Azure HorizonDB: Enterprise-Ready Postgres, Engineered for the AI Era
Affan Dar, Vice President of Engineering, PostgreSQL at Microsoft Charles Feddersen, Partner Director of Program Management, PostgreSQL at Microsoft Today at Microsoft Build, we’re pleased to announce the public preview of Azure HorizonDB, a new enterprise-ready Postgres-compatible database service designed to meet the needs of modern AI applications, alongside a set of enhancements to our PostgreSQL tooling in Visual Studio Code to further streamline the developer experience. Postgres is rapidly solidifying its role as a foundational layer in modern data architectures, with accelerating adoption across industries. For developers, it has become the preferred platform for new application development, driven by its extensible architecture, mature extension ecosystem, and adherence to open standards and APIs. At the same time, enterprises are choosing Postgres to re-platform and modernize existing systems, taking advantage of its ability to support a broad range of operational workloads while enabling advanced capabilities such as vector-based data access all within a single, interoperable platform. A Postgres Platform Grounded in Security, Resilience, Scale, and Performance Azure HorizonDB is purpose-built to meet these demands, combining the flexibility developers expect from Postgres with the operational rigor enterprises require. It extends the core Postgres engine with cloud-native capabilities such as integrated identity, fine-grained network and security controls, and seamless lifecycle management, while preserving full compatibility with the open ecosystem of extensions and tools. At the same time, HorizonDB introduces advanced, natively integrated capabilities like vector data support and AI model management, enabling new classes of intelligent applications without sacrificing transactional integrity or developer productivity. These capabilities are backed by a platform designed for enterprise performance and scale. HorizonDB supports databases up to 128 TB, scales out with up to 15 read replicas for high-throughput workloads, and delivers sub-millisecond commit latency across availability zones for low-latency transactions and high availability. This combination is critical for modern applications that require consistent performance under load, including high-concurrency transactional systems, real-time AI-driven interactions, and globally distributed services. The result is a unified platform that scales from the first line of code to globally distributed, mission-critical systems. Enterprise adoption ultimately depends on trust in the platform itself. Azure HorizonDB delivers this with native integration into Microsoft Entra ID for centralized identity and access control, private endpoints for network isolation, and built-in encryption to protect data at rest and in transit. These capabilities are essential for meeting compliance requirements and enabling organizations to run mission-critical workloads with confidence, without added complexity. This foundation is critical for any application, but it becomes indispensable for AI, where secure access to data and controlled model interaction underpin every intelligent experience. Building on this, HorizonDB introduces a set of integrated AI capabilities designed to bring intelligence directly into the database. Run Fast, Memory-Efficient Vector Search with DiskANN HorizonDB brings high-performance vector search directly into Postgres through DiskANN with spherical quantization. This enables efficient, low-latency similarity search at scale while significantly reducing memory and storage overhead. Spherical quantization works by normalizing vectors and encoding them into compact representations that preserve angular distance, allowing the system to compare vectors efficiently with minimal loss in accuracy. The result is the ability to index and query large embedding datasets within the transactional engine itself, making vector search a first-class capability rather than an external dependency. "HorizonDB is compelling because it brings a PostgreSQL-compatible foundation, AI-native capabilities and enterprise-grade controls closer to the operational data layer." Jennings Balavari, Founder, Opsen AI Build Smarter Apps with Hybrid Search in Postgres HorizonDB supports hybrid search by combining vector similarity through pgvector with full-text search enabled via the pg_textsearch extension, allowing applications to match both semantic meaning and precise keyword relevance in a single query. This enables more accurate, context-aware results, such as blending intent-driven retrieval with exact term matching for search, recommendations, or RAG scenarios. By unifying these capabilities within Postgres, HorizonDB improves result quality while simplifying application design without the need for external search systems. Operationalize AI with Built-In AI Model Management Working with vectors requires models to generate, interpret, and evolve embeddings, making model lifecycle a core part of the application stack. HorizonDB introduces integrated AI model management to simplify how models are registered, versioned, and governed alongside data, including built-in support for generative GPT models and ranking models. For example, GPT models can be used to generate summaries, responses, or structured outputs directly from application data, while ranking models enable relevance scoring for search results or recommendations over vector results. By managing these models alongside the data they operate on, HorizonDB ensures consistency, traceability, and control, creating a unified environment where models and data evolve together. “As we build a multi-tenant, AI-driven commerce platform, HorizonDB has been particularly compelling in two areas: scale and how close AI capabilities are to the data itself. Running vector search, filtering, and model-driven workflows directly inside the database removes a lot of the complexity we’d normally manage across separate services." James Frawley, CIAO, ReFiBuy Bring AI into SQL with AI Functions With models managed in place, AI Functions provide a direct way to invoke them from within SQL and application logic. These functions are implemented through the azure_ai extension, which brings model invocation directly into the Postgres engine. This allows developers to embed inference into queries and transactions, eliminating the need for external orchestration. By bringing model execution closer to the data, AI Functions reduce latency, simplify application design, and make intelligent behavior a natural extension of existing Postgres workloads. "What stood out with HorizonDB is that it aligns closely with how we already think about the problem. Instead of stitching together multiple components, it brings transactional data, vector search, and AI capabilities into a single platform, which simplifies the architecture without forcing a complete rethink." Mohsin Shafqat, Director Software Engineering for Nasdaq Boardvantage®, Nasdaq Run Reliable, Event-Driven Workflows with AI Pipelines Finally, AI Pipelines operationalize these capabilities through reliable, event-driven workflows for model execution and data processing. Pipelines execute on data changes, enabling real-time asynchronous reactions without external orchestration and ensuring consistent, repeatable behavior as data evolves. Combined with model management and AI Functions, they turn embedded intelligence into something that can be run, scaled, and trusted in production, while inheriting the database’s high availability and failover characteristics for resilience. Pipelines can also be visualized and observed in real time through the Visual Studio Code extension for PostgreSQL, giving developers and operators immediate visibility into execution flow, state, and outcomes Modern Unified Experience for Data, AI, and Operations in VS Code As intelligence becomes a core part of the data platform, the developer and operator experience becomes equally critical. HorizonDB extends seamlessly into Visual Studio Code with enhanced PostgreSQL tooling that works across any Postgres deployment, not just HorizonDB. Features like AI-assisted query plans and integrated monitoring enable faster debugging and optimization, helping teams understand both database performance and AI-driven behaviors. At the same time, for Azure-based deployments, the experience is deeply integrated with platform capabilities, enabling management of networking configuration, server parameters, and server logs directly from the development environment, streamlining operations across application and infrastructure layers. Azure HorizonDB brings together enterprise-grade security, deep Postgres compatibility, and a modern AI-native data platform, all engineered for developers. It scales efficiently across workloads, from transactional systems to intelligent applications, while delivering a world-class, Azure-integrated experience in Visual Studio Code for both developers and operators. Ready to get started with Azure HorizonDB? Azure HorizonDB is now available in public preview in Australia East, Central US, Sweden Central, West US 2, and West US 3 regions. Additionally, East US, Canada Central, Indonesia Central, Italy North, Japan East, Korea Central, and Poland Central will be available in the coming weeks. You can get started today by creating a new HorizonDB instance using the Azure portal, API’s, or the Visual Studio Code extension for PostgreSQL to begin exploring these capabilities firsthand. To learn more, dive deeper into our documentation and sign-up today to try AI model management in a limited preview.July 2026 Recap: Azure Database for PostgreSQL
Features PgBouncer: Update to 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝟭.𝟮𝟱.𝟮 Azure Database for PostgreSQL Flexible Server now supports PgBouncer 1.25.2, keeping the built-in connection pooler aligned with the latest community release. PgBouncer helps applications efficiently manage large numbers of idle and short-lived connections with low overhead. This update includes the latest community security and stability fixes, including fixes for multiple CVEs affecting network packet parsing, SCRAM authentication, error handling, and admin command authorization - strengthening the reliability and security of the managed connection pooling experience. Documentation: PgBouncer in Azure Database for PostgreSQL Maintenance Events: Programmatic control through Rest APIs and Azure CLI Azure Database for PostgreSQL Flexible Server now supports new Maintenance Events REST APIs and Azure CLI commands, giving you more ways to programmatically view and manage planned maintenance. You can view upcoming maintenance, review maintenance history, reschedule eligible maintenance for up to 14 days, and apply maintenance on demand through REST APIs and Azure CLI. These capabilities make it easier to into automation integrate maintenance management, scripts, internal tooling, and operational workflows. The REST APIs are available starting with the 2026-04-01-preview API version, while the same maintenance capabilities are available in Azure CLI version 2.88.0 and later. Documentation: Maintenance Events REST API Documentation: Azure CLI Maintenance Event Commands India South Central now generally available We’re excited to announce that Azure Database for PostgreSQL Flexible Server is now generally available in the India South Central region. You can now build and run production-ready cloud applications closer to your users, with the flexibility and control of a fully managed PostgreSQL service. Expanding PostgreSQL Extensibility with pgPointCloud, RDKit, and plpgsql_check Azure Database for PostgreSQL Flexible Server now supports three additional PostgreSQL extensions, expanding the range of specialized workloads you can run on a fully managed PostgreSQL service. pgPointCloud: Enables you to store, compress, and query large-scale LiDAR and 3D point-cloud data directly in PostgreSQL, supporting spatial workloads across areas such as geospatial analytics, autonomous systems, agriculture, research, and other data-intensive scenarios. RDKit: Brings cheminformatics capabilities to PostgreSQL, helping pharmaceutical, chemical, and research teams work with molecular data, fingerprints, similarity searches, and indexing support. plpgsql_check: Helps developers validate PL/pgSQL code and improve code quality, especially for teams modernizing database applications or migrating procedural SQL workloads to Azure Database for PostgreSQL Flexible Server. Together, these extensions make it easier to bring advanced data types, domain-specific analytics, and developer tooling closer to your PostgreSQL applications on Azure. See the full list of all available extensions in Azure Database for PostgreSQL in our learn documentation. Azure PostgreSQL Learning Bytes Take Control of PostgreSQL Maintenance Azure Database for PostgreSQL Flexible Server now gives you more control over planned maintenance events. With self-service maintenance controls in the Azure portal, you can view upcoming maintenance, reschedule eligible maintenance to a more convenient time, apply updates when you're ready, and review maintenance history after completion. These capabilities help reduce operational risk and make it easier to align maintenance with your business schedule. Whether you're managing production workloads, preparing for a major release, or avoiding peak business periods, these controls provide greater flexibility and visibility so you can plan maintenance with confidence. Learn more: Read the full blog post, Take Control of Your PostgreSQL Maintenance, for details on the maintenance experience and how to use it.Announcing 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_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_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.AI-assisted Oracle-to-PostgreSQL schema conversion in Visual Studio Code
By AI Omar Rajawat, Pranay Lohia, Gautam Juneja, Vikas Nimmagadda, Anil Dogra, Aditya Duvuri We’re seeing significant interest in migrating database workloads from Oracle to Azure Database for PostgreSQL. Historically, schema conversion has been one of the most technically challenging and expensive steps in that journey, demanding specialist knowledge of both engines and stretching migration timelines before a single row of data moves. Recent advances in AI-assisted schema conversion are changing that, turning what used to be a long, manual effort into a faster, more accessible, and lower-cost proposition. This post looks at what that shift means in practice for teams moving to Azure Database for PostgreSQL flexible server. Oracle schema conversion is where the complexity of translating schema and code objects to PostgreSQL becomes visible. Packages, procedures, triggers, custom types, and dependencies built up over years must be mapped to PostgreSQL-compatible definitions while preserving the relationships that make the schema work. Generally available since May 2026, the feature is built into the PostgreSQL extension for Visual Studio Code, published by Microsoft. It helps teams convert Oracle schema and code objects — tables, views, constraints, packages, procedures, functions, and triggers — into PostgreSQL-compatible definitions for Azure Database for PostgreSQL flexible server, with no separate conversion utility to install and no disconnected workflow to manage. It brings schema discovery, conversion, compile validation, and review into one project-based experience. Teams can connect to Oracle, select schemas, and configure a Microsoft Foundry connection in the same project. The extension then translates Oracle-specific constructs, compiles and syntax-checks converted DDL into scratch schemas on Azure Database for PostgreSQL flexible server and surfaces unresolved items as review tasks that teams can work through with GitHub Copilot agent mode. Why schema conversion deserves a better workflow Traditional conversion tools can produce a useful first pass, but the long tail of the process is rarely solved by generating replacement DDL alone. Teams still need clear answers to practical questions: What converted successfully? What needs attention? Which Oracle constructs require a PostgreSQL design decision? Which items should be reviewed first? We designed the schema conversion experience around those questions. The goal is not to hide complexity behind a single score. It is to help teams make steady progress while keeping the work visible and reviewable. What the schema conversion experience provides The experience guides teams through a schema conversion project rather than a collection of separate scripts. It discovers the selected Oracle schemas and converts both the relational model and the code that runs on it: tables, indexes, sequences, primary key, unique, check and foreign key constraints, views and materialized views, synonyms, and Oracle object types — along with the PL/SQL that is usually the hardest part of the migration. Packages and package bodies, package-level state, standalone procedures and functions, and triggers are translated into PostgreSQL functions, procedures, and trigger functions. Oracle-specific constructs are mapped to PostgreSQL equivalents rather than dropped or stubbed out. REF CURSOR and SYS_REFCURSOR become PostgreSQL refcursor; CLOB and BLOB columns become text and bytea ; and NUMBER and VARCHAR2 are mapped by precision and length to their closest PostgreSQL types. Oracle date functions such as ADD_MONTHS, LAST_DAY, MONTHS_BETWEEN, and TRUNC are resolved through the orafce extension, which the project detects and flags for you before deployment. Every converted definition is compiled against scratch schemas on Azure Database for PostgreSQL flexible server, so the deployment script you end up with is an organized, dependency-ordered set of PostgreSQL SQL artifacts that has already been proven to build. Objects that still require human judgment are surfaced as review tasks. Teams can inspect the source and converted definitions side by side, work through the remaining items, and use GitHub Copilot agent mode for guided assistance — keeping automation and human review in the same workflow. How it works: the system architecture Under the hood, the conversion engine follows one principle — the language model is a single, bounded stage; it never has the first or the last word. Deterministic steps decide what the model sees and what it is allowed to produce. Deterministic in. Rule-based extraction reads the Oracle DDL and metadata, then a dependency-graph decomposition splits the estate into bounded, dependency-ordered chunks — so every object is converted in the context that keeps it correct. Bounded conversion. A tiered model strategy through the Microsoft Foundry connection translates each chunk with structured, contract-wrapped input and output. Even very large PL/SQL packages are split and converted member by member, so nothing is trusted as a monolith. Deterministic out. Converted objects pass through review, then compile-and-verify against scratch schemas on Azure Database for PostgreSQL flexible server, and finally dependency-ordered deploy assembly. Unresolved items become review tasks, and every object carries a per-object audit trail. A continuous-improvement loop closes the system: the engineering team maintains a versioned regression suite of supported conversion patterns, and an executable benchmark tracks regressions as the pipeline evolves. Proven in production The approach has been exercised on real enterprise estate. Across representative production runs totaling more than 60,000 schema objects; conversion reached roughly 98% overall — with several schemas converting at a full 100%. The hardest tail, PL/SQL package members, now compiles at 96% across more than 20,000 members thanks to targeted coverage and a resilient compile stage. Conversion outcome and review status are separate measures. Objects that convert and compile cleanly are safe to deploy as they are; the rest are deliberately routed into a prioritized review queue rather than silently accepted. In a representative single-schema run, no object ended in a hard conversion failure, and a cleanly generated object can still involve a PostgreSQL design decision. That is the workflow operating as intended: automation absorbs the volume, and review tasks to keep the remaining judgment calls visible, ordered, and auditable. Measured, not asserted: the SchemaBench eval Quality is verified by running it. SchemaBench, the evaluation framework, deploys each converted schema to a live PostgreSQL database and probes real behavior — whether constraints still fire and whether objects still resolve — rather than comparing DDL text. It scores seven weighted dimensions: semantic fidelity, structure, constraints, completeness, performance, target idioms, and maintainability, behind hard gates. On the e-commerce benchmark, the strongest model scored 96.1 overall with 100% semantic fidelity and a ~98% behavioral-probe pass rate. Every failure a migration hits becomes a permanent regression test the next run has to pass. Learn more: Oracle to Azure Database for PostgreSQL schema conversion overviewFaster, Safer Version Upgrades for Databases with Large Objects
By Varun Dhawan, Ilan Benschikovski, and Alexander Kukushkin - Azure PostgreSQL, Microsoft Faster, Safer Upgrades for Databases with Large Objects TL;DR: We improved major version upgrades for PostgreSQL databases with very high large-object counts. For upgrades targeting PostgreSQL 15 and later, large-object metadata is now handled more efficiently, reducing memory/temp-space pressure and helping previously risky upgrades complete more reliably. Why this matters Some PostgreSQL workloads store documents, images, PDFs, scanned files, or attachments as large objects (LOBs). In normal operations this is fine. But during a major version upgrade, very high LOB counts could make the schema dump step slow, memory-heavy, or fail. This improvement is about making that upgrade path safer and more predictable for Azure Database for PostgreSQL flexible server customers. What changed? During a major version upgrade, PostgreSQL uses pg_upgrade , which internally runs pg_dump to move schema and metadata into the new version. For databases with millions of large objects, the older upgrade path handled large-object metadata one object at a time. That created high memory and temporary-space pressure during the schema dump phase. This fix changes the upgrade path. Instead of processing large-object metadata one object at a time, PostgreSQL now transfers that metadata in bulk. The actual large-object data is not changed; only the upgrade metadata handling is improved. Why this is different This improvement builds on upstream PostgreSQL work that makes large-object metadata handling more efficient during upgrades. We brought that benefit into Azure Database for PostgreSQL flexible server for supported PostgreSQL 15+ upgrade targets, so customers with large-object-heavy workloads can benefit without waiting for a future PostgreSQL major version. Before vs after Area Before After Metadata handling One operation per large object Bulk metadata transfer Memory/temp pressure Grew heavily with LOB count Much flatter and more predictable High LOB counts Risk of OOM or temp-space failure Completes more reliably for PostgreSQL 15+ targets Customer workaround vacuumlo + scale-up often needed Less reliance on LOB-specific workarounds for PostgreSQL 15+ targets In plain English: the upgrade no longer has to carry paperwork for every large object one by one. It moves the metadata in bulk, which makes the upgrade faster, safer, and less likely to fail at very high LOB counts. The numbers We tested upgrades from PostgreSQL 13 with large-object counts ranging from 10M to 500M. The older path is represented by PostgreSQL 13 → 14. The improved path is represented by PostgreSQL 13 → 15. Note: These figures come from a multi-database test where large objects were spread across 100 databases. Because pg_dump runs per database, single-database workloads with the same total large-object count may see different runtimes. Cap Outcome summary Large objects Older path Improved path What changed 10M 48 min 16 min 3x faster 20M 1h 02m 18 min 3.4x faster 30M 2h 41m 21 min 7.6x faster 50M Failed at higher scale 26-30 min Now completes 100M Failed 54 min Now completes 500M Failed 4h 01m Now completes Key takeaway: this is not just faster. At higher LOB counts, the improvement changes the outcome from upgrade fails to upgrade completes. Who benefits from this? You should care if your database stores large binary content using PostgreSQL large objects. Workload pattern Why it matters Document management PDFs, contracts, scans, and archived files Attachment-heavy apps Files stored inside PostgreSQL instead of external storage Legacy apps using lo APIs LOBs may have accumulated for years Image/archive systems Millions of binary objects can build up quietly Previous upgrade failures Failures during schema dump may map to this scenario Copy/paste: check your large-object count Run these checks in each database you plan to upgrade. 1. Count large objects in the current database -- Count PostgreSQL large objects in the current database SELECT current_database() AS database_name, count(*) AS large_object_count FROM pg_largeobject_metadata; 2. Check large-object storage footprint -- Estimate large-object data and metadata size SELECT pg_size_pretty(pg_total_relation_size('pg_largeobject'::regclass)) AS large_object_data_size, pg_size_pretty(pg_total_relation_size('pg_largeobject_metadata'::regclass)) AS large_object_metadata_size; 3. Understand ownership and ACL shape -- Inspect large-object metadata shape SELECT count(*) AS total_large_objects, count(lomacl) AS large_objects_with_custom_acl, count(DISTINCT lomowner) AS distinct_large_object_owners FROM pg_largeobject_metadata; 4. Find top large-object owners -- Top large-object owners SELECT lomowner::regrole AS owner, count(*) AS large_object_count FROM pg_largeobject_metadata GROUP BY lomowner ORDER BY large_object_count DESC LIMIT 10; What should I do before my next major version upgrade? If your situation is... Recommended action Target is PostgreSQL 15 or later Target PostgreSQL 15 or later to benefit from improved large-object metadata handling. Target is PostgreSQL 14 or earlier Prefer PostgreSQL 15+ where possible; very high LOB counts may still hit older-path limitations. Very large or unusual database Restore a copy and rehearse the upgrade before production. Suspected orphan LOBs Consider vacuumlo only after testing. It can delete valid LOBs if your app uses custom references. Any major version upgrade Keep healthy free space and leverage pre-upgrade validation checks to validate extension/schema compatibility first. Bottom line If large objects were making your PostgreSQL upgrade risky, this improvement makes the upgrade path safer and more predictable. For large-object-heavy databases, upgrades targeting PostgreSQL 15 and later now show faster runtime, lower memory/temp-space pressure, and successful validation up to 500M large objects. Learn more Major version upgrades in Azure Database for PostgreSQL flexible server How to perform a major version upgrade PostgreSQL vacuumlo documentationTop 10 Performance Optimization Techniques for Azure Database for PostgreSQL Flexible Server
Introduction Performance optimization is one of the most common challenges faced by organizations running business-critical workloads on Azure Database for PostgreSQL flexible server. As your workloads grow it’s common to encounter high CPU utilization, storage bottlenecks, autovacuum issues, excessive temporary file generation, and connection saturation. The good news is that Azure PostgreSQL flexible server provides several built-in capabilities to help optimize performance, improve scalability, and reduce operational overhead. This article explores ten practical techniques that can significantly improve database performance and reliability. 1. Choose the Right Compute SKU Performance starts with selecting the appropriate compute tier. Azure PostgreSQL flexible server offers: Pricing tier Target workloads Burstable Designed for workloads that don't require full CPU performance continuously. Best suited for proof-of-concept environments, and development builds. Not recommended for production workloads. General Purpose Provides a balance between CPU and memory with scalable I/O throughput, making it suitable for most production workloads. Examples include servers for hosting web applications, mobile apps, and enterprise applications. Memory Optimized Suitable for high-performance database workloads that require in-memory performance for larger buffer cache sets, and higher concurrency. Examples include servers for processing real-time data and high-performance transactional or analytical apps. Learn more about Compute Tiers here. 2.Enable and Use Query Store Query Store is one of the most powerful performance tools available. Query Store automatically captures the following and keeps them available for review: Query execution statistics Runtime metrics Wait event information Historical execution trends It organizes the data into time windows, so you can spot database usage patterns. Data for all users, databases, and queries is stored in a database named azure_sys in the Azure Database for PostgreSQL instance. It’s generally recommended to monitor query store from Azure tools, KQL, etc. Learn more about Query store here. You can also view some useful scenario for query store and some Best Practices for Query store 3.Leverage Built-In PgBouncer Connection Pooling PostgreSQL uses a process-per-connection model, which means every connection consumes memory and CPU resources. Azure PostgreSQL flexible server provides built-in PgBouncer support for eligible SKUs . PgBouncer allows multiple application sessions to reuse open backend connections and significantly reduces overhead. Benefits include: Lower memory consumption Faster connection handling Improved application scalability Reduced CPU overhead Learn more about PgBouncer here 4.Use Azure Troubleshooting Guides One underutilized feature is the built-in troubleshooting experience available directly in the Azure portal. Guides are available for: CPU troubleshooting Memory troubleshooting IOPS analysis Temporary files Autovacuum monitoring Autovacuum blockers These tools provide actionable recommendations and visualizations without requiring external monitoring solutions. Learn more about Troubleshooting Guides here. 5. Monitor and Tune Autovacuum Autovacuum is critical for maintaining PostgreSQL performance. Without proper vacuuming: Dead tuples accumulate Table bloat increases Statistics are not refreshed regularly Query performance degrades Transaction ID wraparound risks emerge Use Azure's built-in Autovacuum Monitoring TroubleshootingGuides to identify: Vacuum lag Blocked autovacuums Table bloat Inefficient cleanup operations Azure now also offers adaptive tuning capabilities to optimize maintenance behavior. Learn more about Autovacuum tuning here 6.Optimize Storage and IOPS Planning Many performance incidents originate from insufficient storage planning rather than inefficient SQL. In Azure PostgreSQL flexible server: Storage and baseline IOPS are closely related. Learn more here. Larger storage allocations provide higher baseline IOPS. Auto-grow prevents storage-related outages Note: Storage can only be scaled up and will always be double in size. SSDv2 auto-grow will allow customized growth settings in future release. For write-heavy workloads, monitoring storage utilization and IOPS is essential. Best practice: Enable Storage Auto-Grow Monitor Read/Write IOPS regularly Scale storage proactively 7.Investigate Temporary File Generation Large sorts and hash operations that exceed available memory spill to disk and generate temporary files. Symptoms include: Sudden Latency Spikes Increased IOPS Slower query execution Azure TroubleshootingGuides provide dedicated temporary-file analysis capabilities that help identify offending queries. Frequent temp file generation often indicates: Missing indexes Undersized work_mem Large sorting operations 8.Use Intelligent Tuning Azure PostgreSQL flexible server includes Intelligent Tuning capabilities. The service continuously observes workload behavior and automatically optimizes parameters related to write operations. Examples of tuning include: checkpoint_completion_target max_wal_size min_wal_size bgwriter settings This reduces administrative effort while helping maintain consistent performance. Learn more about Intelligent Tuning here. 9.Optimize Checkpoints and Write Workloads Checkpoint spikes frequently appear in escalations involving high IOPS and latency. Aggressive checkpoint activity can: Generate excessive disk writes Increase latency Consume IOPS capacity Monitoring checkpoint behavior and ensuring WAL parameters are properly configured can significantly improve write-intensive workloads. Azure intelligent tuning can assist in this area as well. 10.Metric Monitoring Optimization should always be data-driven. You should track the following: CPU utilization Memory pressure Active Connections Oldest Query IOPS consumption Combining Azure Metrics, Query Store, and PostgreSQL statistic views allows teams to distinguish between normal workload spikes and true performance degradation. PostgreSQL statistics views provide valuable workload insights. For example, pg_stat_activity can be used to identify long-running or blocking queries, pg_stat_user_tables helps track dead tuples, vacuum activity, and statistics refreshes, while pg_stat_statements (if enabled) help identify the most resource-intensive queries by execution time and frequency. Learn more about Metric here Conclusion Performance optimization in Azure Database for PostgreSQL flexible server is not just about changing a few parameters and hoping for better results. It requires a structured approach that combines workload understanding, proactive monitoring, proper sizing, query optimization, and platform-native capabilities. By leveraging Query Store, PgBouncer, Intelligent Tuning, Autovacuum Monitoring, Azure Metrics, and Troubleshooting Guides, you can significantly improve database efficiency while reducing operational effort. References Compute Options - Azure Database for PostgreSQL | Microsoft Learn Query Store in Azure Database for PostgreSQL Flexible Server - Azure Database for PostgreSQL | Microsoft Learn PgBouncer in Azure Database for PostgreSQL Flexible Server - Azure Database for PostgreSQL | Microsoft Learn Autovacuum Tuning - Azure Database for PostgreSQL | Microsoft Learn Intelligent Tuning in Azure Database for PostgreSQL Flexible Server - Azure Database for PostgreSQL | Microsoft LearnLog 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 parametersFrom RAG to agents: Build AI pipelines inside Azure HorizonDB
By Abe Omorogbe, Navya Teja Gajula, Binnur Gorer, B Harsha Kashyap, Krishnakumar Ravi (KK) from Microsoft PostgreSQL AI team If you’ve ever shipped a RAG app, this will feel familiar. Your data lives in Postgres. But the pipeline that turns that data into vectors lives somewhere else, spread across external services, queues, and retry logic. And when the embedding API hiccups mid-batch? That’s a 2 a.m. production incident. You didn’t set out to build your own embedding service. You just wanted to search your documents. And RAG is only the beginning. The moment AI works on your data: extraction, summarization, reranking, keeping embeddings fresh, or powering agent, you’re back to stitching together more services, queues, and glue code, all outside the database. AI pipelines in Azure HorizonDB (Preview) removes that entire stack. Define your workflows steps like chunking, embeding, extracting, and generating in SQL, and HorizonDB runs them as AI pipelines next to your data. No orchestrator. No glue code. Just Postgres. In this post we'll cover: The external-orchestrator issue that every AI on Postgres team eventually hits What AI pipelines are, and the four-part anatomy that makes them click Use cases worth trying: semantic search, knowledge extraction, content generation, smarter reranking, and always-fresh embeddings How to watch your pipelines run as live graphs in VS Code How to spin up HorizonDB and run your first pipeline today 🚀 Try it on Azure HorizonDB. AI pipelines are built into Microsoft's new PostgreSQL cloud service, no extra infrastructure to stand up. Write ai.create_pipeline(...), call ai.run(...), and it runs. Get started in HorizonDB → AI preprocessing runs outside the database, far from your data The standard way to get data into a vector store looks reasonable on a whiteboard: a service reads source rows, calls an embedding API, and writes chunks back to Postgres. However, some interesting issues often occur in production. The embedding API fails mid-batch, and there's no shared checkpoint showing which rows were completed. You rerun the job, and the extra API calls increases cost. A worker crashes after writing chunks but before flipping the parent row's processed flag. Now your embeddings are quietly inconsistent, and nobody knows. Every one of these is the same missing primitive: durable, checkpointed execution that lives where your data lives. External orchestrators can do it, but now you're operating a second service just to feed the first one. AI pipelines move that logic into HorizonDB itself. The source, the steps, the sink, and the full run history are all SQL protected by the same transactions, backups, and point-in-time restore your data already has. The database is already where your data commits. It's a natural place for the pipeline to live too. Anatomy of an AI pipeline in HorizonDB are optional and can be adjusted as needed. A pipeline has four parts: Source: where rows come from. A table_source(...) over a HorizonDB table, optionally with an incremental_column so the pipeline skips rows it already processed. Steps: the AI operations that transform each row, in order. Each step appends columns to the in-flight batch. Sink: where results land, ready for use by your AI apps or agent. Trigger: 'on_change' (run automatically when source rows change) or 'manual' (run only when you call ai.run()). Those four parts give the pipeline its shape. The steps are where you define the AI work itself, using composable building blocks: Step What it does ai.chunk() Split long text into overlapping chunks ai.embed() Generate vector embeddings ai.extract() Pull structured fields out of text with an LLM ai.generate() Generate text from a prompt (i.e content generation, classify, summarize and more) ai.rank() Score documents against a query How the pieces fit together. The ai.* API gives you the AI pipeline shape: sources define where data comes from, steps define the AI work to perform, sinks define where results land, and triggers define when the pipeline runs. Under the covers, HorizonDB turns that definition into a durable execution graph, where each step can be checkpointed, retried, and resumed if something fails. Built on open source. That durability isn't magic, every AI pipeline compiles down to a graph that runs on pg_durable, Microsoft's open-source durable-execution engine for PostgreSQL (built on the duroxide Rust runtime). The ai.* API is the AI-shaped surface (sources, steps, sinks, triggers) and pg_durable is the general-purpose engine underneath that handles checkpointing, retries, and crash recovery. So, your pipelines stand on a transparent, inspectable foundation you can read, and run on any Postgres 17 & 18. No black box, no lock-in. Use case 1: Semantic search over your data This is one of the most popular use cases. Turn a table of documents into searchable vectors, durably, and keep them fresh as the data changes. That last part matters: in production, documents are edited, added, and deleted constantly, and every change needs the right chunks and embeddings updated without reprocessing the entire corpus or leaving stale vectors behind. With AI pipelines, HorizonDB can track those incremental updates for you. Chunk the body, embed each chunk, and land the result in a DiskANN-indexed table. -- Define the pipeline: source -> chunk -> embed -> sink. SELECT ai.create_pipeline( name => 'rag_pipeline', source => ai.table_source(table_name => 'documents'), steps => ARRAY[ ai.chunk(input => 'content', chunk_size => 512, overlap => 64), ai.embed(model => 'default-embedding', input => 'chunk_text', dimensions => 1536) ], trigger => 'on_change', -- re-embed automatically as rows change sink => ai.table_sink('rag_pipeline_output') ); -- Run it SELECT ai.run('rag_pipeline'); -- Search your data SELECT chunk_text, embedding <=> azure_openai.create_embeddings('text-embedding-3-small', 'how does vector search work?')::vector AS distance FROM rag_pipeline_output ORDER BY distance LIMIT 3; 📘 Read more details in the AI Pipelines documentation That's the entire ingestion layer; chunking, embedding, checkpointing, retries, and sink writes in one definition. Because trigger => 'on_change', the pipeline updates embeddings whenever source rows change, processing only what is new or modified instead of redoing the whole corpus. Your vectors stay in sync with your data, and your ingestion work stays efficient as the dataset grows. Point a query at the DiskANN index and you've got production semantic search without a single line of application glue. That's the whole loop: define, run, inspect. The embedding service you were about to build the queue, the workers, the retry logic, the checkpoint table, the 2 a.m. production incident doesn't happens. Why it's better than an external service: a failure in ai.embed() never re-runs ai.chunk(), each step is a durable node. If the database restarts mid-run, it resumes from the last checkpointed batch, not row zero. Use case 2: Turn unstructured text into structured metadata Support tickets, contracts, product reviews, research papers are full of structure that's locked inside unstructured documents. ai.extract() pulls named fields out of text and merges them into the metadata JSONB column, so you can filter and aggregate on things an LLM read for you. SELECT ai.create_pipeline( name => 'extraction_pipeline', source => ai.table_source(table_name => 'documents'), steps => ARRAY[ ai.chunk(input => 'content'), ai.extract( input => 'chunk_text', data => ARRAY['topics: string - the main topics discussed', 'entities: string - named people, products, or places'] model => 'my-gpt' -- optional, the default model when AI model management is activate ) ], sink => ai.table_sink('extraction_pipeline_output') ); SELECT ai.run('extraction_pipeline'); -- Now query the structured fields the LLM extracted: SELECT doc_id, metadata->'topics' AS topics, metadata->'entities' AS entities FROM extraction_pipeline_output; 📘 Read more details in the AI Pipelines documentation You describe each field as a label: description string in the ai.extract step, and HorizonDB does the rest durably, in bulk, with the same retry-and-resume guarantees. Each field is a label, either a bare name like product, or the detailed form name: type - description (for example `sentiment: number - sentiment score from 1 to 5`). HorizonDB does the rest, durably, in bulk, with the same retry-and-resume guarantees. Use case 3: Summarize and rewrite content at scale ai.generate() runs an LLM prompt against every row, perfect for bulk summarization, classification, tone normalization, or generating titles. Because it's a pipeline, "summarize 4 million documents" becomes a job that survives restarts instead of a script you have to monitor overnight. SELECT ai.create_pipeline( name => 'summary_pipeline', source => ai.table_source(table_name => 'documents'), steps => ARRAY[ ai.chunk(input => 'content'), ai.generate( input => 'chunk_text', system_prompt => 'Create a concise summary in 50 words or fewer.' model => 'my-gpt' -- optional, the default model when AI model management is activate ) ], sink => ai.table_sink('generation_pipeline_output') ); SELECT ai.run('summary_pipeline'); -- Now query the generated text: SELECT doc_id, left(generated_text, 100) AS summary_preview FROM generation_pipeline_output WHERE generated_text IS NOT NULL LIMIT 5; 📘 Read more details in the AI Pipelines documentation Swap the system_prompt and the same shape becomes a classifier ("Label this ticket as billing, bug, or feature request"), a translator, or a headline generator. The instruction goes in system_prompt; the result lands in generated_text. Use case 4: Keep embeddings fresh, and re-embed cleanly when the model changes This is where AI pipelines become especially useful. In a real AI app, two things change constantly: your data and your model. AI pipelines are designed to handle both changes directly. Your data changes. Set incremental_column and an on_change trigger, and the pipeline only embeds new or changed rows, automatically, forever, until you pause or drop it. SELECT ai.create_pipeline( name => 'rag_pipeline', source => ai.table_source( table_name => 'documents', incremental_column => 'updated_at' -- only process what changed ), steps => ARRAY[ ai.chunk(input => 'content'), ai.embed(model => 'default-embedding', input => 'chunk_text', dimensions => 1536) ], trigger => 'on_change', sink => ai.table_sink('rag_pipeline_output') ); Your model changes. Bump the model or the dimensions, then run a single, resumable backfill, no migration script, no babysitting: TRUNCATE rag_pipeline_output; SELECT ai.backfill('rag_pipeline'); 📘 Read more details in the AI Pipelines documentation The backfill runs as one durable instance. If the database restarts mid-backfill, it picks up from the last checkpointed batch instead of starting over. The painful "re-embed everything" migration becomes a one-liner you can actually trust. Watch your pipelines run as live graphs in VS Code A pipeline you can see is a pipeline you can trust. Install the PostgreSQL extension for VS Code, connect to HorizonDB, then right-click your database and open Pipelines & Workflows → AI Pipelines. Select any run and the center pane renders the execution as a color-coded graph: Blue 🔵 : source and sink (where data enters and exits) Green 🟢 : processing steps (chunk, embed, extract, generate, rank) Pink 🟣 : external model and service calls For each run you can read the status (completed, running, failed), the run ID for traceability, start time and duration for performance, and a link back to the pipeline definition. When a run fails, open the graph and jump straight to the step where execution stopped, no log spelunking. Get Started: Try It Now We have a few demoes of AI pipelines in action: Resource Link Microsoft Build AI Pipeline Demo Simplify app dev with cloud-native PostgreSQL in Azure HorizonDB | DEM364 Microsoft Build AI Pipeline GitHub AI Pipelines Demo GitHub Repo | DEM364 Microsoft Mechanic Demo AI Pipeline Demo on Microsoft Mechanic Documentation AI pipelines on HorizonDB Enabling AI pipelines takes minutes: enable to azure_ai, pg_durable, vector and pg_diskann extensions and you can get started. -- On Azure HorizonDB — the extensions are built in. CREATE EXTENSION IF NOT EXISTS pg_durable; CREATE EXTENSION IF NOT EXISTS azure_ai; CREATE EXTENSION IF NOT EXISTS vector; CREATE EXTENSION IF NOT EXISTS pg_diskann; That's it, your PostgreSQL database can now run AI pipelines Learn more MS Learn AI pipelines on HorizonDB: Azure HorizonDB Preview pg_durable on GitHub (open source) MS Learn Durable Functions on HorizonDB Scalable vector search with DiskANN PostgreSQL extension for VS Code
