31 March – 01 April 2025
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
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Latest news and updates from the PostgreSQL global community
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Monitoring, high availability, and security
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Streamlined migration from Oracle, Microsoft SQL Server, and other systems
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Query optimization
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Scalability, sharding and partitioning
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AI applications in DBMS
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PostgreSQL compatibility with other software
Talks
Talks archive
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Дмитрий Муканин
Роман КатунцевWhat does a modern application developer need to transition from familiar cloud-based solutions to SQL databases?
This talk is a practical report on how we transformed an SQL database into a NoSQL-like solution with a developer-friendly interface. We’ll discuss:
What application developers expect from a data framework
How to implement declarative DDL, access control, and automation for reading and writing data
Simplified DML operations, pagination, and other essential features for application development
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Andrey Zubkov PostgresProFor over a year, Postgres Pro has provided extended vacuum statistics, reflecting its operation on individual relations.
We've started receiving observations from production databases of clients that include these statistics. It has been quite successful, and in 2024, we began actively promoting vacuum statistics in PostgreSQL. In this talk, we will review what these statistics can tell us about the complex life of vacuum, using real-world data from live systems.
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Александр Попов PostgresProOver the past year, pgpro_redefinition has undergone significant enhancements. In this talk, we’ll review the key updates, improvements, and new capabilities that have been introduced.
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Евгений Бузюркин PostgresPro
Дарья Барсукова НГУ
Рустам Хамидуллин PostgresProIn 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.
Photos
Photo archive