract text with AWS Textract, and feed the results into RAG systems for intelligent document search. It's an exciting time to be building these systems—but also a frustrating one when things go wrong.

If you've ever stared at your AWS SQS console watching "Messages in Flight" tick upward while your document processing pipeline mysteriously stalls, you're not alone. This metric is like the "check engine" light of distributed systems—it tells you something's wrong, but not what or where.

This guide will teach you how to systematically debug stuck SQS messages in document processing pipelines, turning you from a confused observer into a confident troubleshooter. We'll walk through the detective work needed to find bottlenecks, fix them, and prevent them from happening again.

Understanding the Architecture

Before diving into debugging, let's establish the common architecture pattern that causes these headaches. Most modern document processing pipelines follow this flow:

Document Processing Pipeline/ 

├── S3 (Document Upload) 

├── Lambda (S3 Trigger) 

├── SQS (Upload Queue) 

├── Lambda (SQS Consumer) 

├── Textract (Text Extraction) 

├── SNS (Completion Notifications) 

├── SQS (Completion Queue) 

├── Lambda (Completion Processor) 

├── Database (Structured Storage) 

└── RAG System (AI/Search)


Here's what each step does:

1. S3: Documents are uploaded here
2. Lambda (S3 Trigger): Triggered by S3 uploads, sends notification to SQS
3. SQS (Upload Queue): Buffers upload notifications
4. Lambda (SQS Consumer): Processes upload notifications, starts Textract jobs
5. Textract: Asynchronously extracts text from documents
6. SNS: Receives Textract completion notifications
7. SQS (Completion Queue): Buffers Textract completion notifications
8. Lambda (Completion Processor): Processes results, stores in database/RAG system

Why all this complexity? Decoupling and resilience. Each SQS queue acts as a shock absorber, preventing upstream spikes from overwhelming downstream services. When someone uploads 1,000 documents at once, SQS ensures they're processed steadily rather than crashing your Textract quota or database.

The trade-off? More moving parts mean more places for things to get stuck.

Decoding "Messages in Flight"

When you see "Messages in Flight" in your SQS console, here's what's actually happening:

Messages in Flight = Messages delivered to a consumer but not yet deleted from the queue

In AWS Lambda + SQS integration, this means:

• SQS delivered the message to your Lambda function
• Lambda is processing (or tried to process) the message
• Lambda hasn't successfully completed and deleted the message yet

This is different from:

• Messages Available: Sitting in queue, not yet delivered to any consumer
• Messages Not Visible: Delivered to consumer, within visibility timeout, actively being processed

High "Messages in Flight" usually indicates one of these problems:

• Lambda function is throwing errors
• Lambda is timing out before completing
• Lambda is hanging on downstream API calls
• Lambda is hitting concurrency limits

The Detective's Toolkit: Systematic Troubleshooting

When messages are stuck, resist the urge to randomly restart services or increase timeouts. Instead, follow this systematic approach:

Step 1: Start with the Dead Letter Queue

This is your most valuable debugging tool and should be your first stop.

What to check:

• Navigate to your SQS queue in the AWS Console
• Look for a configured Dead Letter Queue (DLQ) in the queue's "Redrive Policy"
• If there's a DLQ, check if it contains messages

Why this matters: The DLQ contains messages that failed processing multiple times. These messages are your smoking gun—they tell you exactly what data is causing failures and why.

How to investigate:

• Use "Poll for messages" on the DLQ
• Examine the message body and attributes
• Look for patterns in failed messages (file types, sizes, formats)
• Check the ApproximateReceiveCount attribute to see how many times processing was attempted

Step 2: Lambda CloudWatch Logs and Metrics

If the DLQ doesn't reveal the issue, examine your Lambda functions.

Key metrics to check:

• Errors: Are your Lambdas throwing exceptions?
• Throttles: Is Lambda hitting concurrency limits?
• Duration: Are functions timing out?
• Invocations: Is Lambda being triggered when messages arrive?

CloudWatch Logs investigation:

• Filter logs for "ERROR", "EXCEPTION", or "TIMEOUT"
• Look for patterns in error messages
• Check if Lambda is successfully calling downstream services
• Correlate error timestamps with SQS message timestamps

Common Lambda issues:

• Memory limits causing out-of-memory errors
• Timeout limits too short for large document processing
• Unhandled exceptions in error scenarios
• Missing error handling for downstream API failures

Step 3: SQS Metrics Correlation

SQS metrics tell the story of message flow through your system.

Key metrics to compare:

• NumberOfMessagesReceived: Messages delivered to consumers
• NumberOfMessagesDeleted: Messages successfully processed
• ApproximateNumberOfMessagesVisible: Messages waiting in queue

What to look for:

• Gap between received and deleted: Messages are being picked up but not completed
• Consistently high visible messages: Consumers aren't keeping up with message volume
• Spiky patterns: Indicates intermittent failures rather than systemic issues

Step 4: Downstream Service Limits

Your Lambda might be failing because downstream services are overwhelmed or hitting limits.

Textract limits to check:

• Concurrent job limits (varies by region)
• Monthly page processing limits
• API throttling (StartDocumentTextDetection calls per second)

Database issues:

• Connection pool exhaustion
• Write capacity limits (DynamoDB)
• Network timeouts
• Schema validation errors

RAG system problems:

• API rate limiting
• Service availability
• Authentication token expiration
• Payload size limits

Step 5: IAM Permissions Audit

Permissions issues often manifest as mysterious failures in logs.

Lambda execution roles to verify:

• SQS permissions: ReceiveMessage, DeleteMessage, GetQueueAttributes
• Textract permissions: StartDocumentTextDetection, GetDocumentTextDetection
• S3 permissions: GetObject for document access
• Database permissions: Appropriate read/write access
• SNS permissions: Publish if Lambda publishes notifications

Quick permission test: Use AWS CLI with the Lambda's role to manually test each permission your function needs.

Common Culprits and Solutions

Based on real-world debugging experience, here are the most frequent causes of stuck SQS messages:

Lambda Timeouts and Memory Issues

Symptoms:

• Functions timing out at exactly the configured timeout limit
• Memory usage spikes in CloudWatch metrics
• Intermittent failures with large documents

Solutions:

• Increase Lambda timeout (max 15 minutes)
• Increase memory allocation (which also increases CPU)
• Implement processing in chunks for large documents
• Add memory monitoring to detect optimization opportunities

Textract Service Quotas and Throttling

Symptoms:

• Errors mentioning "ProvisionedThroughputExceededException"
• Consistent failures after processing a certain number of documents
• Regional quota errors

Solutions:

• Implement exponential backoff retry logic
• Request quota increases through AWS Support
• Distribute processing across multiple regions
• Implement queue-based throttling to stay within limits

Database Connection Failures

Symptoms:

• Connection timeout errors in Lambda logs
• Database connection pool exhaustion
• Intermittent database unavailability

Solutions:

• Implement connection pooling with proper lifecycle management
• Add database health checks before processing
• Use connection retry logic with exponential backoff
• Consider using RDS Proxy for connection management

Malformed Messages and Data Validation Errors

Symptoms:

• Consistent failures with specific document types
• JSON parsing errors in Lambda logs
• Schema validation failures

Solutions:

• Add input validation at the beginning of Lambda functions
• Implement graceful error handling for malformed data
• Use try-catch blocks around data parsing operations
• Send malformed messages to a separate queue for manual review

Visibility Timeout Misconfigurations

Symptoms:

• Messages being processed multiple times
• Duplicate entries in database or RAG system
• Functions completing successfully but messages reappearing

Solutions:

• Set visibility timeout to 6x your Lambda timeout
• Implement idempotency in your processing logic
• Use message deduplication for critical workflows
• Monitor for duplicate processing patterns

Prevention: Building Observable Pipelines

Once you've fixed the immediate issue, implement monitoring to catch problems early:

CloudWatch Alarms for Proactive Monitoring

Set up alarms for:
SQS Messages in Flight > threshold: Indicates processing issues
Lambda Error Rate > 1%: Catches intermittent failures
DLQ Messages > 0: Immediate notification of failed messages
Textract Job Failure Rate: Monitors downstream service health

Dashboard Design for Quick Troubleshooting

Create a dashboard showing:

• SQS queue depths and processing rates
• Lambda invocation, error, and duration metrics
• Textract job success/failure rates
• Database connection and query performance
• End-to-end processing time percentiles

Error Handling and Retry Strategies

Implement robust error handling:

• Exponential backoff: For transient failures
• Circuit breakers: To prevent cascading failures
• Dead letter queues: For messages that can't be processed
• Idempotency: To safely retry operations
• Structured logging: For easier debugging

Testing at Scale

Regularly test your pipeline with:

• Load testing: Simulate high document volumes
• Chaos engineering: Introduce failures to test resilience
• Document variety: Test with different file types and sizes
• Edge cases: Test with malformed or oversized documents

Conclusion

Debugging stuck SQS messages in document processing pipelines doesn't have to be a mysterious art. By following a systematic approach—starting with the Dead Letter Queue, examining Lambda metrics and logs, correlating SQS metrics, checking downstream services, and verifying permissions—you can quickly identify and resolve bottlenecks.

The key insights to remember:

• "Messages in Flight" means processing issues, not queuing issues
• Dead Letter Queues are your best debugging friend
• Lambda timeouts and memory limits are frequent culprits
• Downstream service limits need proactive monitoring
• Good observability prevents most debugging sessions

As document processing becomes more critical to business operations, building resilient, observable pipelines isn't just a technical nice-to-have—it's a business necessity. The time you invest in proper monitoring and error handling will save you countless hours of debugging in production.


The next time you see those "Messages in Flight" numbers climbing, you'll know exactly where to look and how to fix it. Happy debugging!