PGConf.Russia 2025

Databases are a core component of any information system, and query performance directly impacts overall efficiency.

Last year, we explored suboptimal queries and optimization techniques, and the topic proved highly relevant. In this second installment, we’ll cover:

Using extended statistics for computed columns

Pagination techniques for improved query handling

Real-world optimization examples from 1C

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.

In this talk, we will discuss what character encodings and collations are. I will explain the issues that can arise from sorting (COLLATION) in databases and show how these problems can be addressed using the COLLATION PROVIDER = ICU feature in PostgreSQL.

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.