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I'm stunned by the illogicality of others, and they are stunned by the fact that I'm a robot." This phrase perfectly describes the peculiarities of my interaction with the world around me. I'm like this robot. Or an alien. I can only guess how the other people see me. But now I know for sure that others consider me at least strange. The feeling is mutual. Many actions of people around me seem completely irrational and illogical to me.

For a long time, this baffled me. I didn't understand what was going on, and considered myself a deep introvert, a withdrawn, gloomy dude who did not understand people and their feelings at all. I kept wondering what was wrong with me…

While pg_probackup 3 is still in the works and not yet available to the public, let’s dive into what’s new under the hood. There’s a lot to unpack — from a completely reimagined application architecture to long-awaited features and seamless integration with other tools. 

TDE comes in many flavors — from encryption at the TAM level to full-cluster encryption and tablespace markers. We take a close look at Percona, Cybertec/EDB, Pangolin/Fujitsu, and show where you lose performance and reliability, and where you gain flexibility.

On top of that, Vasily Bernstein, Deputy head of product development, and Vladimir Abramov, senior security engineer, will share how Postgres Pro Enterprise implements key rotation without rewriting entire tables — and why AES-GCM was the clear choice.

How did engineers in the past manage to measure electrical power without modern microchips and DSPs? This article explores the Energomera CE6806P, a device created in 2006 for verifying electricity meters, yet built using 1980s-era technology.

We’ll take a closer look at its design, principles of operation, and how discrete-analog solutions were used to achieve high accuracy. The Energomera is a fascinating example of engineering and ingenuity, giving us a unique perspective on the evolution of electrical measurement devices.

This world needs a new theory — a theory that could describe all the theories on the planet. A theory that could easily describe philosophy, mathematics, physics, and psychology. The one that makes all kinds of sciences computable.

This is exactly what we are working on. If we succeed, this theory will become the unified meta-theory of everything.

A year has passed since our last publication, and our task is to share the progress with our English-speaking audience. This is still not a stable version; it’s a draft. Therefore, we welcome any feedback, as well as your participation in the development of the links theory.

As with everything we have done before, the links theory is published and released into the public domain — it belongs to humanity, that means, it is yours. This work has many authors, but the work itself is far more important than any specific authorship. We hope that today it can become useful to more people.

We invite you to become a part of this exciting adventure.

September 25th marks the release of PostgreSQL 18. This article covers the March CommitFest and concludes the series covering the new features of the upcoming update. This article turned out quite large, as the last March CommitFest is traditionally the biggest and richest in new features.

You can find previous reviews of PostgreSQL 18 CommitFests here: 2024-07, 2024-09, 2024-11, 2025-01.

The story of the PostgreSQL logo was shared by Oleg Bartunov, CEO of Postgres Professional, who personally witnessed these events and preserved an archive of correspondence and visual design development for the database system.

Our iconic PostgreSQL logo — our beloved “Slonik” — has come a long way. Soon, it will turn thirty! Over the years, its story has gathered plenty of myths and speculation. As a veteran of the community, I decided it’s time to set the record straight, relying on the memories of those who were there. Who actually came up with it? Why an elephant? How did it end up in a diamond, and how did the Russian word “slonik” become a part of the global IT vocabulary?

Now that pgpro-otel-collector has had its public release, I’m excited to start sharing more about the tool — and to kick things off, I’m launching a blog series focused entirely on the Collector.

The first post is an intro — a practical guide to installing, configuring, and launching the collector. We’ll also take our first look at what kind of data the collector exposes, starting with good old Postgres metrics.

It all started as a joke by the office coffee machine. But, as with every decent joke, it suddenly sounded worth trying — and before we knew it, we were knee-deep in an experiment that turned out to be anything but trivial, complete with a whole minefield of gotchas.

It began simply: while everyone else was busy debating hardware tuning and squeezing out extra TPS from their systems, we thought — why not just shove a huge chunk of data into PostgreSQL and see how it holds up? Like, really huge. Say, a one-petabyte database. Let’s see how it survives that.

It was December 10, the boss wanted the report by January 20, and New Year was less than a month away. And that itch that all engineers know? It hit hard.

You may have already read in the news that on the eve of Cosmonautics Day, a new stable release of Angie 1.9.0 was released, an nginx fork that continues to be developed by the team of former nginx developers. Approximately every quarter, we try to release new stable versions and delight users with numerous improvements. This release is no exception, but it's one thing to read a dry changelog and quite another to get to know the functionality in more detail, to learn how and in which cases it can be applied.

The list of innovations that we will discuss in more detail:

— Saving shared memory zones with cache index to disk;
— Persistent switching to a backup group of proxied servers;
— 0-RTT in the stream module;
— New busy status for proxied servers in the built-in statistics API;
— Improvements to the ACME module, which allows automatic obtaining of Let's Encrypt TLS certificates and others;
— Caching TLS certificates when using variables.

When there’s no filter on the partitioning key, local indexes turn into a marathon across partitions. The new gbtree keeps a single catalog of keys and jumps straight to the row by primary key. In this article, we’ll show the algorithm, real numbers and limitations (primary key is mandatory, ON CONFLICT does not work) — and where this eases the pain in CRM/billing.

Statistically, September CommitFests feature the fewest commits. Apparently, the version 18 CommitFest is an outlier. There are many accepted patches and many interesting new features to talk about.

If you missed the July CommitFest, get up to speed here: 2024-07.

OpenStreetMap (OSM) is a global project formed around a geographic information database which is being filled by all comers — both enthusiasts and interested companies. Anybody can contribute, but the openness has its downside: incorrect edits often get into the database. Hence plenty of validators of OSM data have been written which allow to maintain the data quality at an acceptable level.

Since 2016 there exists an open source subway preprocessor that validates (generates error reports) rapid transit routes in OSM for completeness and logical/topological errors, and converts them into formats that are suitable for routing and rendering, e.g. GTFS. Besides OSM data it takes a list of public transport (PT) networks which contains the checking information about the number of lines, stations etc. per a PT network. The preprocessor has successfully proven itself in the preparation of PT data for applications such as Maps.me and Organic Maps.

In this article, I would like to share an approach to detecting one of the types of errors that occur quite often in OSM data and automatic detection of which is somewhat challenging. It's an accidental loss of a station from a route. The source code of the validator and the described algorithm are open source. But first, let's define the concepts used to represent PT data in OpenStreetMap.

The myth of the magical fast=true parameter is still alive and well, but in distributed databases, another contender appears: distributed=true. Neither one will save you if you don’t rethink your schema, sharding keys, sequences, queries, and migration process. We walk through every corner with a clear-eyed approach — from choosing sharding keys and colocated tables to CDC, topologies, and foreign key constraints — showing where performance really improves, where it gets more expensive, and how to deal with it.

START is an open-source LLM designed for precise calculations and code verification. It addresses two major issues that most standard models face: hallucinations and errors in multi-step calculations. This article explains why these problems arise and how START solves them.

Declarative partitioning may sound complex, but in reality it’s just a way to tell your database how best to organize large tables — so it can optimize queries and make maintenance easier. Let’s walk through how it works and when declarative partitioning can save the day.

Picture this: a mission-critical SQL query is crawling along. Not for an hour. Not for two. Fifteen hours. A full workday of the system slowly grinding through data while the business bleeds money and users teeter on the edge of a nervous breakdown. And then — cue the dramatic music — in walks the performance engineer.

After a few hours of intense analysis and a couple of pinpoint code tweaks, the same query that took 15 hours now completes in just… two minutes. Sounds like magic? Nope. This is the thrilling (and very real) world of performance engineering.

How to compare the efficiency of SQL query plans? “Measure the execution time, of course!” — an experienced reader would say. And they would be absolutely right: from a practical perspective, the more efficient DBMS is the one that delivers higher TPS. However, sometimes we need to design a system that doesn't exist yet or predict behavior under loads that haven't occurred yet. In such cases, we need a characteristic that allows us to perform a qualitative analysis of a plan or compare two plans. This post is dedicated to one such characteristic — the number of data pages read.

We at Verilog Meetup constructed an exam/interview problem that has an interesting property: if a student tries to figure out a solution by thinking by himself, he usually succeeds; however if he dumps the problem on ChatGPT, the solution fails (does not pass the automated test), and the student goes into a death spiral of futility, kicking ChatGPT to get the solution right.

There is nothing weird about the problem, we do this in the industry all the time:

Migration from Oracle to vanilla PostgreSQL hits roadblocks with packages, autonomous transactions, and collections—they simply don’t exist there. We’ll break down why ora2pg stumbles, how native implementations of these mechanisms in Postgres Pro Enterprise make life easier, and how ora2pgpro translates PL/SQL semantically correctly, without hacks or crude regex.