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Vinayak Savale
Vinayak Savale

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Day 4 – Handling Failures with Dead-Letter Queues (DLQ) in Azure Service Bus

Even in the most reliable systems, not every message can be processed successfully. πŸ‘‰ What happens to those β€œpoison messages”?

This is where Dead-Letter Queues (DLQs) come in.

πŸ”Ή What is a Dead-Letter Queue?
A sub-queue attached to every Queue or Topic Subscription in Azure Service Bus.

Stores messages that cannot be delivered or processed successfully.

Prevents endless retries or message loss.

πŸ”Ή Common Reasons Messages Go to DLQ
βœ” Message exceeds TTL (Time-To-Live).

βœ” Message is too large.

βœ” Max Delivery Count exceeded (message kept failing processing).

βœ” Explicit dead-lettering by application.

βœ… Real-World Example
Imagine a payment processing system:

A malformed message without PaymentId keeps failing.

Instead of blocking the queue forever, it moves to DLQ for later inspection.

This ensures:

Healthy messages keep flowing.

Faulty messages can be debugged.

πŸ”Ή Accessing DLQ in .NET
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πŸ”Ή Key Benefits of DLQ
βœ” Reliability – Messages aren’t lost.

βœ” Debugging – Root cause analysis of failed messages.

βœ” Isolation – Poison messages don’t block valid ones.

πŸ”Ή Best Practices
Monitor DLQ regularly β†’ Don’t let it overflow.

Automate processing β†’ Create a retry mechanism.

Alerting β†’ Set up Azure Monitor alerts for DLQ growth.

πŸ”Ή Wrapping Up
Dead-Letter Queues are your safety net in message-driven systems. They ensure your application remains resilient and messages are never silently lost.

In Day 5, we’ll explore Duplicate Detection in Azure Service Bus β€” how to prevent the same message from being processed multiple times.

πŸ’¬ How are you handling poison messages in your systems? Do you rely on DLQ or custom error handling?

Azure #ServiceBus #DotNet #Messaging #DeadLetterQueue #CloudComputing #Microservices #Resilience #SystemDesign #ErrorHandling

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