Episode

How Small Postgres Metadata Tables Quietly Throttle Your Largest Queries

Podcast
The Good Tech Companies
Published
Jul 1, 2026
Duration seconds
464
Processing state
not_requested
Canonical source
https://share.transistor.fm/s/c9c4e87e
Audio
https://media.transistor.fm/c9c4e87e/265c3b43.mp3
JSON
/v1/public/podcasts/the-good-tech-companies-6882802/episodes/how-small-postgres-metadata-tables-quietly-throttle-your-largest-queries
Markdown
/podcast/the-good-tech-companies-6882802/how-small-postgres-metadata-tables-quietly-throttle-your-largest-queries.md

Actions

  • POST https://stenobird.com/v1/public/podcasts/the-good-tech-companies-6882802/episodes/how-small-postgres-metadata-tables-quietly-throttle-your-largest-queries/transcription-requests
    Idempotently request low-priority transcript generation for this episode.
  • GET https://stenobird.com/podcast/the-good-tech-companies-6882802/how-small-postgres-metadata-tables-quietly-throttle-your-largest-queries.md
    Read the agent-friendly Markdown representation of this episode resource.

Summary

This story was originally published on HackerNoon at: https://hackernoon.com/how-small-postgres-metadata-tables-quietly-throttle-your-largest-queries . Slow Postgres dashboards may not be caused by your fact table. Learn how stale metadata statistics break joins and fix it in minutes. Check more stories related to undefined at: https://hackernoon.com/c/undefined . You can also check exclusive content about #postgres-stale-statistics , #postgres-join-performance , #time-series-database , #postgres-metadata-table , #postgresql-shared_buffers , #postgres-hash-join , #pg_stat_user_tables-audit , #good-company , and more. This story was written by: @tigerdata . Learn more about this writer by checking @tigerdata's about page, and for more stories, please visit hackernoon.com . Most engineers blame slow time-series queries on massive fact tables, but a small metadata table with stale Postgres statistics can silently become the real bottleneck. This guide shows how outdated ANALYZE data misleads the planner into choosing inefficient joins, how to detect row-estimate mismatches with EXPLAIN, and how to fix them using autovacuum tuning, targeted audits, or selective denormalization.