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I compiled and uploaded zip files with latest orafce and plpgsql_check for PostgreSQL 17 and PostgreSQL 18 - I used Microsoft Visual C 2022.

Setup:

You’ve probably seen the incident pattern:

1. Postgres backends start disappearing.
2. dmesg / journalctl -k shows the kernel OOM killer reaping postgres.
3. Someone spots “out of memory” and reflexively recommends: “Increase work_mem.”

That recommendation is frequently backwards for OS OOM kills.

For years now pgAdmin builds on Windows have been code signed using a certificate installed on a build machine in AWS. That certificate is expiring, so I had to look for a new one (I no longer work at a company that supports Windows that can provide one), and much to my annoyance I found that requirements have changed and a secure physical device is required for key storage.

As AI capabilities continue to evolve and integrate more deeply into our applications, we’re faced with interesting architectural decisions about how to expose our data to large language models (LLMs). Two approaches that have gained significant traction are Retrieval Augmented Generation (RAG) servers (such as pgEdge RAG Server) and Model Context Protocol (MCP) servers (such as pgEdge Natural Language Agent).

As AI capabilities continue to evolve and integrate more deeply into our applications, we’re faced with interesting architectural decisions about how to expose our data to large language models (LLMs). Two approaches that have gained significant traction are Retrieval Augmented Generation (RAG) servers (such as pgEdge RAG Server) and Model Context Protocol (MCP) servers (such as pgEdge Natural Language Agent).

I like reproducible development. I also like short feedback loops. Combining both for pgrx was… educational. 🙂 In this post, I share the mistakes, the small pains, and the fixes I used to get a working VS Code dev container for a Rust project that builds PostgreSQL extensions with pgrx. If you’re writing extensions or using pgrx in a team, this will save you a few grey hairs.

TL;DR:

New post up on on the ClickHouse blog:

New post up on on the ClickHouse blog:

I wanted to optimize away parsing the key/value pairs from the [pg_clickhouse] pg_clickhouse.session_settings GUC for every query by pre-parsing it on assignment and assigning it to a separate variable. It took a few tries, as the GUC API requires quite specific memory allocation for extra data to work properly. It took me a few tries to land on a workable and correct solution.

With PostgreSQL 18 introducing built-in OAuth 2.0 and OpenID Connect (OIDC) authentication, tools like pg_oidc_validator have become an essential part of the ecosystem by enabling server-side verification of OIDC tokens directly inside PostgreSQL. If you’re new to the topic, make sure to read our earlier posts explaining the underlying concepts and the need for external validators:

Everyone wants high availability, and that’s completely understandable. When an app goes down, users get frustrated, business stops, and pressure builds.

But here’s the challenge: high availability often feels like a big monster. Many people think, If I need to set up high availability, I must master every tool involved. And there’s another common belief too: Open-source tools are not enough for real HA, so I must buy paid tools.

A useful new feature was introduced on 11 December 2025: Allow expiration of the oldest full backup regardless of current retention. Details are available in commit bf2b276.

One of the principal and most powerful components of the pgEdge Agentic AI Toolkit is the pgEdge Postgres MCP Server. In just over a year MCP (Model Context Protocol), initially developed by Anthropic, has become the standard way to connect LLMs to external data sources and tools.

We are delighted today to be announcing the beta release of our pgEdge Agentic AI Toolkit for Postgres.  We’ve had the benefit of collaborating on real-world Postgres-based AI applications the past two years with a number of leading customers, and this product announcement is the outgrowth of this learning.We listened to customers as they refined their AI strategies in response to the rapid evolution of LLMs, Agentic AI and integration technologies such as the Model Context Protocol (MCP), and as we did so a few things sto

© Laurenz Albe 2025

Most major Linux distributions have upgraded to the GNU C library version 2.28 or later. Therefore, there is a growing awareness that an operating system upgrade can lead to index corruption. However, rebuilding all your indexes can slow down the upgrade process considerably. In this article, I want to discuss how you can avoid rebuilding more indexes than necessary.

A Journey Through PostgreSQL Events

• Introduction
• Community recognition (transparency, inclusivity, organisational balance)
• If I organise a PostgreSQL event — does it have to be recognised?
• Are community-recognised events better than those that aren’t?
• Conferences — a quick flyover (pgDays, pgConfs, FOSDEM, PGConf.dev etc.)
• PostgreSQL User Groups / meet-ups
• Key takeaways
• Which PostgreSQL event to attend?

Quick announcement to say that I’ve replaced the ancient markdown parser with a new one, discount, which supports tables, code fences, definition lists, and more. I reindexed pg_clickhouse this morning and it’s sooo nice to see the table properly formatted.

PostgreSQL 17 introduced streaming I/O – grouping multiple page reads into a single system call and using smarter posix_fadvise() hints. That alone gave up to ~30% faster sequential scans in some workloads, but it was still strictly synchronous: each backend process would issue a read and then sit there waiting for the kernel to return data before proceeding. Before PG17, PostgreSQL typically read one 8kB page at a time.

Earlier this year we started a program (“Developer U”) to help colleagues who show promise for PostgreSQL Development to become contributors. Meet: Nishant Sharma, Staff SDE, who maintains a list of places in the world he has yet to visit.

Data privacy regulations such as GDPR, CCPA, and HIPAA have made it increasingly important for organisations to protect personally identifiable information (PII) in their databases. Whether you're creating a development environment from production data, sharing datasets with third parties, or simply trying to minimise risk, you'll often need to anonymise sensitive data whilst maintaining the structure and relationships within your database.I've been working on a tool to address this need: .

On December 11, the PGDay CERN 2026 CfP committee met to finalize the talk selection. The committee members are listed here: