Last Call: Join Live the PostgreSQL Community at POSETTE: An Event for Postgres 2026 (T‑1 week)
In just one week, the PostgreSQL community gathers again for one of the most anticipated global moments of the year: POSETTE: An Event for Postgres 2026. From June 16–18, this free and fully virtual event brings together PostgreSQL contributors, engineers, architects, and practitioners across 4 livestreams, 44 talks, and 50 speakers. But you might be wondering, why should I participate in POSETTE during the livestreams? Why join it live? Explore the schedule and choose your livestreams on the official site: Join POSETTE: An Event for Postgres 2026 Why joining live makes all the difference Yes, every talk will be available afterward. But the real value of POSETTE: An Event for Postgres 2026 happens while it is unfolding live. Be part of the virtual hallway track Participating live gives you access to the #posetteconf Discord channel, where attendees and speakers interact in real time, asking questions, sharing perspectives, and comparing approaches. This is where conversations extend beyond the talks and where ideas are challenged and refined collectively. Learn and validate your thinking in real time POSETTE is not just about listening. It is about sharpening how you think about PostgreSQL: Are you partitioning effectively? Are you approaching replication with the right mental model? Are your performance strategies aligned with how PostgreSQL actually behaves? Joining live means you can test those ideas immediately with people who build and run PostgreSQL systems at scale. Connect with practitioners solving the same problems A recurring insight from POSETTE participants is how often they discover others facing the same challenges, whether around scaling, performance, or operability. That shared experience often leads to the most valuable takeaways: not just what works, but why. What makes this year’s speakers worth your time POSETTE: An Event for Postgres 2026 brings together a diverse set of voices: PostgreSQL core contributors Engineers and architects working on production systems Specialists in performance, replication, and security Developers shaping how PostgreSQL is used in modern applications Azure Database for PostgreSQL and Azure HorizonDB engineers and experts These are practitioners who have built, debugged, and scaled real systems, and who are ready to share what they learned. Learn directly from the POSETTE speakers who are shaping PostgreSQL Bruce Momjian: understanding PostgreSQL from the inside out Read Bruce Momjian’s interview Bruce Momjian, a co-founder and core member of the PostgreSQL Global Development Group, has spent decades helping people understand how PostgreSQL really works. His session on the write-ahead log (WAL) reflects that same focus: taking something fundamental but often misunderstood and making it approachable. If you want to move beyond “using” PostgreSQL and start truly understanding its internals, how durability, recovery, and replication actually function, this is a rare opportunity to learn directly from someone who has helped build the system. Chris Ellis: making PostgreSQL practical for developers Read Chris Ellis’s interview Chris Ellis focuses on how to turn PostgreSQL’s extensive feature set into practical design choices. His session on design patterns highlights a reality many developers face: PostgreSQL offers powerful primitives, but knowing how to combine them effectively is what makes the difference. His work consistently centers on simplifying application architecture by using the database well, rather than pushing complexity into application code. Chun Lin Goh: understanding performance in real environments Read Chun Lin Goh’s interview Chun Lin Goh brings a cloud architecture and observability perspective to PostgreSQL performance. His session on performance degradation in burstable environments shows how the database behaves under real-world infrastructure constraints. This is especially relevant if you run PostgreSQL in cloud environments, where system behaviour, not just query design,can have a major impact. Derk van Veen: lessons from real-world partitioning Read Derk van Veen’s interview Derk van Veen’s work is grounded in hands-on experience operating PostgreSQL at scale. His partitioning session focuses not just on how to do things right, but also on what can go wrong,and why. Partitioning decisions often look simple early on but have long-term consequences. Learning from real mistakes and trade-offs is what makes these sessions so valuable. Hari Kiran: thinking deeply about replication Read Hari Kiran’s interview Hari Kiran’s session explores logical decoding and replication, two foundational aspects of how PostgreSQL systems scale and integrate with other systems. If your work involves distributed systems, data pipelines, or event-driven architectures, understanding these mechanics is essential. Jimmy Angelakos: uncovering subtle behavior Read Jimmy Angelakos’s interview Jimmy Angelakos focuses on practical, often overlooked aspects of PostgreSQL behavior. His session on NOTIFY highlights how features that seem simple on the surface can introduce complexity in real systems. These are exactly the kinds of nuances that can save hours or days of debugging in production. Sakshi Nasha: securing PostgreSQL for production Read Sakshi Nasha’s interview Sakshi Nasha’s work emphasizes security and production readiness. Her session on securing PostgreSQL reflects a broader shift: as PostgreSQL becomes central to more systems, security needs to be built in from the start. Her perspective is especially relevant for teams moving from development environments into production systems. Taiob Ali: connecting community and real-world usage Read Taiob Ali’s interview Taiob Ali brings a strong community-driven perspective to PostgreSQL, shaped by experience as both a practitioner and an advocate. Sessions like his often help bridge the gap between concepts and how PostgreSQL is actually used across different teams and environments. Xuneng Zhou: an independent perspective from the ecosystem Read Xuneng Zhou’s interview As an independent PostgreSQL hacker, Xuneng Zhou represents a perspective deeply rooted in the open source ecosystem itself. That viewpoint often brings a focus on fundamentals, experimentation, and how PostgreSQL evolves over time, valuable context for anyone who wants to understand not just where PostgreSQL is today, but where it is heading. This is more than a conference POSETTE: An Event for Postgres 2026 is a shared moment for the PostgreSQL community. It is an opportunity to: Learn from practitioners and contributors Challenge assumptions and refine your thinking Understand where PostgreSQL is heading next Stepping into the livestream is not just about attending talks, it is about participating in that moment. Your next step: join live If PostgreSQL is part of your work, or becoming central to it, the best way to experience POSETTE: An Event for Postgres 2026 is live. Pick the sessions that matter to you. Add the livestreams to your calendar. Join the discussion as it happens. Start here: Check out the POSETTE schedule to figure out which livestreams & which talks are for you. For any other answer you may still have, don't forget to take a look at the Ultimate Guide to POSETTE: An Event for Postgres 2026 See you live at POSETTE Whether you are exploring PostgreSQL internals, building modern applications, or scaling production systems, POSETTE: An Event for Postgres 2026 is where those conversations come together. See you at POSETTE on 16-18 June 2026.Ultimate Guide to POSETTE: An Event for Postgres, 2026 edition
POSETTE: An Event for Postgres 2026 is back for its 5th year: free, virtual, and unapologetically all about Postgres. No travel budget required and no jet lag involved. Just your laptop, a decent internet connection, and curiosity. This year the POSETTE 2026 schedule has 4 livestreams (16-18 June) with 44 talks at ~25 minutes each—covering everything from query performance and partitioning to Postgres 19 features, extensions, and use cases. Which is awesome but also a bit of work to figure out which talks are for you. Hence this ultimate guide post. Every talk will land on YouTube afterward (un-gated, of course) so if you miss anything you care about, you can watch it later. But if you can catch a livestream in June, do it. That’s when the “virtual hallway track” happens on Discord—where you can ask the POSETTE speakers questions and compare notes with other attendees. Meeting other attendees who have the same weird Postgres problems you do can be reassuring somehow. And yes, there will be swag. This guide is your cheat sheet: I’ve categorized and tagged all 44 talks so you don’t have to read 44 abstracts back-to-back. In this post you'll get: “By the numbers” summary Map of the 44 talks 2 Keynote sessions 23 Postgres core talks 11 Postgres ecosystem talks 8 Azure Database talks Why participate on the virtual hallway track on Discord A big thank you to our amazing speakers Join us for POSETTE 2026 & mark your calendars Official POSETTE 2026 Trailer “By the numbers” summary for POSETTE 2026 Here’s a quick snapshot of what you need to know about POSETTE this year: 3 days 16-18 June 2026 4 livestreams In Americas & EMEA time zones but of course you can watch from anywhere 44 talks All free, all virtual 2 invited keynotes Driving Postgres forward at Microsoft (Livestream 1), and Postgres 19 Hackers Panel: What’s In, What’s Out, & What’s Next (Livestream 2) 25 minutes Average length per talk ~1100 minutes Total minutes in POSETTE 2026 talks 50 speakers POSETTE 2026 speakers include PostgreSQL hackers and contributors, users, application developers, PG community members, Azure engineers, & Azure customers 6 keynote speakers Affan Dar & Charles Feddersen (Livestream 1); and Álvaro Herrera, Heikki Linnakangas, Melanie Plageman, & Thomas Munro (Livestream 2) 19 countries Speakers reside in 19 different countries 23 companies Speakers hail from 23 different companies 17.6% CFP acceptance rate 42 talks selected from 238 submisssions 75% general Postgres talks 33 talks are not cloud-specific at all, they’re about the Postgres technology & ecosystem 25% Azure-related talks 11 of 44 talks feature Azure Database for PostgreSQL or Azure HorizonDB 1 organizing company Organized by the Postgres team at Microsoft, in partnership with AMD 17 languages Published talk videos will have captions available in 17 languages, including English, Czech, Dutch, French, German, Hindi, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Turkish, Ukrainian, and Chinese Simplified & Chinese Traditional Map of the 44 talks To help you quickly navigate all 44 talks, here’s a map of the high-level categories and detailed topics. : A map of the POSETTE 2026 talks—high-level categories and detailed tags to help you find what you care about 2 Keynote sessions Affan Dar and Charles Feddersen lead the PostgreSQL engineering and product teams at Microsoft, In this keynote, they’ll walk through how Microsoft is contributing to Postgres, both upstream in the open source project and in the cloud database service they build on top of it. Driving Postgres forward at Microsoft, by Affan Dar & Charles Feddersen (Azure Database for PostgreSQL, Azure HorizonDB, VS Code, Dev tools, community, Postgres hacking, open source, PosetteConf, livestream-1) Want to understand how Postgres features get decided? This keynote panel with 4 PostgreSQL committers & hackers will peel back the curtain. You’ll hear what made it into Postgres 19, what didn’t (and why), and get a sneak peek into a few of the things in the oven for Postgres 20. Postgres 19 Hackers Panel: What’s In, What’s Out, & What’s Next, by Álvaro Herrera, Heikki Linnakangas, Melanie Plageman, & Thomas Munro (Postgres 19, Postgres hacking, panel, open source, collaboration, multithreading, livestream-2) 23 Postgres core talks Data Modeling JSON in PostgreSQL - evil data type or just needs to be tamed?, by Boriss Mejias (JSON, performance, data modeling, livestream-1) PostgreSQL Design Patterns, by Chris Ellis (data modeling, SQL, PG use cases, livestream-1) Graph Data Exploring property graphs with SQL/PGQ in PostgreSQL, by Ashutosh Bapat (SQL/PGQ, graph data, data modeling, Postgres 19, livestream-4) LISTEN/NOTIFY LISTEN Carefully: How NOTIFY Can Trip Up Your Database, by Jimmy Angelakos (LISTEN/NOTIFY, PG use cases, triggers, livestream-4) Performance Maintaining Large Tables in PostgreSQL, by Sarat Balijepalli (WAL, performance, scaling Postgres, vacuum, autovacuum, statistics, partitioning, monitoring, livestream-3) My Postgres partitioning cookbook, by Derk van Veen (partitioning, PG use cases, data modeling, performance, livestream-4) PostgreSQL 17 vs 18: Side‑by‑Side Performance Wins in Real‑World Queries, by Divya Bhargov (performance, PG use cases, livestream-3) Vacuuming Enhancements in PostgreSQL 18: Faster, Smarter, More Predictable, by Shashikant Shakya (vacuum, async IO, monitoring, performance, livestream-4) PG Internals Linux and PostgreSQL in the Multiverse of Connections, by Josef Machytka (Linux, PG internals, connection pooling, livestream-2) pg_stats: How Postgres Internal Stats Work, by Richard Yen (statistics, pg_stats, PG internals, query planner, livestream-2) Postgres isn’t slow, your storage is, by Sai Srirampur (storage, IO, performance, livestream-3) PostgreSQL queues done right with PgQ, by Alexander Kukushkin (queues, PG internals, extensions, livestream-2) random_page_cost in Postgres - why the default is 4.0 and should you lower it?, by Tomas Vondra (PG internals, IO, performance, livestream-1) The Wonderful World of WAL, by Bruce Momjian (WAL, PG internals, replication, livestream-3) What's new with constraints in Postgres 18, by Gülçin Yıldırım Jelínek (constraints, data modeling, livestream-2) Postgres Hacking Fuzzing PostgreSQL, by Adam Wolk (PG internals, testing, Dev tools, libpq, security, livestream-1) Journey of developing a performance optimization feature in PostgreSQL, by Rahila Syed (Postgres hacking, PG internals, performance, WAL, replication, livestream-4) The Hitchhiker’s Guide to PostgreSQL Hacking: Don’t Panic, Just Start Small, by Xuneng Zhou (Postgres hacking, PG internals, community, livestream-2) Replication Past, Present, and Future: Logical Decoding and Replication in PostgreSQL, by Hari Kiran (replication, logical decoding, PG internals, livestream-4) Where Does My INSERT Go? A Logical Replication Story, by Hamid Akhtar (replication, PG internals, WAL, livestream-4) Security From Dev to Prod: Securing Postgres the Right Way, by Sakshi Nasha (security, roles, PG use cases, extensions, monitoring, livestream-4) From trust to Tokens: A Short History of PostgreSQL Authentication, by Murat Tuncer (authentication, security, livestream-2) PostgreSQL vs. SQL Server: Security Model Differences, by Taiob Ali (security, authentication, SQL Server, roles, livestream-1) 11 Postgres ecosystem talks Analytics pg_lake: Postgres as a lakehouse, by Marco Slot (pg_lake, extensions, OLAP, data warehouse, Iceberg, DuckDB, analytics, livestream-2) Apache AGE Querying & Visualizing Graphs in Postgres with Apache AGE, by Christian Miles (Apache AGE, graph data, data visualization, SQL/PGQ, Azure HorizonDB, livestream-1) Autotuning Building safety tooling for risk-free AI tuning of Postgres: Fast cars need fast brakes, by Mohsin Ejaz (autotuning, AI, performance, monitoring, livestream-2) Change Data Capture Building Event-Driven Systems with PostgreSQL Logical Replication and Drasi, by Diaa Radwan (Drasi, replication, WAL, CDC, livestream-3) Citus Move Less, Move Faster: Speeding Up Citus Cluster Scaling, by Muhammad Usama (Citus, extensions, performance, scaling Postgres, livestream-4) Dev Tools An MCP for your Postgres DB, by Pamela Fox (MCP, AI, Python, Dev tools, livestream-1) pgcov: Bringing Real Test Coverage to PostgreSQL Code, by Pavlo Golub (testing, Postgres hacking, Dev tools, extensions, CI/CD, livestream-3) PostgreSQL Tooling Across AI Editors and Agents, by Matt McFarland (Dev tools, VS Code, Cursor, AI, data visualization, Apache AGE, graph data, Azure, MCP, Copilot, livestream-1) Django PostgreSQL Generated Columns by Example, by Paolo Melchiorre (app dev, Django, generated columns, livestream-2) Kubernetes Quorum-Based Consistency for Cluster Changes with CloudNativePG Operator, by Jeremy Schneider & Leonardo Cecchi (CloudNativePG, Kubernetes, PG use cases, livestream-3) Performance Modelling Postgres Performance Degradation on Burstable Cloud Instances, by Chun Lin Goh (performance, burstable, compute, QA, livestream-4) 8 Azure Database for PostgreSQL & Azure HorizonDB talks AI-related talks From Queries to Agents: The Next Era of Data Retrieval on PostgreSQL, by Abe Omorogbe (AI, MCP, Azure Database for PostgreSQL, graph data, Apache AGE, Azure HorizonDB, livestream-3) Production RAG at Scale with Azure Database for PostgreSQL, by Julia Schröder Langhaeuser & Paula Santamaría (Azure Database for PostgreSQL, AI, RAG, PG use cases, livestream-3) AMD Choose the Right Azure Infrastructure to Improve Postgres Performance by Over 60%, by Andrew Ruffin (AMD, performance, Azure, compute, Azure Database for PostgreSQL, livestream-1) Azure HorizonDB Why we built Azure HorizonDB for PostgreSQL, by Dingding Lu (Azure HorizonDB, scaling Postgres, livestream-3) Flexible Server pg_duckdb in Action: Accelerating Analytics on Azure Database for PostgreSQL, by Nitin Jadhav (DuckDB, Azure Database for PostgreSQL, extensions, OLAP, analytics, performance, livestream-4) The Rise of PostgreSQL as the Everything Database, by Varun Dhawan (Postgres history, extensions, graph data, Apache AGE, Azure Database for PostgreSQL, DuckDB, Citus, livestream-3) What I’ve Learned Teaching Postgres to 200+ field engineers at Microsoft, by Paula Berenguel (training, Azure, Postgres skilling, livestream-1) Oracle to Postgres Migrating VLDBs from Oracle to Azure Database for PostgreSQL, by Adithya Kumaranchath (migration, Azure Database for PostgreSQL, Oracle to Postgres, livestream-2) Why participate in the virtual hallway track on Discord If you’ve checked out the schedule and plan to watch some of the talks, you might still be wondering: why join live—and why bother with the virtual hallway track on Discord? Here’s how a few of last year’s attendees described the experience: “Very impressed by all the speakers and content I am absolutely shattered as there was so much great content in all the talks over the past 3 days but I have probably learnt more in these sessions than I could have in months of reading up.” “Want to let y’all know how much I got from this onine conference, the speakers were excellent, well-prepared and well-presented. The hosts were informative, engaging, & amusing. The discord hallway channel made me feel connected. I learned a lot and found some new inspiration. I’ll be back next year!” “I have no idea how I’m going to summarise all the interesting stuff for coworkers.” The common thread: the live, shared experience—being able to ask questions, compare notes, and learn alongside other people in real time. How to join the virtual hallway track Head to the #posetteconf channel on Discord (on the Microsoft Open Source Discord) That’s where speakers and attendees hang out during the livestreams—it’s where you can ask questions, share reactions, and just say hi Big thank you to our amazing speakers Every great event starts with great talks—and great talks start with great speakers. Want to learn more about the people behind these talks? Visit the POSETTE 2026 Speaker page Click a speaker’s bio to see their written interview (if available) If a speaker has been a guest on the Talking Postgres podcast in the past, then you’ll find a link to their episode there, too Join us for POSETTE 2026! Mark your calendars I hope you join us for POSETTE 2026. Consider yourself officially invited. As part of the talk selection team, I’m definitely biased—but I truly believe these speakers and talks are worth your time. I’ll be hosting Livestream 1 and you’ll find me in the #posetteconf Discord chat. I hope to see you there. And please: tell your Postgres friends, so they don’t miss out! 🗓️ Add the livestreams to your calendar Livestream 1: Tue 16 June, 8am–2pm PDT (UTC-7) [ register for updates ] and/or [ add to calendar ] Livestream 2: Wed 17 June, 8am–2pm CEST (UTC+2) [ register for updates ] and/or [ add to calendar ] Livestream 3: Wed 17 June, 8am–2pm PDT (UTC-7) [ register for updates ] and/or [ add to calendar ] Livestream 4: Thu 18 June, 8am–2pm CEST (UTC+2) [ register for updates ] and/or [ add to calendar ] Watch last year’s POSETTE 2025 talks in advance: And if you want to get ready, you can watch talks from the POSETTE 2025 playlist on YouTube anytime, anywhere. Lots of solid, useful, and evergreen Postgres talks in there. “Official Trailer” for POSETTE 2026 is on YouTube To help more developers, community members, and Postgres users discover POSETTE 2026, our team created this short video trailer. Take a peek and share it with friends as an invitation of sorts. We’re trying to make sure that people don’t miss their opportunity to be part of the livestreams and ask questions on the discord during the conference (as well as watch the talks on YouTube after the event is over.) Watch and share the trailer: Official Trailer for POSETTE: An Event for Postgres 2026 Acknowledgements & Gratitude I’ve already thanked the 50 amazing speakers above. In addition, thanks go to Silvano Coriani, Cornelia Biacsics, Aaron Wislang, and My Nguyen for reviewing parts of this post before publication. I also want to thank the team at AMD for their partnership and support of POSETTE this year! And of course, big thank you to the POSETTE 2026 organizing team and POSETTE talk selection team—without you, there would be no POSETTE! Figure 3: Visual invitation to join the virtual hallway track for POSETTE 2026 on the Microsoft Open Source Discord, so you can chat with the speakers & others in the Postgres communityUnexpected PITR Charges from restorableDroppedDatabases After BC → Hyperscale Migration
Why This Behavior Is by Design When migrating an Azure SQL Database from Business Critical (BC) to Hyperscale using a manual cutover, some customers notice unexpected Point-in-Time Restore (PITR) backup storage charges appearing under the following resource: /Microsoft.Sql/servers/<server>/restorableDroppedDatabases/<database> At first glance, this can be confusing—especially when: No customer-initiated drop or delete was performed The database is online and healthy post-migration Test migrations may not have shown similar charges This post explains why this happens, why it is expected by design, and how these charges naturally expire. The Observed Scenario After a BC → Hyperscale manual cutover, customers may see PITR charges tied to: restorableDroppedDatabases/<database-name> Despite the database being active and available in Hyperscale, these charges start appearing immediately after the migration cutover and gradually decrease over time. Why Does the Database Appear as “Dropped”? During a manual cutover migration, Azure SQL performs an internal platform-driven workflow to complete the transition between architectures. From a control-plane perspective: The source Business Critical logical database is internally dropped This drop is not initiated by the customer It is a required system step to complete the Hyperscale migration Telemetry confirms that the migration workflow transitions through states such as: Internal drop of the source physical and logical database Cleanup of metadata and completion of the migration This entire sequence completes within seconds and is fully platform managed. Why Are Backup Charges Generated? Although the source BC database is internally dropped, its pre-migration PITR backups are still retained according to the configured backup retention period. Here’s the key point: Backups taken before upgrading to Hyperscale are retained and billed using the dropped-database backup billing model. Because the source database is now considered dropped (from the BC perspective): The 1× database-size discount no longer applies The full data file size is added to the billable backup size Charges appear under restorableDroppedDatabases This behavior is explicitly documented as expected in internal Azure SQL billing guidance. Why Do Charges Decrease Over Time? These charges are not permanent. They: Decrease daily Continue only while the pre-migration PITR backups are retained Automatically stop once the retention window expires In practical terms: Charges stop when: days_since_migration > configured_backup_retention_days No cleanup action is required from the customer—the platform handles this automatically. Why Didn’t Test Migrations Show Similar Charges? In many reported cases, test or smaller databases migrated using the same method did not generate noticeable charges. This can be explained by two documented optimizations: Backup size threshold – very small backup footprints are not charged Low activity optimization – inactive or low-change databases generate fewer snapshots As a result, smaller or lightly used test databases may fall below the billing threshold, while larger production databases do not. Is This a Billing Error or Credit Scenario? No. Although the operation is platform-driven: The behavior is by design The charges are for temporary retention of valid PITR backups They naturally expire based on retention Therefore, this scenario is not considered a billing defect and does not typically warrant credits. How Can Customers Reduce Charges Faster? If needed, customers can: Reduce the PITR backup retention period (minimum is 1 day) Wait up to 24 hours for billing to reflect the change This shortens how long the pre-migration backups are retained and billed. FAQ – restorableDroppedDatabases Charges After BC → Hyperscale Migration Q1: Why am I seeing PITR charges for restorableDroppedDatabases when my database is still online? A: During a Business Critical → Hyperscale manual cutover, Azure SQL internally drops the source BC database as part of the migration workflow. While the Hyperscale database is active and healthy, the pre‑migration BC backups are retained and billed under restorableDroppedDatabases. Q2: Did the customer initiate a drop or delete operation? A: No. This drop is platform‑driven and required to complete the migration. It is not initiated by the customer. Q3: What exactly is being billed? A: The charges are for Point‑in‑Time Restore (PITR) backups taken before the migration. These backups are retained according to the configured backup retention period and are billed using the dropped database billing model. Q4: Why does the cost appear higher than expected? A: Once a database is considered “dropped” (from the BC perspective), the 1× database-size discount no longer applies, and the full data file size is included in the billable backup size. Q5: Will these charges continue indefinitely? A: No. The charges decrease daily and automatically stop once the pre‑migration backups expire based on the configured PITR retention period. Q6: Why didn’t this happen with smaller or test databases? A: Smaller or low‑activity databases may fall below the backup billing threshold, or benefit from low‑activity snapshot optimizations, resulting in no visible charges. Q7: Is this a billing bug or credit-worthy scenario? A: No. This behavior is by design and expected. The charges reflect valid backup retention and do not typically qualify for credits. Q8: Can the customer reduce these charges sooner? A: Yes. The customer can reduce the PITR backup retention period (minimum 1 day). Billing changes usually reflect within up to 24 hours. Key Takeaways The behavior is expected and by design Charges come from pre-migration BC backups, not the active Hyperscale database The database was internally dropped as part of migration, not by the customer Charges decrease daily and stop automatically No action is required unless the customer wants to reduce retention early Final Note As of the time of writing, this behavior is not clearly described in public customer-facing documentation, which explains why it often appears unexpected. Awareness of this mechanism can help set correct expectations when planning BC → Hyperscale manual cutover migrations.AZ-500: Microsoft Azure Security Technologies Study Guide
The AZ-500 certification provides professionals with the skills and knowledge needed to secure Azure infrastructure, services, and data. The exam covers identity and access management, data protection, platform security, and governance in Azure. Learners can prepare for the exam with Microsoft's self-paced curriculum, instructor-led course, and documentation. The certification measures the learner’s knowledge of managing, monitoring, and implementing security for resources in Azure, multi-cloud, and hybrid environments. Azure Firewall, Key Vault, and Azure Active Directory are some of the topics covered in the exam.Scaling PostgreSQL at OpenAI: Lessons in Reliability, Efficiency, and Innovation
At POSETTE: An Event for Postgres 2025, Bohan Zhang of OpenAI delivered a compelling talk on how OpenAI has scaled Azure Database for PostgreSQL- Flexible Server to meet the demands of one of the world’s most advanced AI platforms running at planetary scale. The Postgres team at Microsoft has partnered deeply with OpenAI for years to enhance the service to meet their performance, scale, and availability requirements, and it is great to see how OpenAI is now deploying and depending on Flexible Server as a core component of ChatGPT. Hearing firsthand about their challenges and breakthroughs is a reminder of what’s possible when innovation meets real-world needs. This blog post captures the key insights from Bohan’s POSETTE talk, paired with how Azure’s cloud platform supports innovation at scale. PostgreSQL at the Heart of OpenAI As Bohan shared during his talk, PostgreSQL is the backbone of OpenAI’s most critical systems. Because PostgreSQL plays a critical role in powering services like ChatGPT, Open AI has prioritized making it more resilient and scalable to avoid any disruptions. That’s why OpenAI has invested deeply in optimizing PostgreSQL for reliability and scale. Why Azure Database for PostgreSQL? OpenAI has long operated PostgreSQL on Azure, initially using a single primary instance without sharding. This architecture worked well—until write scalability limits emerged. Azure’s managed PostgreSQL service provides the flexibility to scale read replicas, optimize performance, and maintain high availability to provide global low latency reads without the burden of managing infrastructure. This is why we designed Azure Database for PostgreSQL to support precisely these kinds of high-scale, mission-critical workloads, and OpenAI’s use case is a powerful validation of that vision. Tackling Write Bottlenecks PostgreSQL’s MVCC (Multi-Version Concurrency Control) design presents challenges for write-heavy workloads—such as index bloat, autovacuum tuning complexity, and version churn. OpenAI addressed this by: Reducing unnecessary writes at the application level Using lazy writes and controlled backfills to smooth spikes Migrating extreme write-heavy workloads with natural sharding keys to other systems. These strategies allowed OpenAI to preserve PostgreSQL’s strengths while mitigating its limitations. Optimizing Read-Heavy Workloads With writes offloaded, OpenAI focused on scaling read-heavy workloads. Key optimizations included: Offloading read queries to replicas Avoiding long-running queries and expensive multi-way join queries Using PgBouncer for connection pooling, reducing latency from 50ms to under 5ms Categorizing requests by priority and assigning dedicated read replicas to high-priority traffic As Bohan noted, “After all the optimization we did, we are super happy with Postgres right now for our read-heavy workloads.” Schema Governance and Resilience OpenAI also implemented strict schema governance to avoid full table rewrites and production disruptions. Only lightweight schema changes are allowed, and long-running queries are monitored to prevent them from blocking migrations. To ensure resilience, we categorized requests by priority and implemented multi-level rate limiting—at the application, connection, and query digest levels. This helped prevent resource exhaustion and service degradation. Takeaway OpenAI’s journey is a masterclass in how to operate PostgreSQL at hyper-scale. By offloading writes, scaling read replicas, and enforcing strict schema governance, OpenAI demonstrated PostgreSQL on Azure meets the demands of cutting-edge AI systems. It also reinforces the value of Azure’s managed database services in enabling teams to focus on innovation rather than infrastructure. We’re proud of the work we’ve done to co-innovate with OpenAI and excited to see how other organizations can apply these lessons to their own PostgreSQL deployments. Check out the on-demand talk “Scaling Postgres to the next level at OpenAI” and many more PostgreSQL community sessions from POSETTE.April 2025 Recap: Azure Database for PostgreSQL Flexible Server
Hello Azure Community, April has brought powerful capabilities to Azure Database for PostgreSQL flexible server, On-Demand backups are now Generally Available, a new Terraform version for our latest REST API has been released, the Public Preview of the MCP Server is now live, and there are also a few other updates that we are excited to share in this blog. Stay tuned as we dive into the details of these new features and how they can benefit you! Feature Highlights General Availability of On-Demand Backups Public Preview of Model Context Protocol (MCP) Server Additional Tuning Parameters in PG 17 Terraform resource released for latest REST API version General Availability of pg_cron extension in PG 17 General Availability of On-Demand Backups We are excited to announce General Availability of On-Demand backups for Azure Database for PostgreSQL flexible server. With this it becomes easier to streamline the process of backup management, including automated, scheduled storage volume snapshots encompassing the entire database instance and all associated transaction logs. On-demand backups provide you with the flexibility to initiate backups at any time, supplementing the existing scheduled backups. This capability is useful for scenarios such as application upgrades, schema modifications, or major version upgrades. For instance, before making schema changes, you can take a database backup, in an unlikely case, if you run into any issues, you can quickly restore (PITR) database back to a point before the schema changes were initiated. Similarly, during major version upgrades, on-demand backups provide a safety net, allowing you to revert to a previous state if anything goes wrong. In the absence of on-demand backup, the PITR could take much longer as it would need to take the last snapshot which could be 24 hours earlier and then replay the WAL. Azure Database for PostgreSQL flexible server already does on-demand backup behind the scenes for you and then deletes it when the upgrade is successful. Key Benefits: Immediate Backup Creation: Trigger full backups instantly. Cost Control: Delete on-demand backups when no longer needed. Improved Safety: Safeguard data before major changes or refreshes. Easy Access: Use via Azure Portal, CLI, ARM templates, or REST APIs. For more details and on how to get started, check out this announcement blog post. Create your first on-demand backup using the Azure portal or Azure CLI. Public Preview of Model Context Protocol (MCP) Server Model Context Protocol (MCP) is a new and emerging open protocol designed to integrate AI models with the environments where your data and tools reside in a scalable, standardized, and secure manner. We are excited to introduce the Public Preview of MCP Server for Azure Database for PostgreSQL flexible server which enables your AI applications and models to talk to your data hosted in Azure Database for PostgreSQL flexible servers according to the MCP standard. The MCP Server exposes a suite of tools including listing databases, tables, and schema information, reading and writing data, creating and dropping tables, listing Azure Database for PostgreSQL configurations, retrieving server parameter values, and more. You can either build custom AI apps and agents with MCP clients to invoke these capabilities or use AI tools like Claude Desktop and GitHub Copilot in Visual Studio Code to interact with your Azure PostgreSQL data simply by asking questions in plain English. For more details and demos on how to get started, check out this announcement blog post. Additional Tuning Parameters in PG17 We have now provided an expanded set of configuration parameters in Azure Database for PostgreSQL flexible server (V17) that allows you to modify and have greater control to optimize your database performance for unique workloads. You can now tune internal buffer settings like commit timestamp, multixact member and offset, notify, serializable, subtransaction, and transaction buffers, allowing you to better manage memory and concurrency in high-throughput environments. Additionally, you can also configure parallel append, plan cache mode, and event triggers that opens powerful optimization and automation opportunities for analytical workloads and custom logic execution. This gives you more control for memory intensive and high-concurrency applications, increased control over execution plans and allowing parallel execution of queries. To get started, all newly modifiable parameters are available now through the Azure portal, Azure CLI, and ARM templates, just like any other server configuration setting. To learn more, visit our Server Parameter Documentation. Terraform resource released for latest REST API version A new version of the Terraform resource for Azure Databases for PostgreSQL flexible server is now available, this brings several key improvements including the ability to easily revive dropped databases with geo-redundancy and customer-managed keys (Geo + CMK - Revive Dropped), seamless switchover of read replicas to a new site (Read Replicas - Switchover), improved connectivity through virtual endpoints for read replicas, and using on-demand backups for your servers. To get started with Terraform support, please follow this link: Deploy Azure Database for PostgreSQL flexible server with Terraform General Availability of pg_cron extension in PG 17 We’re excited to announce that the pg_cron extension is now supported in Azure Database for PostgreSQL flexible server major versions including PostgreSQL 17. This extension enables simple, time-based job scheduling directly within your database, making maintenance and automation tasks easier than ever. You can get started today by enabling the extension through the Azure portal or CLI. To learn more, please refer Azure Database for PostgreSQL flexible server list of extensions. Azure Postgres Learning Bytes 🎓 Setting up alerts for Azure Database PostgreSQL flexible server using Terraform Monitoring metrics and setting up alerts for your Azure Database for PostgreSQL flexible server instance is crucial for maintaining optimal performance and troubleshooting workload issues. By configuring alerts, you can track key metrics like CPU usage and storage etc. and receive notifications by creating an action group for your alert metrics. This guide will walk you through the process of setting up alerts using Terraform. First, create an instance of Azure Database for PostgreSQL flexible server (if not already created) Next, create a Terraform File and add these resources 'azurerm_monitor_action_group', 'azurerm_monitor_metric_alert' as shown below. resource "azurerm_monitor_action_group" "example" { name = "<action-group-name>" resource_group_name = "<rg-name>" short_name = "<short-name>" email_receiver { name = "sendalerts" email_address = "<youremail>" use_common_alert_schema = true } } resource "azurerm_monitor_metric_alert" "example" { name = "<alert-name>" resource_group_name = "<rg-name>" scopes = [data.azurerm_postgresql_flexible_server.demo.id] description = "Alert when CPU usage is high" severity = 3 frequency = "PT5M" window_size = "PT5M" enabled = true criteria { metric_namespace = "Microsoft.DBforPostgreSQL/flexibleServers" metric_name = "cpu_percent" aggregation = "Average" operator = "GreaterThan" threshold = 80 } action { action_group_id = azurerm_monitor_action_group.example.id } } 3. Run the terraform initialize, plan and apply commands to create an action group and attach a metric to the Azure Database for PostgreSQL flexible server instance. terraform init -upgrade terraform plan -out <file-name> terraform apply <file-name>.tfplan Note: This script assumes you have already created an Azure Database for PostgreSQL flexible server instance. To verify your alert, check the Azure portal under Monitoring -> Alerts -> Alert Rules tab. Conclusion That's a wrap for the April 2025 feature updates! Stay tuned for our Build announcements, as we have a lot of exciting updates and enhancements for Azure Database for PostgreSQL flexible server coming up this month. We’ve also published our Yearly Recap Blog, highlighting many improvements and announcements we’ve delivered over the past year. Take a look at our yearly recap blog here: What's new with Postgres at Microsoft, 2025 edition We are always dedicated to improving our service with new array of features, if you have any feedback or suggestions we would love to hear from you. 📢 Share your thoughts here: aka.ms/pgfeedback Thanks for being part of our growing Azure Postgres community.
