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I have been doing Oracle-to-PostgreSQL migrations for over last decades across enterprises, cloud platforms, and everything in between. I have used commercial tools, cloud-native services, and custom scripts. And when it comes to table DDL conversion, I keep coming back to the same tool: Ora2pg. Not because it is the flashiest or the easiest to set up, but because once you understand its configuration model, nothing else comes close to the control it gives you.

Patroni is a widely used solution for managing PostgreSQL high availability. It provides a robust framework for automatic failover, cluster management, and operational simplicity in PostgreSQL environments. Patroni offers many powerful features that make PostgreSQL clusters easier to manage while maintaining reliability and operational flexibility.

I’ll be honest with you. I’m old school. My IDE is vim. My PostgreSQL client is psql. I’ve been using it for almost 20 years and I still think it’s the best PostgreSQL client out there. I might be biased. When I joined a new team recently, I noticed something: nobody was using psql. Everyone was connecting through their IDE. Which, fair enough, IDEs have saved connection profiles. You click, you’re in.

Introduction

Most PostgreSQL users use prepared statements as a way to boost performance and prevent SQL injection. Fewer people know that the query planner silently changes the execution plan for prepared statements after exactly five executions.

This behavior often surprises engineers because a query plan can suddenly shift—sometimes dramatically, even though the query itself hasn’t changed. The reason lies in the planner’s handling of custom plans vs generic plans, controlled by the parameter plan_cache_mode.

From March 23 to March 26, the following contributions were made to PostgreSQL at SREcon26 (Americas):

PostgreSQL booth volunteers:

• Aya Griswold
• Erika Miller
• Gabrielle Roth
• Jennifer Scheuerell
• Umair Shahid
• Alex Wood

PostgreSQL speakers:

In my current position as Database Architect at DRW, I talk with end users more than I ever did in my life. Our end users are application developers who look at PostgreSQL from a very utilitarian perspective. Trust me, they do not care whether Postgres is the most advanced DBMS or not. They are very pragmatic: they need a database that will help them accomplish their goals: write the data fast, store reliably, read anything in milliseconds, and run analytics.

CTEs are often the first feature developers reach for beyond basic SQL, and often the only one.

But the popularity of CTEs usually has less to do with modernizing code and more to do with the promise of imperative logic. For many, CTE acts as an easy to understand remedy for 'scary queries' and way how to force execution order on the database. The way how many write queries is as if they tell optimizer "first do this, then do that".

On 20th of March 2026, Andrew Dunstan committed patch: json format for COPY TO   This introduces the JSON format option for the COPY TO command, allowing users to export query results or table data directly as a stream of JSON objects (one per line, NDJSON style).   The JSON format is currently supported only … Continue reading "Waiting for PostgreSQL 19 – json format for COPY TO"

pg_duckpipe is a PostgreSQL extension for real-time CDC to DuckLake columnar tables. This month: transparent query routing, append-only changelog, fan-in streaming, partitioned table support, and more.

Have you noticed how many new PostgreSQL meetups have appeared over the past few months?

Just to name a few examples:

Recently on the pgsql-performance mailing list, a question popped up regarding a Top-N query gone wrong. On the surface, the query merely fetched the latest 1000 rows through a join involving a few CTEs in a dozen tables with a few million rows distributed among them.

Every migration unfolds a story. Here are the chapters most teams miss.

On 20th of March 2026, Amit Kapila committed patch: Add support for EXCEPT TABLE in ALTER PUBLICATION.   Following commit fd366065e0, which added EXCEPT TABLE support to CREATE PUBLICATION, this commit extends ALTER PUBLICATION to allow modifying the exclusion list.   New Syntax: ALTER PUBLICATION name SET publication_all_object [, ...

What is the best way to manage database schema migrations in 2026?

Since this sort of thing is getting easier with AI tooling, I spent some time doing a survey across a bunch of recognizable multi-contributor open source projects to see how they do database schema change management.

Intelligent Automation to Artificial Intelligence in three levels.

Today I added openSUSE Leap 16.0 support to the PostgreSQL RPM repository. Please find all details here:
https://zypp.postgresql.org/

You can find the repository RPM here: https://zypp.postgresql.org/repopackages/

This release includes all packages, including the "extras" repository.

Report any issues here: https://github.com/pgdg-packaging/pgdg-rpms/issues/new

PaulWhalen’s talk recording is now available! Enjoy!

Introduction: The Market Is Talking About AI, but the Deeper Change Is Architectural

The database market is full of confident declarations right now. One vendor says the cloud data warehouse era is ending. Another argues that AI is redrawing the database landscape. A third claims that real-time analytics is now the center of gravity. Each story contains some truth, and each vendor naturally presents itself as the answer.

Your PostgreSQL HA cluster promotes a new primary. Patroni says everything is healthy. But your application is still talking to the old, dead node. Welcome to the OCI VIP problem.

If you have built PostgreSQL high availability clusters on AWS or Azure, you have probably gotten comfortable with how virtual IPs work. You assign a VIP, your failover tool moves it, and your application reconnects to the new primary. Clean. Simple. Done.

Introduction

Most people who work with PostgreSQL eventually learn two commands for query tuning: EXPLAIN and EXPLAIN ANALYZE.

EXPLAIN shows the planner’s chosen execution plan, and EXPLAIN ANALYZE runs the query and adds runtime statistics. For most tuning tasks, this already provides a wealth of information.

But what many people don’t realize is that EXPLAIN has a handful of other options that can make troubleshooting much easier. In some cases they answer questions that EXPLAIN ANALYZE alone cannot.