Expert-level Postgres monitoring tool designed for humans and AI systems

Built for senior DBAs, SREs, and AI systems who need rapid root cause analysis and deep performance insights. This isn't a tool for beginners — it's designed for Postgres experts who need to understand complex performance issues in minutes, not hours.

Part of Self-Driving Postgres - postgres_ai monitoring is a foundational component of PostgresAI's open-source Self-Driving Postgres (SDP) initiative, providing the advanced monitoring and intelligent root cause analysis capabilities essential for achieving higher levels of database automation.

• Top-down troubleshooting methodology: Follows the Four Golden Signals approach (Latency, Traffic, Errors, Saturation)
• Expert-focused design: Assumes deep Postgres knowledge and performance troubleshooting experience
• Dual-purpose architecture: Built for both human experts and AI systems requiring structured performance data
• Comprehensive query analysis: Complete pg_stat_statements metrics with historical trends and plan variations
• Active Session History: Postgres's answer to Oracle ASH and AWS RDS Performance Insights
• Hybrid storage: Prometheus for metrics, Postgres for query texts — best of both worlds

📖 Read more: postgres_ai monitoring v0.7 announcement - detailed technical overview and architecture decisions.

This tool is NOT for beginners. It requires extensive Postgres knowledge and assumes familiarity with:

• Advanced Postgres internals and performance concepts
• Query plan analysis and optimization techniques
• Wait event analysis and system-level troubleshooting
• Production database operations and incident response

If you're new to Postgres, consider starting with simpler monitoring solutions before using postgres_ai.

Experience the full monitoring solution: https://demo.postgres.ai (login: demo / password: demo)

1. Troubleshooting dashboard - Four Golden Signals with immediate incident response insights
2. Query performance analysis - Top-N query workload analysis with resource consumption breakdowns
3. Single query analysis - Deep dive into individual query performance and plan variations
4. Wait event analysis - Active Session History for session-level troubleshooting
5. Backups and DR - WAL archiving monitoring with RPO measurements

• Collection: pgwatch v3 (by Cybertec) for metrics gathering
• Storage: Prometheus for time-series data + Postgres for query texts
• Visualization: Grafana with expert-designed dashboards
• Analysis: Structured data output for AI system integration

Infrastructure:

• Linux machine with Docker installed (separate from your database server)
• Docker access - the user running postgres_ai must have Docker permissions
• Access (network and pg_hba) to the Postgres database(s) you want to monitor

Database:

• Supports Postgres versions 14-17
• pg_stat_statements extension must be created for the DB used for connection

WARNING: Security is your responsibility!

This monitoring solution exposes several ports that MUST be properly firewalled:

• Port 3000 (Grafana) - Contains sensitive database metrics and dashboards
• Port 58080 (PGWatch Postgres) - Database monitoring interface
• Port 58089 (PGWatch Prometheus) - Database monitoring interface
• Port 59090 (Prometheus) - Metrics storage and queries
• Port 59091 (PGWatch Prometheus endpoint) - Metrics collection
• Port 55000 (Flask API) - Backend API service
• Port 55432 (Demo DB) - When using --demo option
• Port 55433 (Metrics DB) - Postgres metrics storage

Configure your firewall to:

• Block public access to all monitoring ports
• Allow access only from trusted networks/IPs
• Use VPN or SSH tunnels for remote access

Failure to secure these ports may expose sensitive database information!

Create a new DB user in the database to be monitored (skip this if you want to just check out postgres_ai monitoring with a synthetic demo database):

-- Create a user for postgres_ai monitoring
begin;
create user postgres_ai_mon with password '<password>';

grant connect on database <database_name> to postgres_ai_mon;

grant pg_monitor to postgres_ai_mon;
grant select on pg_stat_statements to postgres_ai_mon;
grant select on pg_stat_database to postgres_ai_mon;
grant select on pg_stat_user_tables to postgres_ai_mon;

-- Create a public view for pg_statistic access (required for bloat metrics on user schemas)
create view public.pg_statistic as
select 
    n.nspname as schemaname,
    c.relname as tablename,
    a.attname,
    s.stanullfrac as null_frac,
    s.stawidth as avg_width,
    false as inherited
from pg_statistic s
join pg_class c on c.oid = s.starelid
join pg_namespace n on n.oid = c.relnamespace  
join pg_attribute a on a.attrelid = s.starelid and a.attnum = s.staattnum
where a.attnum > 0 and not a.attisdropped;

grant select on public.pg_statistic to pg_monitor;
alter user postgres_ai_mon set search_path = "$user", public, pg_catalog;
commit;

One command setup:

# Download the CLI
curl -o postgres_ai https://gitlab.com/postgres-ai/postgres_ai/-/raw/main/postgres_ai \
  && chmod +x postgres_ai

Now, start it and wait for a few minutes. To obtain a PostgresAI access token for your organization, visit https://console.postgres.ai (Your org name → Manage → Access tokens):

# Production setup with your Access token
./postgres_ai quickstart --api-key=your_access_token

Note: You can also add your database instance in the same command:

./postgres_ai quickstart --api-key=your_access_token --add-instance="postgresql://user:pass@host:port/DB"

Or if you want to just check out how it works:

# Complete setup with demo database
./postgres_ai quickstart --demo

That's it! Everything is installed, configured, and running.

• Grafana Dashboards - Visual monitoring at http://localhost:3000
• Postgres Monitoring - PGWatch with comprehensive metrics
• Automated Reports - Daily performance analysis
• API Integration - Automatic upload to PostgresAI
• Demo Database - Ready-to-use test environment

For developers:

./postgres_ai quickstart --demo

Get a complete monitoring setup with demo data in under 2 minutes.

For production:

./postgres_ai quickstart --api-key=your_key
# Then add your databases
./postgres_ai add-instance "postgresql://user:pass@host:port/DB"

# Instance management
./postgres_ai add-instance "postgresql://user:pass@host:port/DB"
./postgres_ai list-instances
./postgres_ai test-instance my-DB

# Service management  
./postgres_ai status
./postgres_ai logs
./postgres_ai restart

# Health check
./postgres_ai health

After running quickstart:

• 🚀 MAIN: Grafana Dashboard: http://localhost:3000 (login: monitoring; password is shown at the end of quickstart)

Technical URLs (for advanced users):

• Demo DB: postgresql://postgres:postgres@localhost:55432/target_database
• Monitoring: http://localhost:58080 (PGWatch)
• Metrics: http://localhost:59090 (Prometheus)

./postgres_ai help

Get your access token at PostgresAI for automated report uploads and advanced analysis.

• Host stats for on-premise and managed Postgres setups
• pg_wait_sampling and pg_stat_kcache extension support
• Additional expert dashboards: autovacuum, checkpointer, lock analysis
• Query plan analysis and automated recommendations
• Enhanced AI integration capabilities

We welcome contributions from Postgres experts! Please check our GitLab repository for:

• Code standards and review process
• Dashboard design principles
• Testing requirements for monitoring components

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

postgres_ai monitoring is developed by PostgresAI, bringing years of Postgres expertise into automated monitoring and analysis tools. We provide enterprise consulting and advanced Postgres solutions for fast-growing companies.

• 💬 Get support
• 📺 Postgres.TV (YouTube)
• 🎙️ Postgres FM Podcast
• 🐛 Report issues
• 📧 Enterprise support