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On 3rd of April 2026, Daniel Gustafsson committed patch: Online enabling and disabling of data checksums   This allows data checksums to be enabled, or disabled, in a running cluster without restricting access to the cluster during processing.   Data checksums could prior to this only be enabled during initdb or when the cluster is … Continue reading "Waiting for PostgreSQL 19 – Online enabling and disabling of data checksums"

Xata core is now available as open source under the Apache 2 license. It adds copy-on-write branching, scale-to-zero compute to Postgres.

In my previous blog, I walked through setting up the pgEdge MCP Server with a distributed PostgreSQL cluster, and connecting Claude to live database data through natural language. In this blog I want to look at a different problem: how do you build AI-powered search over your own content, without adding a separate vector database to your infrastructure?This is where the pgEdge Vectorizer and RAG Server come in.

Every year, PostgreSQL gets faster. Researchers benchmarking the optimizer from version 8 through 16 found an average 15% performance improvement per major release. That’s a decade of consistent, measurable progress. The project has been doing this since 1996. So when a headline claimed Linux 7.0 just halved PostgreSQL throughput, DBAs, Sys Admins, and DevOps started panicking (in particular, those working with Ubuntu 26.04 LTS which plan to ship Linux kernel 7.

Most AI projects do not fail because the model is weak. They fail because the seams around the model break under real-world constraints such as data truth, governance, and production reality.

This was my first time attending PGConf.India. That is a conference I wanted to visit for quite a while, heard good things about the it, but never had a chance before. During past years it overlapped with another conference I’m attending in Germany - but this year it worked out! Overall this is the 9th Indian PostgreSQL Conference, with no signs of slowing down. Stage at PGConf India 2026 The conference is well attended, and very vibrant.

This article examines how managed database services deliberately suppress access to the physical replication stream, turning operational convenience into permanent lock-in. It makes the case for a cloud-neutral stack — PostgreSQL, Kubernetes, and CloudNativePG — as the only architecture that returns full operational sovereignty to the organisation that owns the data.

Deploy ParadeDB on Railway with one click. Full-text search, vector search, and hybrid search over Postgres — now available on your favorite cloud platform.

In response to a generous corpus of real-world user feedback, we’ve been hard at work the past week adding a slew of updates to pg_clickhouse, the query interface for ClickHouse from Postgres. As usual, we focused on improving pushdown, especially for various date and time, array, and regular expression functions.

The Toulouse PostgreSQL User Group met on April 7, 2026 organized by

• Geoffrey Coulaud
• Xavier SIMON
• Jean-Christophe Arnu

Speakers:

Introduction

One of the most useful debugging tools in modern PostgreSQL is the wait event system. When a query slows down or a database becomes CPU bound, a natural question is: “What are sessions actually waiting on?” Postgres exposes this information through the pg_stat_activity view via two columns:

wait_event_type wait_event

These fields reveal what the backend process is blocked on at a given moment. Among the different wait types, one category tends to cause confusion:

A few days ago, Shaun Thomas published an article over on the pgEdge blog called [Checkpoints, Write Storms, and You]. Sadly a lot of corporate blogs don’t have comment functionality anymore.

One GitHub issue turned into an extension sprint. 32 new additions, 504 in total, say a lot about where PostgreSQL is headed.

There is a point in every security tool’s life where adding one more feature is less important than removing one more obstacle.

That is what makes column_encrypt v4.0 interesting.

In today’s E121 of “5mins of Postgres” we're talking about the upcoming Postgres 19 release, and how a change in the Postgres instrumentation handling reduces overhead of timing measurements in EXPLAIN ANALYZE using the RDTSC instruction, and why this will allow turning on for more workloads. We dive into the recently committed change that I (Lukas) authored together with Andres Freund and David Geier. See the full transcript with examples below. Share this episode: Click here to share this…

Every database has to reconcile two uncomfortable truths: memory is fast but volatile, and disk is slow but durable. Postgres handles this tension through its Write-Ahead Log (WAL), which records every change before it happens. But the WAL can't grow forever. At some point, Postgres needs to flush all those accumulated dirty pages to disk and declare a clean starting point. That process is called a checkpoint, and when it goes wrong, it can bring throughput to its knees.

On 5th of April 2026, Andrew Dunstan committed patch: Add pg_get_database_ddl() function   Add a new SQL-callable function that returns the DDL statements needed to recreate a database. It takes a regdatabase argument and an optional VARIADIC text argument for options that are specified as alternating name/value pairs. The following options are supported: pretty (boolean) … Continue reading "Waiting for PostgreSQL 19 – new pg_get_*_ddl() functions"

Recently, we encountered a production incident where PostgreSQL 16.8 became unstable, preventing the application from establishing database connections. The same behavior was independently reproduced in a separate test environment, ruling out infrastructure and configuration issues. Further investigation identified the pg_prewarm extension as the source of the problem.

In my first post about pgcollection, I introduced the collection type to address the challenge of migrating Oracle associative arrays keyed by strings to PostgreSQL. For integer-keyed associative arrays, I noted that native PostgreSQL arrays work well enough for simple cases. That holds true until the keys are sparse.

Consider this Oracle pattern:

A practical blueprint for secure, private, high-performance AI systems