[Azure Service Bus] JMS messages getting dead-lettered
The article discusses a problem where numerous messages end up in the dead letter queue (DLQ) when the JMS service bus consumer connects to the Azure Service Bus using Qpid jars. The reason for the messages being dead-lettered is that they have reached the maximum delivery count.
The fundamental issue stems from Apache Qpid's message handling. Qpid utilizes a local buffer to prefetch messages from the Azure Service Bus, storing them prior to delivery to the consumer. The complication occurs when Qpid prefetches an excessive number of messages that the consumer is unable to process within the lock duration. Consequently, the consumer is unable to acknowledge or finalize the processing of these messages before the lock expires, leading to an accumulation of messages in the Dead Letter Queue (DLQ).
To address this problem, it is crucial to either turn off Qpid's local buffer or modify the prefetch count. Disabling prefetching is achievable by setting jms.prefetchPolicy.all=0 in the JMS URL. This configuration allows the JMS client to directly consume messages from the Azure Service Bus, circumventing Qpid's local buffer. Consequently, the consumer can process messages at a suitable pace, guaranteeing smooth processing and issue-free completion.
Why is Prefetch not the default option in Microsoft .NET/Java/Python libs?
Published on:
Learn moreRelated posts
Azure Storage - TLS 1.0 and 1.1 retirement
Overview TLS 1.0 and 1.1 retirement on Azure Storage was previously announced for Nov 1st, 2024, and it was postponed recently to 1 year later...
Efficient Management of Append and Page Blobs Using Azure Storage Actions
Overview In Azure Storage, Blob Lifecycle Management (BLM) allows you to automate the management of your data based on rules defined by...
[Azure AI Search] Internal Server Error when creating CMK encrypted objects
Scenario Customers follow the Microsoft doc to create CMK encrypted objects (data source, index etc.), but get the 500 Internal Serv...
Optimizing Azure Table Storage: Automated Data Cleanup using a PowerShell script with Azure Automate
Scenario This blog’s aim is to manage Table Storage data efficiently. Imagine you have a large Azure Table Storage that accumulates logs from ...
Optimizing Azure Table Storage: Automated Data Clean-up using a PowerShell script with Azure Automat
Scenario This blog’s aim is to manage Table Storage data efficiently. Imagine you have a large Azure Table Storage that accumulates logs from ...
Restoring Soft-Deleted Blobs with multithreading in Azure Storage Using C#
Blob soft delete is an essential feature that safeguards your data against accidental deletions or overwrites. By retaining deleted data for a...
Performing simple Azure Table Storage REST API operations using curl command.
The blog provides guidance to perform simple Table Storage REST API operations such as Create table, Delete Table, Insert entity, Delete entit...
Bulk delete all the old jobs from the batch account
Deleting a Job also deletes all Tasks that are part of that Job, and all Job statistics. This also overrides the retention period for Task dat...
Utilizing Azure Storage and Runbooks for scheduled automated backups of Azure SQL Databases
In this article, we are going to provide detailed steps to create a scheduled Azure SQL Database backup to storage account using automation. T...
Subscribe to Azure Storage Blob Lifecycle Policy Events
The LifecyclePolicyCompleted event is generated when the actions defined by a lifecycle management policy are performed. Refer - Opt...