sql-splitter 6.1.0

Advanced MySQL SQL Parser with Visualization Component Support

SQL Splitter 🎯

Advanced MySQL SQL Parser with Visualization Component Support

🚀 Features

• 🎨 Field Type Classification: Automatically categorizes fields as column, aggregation, expression, or computed
• 🔗 Aggregation Scope Tracking: Tracks which tables are involved in aggregation functions like COUNT(*)
• 📊 Visualization-Ready Output: Enhanced JSON format perfect for SQL diagram generation
• 🛠️ Advanced JOIN Detection: Handles complex nested JOINs and old-style comma-separated syntax
• 🏷️ Smart Alias Resolution: Context-aware alias mapping and resolution
• 🐬 MySQL Compatibility: Full MySQL syntax support with normalization
• 📋 Comprehensive Metadata: Provides detailed parsing information for debugging and visualization

📦 Installation

pip install sql-splitter

Or install from source:

git clone https://github.com/alexkwok22/sql-splitter.git
cd sql-splitter
pip install -e .

🎯 Quick Start

Basic Usage

from sql_splitter import SQLParserAST

# Initialize parser
parser = SQLParserAST()

# Parse SQL query
sql = """
SELECT 
    users.name,
    COUNT(*) as total_orders,
    SUM(orders.amount) as total_revenue
FROM users 
JOIN orders ON users.id = orders.user_id 
WHERE users.status = 'active'
GROUP BY users.name
"""

result = parser.parse(sql)
print(result)

Enhanced JSON Output

{
  "success": true,
  "fields": [
    {
      "table": "users",
      "field": "users.name",
      "alias": "name",
      "fieldType": "column",
      "involvedTables": ["users"]
    },
    {
      "table": null,
      "field": "COUNT(*)",
      "alias": "total_orders",
      "fieldType": "aggregation",
      "aggregationScope": ["users", "orders"],
      "involvedTables": ["users", "orders"]
    },
    {
      "table": "orders",
      "field": "SUM(orders.amount)",
      "alias": "total_revenue", 
      "fieldType": "aggregation",
      "involvedTables": ["orders"]
    }
  ],
  "tables": ["users", "orders"],
  "joins": [
    {
      "type": "JOIN",
      "leftTable": "users",
      "leftField": "id",
      "rightTable": "orders", 
      "rightField": "user_id",
      "condition": "users.id = orders.user_id"
    }
  ],
  "whereConditions": ["users.status = 'active'"],
  "parser": "sqlsplit",
  "metadata": {
    "aliasMapping": {},
    "aggregationFields": ["total_orders", "total_revenue"],
    "computedFields": [],
    "unresolved": {
      "aliases": [],
      "fields": []
    }
  }
}

🎨 Visualization Components Support

Field Type Classification

SQL Splitter automatically classifies fields into four types:

• column: Simple table columns (users.name)
• aggregation: Aggregate functions (COUNT(*), SUM(amount))
• expression: Complex expressions (DATE_FORMAT(created_at, '%Y-%m'))
• computed: Conditional logic (CASE WHEN status = 1 THEN 'active' END)

Aggregation Scope Tracking

For visualization components, aggregation functions include aggregationScope to show which tables are involved:

# COUNT(*) shows all tables in the query
{
  "field": "COUNT(*)",
  "fieldType": "aggregation",
  "aggregationScope": ["users", "orders", "products"]  # All related tables
}

# Specific aggregations show only relevant tables
{
  "field": "SUM(orders.amount)",
  "fieldType": "aggregation", 
  "aggregationScope": ["orders"]  # Only orders table
}

🛠️ Advanced Features

MySQL Normalization

from sql_splitter import MySQLCompatibleNormalizer

normalizer = MySQLCompatibleNormalizer()
normalized_sql, rules, errors = normalizer.normalize_query(sql)

Old-Style JOIN Conversion

Automatically converts old-style comma-separated JOINs:

-- Input: Old-style
SELECT * FROM users a, orders b WHERE a.id = b.user_id

-- Output: Modern JOIN
SELECT * FROM users a JOIN orders b ON a.id = b.user_id

Context-Aware Alias Resolution

Handles complex alias scenarios:

# Resolves aliases like 'u' -> 'users', 'o' -> 'orders'
"metadata": {
  "aliasMapping": {
    "u": "users",
    "o": "orders"
  }
}

📚 Documentation

• Quick Start Guide - Get started in 5 minutes
• API Documentation - Complete API reference
• Expected Format - JSON output specification
• Examples - Real-world usage examples

🧪 Testing

# Run basic tests
python -m pytest tests/

# Run with coverage
python -m pytest tests/ --cov=sql_splitter --cov-report=html

📋 Requirements

• Python 3.7+
• No external dependencies (pure Python implementation)

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add 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 file for details.

🙏 Acknowledgments

• Built for SQL visualization component developers
• Supports complex MySQL queries and edge cases
• Designed with performance and accuracy in mind

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Made with ❤️ for the SQL visualization community