Understanding Azure SQL Long-Term Retention Immutability Configuration with Terraform and AzAPI
Executive Summary Organizations using Azure SQL Database Long-Term Retention (LTR) backup policies may encounter failures when attempting to disable backup immutability while also specifying an immutability mode in the same request. In the investigated scenario, the customer observed that the operation succeeded when performed through ARM templates but failed when executed through Terraform using AzAPI-based resources. The investigation determined that the Azure SQL resource provider enforces validation rules that prevent TimeBasedImmutabilityMode from being specified when TimeBasedImmutability is set to Disabled. The issue was not caused by the Azure SQL service itself, but rather by how configuration values were being submitted through Terraform and AzAPI resource updates. The recommended mitigation is to ensure that immutability mode is omitted or explicitly set to null when disabling time-based immutability. This allows the request to comply with the resource provider's validation requirements. Introduction Azure SQL Database supports immutable Long-Term Retention (LTR) backups to help organizations meet compliance, governance, and data protection requirements. These policies allow administrators to control whether retained backups can be modified or deleted. During an investigation involving Terraform and AzAPI deployments, a customer reported inconsistent behavior when attempting to disable backup immutability. While equivalent ARM template operations completed successfully, Terraform-based deployments generated validation errors. This article explains the observed behavior, the investigation findings, the confirmed root cause, and the recommended mitigation. Issue Description Reported Symptoms The customer reported the following behavior: Enabling and managing Long-Term Retention backup immutability worked successfully. Configurations involving immutability mode settings could be applied successfully under certain conditions. Attempts to disable immutability through Terraform resulted in failures. Similar operations appeared to succeed when performed using ARM templates. Technical Environment The discussion confirmed the following components: Azure SQL Database Long-Term Retention (LTR) backup policies Backup immutability configuration Terraform deployments AzAPI resources and AzAPI resource updates ARM template deployments Azure SQL Resource Provider validation logic Expected Behavior When administrators disable backup immutability, the configuration update should be accepted and the policy should transition to a disabled state. Actual Behavior Requests submitted through Terraform/AzAPI included both: TimeBasedImmutability = Disabled TimeBasedImmutabilityMode = Unlocked The Azure SQL resource provider rejected this configuration, returning an error indicating that an immutability policy mode cannot be specified when backup immutability is not enabled. Investigation and Troubleshooting 1. Initial Customer Question The customer sought clarification on whether enabling, disabling, locking, and unlocking backup immutability should all be possible through AzAPI resources and whether a product issue existed. 2. Review of Azure SQL Resource Provider Behavior The support team reviewed requests submitted to the Azure SQL resource provider and compared successful and unsuccessful operations. The investigation focused on configuration differences between ARM template deployments and Terraform-driven updates. Confirmed Finding When ARM templates disabled immutability, the request contained: TimeBasedImmutability = Disabled and did not include an immutability mode parameter. Confirmed Finding When Terraform attempted to disable immutability, the request included both: TimeBasedImmutability = Disabled TimeBasedImmutabilityMode = Unlocked This resulted in a validation failure from the Azure SQL resource provider. 3. Validation of Error Behavior The team verified that the error was generated by the Azure SQL resource provider and was reproducible outside Terraform, including equivalent testing through ARM deployments when the conflicting parameter combination was supplied. Confirmed Error The resource provider returned an error equivalent to: Cannot set immutability policy mode when backup immutability is not enabled. 4. Assessment of Terraform and AzAPI Behavior The investigation identified an important behavioral difference. Terraform itself did not yet expose dedicated Time-Based Immutability parameters in its SQL modules. As a result, the customer was using AzAPI resources to perform direct REST-based operations. The team discovered that: azapi_resource behaved as expected. azapi_resource_update could retrieve and reuse an existing property value when no value was explicitly provided. This behavior caused the immutability mode value to persist unexpectedly during updates. 5. Reproduction and Verification The engineering discussion included review and validation of the reported behavior. Testing confirmed that requests containing immutability mode while immutability was disabled were expected to fail due to platform validation. Root Cause Confirmed Root Cause The failure occurred because the update request attempted to disable backup immutability while simultaneously providing a value for TimeBasedImmutabilityMode. Azure SQL validation rules require immutability mode to be associated only with an enabled immutability configuration. When immutability is disabled, an immutability mode must not be supplied. An additional contributing factor was the behavior of azapi_resource_update, which could retain a previously configured immutability mode value when no new value was explicitly provided. Consequently, requests unintentionally included an immutability mode even though the intent was to disable immutability entirely. The available evidence supports this conclusion through: Comparison of successful ARM template requests and failing Terraform requests. Reproduction of the same validation behavior by the Azure SQL resource provider. Validation of the AzAPI update behavior involving retained values. Mitigation and Resolution Recommended Mitigation When disabling backup immutability: Set TimeBasedImmutability to Disabled. Do not provide TimeBasedImmutabilityMode. Terraform/AzAPI Workaround The investigation determined that explicitly setting: TimeBasedImmutabilityMode = null prevents the previous value from being reused and allows the request to be processed correctly. Configuration Matrix Discussed The support team identified the following expected behavior: Operation Immutability Mode Requirement Locking backups Mode should be set to Locked Unlocking while remaining enabled Mode may be supplied and is recommended for clarity Disabling immutability Mode should not be supplied This guidance was explicitly discussed during the investigation. Validation After applying the mitigation: The disable operation should complete without the immutability mode conflict. Requests should no longer trigger the Azure SQL validation error related to immutability mode usage. Recommendations and Best Practices Recommendations Supported by the Investigation Ensure that immutability mode is not included when disabling backup immutability. Review Terraform templates for dynamically generated properties that may continue to emit previously populated values. When using AzAPI update resources, explicitly manage nullable properties where supported to avoid unintended value persistence. Important Considerations Behavior may vary depending on: Azure SQL API version Terraform provider version AzAPI provider implementation details Existing Long-Term Retention backup state Whether previously locked backups exist Always validate deployment behavior in a non-production environment before applying configuration changes broadly. Conclusion This investigation demonstrated that the inability to disable Azure SQL Long-Term Retention backup immutability was not caused by a platform defect in Azure SQL. Instead, the failure occurred because requests attempted to specify an immutability mode while immutability itself was being disabled. The issue was further influenced by AzAPI update behavior that could preserve previously configured values unless explicitly cleared. Setting the immutability mode to null, or removing it entirely when disabling immutability, resolved the problem. The key technical takeaway is that TimeBasedImmutabilityMode and TimeBasedImmutability must be configured consistently with Azure SQL resource provider validation rules, particularly during infrastructure-as-code deployments. Public Documentation Azure SQL Database Long-Term Retention documentation Azure SQL Backup Immutability documentation ARM/Bicep resource documentation for backup Long-Term Retention policies Terraform provider documentation for Azure SQL DatabaseFebruary 2026 Recap: Azure Database for PostgreSQL
Hello Azure Community, We’re excited to share the February 2026 recap for Azure Database for PostgreSQL, featuring a set of updates focused on speed, simplicity, and better visibility. From Terraform support for Elastic Clusters and a refreshed VM SKU selection experience in the Azure portal to built‑in Grafana dashboards, these improvements make it easier to build, operate, and scale PostgreSQL on Azure. This recap also includes practical GIN index tuning guidance, enhancements to the PostgreSQL VS Code extension, and improved connectivity for azure_pg_admin users. Features Terraform support for Elastic Clusters - Generally Available Dashboards with Grafana - Generally Available Easier way to choose VM SKUs on portal – Generally Available What’s New in the PostgreSQL VS Code Extension Priority Connectivity to azure_pg_admin users Guide on 'gin_pending_list_limit' indexes Terraform support for Elastic Clusters Terraform now supports provisioning and managing Azure Database for PostgreSQL Elastic Clusters, enabling customers to define and operate elastic clusters using infrastructure‑as‑code workflows. With this support, it is now easier to create, scale, and manage multi‑node PostgreSQL clusters through Terraform, making it easier to automate deployments, replicate environments, and integrate elastic clusters into CI/CD pipelines. This improves operational consistency and simplifies management for horizontally scalable PostgreSQL workloads. Learn more about building and scaling with Azure Database for PostgreSQL elastic clusters. Dashboards with Grafana — Now Built-In Grafana dashboards are now natively integrated into the Azure Portal for Azure Database for PostgreSQL. This removes the need to deploy or manage a separate Grafana instance. With just a few clicks, you can visualize key metrics and logs side by side, correlate events by timestamp, and gain deep insights into performance, availability, and query behavior all in one place. Whether you're troubleshooting a spike, monitoring trends, or sharing insights with your team, this built-in experience simplifies day-to-day observability with no added cost or complexity. Try it under Azure Portal > Dashboards with Grafana in your PostgreSQL server view. For more details, see the blog post: Dashboards with Grafana — Now in Azure Portal for PostgreSQL. Easier way to choose VM SKUs on portal We’ve improved the VM SKU selection experience in the Azure portal to make it easier to find and compare the right compute options for your PostgreSQL workload. The updated experience organizes SKUs in a clearer, more scannable view, helping you quickly compare key attributes like vCores and memory without extra clicks. This streamlined approach reduces guesswork and makes selecting the right SKU faster and more intuitive. What’s New in the PostgreSQL VS Code Extension The VS Code extension for PostgreSQL helps developers and database administrators work with PostgreSQL directly from VS Code. It provides capabilities for querying, schema exploration, diagnostics, and Azure PostgreSQL management allowing users to stay within their editor while building and troubleshooting. This release focuses on improving developer productivity and diagnostics. It introduces new visualization capabilities, Copilot-powered experiences, enhanced schema navigation, and deeper Azure PostgreSQL management directly from VS Code. New Features & Enhancements Query Plan Visualization: Graphical execution plans can now be viewed directly in the editor, making it easier to diagnose slow queries without leaving VS Code. AGE Graph Rendering: Support is now available for automatically rendering graph visualizations from Cypher queries, improving the experience of working with graph data in PostgreSQL. Object Explorer Search: A new graphical search experience in Object Explorer allows users to quickly find tables, views, functions, and other objects across large schemas, addressing one of the highest-rated user feedback requests. Azure PostgreSQL Backup Management: Users can now manage Azure Database for PostgreSQL backups directly from the Server Dashboard, including listing backups and configuring retention policies. Server Logs Dashboard: A new Server Dashboard view surfaces Azure Database for PostgreSQL server logs and retention settings for faster diagnostics. Logs can be opened directly in VS Code and analyzed using the built-in GitHub Copilot integration. This release also includes several reliability improvements and bug fixes, including resolving connection pool exhaustion issues, fixing Docker container creation failures when no password is provided, and improving stability around connection profiles and schema-related operations. Priority Connectivity to azure_pg_admin Users Members of the azure_pg_admin role can now use connections from the pg_use_reserved_connections pool. This ensures that an admin always has at least one available connection, even if all standard client connections from the server connection pool are in use. By making sure admin users can log in when the client connection pool is full, this change prevents lockout situations and lets admins handle emergencies without competing for available open connection slots. Guide on 'gin_pending_list_limit' indexes Struggling with slow GIN index inserts in PostgreSQL? This post dives into the often-overlooked gin_pending_list_limit parameter and how it directly impacts insert performance. Learn how GIN’s pending list works, why the right limit matters, and practical guidance on tuning it to strike the perfect balance between write performance and index maintenance overhead. For a deeper dive into gin_pending_list_limit and tuning guidance, see the full blog here. Learning Bytes Create Azure Database for PostgreSQL elastic clusters with terraform: Elastic clusters in Azure Database for PostgreSQL let you scale PostgreSQL horizontally using a managed, multi‑node architecture. With Elastic cluster now generally available, you can provision and manage elastic clusters using infrastructure‑as‑code, making it easier to automate deployments, standardize environments, and integrate PostgreSQL into CI/CD workflows. Elastic clusters are a good fit when you need: Horizontal scale for large or fast‑growing PostgreSQL workloads Multi‑tenant applications or sharded data models Repeatable and automated deployments across environments The following example shows a basic Terraform configuration to create an Azure Database for PostgreSQL flexible server configured as an elastic cluster. resource "azurerm_postgresql_flexible_server" "elastic_cluster" { name = "pg-elastic-cluster" resource_group_name = <rg-name> location = <region> administrator_login = var.admin_username administrator_password = var.admin_password version = "17" sku_name = "GP_Standard_D4ds_v5" storage_mb = 131072 cluster { size = 3 } } Conclusion That’s a wrap for the February 2026 Azure Database for PostgreSQL recap. We’re continuing to focus on making PostgreSQL on Azure easier to build, operate, and scale whether that’s through better automation with Terraform, improved observability, or a smoother day‑to‑day developer and admin experience. Your feedback is important to us, have suggestions, ideas, or questions? We’d love to hear from you: https://aka.ms/pgfeedback.January 2026 Recap: Azure Database for PostgreSQL
Hello Azure Community, We’re kicking off the year with important updates for Azure Database for PostgreSQL. From Premium SSD v2 features now available in public preview to REST API feature updates across developer tools, this blog highlights what’s new and what’s coming. Terraform Adds Support for PostgreSQL 18 – Generally Available Ansible module update - Generally Available Achieving Zonal Resiliency with Azure CLI - Generally Available SDKs Released : Go, Java, JavaScript, .NET and Python – Generally Available What’s New in Premium SSD v2 - Public Preview Latest PostgreSQL minor versions January 2026 Maintenance Release Notes Terraform Adds Support for PostgreSQL 18 Azure Database for PostgreSQL now provides support for PostgreSQL 18 which allows customers to create new servers with PostgreSQL 18 version and upgrade existing ones using Terraform. This update makes it easier to adopt PostgreSQL 18 on Azure while managing both provisioning and upgrades through consistent Terraform workflows. Learn more about using the new terraform resource Ansible Module Update A new Ansible module is now available with support for the latest GA REST API features, enabling customers to automate provisioning and management of Azure Database for PostgreSQL resources. This includes support for Elastic Clusters provisioning, deployment of PostgreSQL instances with PostgreSQL 18, and broader adoption of newly released Azure Database for PostgreSQL capabilities through Ansible. Learn more about using Ansible module with latest REST API features Achieve zonal resiliency with Azure CLI We have released updates to the Azure CLI that allow users to enable zone‑redundant high availability (HA) by default using a new --zonal-resiliency parameter. This parameter can be set to enabled or disabled. When --zonal-resiliency is enabled, the service provisions a standby server in a different availability zone than the primary, providing protection against zonal failures. If zonal capacity is not available in the selected region, you can use the --allow-same-zone flag to provision the standby in the same zone as the primary. Azure CLI commands: az postgres flexible-server update --resource-group <resource_group> --name <server> --zonal-resiliency enabled --allow-same-zone</server></resource_group> az postgres flexible-server update --resource-group <resource_group> --name <server> --zonal-resiliency Disabled</server></resource_group> az postgres flexible-server create --resource-group <resource_group> --name <server> --zonal-resiliency enabled --allow-same-zone</server></resource_group> Learn more about how to configure high availability on Azure Database for PostgreSQL. SDKs Released : Go, Java, JavaScript, .NET and Python We have released updated SDKs for Go, Java, JavaScript, .NET, and Python, built on the latest GA REST API (2025‑08‑01). These SDKs enable developers to programmatically provision, configure, and manage Azure Database for PostgreSQL resources using stable, production‑ready APIs. It also adds the ability to set a default database name for Elastic Clusters, simplifying cluster provisioning workflows, support for PostgreSQL 18. To improve developer experience and reliability, operation IDs have been renamed for clearer navigation, and HTTP response codes have been corrected so automation scripts and retries behave as expected. Learn More about .NET SDK Learn more about Go SDK Learn more about Java SDK Learn more about Javascript SDK Learn more about Python SDK What’s New in Premium SSD v2: Public Preview Azure Database for PostgreSQL Flexible Server now supports a broader set of resiliency and lifecycle management capabilities on Premium SSD v2, enabling production‑grade PostgreSQL deployments with improved durability, availability, and operational flexibility. In this preview, customers can use High Availability (same‑zone and zone‑redundant), geo‑redundant backups, in‑region and geo read replicas, geo‑disaster recovery (Geo‑DR), and Major Version Upgrades on SSDv2‑backed servers, providing both zonal and regional resiliency options for mission‑critical PostgreSQL workloads. These capabilities help protect data across availability zones and regions, support compliance and disaster‑recovery requirements, and simplify database lifecycle operations. Premium SSD v2 enhances these resiliency workflows with higher and independently scalable IOPS and throughput, predictable low latency, and decoupled scaling of performance and capacity. Customers can provision and adjust storage performance without over‑allocating disk size, enabling more efficient capacity planning while sustaining high‑throughput, low‑latency workloads. When combined with zone‑resilient HA and cross‑region data protection, SSDv2 provides a consistent storage foundation for PostgreSQL upgrades, failover, backup, and recovery scenarios. These capabilities are being expanded incrementally across regions as the service progresses toward general availability For more details, see Premium SSDv2 Latest Postgres minor versions: 18.1, 17.7, 16.11, 15.15, 14.20, 13.23 Azure Database for PostgreSQL now supports the latest PostgreSQL minor versions: 18.1, 17.7, 16.11, 15.15, 14.20, and 13.23. These updates are applied automatically during planned maintenance windows, ensuring your databases stay up to date with critical security fixes and reliability improvements no manual action required. This release includes two security fixes and over 50 bug fixes across indexing, replication, partitioning, memory handling, and more. PostgreSQL 13.23 is the final community release for version 13, which has now reached end-of-life (EOL). Customers still using PostgreSQL 13 on Azure should review their upgrade options and refer to Azure’s Extended Support policy for more details. For details about the minor release, see PostgreSQL community announcement. January 2026 Maintenance Release Notes We’re excited to announce the January 2026 version of Azure Database for PostgreSQL maintenance updates. This new version delivers major engine updates, new extensions, Elastic clusters enhancements, performance improvements, and critical reliability fixes. This release introduces expands migration and Fabric mirroring support, and adds powerful analytics, security, and observability capabilities across the service. Customers also benefit from improved Query Store performance, new WAL metrics, enhanced networking flexibility, and multiple Elastic clusters enhancements. All new servers are automatically onboarded beginning January 20, 2026, with existing servers upgraded during their next scheduled maintenance. For a complete list of features, improvements, and resolved issues, see the full release notes here. Azure Postgres Learning Bytes Managing Replication Lag with Debezium Change Data Capture (CDC) enables real‑time integrations by streaming row‑level changes from OLTP systems like PostgreSQL into event streams, data lakes, caches, and microservices. In a typical CDC pipeline, Debezium captures changes from PostgreSQL and streams them into Kafka with minimal latency. However, during large bulk updates that affect millions of rows, replication lag can spike significantly, impacting replication lag. This learning byte walks through how to detect and mitigate replication lag in Azure Database for PostgreSQL when using Debezium. Detect Replication Lag: Start by identifying where lag is building up in the system. Monitor replication slots and lag: Use the following query to inspect active replication slots and measure how far behind they are relative to the current WAL position: SELECT slot_name, active_pid, confirmed_flush_lsn, restart_lsn, pg_current_wal_lsn(), pg_size_pretty( ( pg_current_wal_lsn() - confirmed_flush_lsn ) ) AS lsn_distance FROM pg_replication_slots; Check WAL sender backend status: Verify whether WAL sender processes are stalled due to decoding or I/O waits: SELECT pid, backend_type, application_name, wait_event FROM pg_stat_activity WHERE backend_type = 'walsender' ORDER BY backend_start; Inspect spill activity : High spill activity indicates memory pressure during logical decoding and may contribute to lag. Large values for spill_bytes or spill_count suggest the need to increase logical_decoding_work_mem, reduce transaction sizes, or tune Debezium connector throughput. SELECT slot_name, spill_txns, spill_count, pg_size_pretty(spill_bytes) AS spill_bytes, total_txns, pg_size_pretty(total_bytes) AS total_bytes, stats_reset FROM pg_stat_replication_slots; Fix Replication Lag: Database and infrastructure tuning Reduce unnecessary overhead and ensure compute, memory, and storage resources are appropriately scaled to handle peak workloads. Connector level tuning Adjust Debezium configuration to keep pace with PostgreSQL WAL generation and Kafka throughput. This includes tuning batch sizes, poll intervals, and throughput settings to balance latency and stability. To learn more about diagnosing and resolving CDC performance issues, read the full blog: Performance Tuning for CDC: Managing Replication Lag in Azure Database for PostgreSQL with DebeziumApril 2025 Recap: Azure Database for PostgreSQL Flexible Server
Hello Azure Community, April has brought powerful capabilities to Azure Database for PostgreSQL flexible server, On-Demand backups are now Generally Available, a new Terraform version for our latest REST API has been released, the Public Preview of the MCP Server is now live, and there are also a few other updates that we are excited to share in this blog. Stay tuned as we dive into the details of these new features and how they can benefit you! Feature Highlights General Availability of On-Demand Backups Public Preview of Model Context Protocol (MCP) Server Additional Tuning Parameters in PG 17 Terraform resource released for latest REST API version General Availability of pg_cron extension in PG 17 General Availability of On-Demand Backups We are excited to announce General Availability of On-Demand backups for Azure Database for PostgreSQL flexible server. With this it becomes easier to streamline the process of backup management, including automated, scheduled storage volume snapshots encompassing the entire database instance and all associated transaction logs. On-demand backups provide you with the flexibility to initiate backups at any time, supplementing the existing scheduled backups. This capability is useful for scenarios such as application upgrades, schema modifications, or major version upgrades. For instance, before making schema changes, you can take a database backup, in an unlikely case, if you run into any issues, you can quickly restore (PITR) database back to a point before the schema changes were initiated. Similarly, during major version upgrades, on-demand backups provide a safety net, allowing you to revert to a previous state if anything goes wrong. In the absence of on-demand backup, the PITR could take much longer as it would need to take the last snapshot which could be 24 hours earlier and then replay the WAL. Azure Database for PostgreSQL flexible server already does on-demand backup behind the scenes for you and then deletes it when the upgrade is successful. Key Benefits: Immediate Backup Creation: Trigger full backups instantly. Cost Control: Delete on-demand backups when no longer needed. Improved Safety: Safeguard data before major changes or refreshes. Easy Access: Use via Azure Portal, CLI, ARM templates, or REST APIs. For more details and on how to get started, check out this announcement blog post. Create your first on-demand backup using the Azure portal or Azure CLI. Public Preview of Model Context Protocol (MCP) Server Model Context Protocol (MCP) is a new and emerging open protocol designed to integrate AI models with the environments where your data and tools reside in a scalable, standardized, and secure manner. We are excited to introduce the Public Preview of MCP Server for Azure Database for PostgreSQL flexible server which enables your AI applications and models to talk to your data hosted in Azure Database for PostgreSQL flexible servers according to the MCP standard. The MCP Server exposes a suite of tools including listing databases, tables, and schema information, reading and writing data, creating and dropping tables, listing Azure Database for PostgreSQL configurations, retrieving server parameter values, and more. You can either build custom AI apps and agents with MCP clients to invoke these capabilities or use AI tools like Claude Desktop and GitHub Copilot in Visual Studio Code to interact with your Azure PostgreSQL data simply by asking questions in plain English. For more details and demos on how to get started, check out this announcement blog post. Additional Tuning Parameters in PG17 We have now provided an expanded set of configuration parameters in Azure Database for PostgreSQL flexible server (V17) that allows you to modify and have greater control to optimize your database performance for unique workloads. You can now tune internal buffer settings like commit timestamp, multixact member and offset, notify, serializable, subtransaction, and transaction buffers, allowing you to better manage memory and concurrency in high-throughput environments. Additionally, you can also configure parallel append, plan cache mode, and event triggers that opens powerful optimization and automation opportunities for analytical workloads and custom logic execution. This gives you more control for memory intensive and high-concurrency applications, increased control over execution plans and allowing parallel execution of queries. To get started, all newly modifiable parameters are available now through the Azure portal, Azure CLI, and ARM templates, just like any other server configuration setting. To learn more, visit our Server Parameter Documentation. Terraform resource released for latest REST API version A new version of the Terraform resource for Azure Databases for PostgreSQL flexible server is now available, this brings several key improvements including the ability to easily revive dropped databases with geo-redundancy and customer-managed keys (Geo + CMK - Revive Dropped), seamless switchover of read replicas to a new site (Read Replicas - Switchover), improved connectivity through virtual endpoints for read replicas, and using on-demand backups for your servers. To get started with Terraform support, please follow this link: Deploy Azure Database for PostgreSQL flexible server with Terraform General Availability of pg_cron extension in PG 17 We’re excited to announce that the pg_cron extension is now supported in Azure Database for PostgreSQL flexible server major versions including PostgreSQL 17. This extension enables simple, time-based job scheduling directly within your database, making maintenance and automation tasks easier than ever. You can get started today by enabling the extension through the Azure portal or CLI. To learn more, please refer Azure Database for PostgreSQL flexible server list of extensions. Azure Postgres Learning Bytes 🎓 Setting up alerts for Azure Database PostgreSQL flexible server using Terraform Monitoring metrics and setting up alerts for your Azure Database for PostgreSQL flexible server instance is crucial for maintaining optimal performance and troubleshooting workload issues. By configuring alerts, you can track key metrics like CPU usage and storage etc. and receive notifications by creating an action group for your alert metrics. This guide will walk you through the process of setting up alerts using Terraform. First, create an instance of Azure Database for PostgreSQL flexible server (if not already created) Next, create a Terraform File and add these resources 'azurerm_monitor_action_group', 'azurerm_monitor_metric_alert' as shown below. resource "azurerm_monitor_action_group" "example" { name = "<action-group-name>" resource_group_name = "<rg-name>" short_name = "<short-name>" email_receiver { name = "sendalerts" email_address = "<youremail>" use_common_alert_schema = true } } resource "azurerm_monitor_metric_alert" "example" { name = "<alert-name>" resource_group_name = "<rg-name>" scopes = [data.azurerm_postgresql_flexible_server.demo.id] description = "Alert when CPU usage is high" severity = 3 frequency = "PT5M" window_size = "PT5M" enabled = true criteria { metric_namespace = "Microsoft.DBforPostgreSQL/flexibleServers" metric_name = "cpu_percent" aggregation = "Average" operator = "GreaterThan" threshold = 80 } action { action_group_id = azurerm_monitor_action_group.example.id } } 3. Run the terraform initialize, plan and apply commands to create an action group and attach a metric to the Azure Database for PostgreSQL flexible server instance. terraform init -upgrade terraform plan -out <file-name> terraform apply <file-name>.tfplan Note: This script assumes you have already created an Azure Database for PostgreSQL flexible server instance. To verify your alert, check the Azure portal under Monitoring -> Alerts -> Alert Rules tab. Conclusion That's a wrap for the April 2025 feature updates! Stay tuned for our Build announcements, as we have a lot of exciting updates and enhancements for Azure Database for PostgreSQL flexible server coming up this month. We’ve also published our Yearly Recap Blog, highlighting many improvements and announcements we’ve delivered over the past year. Take a look at our yearly recap blog here: What's new with Postgres at Microsoft, 2025 edition We are always dedicated to improving our service with new array of features, if you have any feedback or suggestions we would love to hear from you. 📢 Share your thoughts here: aka.ms/pgfeedback Thanks for being part of our growing Azure Postgres community.
