Can I compare my PostgreSQL databases across different development stages?

Absolutely. PG Analytics supports configuring up to four environments (LOCAL, DEV, STG, PROD). You can easily use tools like `compare_envs` to quickly identify row count differences and other discrepancies across these environments.

What specific insights can PG Analytics provide about my PostgreSQL databases?

It offers comprehensive schema discovery (table sizes, column details, empty tables), data exploration (recent rows, column stats), relationship mapping (foreign keys, constraints), performance insights (index usage, slow query candidates), and data quality monitoring (null reports, duplicate checks, table health).

Is PG Analytics safe to use in production environments?

Yes, PG Analytics is designed to be entirely read-only, ensuring no data is ever modified. It includes robust security features such as identifier validation, row limits on queries, and statement timeouts to prevent any unintended impact.

PG Analytics: PostgreSQL Schema, Performance & Data Quality

PG Analytics is an MCP server designed as a general-purpose "DBA-lite" toolkit for PostgreSQL. It offers any MCP client instant visibility into your database's schema structure, data quality, relationships, and performance, including multi-environment comparisons. Exposing over 20 read-only tools, it helps developers and DBAs understand their database state without needing direct database access or complex queries, making database management and debugging more accessible.

Características Principales

01Comprehensive Schema Discovery: Tools for high-level database overviews, row counts, table sizes, column details, and finding empty tables.

02Relationship Mapping: View all constraints, analyze bidirectional foreign key relationships, and compare row counts across multiple environments (DEV/STG/PROD).

03Data Quality Monitoring: Check table health (row count, last inserted/updated), generate null percentage reports for columns, and detect duplicate rows based on specified criteria.

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05Data Exploration: Peek at recent rows, get distinct value counts, and retrieve statistics (min, max, avg, null count) for any column.

06Performance Insights: Monitor index usage, identify slow query candidates (tables with high sequential scans), and estimate table bloat that may require VACUUM.

Casos de Uso

01Monitoring PostgreSQL database health, schema structure, and data quality across different development, staging, and production environments.

02Identifying potential performance bottlenecks within PostgreSQL, such as missing indexes or tables needing vacuuming.

03Facilitating data exploration and debugging by providing quick insights into table contents, column statistics, and data integrity issues.