PGConf.Russia 2025

Two years ago, I spoke about data retrieval queries, where the LINQ to DB library offers almost limitless possibilities.

This talk is about data modification operations. LINQ to DB allows you to execute UPDATE + JOIN queries, INSERT FROM SELECT, supports the MERGE operator, row-level locking with SELECT FOR UPDATE, temporary table creation, and features a fast data insertion mechanism.

I will also discuss the linq2db. EntityFrameworkCore project — a solution for those who aren’t ready to leave EF Core but still want the capabilities of LINQ to DB. I’ll share our experience implementing LINQ to DB in a large-scale project.

This talk explores the challenges we encountered — and solved — while migrating massive databases from Oracle to PostgreSQL.

One of the most complex aspects of this process was rebuilding foreign keys (FKs). To overcome these challenges, we had to dive deep into the internal workings of FK creation and validation.

Key topics include:

Does ALTER TABLE ... ADD CONSTRAINT ... FOREIGN KEY have an execution plan?

Can an ordinary user influence this process?

What locks are applied during FK creation and validation?

Also, we’ll introduce a first-principles method for investigating performance issues, applying it to a real-world FK creation bottleneck.

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.

Sometimes, issues arise with the operation of PostgreSQL on Linux. We will define a classification of these scenarios and provide solutions, from simple to complex. Which failures lead to data corruption? Which scenarios are more dangerous than others, and what should be monitored? We will review logging examples from DBM.P Skala-R and analyze debugging scenarios, including OS libraries, Linux kernel, drivers, and kernel modules to identify causes and resolve errors.