PGConf.Russia 2025

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).

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

The execution time of SQL queries depends on factors like indexes and up-to-date statistics. In most cases, optimizing slow queries helps resolve database performance issues.

But what if classic query optimization doesn’t work? What if the system keeps behaving unpredictably — or worse, crashes, leading to frustration, panic, and even despair?

In this talk, we’ll explore how Postgres Professional’s performance engineers tackle these challenges, look at the tools they use, what’s still missing, and where PostgreSQL performance optimization is headed

Many developers often face performance issues in the systems they develop. One common solution for optimizing slow business processes is parallelization. But what do you do if the bottleneck is the data insertion into the database, which needs to maintain atomicity?

In this talk, I’ll explain how to speed up data insertion by parallelizing the process in Spring, while ensuring the atomicity of the entire operation. We'll cover batch updates in Spring and PostgreSQL, discuss why updates are heavy operations, and explore ways to speed up the process in the current tech stack. Additionally, I will present other approaches to maintaining atomicity and demonstrate their differences in benchmarks.

This will be useful for practicing engineers.