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PostgreSQL community introduces radar and pg_statviz for agentless observability

Brief written by the FOSSWire AI editor from Planet PostgreSQL How we write

1 min read

Key Takeaway

New open-source tools radar and pg_statviz provide agentless diagnostic snapshots and in-database time-series analysis for PostgreSQL.

Summary

During the PGConf.EU 2026 conference in Valencia, new open-source diagnostic and observability tools for PostgreSQL were presented to the community. The featured tools, radar and radar-analyst, aim to solve the problem of post-incident support by providing agentless snapshots of database metadata and host configurations.

Additionally, the presentation of pg_statviz introduces a method for performing in-database time-series analysis using existing cumulative statistics. These developments, presented alongside discussions on community onboarding and observability, represent a push toward more accessible and deterministic database monitoring and troubleshooting workflows.

Technical breakdown of new PostgreSQL observability tools

  • radar (Diagnostic Tool): A single static Go binary designed to collect a snapshot of PostgreSQL metadata, including catalogs, statistics, and configurations, into a compressed archive without accessing row data or passwords.
  • radar-analyst (Analysis Engine): An automated tool that processes radar snapshots using deterministic rules to provide verdicts and fixes, with an optional LLM layer used only for summarizing findings rather than generating them.
  • pg_statviz (Time-Series Analysis): A tool that captures PostgreSQL's internal cumulative statistics and stores them in local database tables, enabling time-series querying and charting without requiring external exporters or agents.
  • AI Integration: Both radar-analyst and pg_statviz utilize vision-capable or text-based LLMs to interpret charts or write briefs, though the core logic remains grounded in deterministic rule-based findings.

Why it matters

These tools reduce the dependency on complex monitoring agents and external exporters for database troubleshooting. By providing agentless, metadata-only snapshots and in-database time-series capabilities, they offer sysadmins and DBAs a more lightweight and secure way to perform diagnostics and observability.

Original reference: postgr.es →