PGConf.Russia 2025

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.

This talk addresses a common issue and touches a bit on application architecture. Temporary tables in database applications are typically used for several purposes. 

Firstly, they are used to store intermediate results when implementing complex data processing algorithms. Secondly, in the case of stored procedures, application servers often place large datasets into temporary tables when they are too large or inconvenient to pass as parameters to stored procedures. 

The handling of temporary tables in different DBMSs is implemented in various ways, which often complicates migration from these systems to Postgres. 

The drawbacks of temporary tables are well-known, which leads to a reasonable desire to replace them, where possible, with other methods that can achieve the same goal. This talk focuses on alternative mechanisms provided by Postgres to solve this problem.

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.

Last year, we introduced built-in fault tolerance support in Postgres Pro Enterprise through BiHA. Our solution allows you to deploy a fault-tolerant Postgres cluster where, in the event of a failure of the primary node, a new primary node (leader) is automatically selected.

However, this brings up the issue of redirecting traffic to the new leader. This can be solved using our Proxima extension or an external TCP proxy server. Both solutions needed to periodically query the BiHA cluster to determine the primary node.

As an alternative, the latest version of BiHA introduced the ability to register custom functions that will be triggered by events such as leader change, node addition/removal, and others. We call this mechanism user callbacks. In this presentation, we’ll explain how the callbacks are implemented and discuss their usage.