PGConf.Russia 2025

There are many administrator consoles out there, ranging from the old-school DBeaver to the ubiquitous pgAdmin. However, we decided to create another one. Why?

In this talk, you’ll learn:

• How can a single installation manage tens of thousands of database instances.

• How many users can the console support.

• How can you inventory your infrastructure without losing your sanity.

• How do you integrate into an existing environment without starting from scratch.

• How to build a security model that earns the trust of leading banks and telecom operators.

• How can you get hands-on experience with all of this.

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.

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.