pypgsvg 1.1.1

Generate SVG Entity Relationship Diagrams from PostgreSQL database dump files

pypgsvg - Lightweight PostgreSQL ERD Generator

pypgsvg is a lightweight, enterprise-ready Python tool that generates interactive Entity Relationship Diagrams (ERDs) from PostgreSQL schema dump files. With only Graphviz as a dependency, it's perfect for enterprise scripting, CI/CD pipelines, and rapid deployment scenarios.

🚀 Enterprise Features

• Zero-dependency Python tool (except Graphviz)
• Interactive SVG output with navigation and selection tools
• Scriptable and automatable for enterprise workflows
• Quick deployment - install and run in seconds
• Self-contained - no database connections required
• Cross-platform support (Windows, macOS, Linux)

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✨ Interactive Features

🎯 Selection & Navigation Tools

• Smart table/edge selection - Click any element to view detailed SQL
• Miniature overview with viewport indicator for large schemas
• Drag & drop containers - Reposition windows anywhere
• Resizable panels - Customize your workspace layout

📋 Copy & Export Tools

• One-click copy - Copy table definitions, foreign keys, or trigger SQL
• Download selection details - Export selected elements as formatted text
• Emoji-free output - Enterprise-friendly plain text exports

🖱️ Interactive Controls

• Minimize/maximize any panel to focus on your work
• Close buttons for distraction-free viewing
• Pan and zoom with mouse or miniature navigator
• Keyboard shortcuts (ESC/R to reset view)

🎨 Visual Enhancements

• Color-coded tables with accessible contrast
• Hover effects for better element identification
• Professional styling suitable for documentation and presentations

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📦 Installation

Lightweight setup - only 2 steps:

1. Install pypgsvg:

   pip install pypgsvg
   

2. Install Graphviz (the only external dependency):

   • macOS: brew install graphviz
   • Ubuntu/Debian: sudo apt-get install graphviz
   • CentOS/RHEL: sudo yum install graphviz
   • Windows: Download from Graphviz.org

That's it! Ready for enterprise deployment.

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🛠️ Enterprise Usage

Quick Start - Schema Analysis

# Generate interactive ERD from schema dump
pypgsvg schema.dump --output database_erd --view

# Enterprise automation (CI/CD ready)
pypgsvg schema.dump --output docs/database_diagram --rankdir TB --node-sep 4

Advanced Enterprise Options

# Large schema optimization
pypgsvg large_schema.dump --packmode graph --rank-sep 3 --hide-standalone

# Custom layout for documentation
pypgsvg schema.dump --rankdir LR --fontsize 20 --node-fontsize 16 --output presentation_erd

Usage

Get Your PostgreSQL Schema

If you don't have a schema dump, generate one with pg_dump:

# Standard schema export (most common)
pg_dump -h your-host -d database -U username -s --no-owner --no-privileges > schema.dump

# Comprehensive export with triggers and functions
pg_dump -h your-host -d database -U username -s -O -F plain --disable-triggers --encoding=UTF8 > schema.dump

Or use our sample schema for testing.

Interactive ERD Generation

Basic usage:

pypgsvg schema.dump --output my_database_erd --view

Enterprise production:

pypgsvg schema.dump \
  --output docs/database_architecture \
  --rankdir TB \
  --node-sep 4 \
  --packmode graph \
  --rank-sep 3 \
  --hide-standalone

The generated SVG includes:

• 🖱️ Interactive selection - Click tables/edges to view SQL details
• 📱 Miniature navigator - Overview panel for large schemas
• 📋 Copy/download tools - Export selected elements
• 🎨 Resizable panels - Customize your workspace
• ⌨️ Keyboard shortcuts - ESC/R to reset view

Note: For full interactivity, open the SVG file locally in your browser. GitHub restricts JavaScript for security.

🎬 Quick Demo

[🎯 View Interactive Example]

Try it yourself:

# Download sample and generate interactive ERD
wget https://github.com/blackburnd/pypgsvg/raw/main/Samples/schema.dump
pypgsvg schema.dump --output demo_erd --view

Scriptable API

Perfect for automation and enterprise workflows:

from pypgsvg import parse_sql_dump, generate_erd_with_graphviz

# Parse schema dump
with open("schema.dump", "r", encoding='utf-8') as file:
    sql_content = file.read()

# Extract database structure  
tables, foreign_keys, triggers, errors = parse_sql_dump(sql_content)

# Generate interactive ERD
if not errors:
    generate_erd_with_graphviz(
        tables=tables,
        foreign_keys=foreign_keys, 
        output_file="enterprise_diagram",
        rankdir='TB',
        packmode='graph'
    )
    print("✅ Enterprise ERD generated successfully!")
else:
    print("⚠️ Parsing errors:", errors)

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🎯 Interactive Components

The generated SVG includes several interactive panels that can be moved, resized, and minimized:

📊 Metadata Panel

Displays comprehensive information about your database schema:

• Schema statistics (table count, columns, relationships)
• Generation parameters used
• File information and timestamps
• Interactive controls (minimize, close, drag to reposition)

🗺️ Miniature Overview

Navigate large schemas effortlessly:

• Interactive minimap with viewport indicator
• Click to jump to specific schema areas
• Drag viewport for precise navigation
• Resizable panel - make it larger for detailed navigation

🔍 Selection Details

View and export detailed SQL information:

• Table definitions with column details and constraints
• Foreign key relationships with full SQL syntax
• Trigger information including execution details
• Copy button for instant clipboard access
• Download button for formatted text export
• Enterprise-friendly emoji-free output option

Example selection output:

📊 Selected Tables
==================
public_franchises
public_association_map
public_ecommerce
...

🔗 Foreign Key Relationships  
============================
🔑 franchise_id → id
ALTER TABLE ONLY public.association_map
    ADD CONSTRAINT association_map_franchise_id_fkey 
    FOREIGN KEY (franchise_id) REFERENCES public.franchises(id) 
    ON DELETE CASCADE;

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🏢 Enterprise Deployment

CI/CD Integration

# GitHub Actions example
- name: Generate Database Documentation
  run: |
    pip install pypgsvg
    sudo apt-get install graphviz
    pypgsvg schemas/production.dump --output docs/database_erd
    
# Docker deployment
FROM python:3.9-slim
RUN apt-get update && apt-get install -y graphviz && rm -rf /var/lib/apt/lists/*
RUN pip install pypgsvg
COPY schema.dump .
RUN pypgsvg schema.dump --output database_diagram

Automation Scripts

#!/bin/bash
# Enterprise schema documentation automation
DATE=$(date +%Y%m%d)
pg_dump -h $DB_HOST -d $DB_NAME -U $DB_USER -s > schema_$DATE.dump
pypgsvg schema_$DATE.dump --output docs/database_erd_$DATE --hide-standalone
echo "✅ Database documentation updated: docs/database_erd_$DATE.svg"

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🧪 Testing & Quality

Enterprise-grade testing with comprehensive coverage:

# Run full test suite (no dependencies required)
python -m pytest tests/tests/

# Generate detailed coverage report
python -m pytest tests/tests/ --cov=src --cov-report=html
open htmlcov/index.html

Quality metrics:

• ✅ 95%+ code coverage
• ✅ 70+ comprehensive tests
• ✅ Enterprise PYTHONPATH support
• ✅ Cross-platform compatibility

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🏗️ Architecture & Performance

Lightweight Design

📦 pypgsvg/
├── 🐍 Pure Python core (~450 lines)
├── 🎨 CSS styling (~200 lines)  
├── ⚡ JavaScript interactivity (~2000 lines)
├── 🧪 Comprehensive tests (~1000+ lines)
└── 📚 Zero runtime dependencies (except Graphviz)

Performance characteristics:

• Fast parsing - Processes large schemas in seconds
• Memory efficient - Minimal footprint for enterprise deployment
• Scalable output - Handles schemas with hundreds of tables
• Quick startup - No database connections or heavy frameworks

Enterprise-Ready Features

• 🔒 Security-focused - No network requirements, processes local files only
• 📋 Audit-friendly - Deterministic output for version control
• 🚀 Container-ready - Minimal Docker image size
• ⚙️ Configurable - Extensive customization options
• 📊 Monitoring - Built-in error reporting and validation

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⚙️ Configuration & Customization

Advanced Layout Options

# Horizontal layout for wide displays
pypgsvg schema.dump --rankdir LR --node-sep 3 --rank-sep 2

# Compact layout for presentations  
pypgsvg schema.dump --packmode graph --fontsize 16 --node-fontsize 14

# Large schema optimization
pypgsvg schema.dump --hide-standalone --esep 8 --rank-sep 4

Table Filtering (Automatic)

Enterprise-focused exclusions for cleaner diagrams:

• Views (vw_*) - Database views
• Backup tables (*_bk, *_backup) - Temporary backup data
• Utility tables (*_temp, *_tmp) - Temporary processing tables
• Log tables (*_log, *_audit) - Audit and logging tables
• Duplicate tables (*_dups, *_duplicates) - Data cleanup tables
• Version tables (*_old, *_archive) - Historical data tables

Color & Accessibility

• WCAG-compliant color palette with proper contrast ratios
• Automatic text color calculation for readability
• Color-blind friendly palette selection
• High-contrast mode for professional presentations

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🔧 Supported Database Features

SQL Parsing Capabilities

• ✅ CREATE TABLE statements with all PostgreSQL data types
• ✅ ALTER TABLE constraints and foreign keys
• ✅ Triggers with BEFORE/AFTER/INSTEAD OF events
• ✅ Primary keys and unique constraints
• ✅ Complex data types (JSON, arrays, custom types)
• ✅ Quoted identifiers and schema-qualified names
• ✅ Unicode support with proper encoding handling

Advanced SQL Features

-- Fully supported constructs
CREATE TABLE "complex_table" (
    id SERIAL PRIMARY KEY,
    data JSONB NOT NULL,
    tags TEXT[],
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Trigger support
CREATE TRIGGER update_modified_time 
    BEFORE UPDATE ON complex_table 
    FOR EACH ROW EXECUTE FUNCTION update_timestamp();

-- Foreign key variations
ALTER TABLE orders 
    ADD CONSTRAINT fk_customer 
    FOREIGN KEY (customer_id) 
    REFERENCES customers(id) 
    ON DELETE CASCADE ON UPDATE RESTRICT;

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🚨 Error Handling & Reliability

Production-ready error management:

• Graceful degradation - Continues processing despite individual parsing errors
• Detailed error reporting - Specific line numbers and context
• Encoding resilience - Handles various character encodings
• Malformed SQL recovery - Attempts to extract partial information
• Validation checks - Ensures output integrity

Enterprise logging:

# Built-in error collection for monitoring
tables, foreign_keys, triggers, errors = parse_sql_dump(sql_content)

if errors:
    for error in errors:
        log.warning(f"Schema parsing issue: {error}")
    # Continue with partial results

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🤝 Contributing

We welcome enterprise users and contributors:

1. Code quality - Follow PEP 8 and maintain >95% test coverage
2. Enterprise focus - Consider automation and deployment scenarios
3. Performance - Optimize for large schemas and CI/CD usage
4. Documentation - Update examples for enterprise use cases
5. Testing - Add tests for new SQL patterns and edge cases

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📋 Dependencies

Minimal dependency footprint for enterprise deployment:

Required

• Python 3.8+ (standard in most enterprise environments)
• Graphviz (system package, widely available)

Development/Testing Only

• pytest>=7.0.0 - Testing framework
• pytest-cov>=4.0.0 - Coverage reporting

That's it! No heavy frameworks, databases, or complex runtime dependencies.

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📄 License & Enterprise Usage

This project is released under the MIT License, making it suitable for:

• ✅ Commercial use in enterprise environments
• ✅ Modification for internal requirements
• ✅ Distribution within organizations
• ✅ Private use without attribution requirements

Perfect for enterprise adoption with minimal legal overhead.