Neues vom PostgreSQL Planet

Cygnet is a PostgreSQL ORM for async Python that refuses to hide the SQL.

AI agents are becoming increasingly capable at operational tasks: summarizing logs, analyzing query plans, identifying anomalies, and assisting with incident response. For databases in particular, this creates an obvious opportunity. Much of day-to-day troubleshooting follows repeatable workflows that lend themselves well to automation.

Every database eventually runs into the same wall: storage costs money, and the data nobody queries anymore costs exactly as much as the data everyone does. A five-year-old row occupies the same expensive block storage as the order that came in thirty seconds ago. Postgres doesn't know the difference, and why would it? That's honestly a common refrain among most database engines.As a result, many have dreamed of fixing this by decoupling compute from storage.

Before starting, I want to put a few things at the top:

Diagnose index scan performance problems by temporarily disabling index scans or bitmap scans and measuring what the planner chooses instead.

I’ve got a new post over on the ClickHouse blog today: What’s New in pg_clickhouse v0.3.2: Postgres 19, TLS, Regex, and Memory. The big news is Postgres 19 support:

PostgreSQL today looks very different from the PostgreSQL many of us started working with over 25 years ago.

A couple months back I posted about maybe adjusting random_page_cost to better reflect how current storage handles random and sequential access. I had a bunch of great discussions about the topic since then, but ultimately I got distracted by other stuff.

Async append lets the planner fan out queries across remote shards in parallel instead of one at a time, but it's a diagnostic switch, not a tuning knob.

Generate valid SQL that looks real, nourishes nothing, and never spoils.

Introduction

I recently had the privilege of speaking at POSETTE 2026 about pg_stats and how Postgres internal statistics work. This post is a written companion to that talk – aimed at giving you a working understanding of what pg_stats is, how it’s populated, and how it shapes the decisions the query planner makes on your behalf.

Imagine a customers table that looks roughly like this:

On June 5 2026, the PostgreSQL User Group Greece met, organized by Eftychia Kitsou and Charis Charalampidi.

Speaker:

• George Capnias
• Kostas Maistrelis

PGDay Boston happened on June 9 2026 Organized by:

`effective_io_concurrency` has changed what it means twice—from a harmonic-series spindle count to a direct request depth to a real async I/O control.

The PostGIS development team is pleased to provide postgis_tiger_geocoder extension. This is the very first release since the break from the PostGIS core. This version requires PostgreSQL 16 and above and should work with any supported PostGIS version.

PostGIS 3.6 series is the last series to include postgis_tiger_geocoder. PostGIS 3.7 will be shipped without postgis_tiger_geocoder.

On 7th of May, 2026, we had the PostgreSQL May Meetup in Berlin. AWS hosted it again, this time we had two speakers from UK and US. The Meetup took place in the Amazontower (EDGE East Side Tower Berlin) in Berlin, across the Uber Arena and with a view at the railway station Warschauer Straße. Celeste Horgan: pg_lake: Unifying transactional and analytical data with Postgres Celeste is Sr.

pgAgent has been my go to scheduling solution for quite some time. Sadly in 6 months it will be completely retired and the pgAgent UI in pgAdmin will be gone. The main reasons I liked pgAgent were:

effective_cache_size doesn't allocate memory, reserve RAM, or control runtime behavior—it merely whispers a number to the query planner to make it smarter…

Recently, I looked into how common polymorphic associations actually are in relational databases — a performance-hostile pattern built around a discriminated foreign key that ORMs (Rails, Django, Hibernate), CRM platforms (Salesforce), and 1C generate automatically.

Parallel queries in PostgreSQL need shared memory sized at runtime, not startup.

Logical replication has been an integral part of Postgres since version 10 released in 2017. It's a very convenient system for synchronizing one or more tables from one running Postgres cluster to another, and the community has embraced it almost without reservation. It's a great feature we've all come to rely on.For all that, it has never been a flawless panacea. Perhaps the most glaring and conspicuous omission in Postgres logical replication is that of sequences.