brokolisql

BrokoliSQL

Universal Data-to-SQL Converter

BrokoliSQL is a Python-based command-line tool designed to facilitate the conversion of structured data files—such as CSV, Excel, JSON, and XML—into SQL INSERT statements. It solves common problems faced during data import, transformation, and database seeding by offering a flexible, extensible, and easy-to-use interface.

Key Features & Advantages

• Multi-format Support: Accepts CSV, XLSX, JSON, and XML as input.
• Database Dialect Flexibility: Generates SQL for PostgreSQL, MySQL, SQLite, and others using the --dialect option.
• Auto Table Creation: Optionally generates a CREATE TABLE statement based on input data.
• Batch Inserts: Improves performance by writing multiple rows per INSERT.
• Python-powered Transformations: Allows column transformations using Python expressions in a JSON configuration.
• Portable & Scriptable: Lightweight CLI tool, easily integrated into data pipelines or automation scripts.
• Robust Column Handling: Automatically normalizes column names and infers SQL data types.
• Open Source & Extensible: Cleanly organized codebase, open to community contributions.

How It Works

The execution process is straightforward:

• Reads the input file (CSV, Excel, JSON, XML).
• Normalizes column names (e.g., Name Id → Name_ID) for SQL compatibility.
• Infers column types (INTEGER, TEXT, etc.).
• Applies optional transformations defined via a Python-based JSON config.
• Generates SQL INSERT INTO statements (and optionally CREATE TABLE).
• Outputs the final SQL code to the specified file.

Installation

Install from PyPI:

pip install brokolisql

Make sure you have Python 3 installed:

python --version

If you're working with the source code, install dependencies with:

pip install -r requirements.txt

Basic Usage

brokolisql --input data.csv --output output.sql --table users

Additional Usage Examples

Use a specific SQL dialect:

brokolisql --input data.csv --output output.sql --table users --dialect mysql

Generate a CREATE TABLE statement:

brokolisql --input data.csv --output output.sql --table users --create-table

Use batch inserts for better performance:

brokolisql --input data.csv --output output.sql --table users --batch-size 100

Specify input format explicitly:

brokolisql --input data.xml --output output.sql --table users --format xml

Apply Python-based transformations:

brokolisql --input data.csv --output output.sql --table users --transform transforms.json

Example of transforms.json:

{
  "transformations": [
    {
      "type": "rename_columns",
      "mapping": {
        "FIRST_NAME": "GIVEN_NAME",
        "LAST_NAME": "SURNAME",
        "PHONE_1": "PRIMARY_PHONE",
        "PHONE_2": "SECONDARY_PHONE"
      }
    },
    {
      "type": "add_column",
      "name": "FULL_NAME",
      "expression": "FIRST_NAME + ' ' + LAST_NAME"
    },
    {
      "type": "add_column",
      "name": "SUBSCRIPTION_AGE_DAYS",
      "expression": "(pd.Timestamp('today') - pd.to_datetime(df['SUBSCRIPTION_DATE'])).dt.days"
    },
    {
      "type": "filter_rows",
      "condition": "COUNTRY in ['USA', 'Canada', 'Norway', 'UK', 'Germany']"
    },
    {
      "type": "apply_function",
      "column": "EMAIL",
      "function": "lower"
    },
    {
      "type": "apply_function",
      "column": "WEBSITE",
      "function": "lower"
    },
    {
      "type": "replace_values",
      "column": "COUNTRY",
      "mapping": {
        "USA": "United States",
        "UK": "United Kingdom"
      }
    },
    {
      "type": "drop_columns",
      "columns": ["INDEX"]
    },
    {
      "type": "add_column",
      "name": "EMAIL_DOMAIN",
      "expression": "EMAIL.str.split('@').str[1]"
    },
    {
      "type": "sort",
      "columns": ["COUNTRY", "CITY", "GIVEN_NAME"],
      "ascending": true
    }

  ]
}

This enables flexible pre-processing logic during data conversion, such as cleaning strings, formatting dates, or extracting information.

Using the Script Directly

If running directly from source:

PYTHONPATH=. python brokolisql/cli.py --input <path_to_input_file> --output <path_to_output_file> --table <table_name>

Example:

PYTHONPATH=. python brokolisql/cli.py --input data.csv --output commands.sql --table products

Project Structure

brokolisql/
├── assets
│   └── banner.txt
├── cli.py
├── dialects
│   ├── base.py
│   ├── generic.py
│   ├── __init__.py
│   ├── mysql.py
│   ├── oracle.py
│   ├── postgres.py
│   ├── sqlite.py
│   └── sqlserver.py
├── examples
│   ├── customers-10000.csv
│   ├── customers-100.csv
│   ├── output.sql
│   └── transforms.json
├── exceptions
│   ├── base.py
│   └── __init__.py
├── output
│   └── output_writer.py
├── services
│   ├── normalizer.py
│   ├── sql_generator.py
│   └── type_inference.py
├── setup.py
├── transformers
│   ├── __init__.py
│   └── transform_engine.py
└── utils
    └── file_loader.py

Contributing

We welcome contributions to improve BrokoliSQL. To contribute:

• Fork the repository.

• Clone it to your machine.

• Create a feature branch:

  git checkout -b my-feature
  

• Make your changes following PEP 8 standards.

• Add or update tests.

• Commit and push your changes:

  git add .
  git commit -m "feat(minor): add X support"
  git push origin my-feature
  

• Open a pull request describing your changes and why they are useful.

License

BrokoliSQL is licensed under the GNU GPL-3.0. See the LICENSE file for more information.

Summary

BrokoliSQL streamlines the process of converting structured data into clean, executable SQL. Whether you're migrating legacy data, seeding databases for development, or automating ingestion workflows, BrokoliSQL is a flexible and reliable tool that adapts to your needs. With support for Python-powered transformations and multiple database dialects, it brings power and simplicity to your data operations.

If you encounter any bugs, have suggestions, or would like to contribute, feel free to open an issue or submit a pull request.