August 2025 Recap: Azure Database for MySQL
We're excited to share a summary of the Azure Database for MySQL updates for the month of August 2025. Join us live on our YouTube channel on September 11, 2025 for an exclusive webinar where we’ll dive deeper into these updates and answer your questions! Watch it live here. Azure Database for MySQL 8.4 - General Availability We’re excited to announce that Azure Database for MySQL now supports MySQL 8.4 in General Availability (GA). This means you can create new MySQL 8.4 servers on Azure fully supported for production workloads. MySQL 8.4 is a long-term supported release from the MySQL community, bringing the latest features and improvements while emphasizing stability. With Azure’s managed service, you get these new capabilities backed by Azure’s enterprise-grade reliability and support. In short, MySQL 8.4 GA opens the door for you to upgrade your databases and future-proof your MySQL environment on Azure. Learn more. Cross subscription and cross resource-group placement in restore/replica provisioning workflow You can now restore a server or create a read replica in a different subscription and resource group in Azure Database for MySQL – Flexible Server. This enhancement offers greater flexibility for cross-environment restores, resource organization, and subscription-level separation, helping meet governance and operational requirements. Learn more. Ability to delete on-demand backup You can now delete on-demand backups in Azure Database for MySQL – Flexible Server, giving you greater control over backup management and storage costs. This feature allows you to remove on-demand backups that are no longer needed, helping maintain a cleaner backup inventory and optimize resource usage. Learn more. Unlocking Regional Insights with the Location Based Capabilities REST API Managing MySQL Flexible Server deployments across Azure regions often means choosing the right Azure region for your MySQL deployment is critical. The new Location-Based Capability Set – List API helps you: Retrieve real-time, region-specific capabilities. Compare SKUs, storage options, backup retention, and HA configurations. Integrate insights into automation pipelines for smarter deployments. This API empowers architects and developers to make informed decisions, reduce misconfigurations, and accelerate deployment cycles. Learn more. Stay Connected We look forward to your feedback as you explore these enhancements and continue building with Azure Database for MySQL. If you have any suggestions or queries about our service, please let us know by emailing us at AskAzureDBforMySQL@service.microsoft.com. You can also submit product ideas and feedback at Azure Database for MySQL Community forum. To learn more about what's new with Flexible Server, see What's new in Azure Database for MySQL - Flexible Server. Stay tuned for more updates and announcements by following us on social media: YouTube | LinkedIn | X. Take care, and thanks for being part of our community!Azure AI Translation Preprocessing Workflow
In today's global enterprise landscape, translation accuracy and efficiency are critical to delivering seamless multilingual experiences. Yet, many organizations struggle with inconsistent results, API errors, and high costs when using machine translation services. That's where our custom-built Azure AI Translation Preprocessing Workflow comes in. 🌐 Solution Introduction The Azure AI Translation Preprocessing Workflow is a purpose-built solution designed to bridge the gap between raw document inputs and high-quality machine translation outcomes. Developed in response to real-world enterprise challenges, this service automates the preprocessing of multilingual documents, ensuring they meet Azure AI Translate’s strict formatting and content standards. By intelligently analyzing, validating, and optimizing files before translation, it empowers organizations to achieve greater accuracy, lower costs, and eliminate common API failures. Whether you're dealing with inconsistent document formats, embedded content, or language detection issues, this workflow provides a scalable and intelligent foundation for enterprise-grade translation pipelines. It’s not just a tool—it’s a strategic enabler for global communication. 🎯 Why This Service? This solution was designed to address real-world challenges faced by enterprise teams using Azure AI Translation Services: 🚀 Azure AI Translate Ready: Fully optimized for Azure's translation service standards. 📊 92% Translation Quality Improvement: Pre-validates and optimizes content for better results. 💰 30% Cost Reduction: Intelligent content filtering reduces unnecessary translation costs. ⚡ Zero API Errors: Format validation prevents common Azure AI service failures. 🔍 Intelligent Analysis: Advanced content segmentation and language detection. ✨ Key Features 🎯 Azure AI Translate Optimization Readiness Scoring (0-100%): Quantifies document preparation for Azure AI Translate. Language Detection: Automatic identification of document languages. Content Segmentation: Optimizes text segments for Azure's 5,000 character limit. Format Compliance: Ensures Strict Open XML Document standards. Translation Quality Enhancement: Intelligent content filtering and preparation. 🛠️ Core Capabilities Multi-format Support: Converts DOCX, DOC, RTF, ODT, TXT → DOCX. Comprehensive Validation: Advanced DOCX format verification. Content Intelligence: Detects and analyzes translatable text. REST API: 9 endpoints with interactive Swagger UI documentation. Background Processing: Async file processing capabilities. Detailed Analytics: Word counting, language hints, content type analysis. 🔧 Technical Excellence FastAPI Framework: High-performance async web framework. LibreOffice Integration: Professional document conversion. Comprehensive Logging: Detailed operation tracking and metadata. Error Handling: Robust validation and error recovery. Docker Ready: Containerization support for easy deployment. 🚀 Real-World Impact This solution was born out of a critical support case where translation failures were impacting production workflows. By automating preprocessing and validation, we not only resolved the immediate issue but also architected a scalable, long-term solution that enhances translation reliability and reduces operational overhead. 📢 Get Involved Explore the GitHub repository, contribute, or adapt the solution to your enterprise needs: 👉 https://github.com/vinod-soni-microsoft/azure-ai-translation-preprocessing-workflow Let’s build smarter, faster, and more reliable translation workflows together.
Best practices for safely performing schema changes in Azure Database for MySQL
Azure Database for MySQL - Flexible Server is built on the open-source MySQL database engine, and the service supports MySQL 8.0 and newer versions. This means that users can take advantage of the flexibility and advanced capabilities of MySQL’s latest features while benefitting from a fully managed database service. While newer versions and features can provide a lot of value, the recent issues identified with MySQL versions 8.0+ makes it important to be aware of potential risks that can occur during certain operations, particularly if you are making online schema changes. Issues with data loss and duplicate keys with Online DDL Online Data Definition Language (DDL) operations are a powerful feature in MySQL, enabling schema changes like ALTER TABLE or OPTIMIZE TABLE with minimal impact on table availability. These operations are designed to reduce downtime by allowing concurrent reads and writes during schema modifications, making them an essential tool for managing active databases efficiently. However, a recent post on the Percona blog, Who Ate My MySQL Table Rows? highlights critical risks associated with MySQL 8.0.x versions after 8.0.27 and all versions beyond 8.4.y. Specifically, the open-source INPLACE algorithm, commonly used for online schema changes, can lead to data loss and duplicate key errors under certain conditions. These issues arise from constraints in the INPLACE algorithm, particularly during ALTER TABLE and OPTIMIZE TABLE operations, exposing vulnerabilities that compromise data integrity and system reliability. These risks are called out in the following bug reports: Bug #115511: Data loss during online ALTER operations with concurrent DML Bug #115608: Duplicate key errors caused by online ALTER operations Documented issues related to the INPLACE algorithm (used for online DDL) can cause: Data Loss: Rows may be accidentally deleted or become inaccessible. Duplicate Keys: Indexes can end up with duplicate entries, leading to data consistency issues and potential replication errors. Problems arise when INPLACE operations, such as ALTER TABLE or OPTIMIZE TABLE, run concurrently with: DML operations (INSERT, UPDATE, DELETE): Modifications to table data during the rebuild. A purge activity: Background cleanup operations for old row versions in InnoDB. These scenarios can lead to anomalies resulting from race conditions and incomplete synchronization between concurrent activities. Impact on Azure Database for MySQL - Flexible Server Customers For Azure Database for MySQL Flexible Server customers using MySQL 8.0+ and all versions after 8.4.y, this issue is particularly critical as it affects: Data Integrity: During schema changes such as ALTER TABLE or OPTIMIZE TABLE run using the INPLACE algorithm, data rows may be lost or duplicated if these operations run concurrently with a DML activity (e.g., INSERT, UPDATE, or DELETE) or background purge tasks. This can compromise the accuracy and reliability of the database, potentially leading to incorrect query results or the loss of critical business data. Replication Instability: Duplicate keys or missing rows can interrupt replication processes, which rely on a consistent data stream across the primary and replica servers. These issues can arise when there are concurrent insertions into the table during schema changes, leading to data inconsistencies between the primary and replicas. Such inconsistencies may result in replication lag, errors, or even a complete breakdown of high-availability setups, requiring manual intervention to restore synchronization. Operational Downtime: Resolving these issues often involves manually syncing data or restoring backups. These recovery efforts can be time-consuming and disruptive, leading to extended downtime for applications and potential business impact. Recommendations for safe schema changes on Azure Database for MySQL flexible servers To minimize the risks of data loss and duplicate keys while making schema changes, follow these best practices: Set old_alter_table=ON to Default to COPY Algorithm Enable the server parameter old_alter_table system variable so that ALTER TABLE operations without a specified ALGORITHM default to using the COPY algorithm instead of INPLACE. This reduces the risk for users who do not explicitly specify the ALGORITHM in their commands. Learn more on how configure server parameters in Azure Database for MySQL. Avoid using ALGORITHM=INPLACE Do not explicitly use ALGORITHM=INPLACE for ALTER TABLE commands, as it increases the risk of data loss or duplicate keys. Back up your data before schema changes Always perform a full on-demand backup of your server before executing schema changes. This precaution ensures data recoverability in case of unexpected issues. Learn more on how to take full on-demand backups for your server. Avoid Concurrent DML during schema changes Schedule schema changes like ALTER TABLE and OPTIMIZE TABLE during application maintenance windows when no concurrent writes activities occur. This minimizes race conditions and synchronization conflicts. Use External Tools for Safer Online Schema Changes Consider using external tools like pt-online-schema-change to modify table definitions without blocking concurrent changes. These tools enable you to make schema changes with minimal impact on availability and performance. Learn more about pt-online-schema-change. Disclaimer: The pt-online-schema-change tool is not managed or supported by Microsoft; use it at your discretion. Mitigation plans To address these risks, we’re actively working to integrate the necessary fixes to ensure a more robust and reliable experience for our customers. New Servers Fully Secured by End of February 2025 All new Azure Database for MySQL Flexible Server instances created after 1 st March 2025, will include the latest fixes, ensuring that schema changes are safeguarded against data loss and duplicate key risks. Rollout for Existing Servers For existing servers, we will roll out patches during upcoming maintenance windows by end of Q1 of Calendar Year 2025 We recommend monitoring your Azure portal for scheduled maintenance windows and Release notes for announcements about critical updates and patches. Priority updates available upon request If you require an urgent update outside of the scheduled maintenance windows, you can contact Azure Support. Provide the necessary server details and an appropriate maintenance window, and our team will work with you to prioritize the patching process. Note that priority patching will be available by February 2025. We recommend monitoring Release notes for announcements about critical updates and patches. Conclusion Safely managing schema changes on MySQL servers requires understanding the risks associated with online DDL operations, such as potential data loss and duplicate keys. To help safeguard data integrity and maintain server stability, implement best practices, for example enabling the COPY algorithm, using offline operations if feasible, or scheduling changes during low activity periods. Fixes are expected by the end of February 2025, and new Azure Database for MySQL flexible servers will be fully protected against these bugs. We will apply updates to existing servers during maintenance windows in Q1 2025. Following the recommendations above will help ensure that you can confidently make schema changes while preserving the reliability and performance of your server.On-Demand Backups in Azure Database for PostgreSQL Flexible Server - Public Preview
In today’s fast-paced world of data management, safeguarding your databases is crucial. Azure Database for PostgreSQL Flexible Server simplifies this process with its robust backup management capabilities. The service automatically creates storage volume snapshots of your entire database instance, including all databases, as part of its scheduled backup routine. You can now create on-demand backups whenever required. On-demand backups can be taken in addition to scheduled automatic backups. This feature is especially useful for scenarios such as preparing for potentially high-risk operations like system upgrades or schema changes or performing periodic refreshes that fall outside your scheduled backup cadence. Key Benefits Flexible Retention: On-demand backups are retained according to your specified backup retention window, with the added flexibility to delete them when they are no longer needed. User Control: You can initiate backups based on your business needs and delete on-demand backups anytime. Seamless Integration: These backups are stored alongside automated backups, but only on-demand backups are user-deletable. Automated backups remain managed by the Flexible Server service to ensure compliance with retention requirements. Cost Optimization: While automated backups are managed by Azure and cannot be deleted, on-demand backups provide you with control over storage costs. Delete these backups once their purpose is served to avoid unnecessary storage expenses. Azure Database for PostgreSQL Flexible Server provides a comprehensive, user-friendly backup solution, giving you the confidence to manage your data effectively and securely. Let us explore how on-demand backups can elevate your database management strategy and provide peace of mind during high-stakes operations. Automated Backups vs On-Demand Backups Feature Automated Backups On-Demand Backups Creation Scheduled by Azure Manually initiated by the user Retention Based on the backup policy Based on the backup policy Deletion Managed by Azure User-controlled Use Cases Regular data protection High-risk operations, ad-hoc needs How to take On-Demand Backups using portal. In the Azure portal, choose your Azure Database for PostgreSQL flexible server. Click Settings from the left panel and choose Backup and Restore. Click Backup now and provide your backup name. 4. Click Trigger. 5. A notification is shown that an On-demand backup trigger has been initiated. How to take On-Demand Backups using CLI. You can run the following command to perform an on-demand backup of a server. az postgres flexible-server backup create [--source-server-name] [--resource-group] [--backup-name] Example : For more information: How to perform On-demand backups using CLI What's Next Once you have taken an on-demand backup based on your business needs, you can retain it until your high-risk operation is complete or use it to refresh your reporting or non-production environments. When the backup is no longer needed, you can delete it to optimize storage costs. To restore or delete on-demand backups, you can use the Azure portal, CLI, or API for seamless management. Limitations: In preview, on-demand backups currently work with general-purpose and Memory-optimized SKUs only. We will add support for burstable SKUs later. Take Control of Your Database Protection Today! The ability to create on-demand backups is critical for managing and safeguarding your data. Whether you're preparing for high-risk operations or refreshing non-production environments, this feature puts flexibility and control in your hands. Get started now: Create your first on-demand backup using the Azure Portal or CLI. Optimize your storage costs by deleting backups when no longer needed. Restore with ease to keep your databases resilient and ready for any challenge. Protect your data effectively and ensure your database is always prepared for the unexpected. Learn more about Azure Database for PostgreSQL Flexible Server and explore the possibilities with on-demand backups today! You can always find the latest features added to Flexible server in this release notes page. We are eager to hear all the great scenarios this new feature helps you optimize, and look forward to receiving your feedback at https://aka.ms/PGfeedback.Autogen: Microsoft’s Open-Source Tool for Streamlining Development
Are you a technical student looking for a tool that can help you generate high-quality code, documentation, and tests for your projects? If so, you might want to check out AutoGen a framework that enables development of large language model (LLM) applications using multiple agents that can converse with each other to solve tasks.
