Page pruning (HOT cleanup) is an optimization allowing to efficiently remove old row versions (tuples) from table blocks. The freed space will be reused for new row versions. The only space occupied by row versions beyond the database's xmin horizon is reclaimed. This article examines the algorithm behind a similar optimization for indexes. If the xmin horizon is held back - by a long-running query or transaction - neither page pruning nor VACUUM can reclaim space, forcing new row versions to be inserted into different blocks. With the standard pgbench test, we demonstrate how significantly performance can degrade when the database horizon is held back, and we analyze the underlying causes. 

Hello, Habr! We continue the series of articles on the innovations of the Tantor Postgres 17.5.0 DBMS, and today we will talk about authorization support via OAuth 2.0 Device Authorization Flow is a modern and secure access method that allows applications to request access to PostgreSQL on behalf of the user through an external identification and access control provider, such as Keycloak, which is especially convenient for cloud environments and microservice architectures (the feature will also be available in PostgreSQL 18). In this article, we'll take a step-by-step look at configuring OAuth authorization in PostgreSQL using Keycloak: configure Keycloak, prepare PostgreSQL, write an OAuth token validator in PostgreSQL, and verify successful authorization via psql using Device Flow.

During load testing of Tantor Postgres databases or other PostgreSQL-based databases using the standard tool pgbench, specialists often encounter non-representative results and the need for repeated tests due to the fact that details of the environment (such as DBMS configuration, server characteristics, PostgreSQL versions) are not recorded. In this article we are going to review author's pg_perfbench, which is designed to address this issue. It ensures that scenarios are repeatable, prevents the loss of important data, and streamlines result comparison by registering all parameters in a single template. It also automatically launches pgbench with TPC-B load generation, collects all metadata on the testing environment, and generates a structured report.

pg_stat_statements is the standard PostgreSQL extension used to track query statistics: number of executions, total and average execution time, number of returned rows, and other metrics. This information allows to analyze query behavior over time, identify problem areas, and make informed optimization decisions. However, in systems with high contention, pg_stat_statements itself can become a bottleneck and cause performance drops. In this article, we will analyze in which scenarios the extension becomes a source of problems, how sampling is structured, and in which cases its application can reduce overhead.