# c77_dbh - PostgreSQL Database Health Extension

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A lightweight, intelligent PostgreSQL extension for database health monitoring and maintenance. Provides actionable insights through JSON-formatted metrics on table bloat, index efficiency, materialized view performance, and more.

## 🔍 Overview

**c77_dbh** embeds health monitoring directly into your PostgreSQL database, eliminating the need for external monitoring tools. It uses statistical analysis to establish dynamic, database-specific thresholds for various health indicators, enabling both technical and non-technical users to identify and address performance issues.

## ✨ Features

- **Intelligent Vacuum Monitoring**: Tracks table bloat with context-aware thresholds and TOAST overhead detection
- **Index Usage Analysis**: Identifies unused or bloated indexes with efficiency metrics and specific recommendations
- **Materialized View Performance**: Monitors refresh times and bottlenecks (I/O, CPU, memory) with historical trending
- **Adaptive Precision**: Automatically leverages `pgstattuple` and `pg_stat_statements` when available, with graceful fallbacks
- **Human-readable Insights**: Provides clear status indicators (Red/Yellow/Green), severity scores (1-5), and actionable recommendations
- **Dashboard-Ready Outputs**: Structured JSON format ideal for integration with monitoring dashboards
- **Low Overhead**: Minimal impact on database performance with configurable collection frequencies

## 📊 Example Output

```json
[
  {
    "metric": "Table Vacuum Health",
    "table_name": "public.large_table",
    "status": "Red",
    "severity": 5,
    "insight": "Table bloat is critically high; TOAST overhead is significant.",
    "action": "Run VACUUM FULL or CLUSTER; review large fields for TOAST impact.",
    "last_vacuum": "2025-03-10 14:30:22",
    "last_autovacuum": "2025-03-12 02:15:44",
    "vacuum_count": 127,
    "dead_tuples": 1543267,
    "live_tuples": 4329856,
    "table_size": "2.3 GB",
    "bloat_estimate": "42% (precise)",
    "details": {
      "median_bloat": "15%",
      "bloat_stddev": "8%",
      "toast_bloat": "22%",
      "explanation": "Includes TOAST bloat in total health assessment."
    }
  }
]
```

## 📋 Requirements

- PostgreSQL 13 or higher
- Optional but recommended:
    - `pgstattuple` extension for precise bloat measurements
    - `pg_stat_statements` extension for enhanced query performance insights
    - `pg_cron` extension for automated statistics collection

## 🛠️ Installation

See the [Installation Guide](INSTALL.md) for detailed installation instructions, including manual installation steps.

## 📘 Usage

### Basic Setup

After installation, enable the extension in your database:

```sql
CREATE EXTENSION c77_dbh;
```

### Collecting Statistics

Initialize the statistics tables:

```sql
-- Update table and vacuum statistics
SELECT c77_dbh_update_vacuum_stats();

-- Update index statistics (including TOAST indexes)
SELECT c77_dbh_update_index_stats();
```

### Monitoring Health

Query health metrics:

```sql
-- Table health with bloat estimates and recommendations
SELECT * FROM c77_dbh_get_vacuum_health();

-- Index health analysis
SELECT * FROM c77_dbh_get_index_health();

-- TOAST index specific analysis
SELECT * FROM c77_dbh_get_toast_health();

-- Materialized view refresh performance
SELECT * FROM c77_dbh_get_mv_health();
```

### Automation

With `pg_cron` extension:

```sql
-- Update vacuum stats hourly
SELECT cron.schedule('update_vacuum_stats', '0 * * * *', 
                    'SELECT c77_dbh_update_vacuum_stats()');

-- Update index stats hourly
SELECT cron.schedule('update_index_stats', '0 * * * *', 
                    'SELECT c77_dbh_update_index_stats()');
```

## 🖥️ Dashboard Integration

The JSON output format makes integration with monitoring dashboards straightforward:

- **Grafana**: Use a PostgreSQL data source with JSON parsing
- **Pganalyze**: Import as custom metrics
- **Custom dashboards**: Use the structured JSON for consistency

## 🔄 Update Frequency Recommendations

| Database Characteristics | Recommended Update Frequency |
|--------------------------|------------------------------|
| High-write production    | Hourly                       |
| Mixed read/write         | Every 6 hours                |
| Read-heavy, stable       | Daily                        |
| Development/staging      | On-demand or daily           |

## 🔧 Advanced Configuration

- Adjust permissions as needed:
  ```sql
  -- Grant read access to a reporting role
  GRANT SELECT ON ALL TABLES IN SCHEMA public TO reporting_role;
  
  -- Grant full access to admin roles
  GRANT ALL ON ALL TABLES IN SCHEMA public TO admin_role;
  ```

- Include in schema backups:
  ```bash
  pg_dump -s -n public mydatabase > schema.sql
  ```

## 📝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## 📜 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 📊 Technical Details

The extension consists of:

- **Tables**:
    - `c77_dbh_vacuum_stats`: Table vacuum statistics
    - `c77_dbh_index_stats`: Index usage and bloat statistics
    - `c77_dbh_mv_stats`: Materialized view refresh metrics

- **Functions**:
    - Data collection functions (e.g., `c77_dbh_update_vacuum_stats()`)
    - Health assessment functions (e.g., `c77_dbh_get_vacuum_health()`)
    - Event trigger functions for materialized views

- **Event Triggers**:
    - Automatically tracks materialized view operations

## 🙏 Acknowledgments

- Inspired by statistical process control principles
- Initial development facilitated by the PostgreSQL community