PGConf.Russia 2025

Covering all the key changes in PostgreSQL 18 during this talk would be quite a challenge — especially since the code freeze is set to happen just a week after the presentation. However, I’ll focus on what has already been finalized.

There are plenty of exciting updates in performance, monitoring, vacuuming (of course!), and beyond. As always, this talk is based on a series of articles reviewing the commit fests for PostgreSQL 18, published on Postgres Professional’s corporate blog on Habr.

Every day, thousands of engineers work tirelessly to make data more accurate, reliable, and up-to-date. But sometimes, we need to do the exact opposite—corrupt it.

Whether it’s masking or replacing sensitive information, or even generating entirely new datasets while preserving key business properties, data obfuscation is a crucial task. It’s essential for testing systems, sharing data with third parties, and more. However, given the complexity of data schemas and business logic, this is far from trivial.

In this talk, we’ll explore the challenges of working with artificial data and discuss various approaches to solving them using PostgreSQL’s built-in features and external extensions.

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

I’ll talk about the challenges you’ll face if you decide to implement a new TableAM. What to choose: Generic XLog or Custom RMGR? Why use a Custom SMGR? How to integrate PostgreSQL allocators into third-party libraries, even if they don't officially support it? What’s missing for a columnar engine (including vectorization and late materialization), and how can we work around those limitations?

In the second part, I’ll dive into the internals of pgpro_tam — a new native table engine for OLAP that supports standard data formats, various SMGRs, and, if needed, third-party schedulers and execution engines, all while adhering to ACID principles. This is designed to achieve the fastest analytics on PostgreSQL (not just plugging in DuckDB).