April 2026 Recap: Azure Database for PostgreSQL
April brought several updates for Azure Database for PostgreSQL, focused on improving developer productivity, strengthening security and connectivity, and helping customers scale and optimize their PostgreSQL workloads. From new Entra ID token refresh libraries across .NET, JavaScript, and Python to simplify authentication, to guidance on migrating from VNet to Private Endpoint capable configurations, we continue to make it easier to build and manage secure applications. We also introduced enhancements to the PostgreSQL VS Code extension and published deep dives on query performance, data modeling, and real-world scaling patterns. We also published a blog on how PostgreSQL enters its AI era, which explores ways with which developers can adapt PostgreSQL to meet the needs of AI-driven and rapidly growing applications, with practical guidance on running and scaling PostgreSQL more effectively in these evolving workloads. POSETTE 2026 Before we dive deeper into the feature updates, POSETTE: An Event for Postgres 2026 is just around the corner, PostgreSQL’s free, virtual conference bringing together the global community. Taking place from June 16–18, the event will feature four livestream tracks with a strong lineup of content, including 44 sessions, 2 keynotes, and 50 speakers. It’s a great opportunity to hear from PostgreSQL experts, learn about the latest trends, and discover real-world best practices across a wide range of topics. Register today for updates and be part of three days of learning, insights, and community-driven discussions across a wide range of PostgreSQL topics. Features Entra-ID token refresh libraries for .NET, JavaScript, and Python: Preview Migrating from VNet to Private Endpoint: Preview New enhancements in the PostgreSQL VS Code Extension Improving Query Performance and Modeling in PostgreSQL Scaling PostgreSQL for Real-World Application Workloads Learning Bytes: Preventing accidental server deletion Entra-ID Token refresh libraries: .NET, JavaScript and Python We’ve introduced Entra ID token refresh libraries for .NET, JavaScript, and Python to simplify how applications authenticate with Azure Database for PostgreSQL using Entra ID. When using Entra ID–based authentication, access tokens are short-lived and need to be refreshed periodically. This often requires additional logic in the application to handle expiration, retries, and reconnection scenarios. These new libraries take care of that complexity by automatically refreshing tokens behind the scenes, so applications can maintain uninterrupted database connections without custom token management. With built-in support for token renewal, these libraries help: Reduce the need for manual token refresh logic in your application code Improve reliability for long-running or connection-pooled workloads Simplify adoption of Entra ID authentication across different language stacks Whether you're building new applications or migrating existing ones to use Entra ID, these libraries make it easier to integrate secure, passwordless authentication while keeping connection handling straightforward. Migrating from VNet to Private Endpoint Azure Database for PostgreSQL flexible server can now be migrated from a VNet‑integrated deployment to a network configuration that supports Private Endpoint connectivity. Servers originally deployed inside a VNet may require greater flexibility in networking management. Private Endpoints provide a simpler and more scalable model. Following migration, private access to the server continues over Azure’s backbone network, dependency on delegated subnets is reduced, and database networking can be better aligned with evolving architectural or organizational standards. The migration can be initiated through Azure CLI, API, or SDK and is designed to be straightforward. Although the operation involves a period of downtime, it enables adoption of Private Endpoint connectivity without recreating the server or manually moving data. After migration, Private Endpoints or firewall rules can be configured based on the desired access model, and infrastructure-as-code templates can be updated accordingly. Read more here: Migrate from VNet to a Private Endpoint Capable Network Configuration | Microsoft Learn New enhancements in the PostgreSQL VS Code Extension The latest release (v1.21) of the PostgreSQL VS Code extension delivers enhancements to query authoring and analysis workflows, improved cross-extension interoperability, reliability improvements across Object Explorer and connection management, and a set of targeted bug fixes. Schema-Aware Query Creation: You can now open a new query directly from a schema in Object Explorer, automatically setting the appropriate search_path so unqualified object names resolve correctly without additional setup. Query Plan Visualization Enhancements: The query plan visualizer now uses PostgreSQL-specific node icons across all views, making it easier to identify scan, join, and aggregate operations during performance analysis. Improved Multi-Extension Compatibility: The extension now coordinates editor ownership with the MSSQL extension when both are installed, reducing duplicate UI actions and avoiding conflicts in query execution workflows. Object Explorer Reliability Improvements: The Object Explorer has been refactored for more consistent refresh, expansion, and reconnection behavior, especially in long-running sessions and databases with many schemas. Enhanced IntelliSense Behavior: IntelliSense now respects the configured search_path, improving the relevance of suggestions and helping you work more efficiently across schemas. Bug Fixes: This release includes fixes across object scripting (including partitioned tables), connection profile handling, Docker container creation, and initial extension setup for improved reliability and stability. Improving Query Performance and Modeling in PostgreSQL This month, we also shared a set of technical blogs highlighting advanced PostgreSQL scenarios and practical guidance for real-world workloads: Guide on workload observability with Query store: This blog dives into how Query Store can be used to gain end-to-end visibility into query performance across both primary and replica nodes. It highlights the importance of understanding query behavior in distributed setups and how bottlenecks can surface differently across nodes. The post also shares practical guidance on using these insights to troubleshoot issues and optimize workload performance effectively. Guide on Common Table Expressions(CTEs) with Data Skew: This deep dive unpacks a complex query planning scenario in PostgreSQL v17, where data skew can lead to unexpected and suboptimal execution plans involving CTEs. It explains why the optimizer may choose inefficient plans and how this impacts real-world workloads. The blog also outlines strategies to diagnose and mitigate these issues, helping users better predict and tune query performance. Guide on PostgreSQL as a Graph Database: This blog explains how PostgreSQL can be leveraged to model and query graph-like relationships, making it highly relevant for AI-driven applications. It demonstrates how relational capabilities can be extended to support graph workloads without introducing additional systems. The post also highlights practical patterns and use cases that enable developers to build more connected, intelligent applications using PostgreSQL as a unified data platform. Scaling PostgreSQL for Real-World Application Workloads Alongside performance tuning and data modeling topics, we also explored how PostgreSQL behaves under real-world application patterns especially in scenarios involving high concurrency, background job processing, and connection-heavy workloads. These blogs focus on common architectural choices developers make and the trade-offs to consider when scaling reliably. Guide on using Postgres as a Job Queue: Thisblog takes a deeper look at the implications of using PostgreSQL as a job queue, a pattern commonly adopted for simplicity and tighter integration. It walks through how queue-like workloads can introduce contention due to frequent updates, row locking, and long-running transactions. The post highlights how these patterns can impact throughput, vacuum efficiency, and overall database health as scale increases. It also discusses when this approach is appropriate, and when teams should consider dedicated queuing systems to avoid performance bottlenecks. Guide on Connection Scaling with Elastic Clusters: This blog dives into the challenges of handling large volumes of concurrent connections, which is a common bottleneck for modern, microservices-based applications. It explains how Elastic Clusters help distribute connections and workload across multiple nodes, improving scalability and resilience under heavy load. The post also touches on connection management patterns, including pooling strategies, and how they work in conjunction with Elastic Clusters to prevent resource exhaustion and ensure consistent performance at scale. Azure Postgres Learning Bytes 🎓 Preventing accidental server deletion In production environments, accidental deletions can lead to significant downtime and data loss. To safeguard critical resources like Azure Database for PostgreSQL servers, Azure provides resource locks that add an extra layer of protection beyond standard role-based access control (RBAC). A commonly used option is the CanNotDelete (Delete Lock), which ensures that a resource cannot be deleted even by users with elevated permissions until the lock is explicitly removed. You can apply a delete lock easily using the Azure CLI by targeting the specific resource: az lock create --name PreventDelete --lock-type CanNotDelete --resource-group <rg-name> --resource-type Microsoft.DBforPostgreSQL/flexibleServers --resource-name <resource-name></resource-name></rg-name> Once applied, any delete operation on the resource will be blocked, helping prevent accidental or unintended deletions during maintenance, deployments, or testing. Locks can be applied at different levels subscription, resource group, or individual resources allowing flexibility based on your protection needs. For more details and step-by-step guidance, read our blog on Preventing accidental deletion of an Azure PostgreSQL Instance.
