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PGConf.Russia 2025

PGConf.Russia is the largest PostgreSQL conference in Russia and the CIS. The event offers technical sessions, hands-on demos of new DBMS features, master classes, networking opportunities, and knowledge exchange with top PostgreSQL community experts. Each year, hundreds of professionals participate, including DBAs, database architects, developers, QA engineers, and IT managers.

Agenda highlights

  • Latest news and updates from the PostgreSQL global community

  • Monitoring, high availability, and security

  • Streamlined migration from Oracle, Microsoft SQL Server, and other systems

  • Query optimization

  • Scalability, sharding and partitioning

  • AI applications in DBMS

  • PostgreSQL compatibility with other software

  • more than
    0 participants
  • 0 speakers
  • 0
    minutes of conversation
  • 63 talks
  • hybrid
    format

Talks

Talks archive

PGConf.Russia 2025
  • Дмитрий Дорофеев
    Дмитрий Дорофеев

    In this talk, I will challenge architectural stereotypes about the impossibility of communication with external systems directly from a DBMS. PostgreSQL offers vast communication capabilities; you just need to know which extensions to use.

    And when extensions aren't enough, you can create your own. For the Luxms BI project, we needed communication with NATS, so we developed our own extension in Rust. I’ll share the GitHub link and my experience of developing PostgreSQL extensions in Rust during this talk.

  • Alexey Lesovsky
    Alexey Lesovsky PostgresPro

    In 2024, Postgres Professional introduced pgpro-otel-collector, a new tool for gathering PostgreSQL telemetry data using OpenTelemetry technologies and standards.

    In this talk, I’ll explain why we chose OpenTelemetry, showcase the features of pgpro-otel-collector, and demonstrate how it helps streamline telemetry collection within a monitoring infrastructure.

  • Евгений Бузюркин
    Евгений Бузюркин PostgresPro
    Дарья Барсукова
    Дарья Барсукова НГУ
    Рустам Хамидуллин
    Рустам Хамидуллин PostgresPro

    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.

All talks

Informational