SQL Antipattern Scanner

SQL Antipattern Scanner is a powerful tool that scans SQL files/queries for antipatterns and generates comprehensive reports in various formats.

Installation

To install SQL Antipattern Scanner, simply run:

pip install sql-antipattern-scanner

Usage

After installation, you can use SQL Antipattern Scanner from the command line:

sql-antipattern-scanner [arguments]

Available arguments are:

• sql_file (positional argument): Path to the SQL file to scan. Optional if --query option is used.
• --query: SQL query string to scan directly. If provided, sql_file argument is not required.
• --format: Output format for the report. Options: json, csv, html. Default: json.
• --output: Output file path for the report. If not provided, prints to console.
• --run-tests: Flag to run unit tests for SQL Antipattern Scanner.

General syntax:

sql-antipattern-scanner [sql_file] [--query QUERY] [--format FORMAT] [--output OUTPUT] [--run-tests]

Note: If both sql_file and --query are provided, the tool will prioritize the --query option.

Examples

1. Scan SQL file and print report to console:

   sql-antipattern-scanner path/to/your/sql_file.sql
   

2. Scan SQL query and save report in HTML format:

   sql-antipattern-scanner --query "SELECT * FROM users WHERE id = 1" --format html --output report.html
   

3. Scan SQL file and save report in CSV format:

   sql-antipattern-scanner path/to/your/sql_file.sql --format csv --output report.csv
   

4. Run unit tests and scan SQL file, saving report in JSON format:

   sql-antipattern-scanner path/to/your/sql_file.sql --run-tests --output report.json
   

5. Run unit tests only:

   sql-antipattern-scanner --run-tests
   

Features

• Detects a wide range of SQL antipatterns
• Generates detailed reports with severity levels and suggestions for improvement
• Supports multiple output formats for easy integration into your workflow
• Fast and efficient scanning of large SQL files
• Includes comprehensive unit tests to ensure reliability

License

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

© 2024 Nicholas G. Piesco. All rights reserved.