PGConf.Russia 2025

So much has been said about PostgreSQL as the result of its program code. But Postgres is not just code. It’s the people who create it, develop it, and... leave a piece of themselves through comments.

What stories can we uncover from the comments in PostgreSQL’s code? We’ll discover what the most popular word is, which comments have been in the code since the very first public commit, how the style of communication has evolved with the product, and how we can see the human side behind the lines of code and comments.

Making changes to tables under production load is always a complex task. For example, when you need to change a column type (e.g., from int to bigint or from timestamp to timestamptz), or move a table to a different tablespace without losing any changes that occur during the data migration.

What if you have hundreds of such tables? With pg-transparent-alter-table, this is no longer a problem. These tasks can be solved with a single simple command:
$ pg_tat -h 0.0.0.0 -d mydb -c "alter table mytable alter column id bigint"

Key features include:

• You can specify any number of alter table commands at once.
• You can modify partitioned tables, supporting both the old inheritance-based partitioning and new declarative partitioning, including multi-level partitioning.
• You can interrupt the process at any stage and continue later without losing progress from previous stages.
• You can change your mind at any time, stop the execution, run "pg_tat --clean," and revert to the original state.
• Custom commands for changing column order.
• PostgreSQL version support: 11-17.

After more than 5 years of existence (previously called transparent-alter-type), the project has become a reliable tool actively used in production. I would like to share my experience and discuss its capabilities.

In this session, we’ll provide an overview of the features of the pg_probackup3 backup utility. 

The new version has completely rethought the application architecture, introduced several long-awaited features, and added integration possibilities with other applications. We’ll dive into these updates and discuss them in detail during the presentation.

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.