PGConf.Russia 2025

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

Many Russian companies are striving to establish themselves in the relational database market by developing their own commercial solutions, often based on forks of vanilla PostgreSQL. But beyond the database itself, customers also need a graphical management tool — Enterprise Manager. Since forking it is extremely challenging, vendors often have to build their own from scratch while considering competitive solutions.

This talk will explore Enterprise Managers from various Russian database vendors, comparing their key features — functionality, usability, and design — to identify which one stands out as the most effective.

As part of the import substitution process, we encountered the need to find a reliable alternative to Oracle. PostgreSQL was considered the primary candidate for replacement, but during the research, the "vanilla" version revealed a number of critical limitations that hindered a successful transition.

These issues included support for autonomous transactions, batch variables, and several other specifics. What is special about pg_settings? Why do we need autonomous transactions, and how do HOLD cursors relate to this? These and other topics will be discussed in our presentation, where we will also share our experience of implementing Postgres Pro Enterprise.

The focus of the presentation is on the challenges of building an application solution on PostgreSQL, specifically a high-load analytical data warehouse. Using the case of the company EVRAZ, we will demonstrate the impact of applying the Data Vault methodology on PostgreSQL and Greenplum databases for developing a unified production performance system.

We will discuss the difficulties and solutions, showcasing query plans for tasks such as updating directories and handling large objects, along with optimization examples. We will highlight coding nuances, problems related to populating the model, and issues with querying from the model.

This session will be useful for those planning to use or already facing challenges with the Data Vault methodology and performance issues in DWH on the open-source stack. We will compare technical implementation options for the Business Vault model layer, considering the specifics of PostgreSQL and Greenplum.

We will also cover 5 real problems that arise when operating a DWH and their solutions:

1. Transferring Business Vault object assembly logic from PostgreSQL to Greenplum.
2. Slower ETL performance when building the current state of Business Vault in PostgreSQL.
3. Slower Data Lineage construction in PostgreSQL and Greenplum.
4. Slow satellite queries in Greenplum.
5. Slow queries with "IN" or "OR" in the Business Vault layer.