PGConf.Russia 2025

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.

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.

As part of the import substitution process, we encountered the need to find a reliable alternative to Oracle. PostgreSQL was considered the primary candidate for replacement, but during the research, the "vanilla" version revealed a number of critical limitations that hindered a successful transition.

These issues included support for autonomous transactions, batch variables, and several other specifics. What is special about pg_settings? Why do we need autonomous transactions, and how do HOLD cursors relate to this? These and other topics will be discussed in our presentation, where we will also share our experience of implementing Postgres Pro Enterprise.

This talk will likely be of interest to network providers who can enhance their cool features by adding something to the syntax to simplify administration, as well as anyone interested in machine learning. We will explore the SQL parsing mechanism, what, where, and how to modify it in order to introduce new syntax constructs. Additionally, I will demonstrate the capabilities of machine learning on tabular data, all within the database, using the new syntax.