PGConf.Russia 2025

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.

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.

Diagnostic Methods: Analyzing the Issue of Long CREATE DATABASE Execution

This talk covers two cases of long CREATE DATABASE execution in PostgreSQL 11, which, in a Corosync/Pacemaker cluster configuration, caused node failover due to a monitoring timeout from the clustering software. The first case is related to a large shared buffers size, while the second involves the use of the WITH OWNER option during database creation and a large pg_shdepend system table (a feature of using the jBPM software).

This talk addresses a common issue and touches a bit on application architecture. Temporary tables in database applications are typically used for several purposes. 

Firstly, they are used to store intermediate results when implementing complex data processing algorithms. Secondly, in the case of stored procedures, application servers often place large datasets into temporary tables when they are too large or inconvenient to pass as parameters to stored procedures. 

The handling of temporary tables in different DBMSs is implemented in various ways, which often complicates migration from these systems to Postgres. 

The drawbacks of temporary tables are well-known, which leads to a reasonable desire to replace them, where possible, with other methods that can achieve the same goal. This talk focuses on alternative mechanisms provided by Postgres to solve this problem.