PGConf.Russia 2025

Dozens of PostgreSQL instances have replaced a single large Oracle database. One of our key business systems was completely redesigned and rebuilt on an import-independent solution.  

This talk will cover the unique challenges, architecture, issues, and operational aspects of this migration. Expect an insightful deep dive into the process!

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

This talk will explore different approaches to storing files in PostgreSQL, including:

• Simple table-based storage

• Large objects with pg_largeobject

• pgpro_sfile – a large object (pgpro_bfile) storage solution

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.