Log Insights in Minutes: A Simpler pgBadger Workflow
Sometimes the fastest way to understand a PostgreSQL workload is not another dashboard. It is a good log report. pgBadger is a PostgreSQL log analysis tool that turns raw PostgreSQL logs into an interactive HTML report. It helps summarize query activity, connection patterns, errors, temporary files, lock waits, autovacuum activity, and more. Earlier guidance for generating pgBadger reports from Azure Database for PostgreSQL Flexible Server focused on exporting logs through Diagnostic Settings, storing them in a storage account, and then using tools such as BlobFuse and jq to extract PostgreSQL log lines from JSON files. That workflow is still useful when customers centralize logs across multiple servers. However, if you are already using the Server logs feature in Azure Database for PostgreSQL Flexible Server, there is a much simpler path. In this post: You’ll learn how to generate a pgBadger HTML report from Azure Database for PostgreSQL Flexible Server by downloading native PostgreSQL .log files directly from the Azure portal. No storage account, BlobFuse mount, or JSON extraction required. Fast path Configure log_line_prefix . Enable Server logs for download. Download the PostgreSQL .log files. Run pgBadger with the matching prefix. Open pgbadger-report.html . Why use this workflow? With Server logs, you can download native PostgreSQL .log files directly from the Azure portal and run pgBadger locally. Older path Simpler path in this blog Diagnostic Settings → Storage account → BlobFuse → JSON extraction → pgBadger Server logs → Download .log files → pgBadger Area Older Diagnostic Settings workflow Server logs workflow Export path Diagnostic Settings to storage account Download .log files directly from the portal Format JSON payloads need extraction Native PostgreSQL .log files Extra tooling BlobFuse and jq JSON parsing None Best suited for Centralized or multi-server logging Quick per-server analysis Outcome Flexible, but more setup Faster path to pgBadger Recommended: Use the Server logs workflow when you want a fast, low-friction way to generate a pgBadger report from one Azure Database for PostgreSQL Flexible Server. When should you use this workflow? Use this workflow when... Use Diagnostic Settings when... You need a quick report for one Flexible Server. You centralize logs from many servers. You want to run pgBadger locally. You need long-term retention or workspace-level querying. You want to avoid JSON extraction. You already have automated log export pipelines. Before you start A machine where you can install or run pgBadger. A working Perl runtime. Git Bash on Windows, so the multi-line shell commands work as shown. Portal access to your Azure Database for PostgreSQL Flexible Server. Permission to update server parameters and enable Server logs. Important: pgBadger can only analyze what PostgreSQL logs capture. To populate query timing and slow-query sections in the report, enable log_min_duration_statement before collecting logs. Logs collected before that change will not include duration data. Workflow overview Task Type Rough effort Install or prepare pgBadger One-time setup per analysis machine 5–10 minutes Configure log_line_prefix One-time setup per server 2–3 minutes Enable Server logs One-time setup per server 2–3 minutes Download logs and run pgBadger Repeatable 2–5 minutes Install or prepare pgBadger on the machine where you will analyze logs. Configure log_line_prefix so pgBadger can parse each log line. Enable Server logs, so PostgreSQL logs are available for download. Download the logs and run pgBadger locally. 💡Pro tip: Start with a narrow log window first. Use one or two hourly log files, confirm the report looks right, and then expand the analysis window if needed. Step 1: Install pgBadger Before generating a report, you need pgBadger available on the machine where you plan to analyze the downloaded PostgreSQL log files. Run this on a Linux VM, WSL, or another Linux-based environment where you can install packages. Note: Azure Cloud Shell may work for quick testing, but package installation and build-tool availability can vary by session. For repeatable analysis, use a Linux VM, WSL, or another environment you control. Copy and run sudo apt-get update && sudo apt-get install -y git perl make gcc && \ git clone https://github.com/darold/pgbadger.git && \ cd pgbadger && \ perl Makefile.PL && \ make && \ sudo make install && \ pgbadger -V What good looks like: The install command completes successfully and pgbadger -V returns the installed pgBadger version. Step 2: Configure log_line_prefix This is a one-time server configuration step. The log_line_prefix parameter controls the beginning of each PostgreSQL log line. pgBadger uses this prefix to extract useful fields such as timestamp, user, database, and process ID. In the Azure portal, open your Flexible Server and go to Server parameters. Search for: Parameter log_line_prefix Set this value %m user=%u db=%d pid=%p: Then select Save. In Server parameters, confirm that the custom value is saved for log_line_prefix . Figure 1: Set log_line_prefix so pgBadger can correctly parse timestamp, user, database, and process ID from each log line. Prefix tokens Token Meaning %m Timestamp with milliseconds %u Username %d Database name %p Process ID After this change, log lines should look like this: Example log line 2026-06-22 19:00:00.070 UTC user=pgadmin db=highcpu pid=3805603: LOG: statement: SELECT 1 FROM pg_extension WHERE extname='pg_stat_statements' The matching pgBadger prefix for this log format is: Matching pgBadger prefix %m user=%u db=%d pid=%p: You will use this same value later in the pgBadger command. What good looks like: The server parameter is saved, and new PostgreSQL log lines begin with timestamp, user, database, and process ID fields that match the pgBadger prefix. Step 3: Enable Server logs for download This is also a one-time setup step. In the Azure portal, open your Flexible Server and go to Server logs. Enable: Portal setting Capture logs for download Set the retention period based on how long you want logs to remain available for download. For example, a 7-day retention period keeps logs available for download for 7 days. In Server logs, enable Capture logs for download and choose the retention window. Figure 2: Enable Capture logs for download and set a retention period long enough to cover the analysis window you want to inspect. What good looks like: After Server logs are enabled, hourly PostgreSQL log files appear in the Server logs blade and can be downloaded from the Azure portal. Once enabled, hourly log files appear in the Server logs blade. The files are named by date and hour, for example: Example log files postgresql_2026_06_22_19_00_00.log postgresql_2026_06_22_20_00_00.log Step 4: Download and organize the logs locally From the Server logs page, select the .log files for the time window you want to analyze and download them. For example, to analyze activity between 19:00 and 21:00 UTC, download: Example files to download postgresql_2026_06_22_19_00_00.log postgresql_2026_06_22_20_00_00.log On your local machine, create a folder for that analysis window. A simple convention is to use the Mon-DD format. Folder name Jun-22 Place the downloaded .log files inside that folder. Your local folder structure should look like this: Folder structure pgbadger-13.1/ pgbadger Jun-22/ postgresql_2026_06_22_19_00_00.log postgresql_2026_06_22_20_00_00.log Step 5: Generate the pgBadger report Open Git Bash from the folder where pgBadger is located. For example, if pgBadger is inside the pgbadger-13.1 folder, open Git Bash from that folder. # Action Command 1 Set the folder FOLDER=Jun-22 2 Confirm files ls -lh ./$FOLDER 3 Run pgBadger Use the full command below. Copy and run FOLDER=Jun-22 ls -lh ./$FOLDER perl -X ./pgbadger -f stderr \ --prefix '%m user=%u db=%d pid=%p:' \ ./$FOLDER/*.log \ -o ./$FOLDER/pgbadger-report.html Command breakdown Part of command Purpose perl -X ./pgbadger Runs pgBadger and suppresses non-critical Perl warnings. -f stderr Parses PostgreSQL stderr log files. --prefix '%m user=%u db=%d pid=%p:' Matches the log_line_prefix set on the server. ./$FOLDER/*.log Analyzes every .log file in the selected folder. -o ./$FOLDER/pgbadger-report.html Writes the HTML report into the same folder. When the command completes successfully, you should see output like this: Expected output Parsed 12134249 bytes of 12134249 (100.00%), queries: 26684, events: 83 LOG: Ok, generating html report... What good looks like: pgBadger finishes parsing the logs and creates pgbadger-report.html in the selected folder. Step 6: Open the report Open the generated report: Copy and run start ./$FOLDER/pgbadger-report.html The report opens in your default browser. The final report is created here: Generated report path Jun-22/pgbadger-report.html What the report can show The pgBadger report gives you a quick view into the workload shape for the selected log window. For example, in a sample run across two hourly log files, pgBadger summarized: Total number of queries. Number of unique normalized queries. Query traffic over time. Events such as errors and fatal messages. Session and connection patterns. Once the report opens, start with Global Stats to confirm the time range, total queries, normalized queries, and query peak. Figure 3: Start with Global Stats to validate the selected time range, total query count, normalized query count, and query peak. Query volume and normalized queries Many raw queries can often reduce to a smaller number of normalized query patterns. This helps identify whether the workload is spread across many different query shapes or dominated by a smaller set of repeated statements. Example: In this sample run, 26,684 queries reduced to 59 normalized query shapes. That suggests the workload is mostly a small set of repeated statements, which can help focus tuning effort. Traffic patterns The SQL Traffic section helps identify spikes, quiet periods, and workload changes over time. Figure 4: Use SQL Traffic to identify query spikes, quiet periods, and workload changes during the selected log window. Figure 5: Review the query breakdown to compare read vs. write volume and query-type distribution for the selected Server logs window. For example, if the report shows a steady baseline followed by a sharp spike, that spike can be correlated with application activity, batch jobs, synthetic tests, or operational events during the same time window. Query duration If query duration shows 0 ms or the slow query sections are empty, it usually means duration logging was not enabled when the logs were collected. In that case, pgBadger can still show query counts and events, but it cannot calculate the slowest queries, total execution time, average duration, or maximum duration. To unlock those timing sections, enable log_min_duration_statement , collect fresh logs, and rerun pgBadger. What pgBadger cannot infer from missing logs pgBadger reports are only as complete as the log data you provide. If PostgreSQL did not log duration, lock waits, temporary files, or autovacuum activity during the selected time window, pgBadger cannot reconstruct those details later. To analyze... Enable before collecting logs Slow queries log_min_duration_statement Lock waits log_lock_waits Temporary files log_temp_files Autovacuum activity log_autovacuum_min_duration Repeatable copy/paste block Reusable command block Change only FOLDER for each new analysis window. Copy and run FOLDER=Jun-22 ls -lh ./$FOLDER perl -X ./pgbadger -f stderr \ --prefix '%m user=%u db=%d pid=%p:' \ ./$FOLDER/*.log \ -o ./$FOLDER/pgbadger-report.html start ./$FOLDER/pgbadger-report.html For another date, change only this line: Update this value FOLDER=Jun-22 Examples: Example folder values FOLDER=Jun-23 FOLDER=Jul-01 FOLDER=Aug-15 Optional: Improve report quality pgBadger can only analyze the information captured in PostgreSQL logs. The default logs may be enough for query frequency, connection activity, and errors. For deeper performance troubleshooting, consider enabling additional logging parameters based on your scenario. Scenario Parameter Suggested value Notes Slow query analysis log_min_duration_statement 1000 Logs statements slower than 1 second. Short controlled test log_min_duration_statement 0 Logs every statement. Use carefully. Lock troubleshooting log_lock_waits on Helps identify lock waits. Temporary file analysis log_temp_files 0 Logs all temporary files. Autovacuum visibility log_autovacuum_min_duration 0 Useful during focused analysis. Useful parameters include: Recommended logging parameters log_lock_waits = on log_temp_files = 0 log_autovacuum_min_duration = 0 To capture query durations, configure: Duration logging log_min_duration_statement = 1000 This logs statements that run longer than 1000 milliseconds. For short test runs, you can temporarily use: Short test run only log_min_duration_statement = 0 Caution: Use log_min_duration_statement = 0 carefully on busy production servers. It logs every statement and can generate a large volume of logs. Duration matters: If duration logging is not enabled, pgBadger can still show query counts and events, but slowest-query, total duration, average duration, and maximum duration sections will be limited or empty. Common mistakes and quick fixes Symptom Likely cause Fix Report is empty Prefix mismatch Match --prefix with log_line_prefix . No duration data Duration logging was not enabled Set log_min_duration_statement before collecting logs. No files visible Server logs disabled or retention expired Enable capture and check retention. pgBadger command fails pgBadger is not in the current folder or path Run pgbadger -V to confirm installation. Common troubleshooting FAQs 1. Report is created but empty This usually means the pgBadger prefix did not match the actual log format. Check the first few lines: Copy and run head -5 ./$FOLDER/*.log Make sure the pgBadger --prefix matches the server’s log_line_prefix . 2. Report shows queries but no duration PostgreSQL logged statements but did not log durations. Enable one of the following, collect fresh logs, and rerun pgBadger: Parameter options log_min_duration_statement = 1000 # or temporarily for testing log_min_duration_statement = 0 3. No .log files are visible Confirm that Server logs are enabled: Portal setting Capture logs for download Also check the retention period. If the retention period has expired, older logs may no longer be available for download. 4. pgBadger command fails Confirm that pgBadger is available in the current folder or installed in your path. Copy and run pgbadger -V If you are running pgBadger from the local folder, use: Copy and run perl -X ./pgbadger Summary For customers already using Azure Database for PostgreSQL Flexible Server logs, the pgBadger workflow is straightforward: Install pgBadger. Configure log_line_prefix . Enable Server logs for download. Download the .log files. Place them in a local date-based folder. Run pgBadger with the matching prefix. Open pgbadger-report.html . Bottom line: Server logs give you the shortest path from Azure Database for PostgreSQL Flexible Server logs to a pgBadger report. Download the native .log files, run pgBadger with the matching prefix, and open the generated HTML report. References pgBadger - source and documentation GitHub pgBadger - project site Azure - Download server logs from the portal Flexible Server Azure - Logging concepts Flexible Server Azure - Configure server parameters via the portal PostgreSQL - log_line_prefix and logging parametersAnnouncing new security, maintenance and analytics features for PostgreSQL at Microsoft Build 2026
At Microsoft Build 2026, we’re announcing a major wave of PostgreSQL innovation across Azure. Alongside the public preview of Azure HorizonDB, we’re delivering a broad set of enhancements for our fully managed open-source PostgreSQL service: Azure Database for PostgreSQL flexible server. These updates span performance, analytics, security, operations, resilience and migration - helping you build faster, operate with more control, secure your workloads, and modernize with confidence. Here’s a quick tour of the top flexible server announcements at Build 2026. Feature Highlights pg_duckdb Extension pg_ivm Extension Defender Security assessments temporal_tables Extension Cross-tenant CMK Automatic Entra token refresh libraries New Powershell module: Az.PostgreSQLFlexibleServer More control over planned maintenance Pre-Upgrade validation checks New Built-in Grafana dashboards Chaos Studio supports Azure Database for PostgreSQL AI-assisted Oracle to PostgreSQL migration Migration Service for Azure Database for PostgreSQL improvements (EDB, AlloyDB) Performance, Scale & Analytics pg_duckdb Extension Generally Available The pg_duckdb extension enables you to accelerate high-performance analytics and data-intensive applications with DuckDB’s SQL engine running inside your Postgres server. We’re pleased to announce pg_duckdb is now generally available in Azure Database for PostgreSQL. The latest version builds on the preview with the latest DuckDB engine improvements and optimized performance. This version adds vectorized execution for faster analytical queries, delivering significant improvements in aggregation performance, along with new support for writing to Azure Blob Storage and querying Parquet data directly from PostgreSQL. These capabilities enable high-performance analytics on your external data and simplify data processing workflows. Learn more: pg_duckdb. pg_ivm Extension Generally Available Materialized views are a useful way to optimize performance for queries that run regularly, but if underlying data becomes stale the result set needs to be recomputed. With the pg_ivm extension you can automatically maintain materialized views as the underlying data changes. This is particularly valuable for large datasets with small incremental changes that need real-time freshness, like dashboards, catalog analytics and SaaS usage reporting. We are pleased to announce the pg_ivm extension is now generally available in Azure Database for PostgreSQL. Learn more: pg_ivm. Security, Auditing & Identity Defender security assessments Preview Microsoft Defender Security Assessments for Azure Database for PostgreSQL enables continuous evaluation of your database security posture, helping identify vulnerabilities and misconfigurations across server and database configurations. Previously limited to reactive threat detection, in the latest preview release, Defender now provides proactive, risk-based insights through assessments tailored to PostgreSQL-specific best practices, delivering more relevant and actionable guidance. This helps you strengthen your security baseline, prioritize remediation, and align with best practices and compliance requirements. Learn more: https://aka.ms/Defender-Assessments-for-PG-Preview temporal_tables Extension Generally Available We’ve had many customer requests to support the temporal_tables extension, which provides built-in support for tracking and querying historical changes to data over time. Temporal tables are now generally available in Azure Database for PostgreSQL. With this extension enabled you can easily perform time-based queries, audit data changes, and maintain historical records without building custom tracking logic, simplifying application development and compliance scenarios. Learn more: temporal_tables Cross-tenant CMK Preview Azure Database for PostgreSQL now supports cross-tenant customer-managed keys (CMK) in public preview, allowing you to encrypt your data at rest using an Azure Key Vault key that resides in a separate Microsoft Entra tenant from the database service. This feature is designed for SaaS providers and enterprises that need to maintain strict separation of duties and ownership of encryption keys, enabling you to retain full control over key lifecycle management while PostgreSQL runs in a service provider’s tenant. Learn more: Data encryption at rest in Azure Database for PostgreSQL Automatic Entra token refresh libraries Preview We’re making it easier to use Entra ID authentication with Azure Database for PostgreSQL throughout the application stack by introducing new token refresh libraries for .NET, JavaScript, and Python. With Entra ID, access tokens are short-lived which can make managing their lifecycle complex in real-world applications. Developers need to be aware of token refresh and build additional handling around token expiration, connection retry, and session continuity. These new libraries remove that friction. By handling Entra token refresh seamlessly in the background, they allow applications to stay connected without interruption and with no custom logic required. The result is a simpler development experience and more resilient applications, especially for long-running or connection-heavy workloads. Across languages, the libraries provide a consistent and streamlined way to adopt secure, passwordless authentication, helping teams focus more on building their applications and less on managing authentication. Learn more: .NET, JavaScript, and Python. Operations, Maintenance & Monitoring New Powershell module: Az.PostgreSQLFlexibleServer Generally Available We’re excited to introduce the newly renamed Az.PostgreSQLFlexibleServer PowerShell module, delivering a streamlined experience for managing Azure Database for PostgreSQL with PowerShell. Building on the capabilities of the previous Az.PostgreSql module, the updated module aligns with the new features in the 2026-01-01 preview REST API. This module brings support for PostgreSQL 18, elastic clusters for scalable workloads and a range of enhancements designed to simplify management and improve performance. Whether you're provisioning new deployments or managing complex environments, this module ensures you can take full advantage of the latest platform capabilities directly from PowerShell. To learn more, visit our official documentation on PowerShell: Az.PostgreSql Module | Microsoft Learn More control over planned maintenance Generally Available We’ve seen many requests to provide more control when a maintenance update is applied to Azure Database for PostgreSQL. Sometimes when a critical workload is running you want to apply the maintenance when you’re ready. Announcing general availability this week, we’re building on the existing System and Custom maintenance window options and adding new self-service maintenance capabilities to the Azure portal. You can now reschedule upcoming maintenance updates for up to two weeks and apply maintenance on demand at a time that suits you. You can also view scheduled maintenance and review your server’s maintenance history after updates are complete. These options help you better align maintenance with your business schedules, reduce disruption during critical workload periods, and minimize the need for support-driven deferral requests. CLI and API support are coming soon. Learn more: https://aka.ms/azure-postgres-reschedule-maintenance Pre-Upgrade validation checks Preview Major version upgrades are critical for staying current with PostgreSQL features, security updates, and performance improvements, but you often discover blockers only after starting the upgrade workflow. Pre-Upgrade Validation Checks lets you validate upgrade readiness before initiating the actual upgrade by running Azure-specific upgrade checks and PostgreSQL pg_upgrade --check validations independently. The shift is simple: you can identify and fix upgrade blockers before the upgrade window begins. The feature surfaces actionable issues across configurations, extensions, dependencies, replication slots, event triggers, and other upgrade-sensitive objects. You can fix blockers, re-run validation until all checks pass, and proceed with the upgrade with greater predictability. Learn more: https://aka.ms/pg-flex-upgrade-checks New Built-in Grafana dashboards Generally Available Grafana dashboards are now built directly into the Azure portal for Azure Database for PostgreSQL - no setup, no extra cost, and no separate service to manage. You can open your PostgreSQL resource in the portal and immediately access prebuilt dashboards for key health and performance signals such as CPU, memory, storage, IOPS, connections, transactions, and availability. The key value is metrics + logs in one place. You can quickly correlate performance spikes with PostgreSQL logs, understand what changed, and troubleshoot faster using the familiar Grafana experience. Dashboards can also be customized, saved to your subscription, and shared across teams for ongoing operations. Learn more: https://aka.ms/azure-postgres-dashboards-grafana Resilience & Business Continuity Chaos Studio supports Azure Database for PostgreSQL Preview No matter how much you prepare, you only really know how good your database disaster recovery plan is when something breaks. With Chaos Studio support for Azure Database for PostgreSQL, you can simulate zone-down scenarios on PostgreSQL HA-enabled instances and validate the resilience of your mission-critical workloads. With Chaos Studio integration, you can proactively test failover behavior and gain confidence in how your applications respond to real-world zonal failures. This feature is currently available through a gated private preview. To get started, submit your subscription details using the form. Once reviewed, our team will enable the feature for your subscription, with guidance to help you begin testing. Getting started is simple: Create a Chaos Studio workspace via the Chaos Studio portal and configure your subscription, resource group, and region. Define the scope and assign the required managed identity and permissions. Review and verify your workspace setup. Browse available scenarios and select the PostgreSQL zone-down scenario. Configure the test (name, duration), then run it from My Library to begin validating failover behavior. With just a few steps, you’ll be able to simulate real-world failure conditions and gain confidence in your application’s resilience. To get started, please submit your details using this link: Private Preview Support for Chaos Studio Migration & Modernization AI-assisted Oracle to PostgreSQL migration Generally Available AI-assisted migration tooling has dramatically lowered the bar for moving between different databases and is changing the way people look at the return on investment for migration. The VS Code PostgreSQL extension comes with AI-Assisted migration tooling which converts Oracle schema and application code to Azure Database for PostgreSQL. This tooling uses GitHub Copilot, Microsoft Foundry, and custom Language Model tools to convert Oracle schema, database code and client applications into the PostgreSQL equivalents, and validates every change against a running flexible server instance. Learn more: Schema conversion, App conversion. Migration Service for Azure Database for PostgreSQL improvements (EDB, AlloyDB) Generally Available We’ve added AlloyDB and EDB Extended Server as new sources for migrating to PostgreSQL in the Azure Database for PostgreSQL Migration Service, with support for both online and offline migration support. Learn more: Migrate from AlloyDB, Migrate from EDB. Looking ahead That wraps up the Build 2026 announcements for Azure Database for PostgreSQL flexible server. There are also many great PostgreSQL technical sessions at Build this week, covering cloud-native app & AI development and migration. To find out more, here's a link to the Build session catalog for PostgreSQL sessions: https://aka.ms/Postgres-on-Azure_Build-2026. We'll continue to build out our roadmap over the coming months to deliver on your asks to improve the performance, security and stability of your PostgreSQL workloads. Check the Microsoft Blog for PostgreSQL for a regular monthly recap where we share the latest enhancements and product updates.February 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.
