PGConf.Russia 2025

In any business-critical system, there comes a need to modify table structures — whether it's adding new columns with expressions based on other columns or converting a non-partitioned table into a partitioned one.

Using standard PostgreSQL tools for large table reorganization often results in significant downtime, as tables become unavailable for writes — and sometimes even for reads — during the process.

In this talk, I’ll introduce dbms_redefinition, a custom-built extension that brings functionality similar to Oracle’s DBMS_REDEFINITION package to PostgreSQL. This solution minimizes downtime to near zero when applying schema changes in production. 

Unlike pg_repack, which does not support structural modifications, dbms_redefinition is useful for all PostgreSQL users, even beyond Oracle migrations.

I’ll share a real-world use case, discuss future development plans, and explore new features in the pipeline.

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.

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.

For DBAs, ensuring database protection and integrity is always a top priority. Along with that comes the need to store data efficiently and compactly, without overloading the network, and ensuring reliable recovery.

In this talk, we’ll discuss a universal solution for backup and archive storage that not only offers effective deduplication but also minimizes network load. TATLIN.BACKUP is a platform for storing PostgreSQL backups or WAL archives with compression, deduplication, and minimal data transmission over the network.

TATLIN.BACKUP is a versatile solution for transferring and storing PostgreSQL archives at remote locations from a central office.