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This is the first in a series of three blog posts covering the new AI functionality coming in pgAdmin 4. In this post, I'll walk through how to configure the LLM integration and introduce the AI-powered analysis reports; in the second, I'll cover the AI Chat agent in the query tool; and in the third, I'll explore the AI Insights feature for EXPLAIN plan analysis.Anyone who manages PostgreSQL databases in a professional capacity knows that keeping on top of security, performance, and schema design is an ongoing endeavour.

In the previous article we covered how the PostgreSQL planner reads pg_class and pg_statistic to estimate row counts, choose join strategies, and decide whether an index scan is worth it. The message was clear: when statistics are wrong, everything else goes with it.

I just gave a new presentation at SCALE titled The Wonderful World of WAL. I am excited to have a second new talk this year. (I have one more queued up.)

In this article I walk you through the journey of adding the pg_crash extension to the new CloudNativePG extensions project. It explores the transition from legacy standalone repositories to a unified, Dagger-powered build system designed for PostgreSQL 18 and beyond. By focusing on the Image Volume feature and minimal operand images, the post provides a step-by-step guide for community members to contribute and maintain their own extensions within the CloudNativePG ecosystem.

The last PG Phriday article focused on the architecture of a Patroni cluster—the how and why of the design. This time around, it’s all about actually building one. I’ve often heard that operating Postgres can be intimidating, and Patroni is on a level above that. Well, I won’t argue on the second count, but I can try to at least ease some of the pain.To avoid an overwhelming deluge consisting of twenty pages of instructions, I’ve split this article into a series of three along these lines:

On 5th of March, 2026, we had the PostgreSQL March Meetup in Berlin. Zalando hosted it again, and like last time it was four regular talks in two parallel tracks. Attendee number was a bit smaller compared to last time, likely because the Meetup was announced late. The Meetup took place in the Hedwig-Wachenheim-Straße in Berlin, right around the corner from the Uber Arena and East Side Gallery. Zalando has an office here, and the first floor is a large meeting and conference area.

To understand how PostgreSQL scans data, we first need to understand how PostgreSQL stores it.

In PostgreSQL, a DELETE operation does not immediately erase data from disk. The MVCC mechanism retains deleted rows as dead tuples, and reading these dead tuples is one viable approach to data recovery. However, this approach has a clear time limitation: once autovacuum completes its cleanup, the dead tuples are physically removed, and recovery methods based on data files become ineffective. At this point, the WAL (Write-Ahead Log) offers an alternative recovery path. Specifically, the **FPW (Full Page Write)** mechanism within WAL is the foundation of this approach.

It is 3 AM. A rogue DELETE just wiped 500,000 customer records. Traditional recovery takes hours and risks collateral damage. This guide shows you how to recover accidental DELETEs and UPDATEs in five steps using PDU — no kernel expertise, no downtime, data back in under a minute.

I'm proposing a very ambitious patch set for PostgreSQL 19. Only time will tell whether it ends up in the release, but I can't resist using this space to give you a short demonstration of what it can do. The patch set introduces three new contrib modules, currently called pg_plan_advice, pg_collect_advice, and pg_stash_advice.

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PostgreSQL uses a cost-based optimizer (CBO) to determine the best execution plan for a given query. The optimizer considers multiple alternative plans during the planning phase. Using the EXPLAIN command, a user can only inspect the chosen plan, but not the alternatives that were considered. To address this gap, I developed pg_plan_alternatives, a tool that uses eBPF to instrument the PostgreSQL optimizer and trace all alternative plans and their costs that were considered during the planning phase.

As a PostgreSQL expert, one of the most common “ghosts” I hunt during database audits is the zombie session. You know the one: a backend process that stays active or idle in transaction, holding onto critical locks and preventing vacuum from doing its job, all because the client disappeared without saying goodbye. In the words of Miracle Max from The Princess Bride, there’s a big difference between mostly dead and all dead.

Part 2 of the Semantic Caching in PostgreSQL series that’ll take you from a working demo to a production-ready system.

© Laurenz Albe 2026

PostgreSQL has supported the (non-standard) ON CONFLICT clause for the INSERT statement since version 9.5. In v19, commit 88327092ff added ON CONFLICT ... DO SELECT. A good opportunity to review the benefits of ON CONFLICT and to see how the new variant DO SELECT can be useful!

Prague PostgreSQL Meetup met on Monday, February 23 for the February Edition - organized by Gulcin Yildirim Jelinek & Mayur B.

Speakers:

The PostgreSQL Database Security Assessment Tool has been updated to conform to the latest version, of the CIS Benchmark fort PostgreSQL

It’s been roughly a year since Andrew Dunstan first proposed an internal training program designed to mentor the next generation of PostgreSQL developers. Last week, the inaugural "Developer U" cohort gathered in for their second in-person session. I caught up with the trainers and participants to get the inside scoop on how the program is evolving.

Trust, Governance, Talent, and the Enterprise Reality of PostgreSQL

Last year at the CIO Summit Mumbai, I had the opportunity to participate in a leadership roundtable with CIOs across banking, fintech, telecom, manufacturing, and digital enterprises.

The session was not a product showcase.

Let’s face it, there are a multitude of High Availability tools for managing Postgres clusters. This landscape evolved over a period of decades to reach its current state, and there’s a lot of confusion in the community as a result.

Every query starts with a plan. Every slow query probably starts with a bad one. And more often than not, the statistics are to blame. But how does it really work? PostgreSQL doesn't run the query to find out — it estimates the cost. It reads pre-computed data from pg_class and pg_statistic and does the maths to figure out the cheapest path to your data.