PGConf.Russia 2025

In PostgreSQL performance testing, benchmarks measure query execution time (latency). To get more reliable results, queries are executed repeatedly, generating a dataset of latency values. Performance is often assessed using standard metrics like the median or mean, but we propose a more advanced approach.

In practice, latency distributions are often multimodal, consisting of multiple underlying distributions with distinct characteristics. In such cases, traditional statistical methods are insufficient, requiring a more detailed analysis of the dataset’s structure.

Our work presents a tool that automatically performs statistical analysis of benchmark results, accounting for dataset-specific features. It detects multimodality, identifies the number and boundaries of dominant modes, and determines key distribution parameters—providing deeper insights into PostgreSQL performance variations.

Experience Report addressing the manual migration of MS SQL Server Stored Procedures and Table-Valued Functions to PL/pgSQL. We chose a manual migration from T-SQL over using a PostgreSQL Extension with an automated translation. The motivation was that the T-SQL code contains non-trivial business logic for which we wanted idiomatic PL/pgSQL code. 

The T-SQL Code used features like 

• Mix of Stored Procedures and Table-valued Functions

• Table variables, (user-defined) table types 

• Recursive Common Table Expressions 

• Optional parameters 

The migration required

• Adopting naming conventions 

• Renaming parameters and local variables 

• Maping table types 

• Mapping table-valued parameters to arrays 

• Mapping table-valued return types to SETOF record

• Translating between arrays and tables 

In this session we will migrate a chain of dependend functions T-SQL functions to PL/pgSQL. We will work around quirks in both T-SQL functions and PL/pgSQL.

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).

In today’s rapidly evolving technology landscape, PostgreSQL has emerged as a popular choice for organizations seeking to leverage the power of open-source databases while maintaining scalability, performance, and flexibility. 

As enterprises migrate from proprietary database systems like Oracle to PostgreSQL, developers face the challenge of adapting existing PL/SQL codebases to PL/pgSQL. Despite the similarities between these two procedural languages, significant differences in their syntax, performance characteristics, and ecosystem features require a thoughtful approach to migration and optimization. 

This session will focus on highlighting the key differences between Oracle’s PL/SQL and PostgreSQL’s PL/pgSQL, providing actionable insights on how to efficiently navigate these differences during migration. A deep dive will be taken into critical areas including compilation methodology, Code Debugging , Exception Handling, work_mem , Bulk Load and more.