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postgres-insight

by InAppUpdated Jul 26, 2026

Empower developers and DBAs to optimize PostgreSQL performance with tools for schema introspection, bloat detection, and query diagnostics. Use pg_analyze_query and pg_recommend_indexes to identify bottlenecks and generate schema improvements, while pg_dry_run_query ensures safe execution. From health monitoring to index optimization, gain actionable insights into your database state without manual overhead.

postgresql
database-management
query-optimization
+5
|

How to pay

Subscribe

Monthly billing

$4.99/month

Predictable monthly cost with included usage. Best for steady, high-volume traffic.

  • Unlimited tools within plan limits
  • One API key, billed once a month
  • Cancel any time
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Overview

postgres-insight provides direct, actionable intelligence into PostgreSQL database performance and structure. It bridges the gap between raw SQL execution and full-scale DBA tooling by offering automated diagnostics for query optimization and schema health.

Key Capabilities

  • pg_health_check: Retrieves server uptime, versioning, and core connection metrics to monitor instance stability.
  • pg_detect_bloat: Identifies dead tuples and transaction ID wraparound risks, providing automated vacuuming guidance.
  • pg_introspect_schema: Maps out table structures, foreign key relationships, and row estimates for complex database navigation.
  • pg_dry_run_query & pg_analyze_query: Evaluates query execution plans via EXPLAIN and EXPLAIN ANALYZE to identify bottlenecks without risking production data integrity.
  • pg_get_table_stats: Extracts cache hit ratios and scan counts to pinpoint underperforming or oversized tables.
  • pg_recommend_indexes: Suggests specific index modifications based on query execution patterns to improve retrieval speeds.

Use Cases

  • A developer needs to debug a slow-running report and uses pg_analyze_query to identify a missing index, followed by pg_recommend_indexes to generate the exact DDL needed to fix it.
  • A database administrator manages high-traffic tables by running pg_detect_bloat to determine if manual vacuuming is required to reclaim space and maintain performance.
  • An engineer onboarding to a new project uses pg_introspect_schema to visualize foreign key dependencies and table relationships before writing complex joining logic.
  • A team performs a safety audit by using pg_dry_run_query to validate that new application migrations perform index-based lookups rather than full sequential scans.

Who This Is For

This server is designed for backend developers, database administrators, and data engineers who work directly with PostgreSQL. It requires a foundational understanding of SQL and query execution plans to interpret the diagnostic output effectively.