PGConf.Russia 2025

So much has been said about PostgreSQL as the result of its program code. But Postgres is not just code. It’s the people who create it, develop it, and... leave a piece of themselves through comments.

What stories can we uncover from the comments in PostgreSQL’s code? We’ll discover what the most popular word is, which comments have been in the code since the very first public commit, how the style of communication has evolved with the product, and how we can see the human side behind the lines of code and comments.

This presentation examines the challenges of migrating an application system from Oracle to Postgres, based on a real-world project. It provides a detailed discussion on logical replication of data from Postgres to ensure the possibility of reverting back to Oracle while maintaining the functionality of legacy reporting and integration.

Currently, WAL files can be compressed with a ratio of up to 6x, but the existing compression system is inefficient.

In this talk, I’ll share my work on improving WAL compression.

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.