PGConf.Russia 2025

t’s well known that pg_upgrade is the go-to tool for fast PostgreSQL upgrades. However, even with this tool, there are cases where the upgrade process takes far longer than expected.

In our case, upgrading a PostgreSQL database with 350,000 tables meant either waiting 3.5 hours or finding a better approach. By digging into the pg_upgrade source code, we discovered a way to speed up the process significantly. In this talk, we’ll share how we did it.

In this session, we’ll provide an overview of the features of the pg_probackup3 backup utility. 

The new version has completely rethought the application architecture, introduced several long-awaited features, and added integration possibilities with other applications. We’ll dive into these updates and discuss them in detail during the presentation.

Covering all the key changes in PostgreSQL 18 during this talk would be quite a challenge — especially since the code freeze is set to happen just a week after the presentation. However, I’ll focus on what has already been finalized.

There are plenty of exciting updates in performance, monitoring, vacuuming (of course!), and beyond. As always, this talk is based on a series of articles reviewing the commit fests for PostgreSQL 18, published on Postgres Professional’s corporate blog on Habr.

In PostgreSQL performance testing, benchmarks measure query execution time (latency). To get more reliable results, queries are executed repeatedly, generating a dataset of latency values. Performance is often assessed using standard metrics like the median or mean, but we propose a more advanced approach.

In practice, latency distributions are often multimodal, consisting of multiple underlying distributions with distinct characteristics. In such cases, traditional statistical methods are insufficient, requiring a more detailed analysis of the dataset’s structure.

Our work presents a tool that automatically performs statistical analysis of benchmark results, accounting for dataset-specific features. It detects multimodality, identifies the number and boundaries of dominant modes, and determines key distribution parameters—providing deeper insights into PostgreSQL performance variations.