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Database Technology

Wednesday, July 15, 2026 · 8 stories, curated & summarized — click any story for the source.

PostgreSQL Releases databasereleases

PostgreSQL 19 Beta 2 Released

The PostgreSQL Global Development Group has published the second beta for version 19. This release continues the stabilization process for the upcoming major version, allowing developers and testers to evaluate new features and performance improvements. As a pre-release candidate, it is intended for testing rather than production deployment.

  • Beta 2 indicates the feature set is largely frozen, focusing now on bug fixes and stability.
  • Testers should verify critical workloads against this build to identify regression issues.
  • Production environments must not upgrade to beta releases; wait for GA.
  • Monitor the PostgreSQL community forums for reported issues specific to this beta.

Percona highlights MySQL 9.7 as a new Long-Term Support release designed to replace the End-of-Life MySQL 8.0. The update introduces features across multiple technical domains aimed at enhancing operational stability. The release is positioned as the next stable foundation for production workloads following the previous LTS cycle.

  • MySQL 8.0 has reached End-of-Life, making 9.7 the recommended LTS path.
  • Release focuses on enhanced stability for long-term production deployments.
  • Feature set spans multiple technical domains within the MySQL environment.
  • Percona provides evaluation of practical utility for database administrators.
THE SHIFTMySQL LTS Transition8.0END OF LIFE9.7NEW LTSStability focus for production

LakeQuest is a 9,846-pair benchmark designed to evaluate question answering systems on noisy, weakly structured data lakes rather than clean corpora. It spans AI/ML metadata, retail banking, and biomedical domains, requiring models to navigate heterogeneous tables and linked metadata. The benchmark focuses on the full retrieve-and-synthesize pipeline, addressing the gap where current tests ignore the complexity of real-world data discovery.

  • Evaluates end-to-end performance over heterogeneous, weakly structured data rather than clean schemas.
  • Covers three distinct domains: AI/ML metadata, retail banking, and biomedical drug information.
  • Requires systems to handle noisy discovery of tables, passages, and linked metadata.
  • Human-validated pairs ensure realistic assessment of retrieve-and-synthesize pipelines.
HOW IT WORKSLakeQuest QA Pipeline1Navigate heterogeneous tables2Discover noisy metadata3Retrieve relevant passages4Synthesize final answer
AWS What's New awsdatabase

Amazon Aurora DSQL now available in Europe (Spain)

AWS has expanded Amazon Aurora DSQL availability to the Europe (Spain) Region, supporting single-Region clusters. This serverless distributed SQL database offers active-active high availability and multi-Region strong consistency. It is designed to provide virtually unlimited scalability and zero infrastructure management for always-available applications.

  • Aurora DSQL is now live in Europe (Spain) for single-Region deployments.
  • Features include active-active HA and multi-Region strong consistency.
  • Serverless architecture eliminates infrastructure management overhead.
  • Enables effortless scaling and resilience for distributed SQL workloads.
  • Expands the global footprint of fast, serverless distributed SQL capabilities.
CHECKLISTAurora DSQL Spain LaunchDeploy single-Region clusters in Europe (Spain)Enable active-active high availabilityEnsure multi-Region strong consistencyEliminate infrastructure management overheadScale effortlessly for distributed workloads

With pgAgent on its way out, Regina Obe is guiding the migration to pg_timetable by detailing how to install it as a Linux service. This second part of the series focuses on the operational configuration required to run the scheduler reliably. Obe is also developing a pg_timetable UI for pgAdmin that mirrors the familiar pgAgent interface to ease the transition for users.

  • pgAgent is being deprecated, making pg_timetable the recommended replacement for job scheduling.
  • New guidance is available for installing pg_timetable as a persistent Linux service.
  • A pgAdmin UI for pg_timetable is in development to replicate the existing pgAgent experience.
  • The new UI pull request is currently being cleaned up before final commit.
CHECKLISTMigrating to pg_timetableInstall pg_timetable as a Linux serviceConfigure operational settings for reliabilityWait for pgAdmin UI final commitReplace deprecated pgAgent scheduling jobs

The Odyssey connection pooler for PostgreSQL and Apache Cloudberry has reached version 1.5.1, bringing extended protocol support and performance improvements for pipelining. New capabilities include shared pools, transaction pooling pinning, and explicit CPU affinity support. The release also adds observability features like queue time metrics, Prometheus exporter enhancements, and SSLKEYLOGFILE support.

  • Improves pipelining performance and fixes protocol violations for better stability.
  • Adds queue time metrics (avg/total wait) and pool_notice_after_waiting_ms for debugging.
  • Supports CPU affinity and shared pools for more granular resource control.
  • Includes SSLKEYLOGFILE support to aid in TLS traffic analysis and debugging.
CHECKLISTOdyssey 1.5.1 Key UpdatesFix protocol violations for stabilityAdd queue time metricsSupport CPU affinityEnable SSLKEYLOGFILE for debugging

Christophe Pettus highlights that setting the event_source GUC alone is insufficient on Windows. The operating system does not recognize the specified source name until it is explicitly registered in the Windows Event Log. This registration step is mandatory for logs to appear correctly in the Event Viewer.

  • Setting event_source GUC does not auto-register the log source on Windows
  • Manual registration in Windows Event Log is required before logging works
  • Unregistered sources will fail to display messages in Event Viewer
  • This is a configuration prerequisite for Windows-specific PostgreSQL deployments

Seph Gentle proposes Schemaboi, an experimental format that embeds self-contained schemas directly into file headers, inspired by the adaptability of HTML and HTTP. This approach ensures data remains readable without external definitions by prioritizing forward, backward, and sideways compatibility. The goal is to enable data format evolution without requiring central coordination or risking data loss.

  • Embeds schemas in file headers to eliminate external definition dependencies.
  • Supports forward, backward, and sideways compatibility for seamless evolution.
  • Enables format changes without central coordination or data loss.
  • Inspired by HTML/HTTP patterns for enduring adaptability.