Uma biblioteca C++ para mapeamento e transformação de dados
Project description
Mapper Lib
A high-performance Python library for data mapping and transformation, built with C++ extensions for maximum efficiency.
📋 Description
Mapper Lib is a specialized library for transforming and mapping complex JSON data structures. It provides functionality for:
- Flattening: Convert nested dictionaries into flat structures
- Field Mapping: Map fields between different formats using JSON configurations
- High Performance: C++ implementation for fast data transformation operations
🚀 Features
- ⚡ High Performance: C++ implementation with Python bindings
- 🔧 Configurable: Mapping based on JSON configuration files
- 🐍 Python Native: Simple and intuitive API for Python
- 🎯 Flexible: Support for customizable separators and parent keys
- 🏗️ Cross-Platform: Support for Windows and Linux
📦 Installation
Prerequisites
- Python 3.7+
- C++ compiler with C++17 support
- pybind11
Installation via pip
pip install -r requirements.txt
pip install -e .
Manual installation
# Clone the repository
git clone https://github.com/compre-sua-peca/csp_mapper.git
cd csp_mapper
# Install dependencies
pip install -r requirements.txt
# Compile and install
python setup.py build_ext --inplace
python setup.py install
💻 Usage Examples
📝 How Configuration Works
The library reads JSON configuration files from the file system. The file must contain a dictionary where each key represents a "trigger" that defines how to map the fields.
Typical configuration file structure:
{
"trigger_1": {
"output_field_1": "input.field.path.1",
"output_field_2": "input.field.path.2"
},
"trigger_2": {
"another_field": "another.field.path"
}
}
Example 1: Basic Mapping
First, create the configuration file mapping_config.json:
{
"user_mapping": {
"nome": "user.personal.name",
"idade": "user.personal.age",
"endereco": "user.address.street",
"cidade": "user.address.city",
"pedido_id": "order.id",
"valor_total": "order.total"
}
}
Then, use Python:
from mapper import mapper
# Create mapper instance
mapper_instance = mapper(sep=".", parent_key="data")
# Nested input data
input_data = {
"user": {
"personal": {
"name": "João Silva",
"age": 30
},
"address": {
"street": "Rua das Flores",
"city": "São Paulo"
}
},
"order": {
"id": "12345",
"total": 99.99
}
}
# Mapping using configuration file
result = mapper_instance.map_dic(
input_dict=input_data,
config_path="mapping_config.json",
trigger="user_mapping"
)
print(result)
Example 2: Mapping with Configuration File
config_file.json:
{
"user_mapping": {
"nome_completo": "user.personal.name",
"idade_usuario": "user.personal.age",
"endereco_completo": "user.address.street",
"cidade_usuario": "user.address.city"
},
"order_mapping": {
"id_pedido": "order.id",
"valor": "order.total"
}
}
Python:
# Mapping using configuration file
result = mapper_instance.map_dic(
input_dict=input_data,
config_path="config_file.json",
trigger="user_mapping"
)
print(result)
Output:
{
"nome_completo": "João Silva",
"idade_usuario": 30,
"endereco_completo": "Rua das Flores",
"cidade_usuario": "São Paulo"
}
Example 3: Mapping without Trigger (Complete Configuration)
# Mapping using entire configuration (without specific trigger)
result = mapper_instance.map_dic(
input_dict=input_data,
config_path="config_file.json"
# trigger=None (default)
)
print(result)
Output:
{
"user_mapping": {
"nome_completo": "João Silva",
"idade_usuario": 30,
"endereco_completo": "Rua das Flores",
"cidade_usuario": "São Paulo"
},
"order_mapping": {
"id_pedido": "12345",
"valor": 99.99
}
}
Example 4: Different Separators
# Use dot as separator
mapper_dot = mapper(sep=".", parent_key="")
result_dot = mapper_dot.map_dic(input_data, "config.json", "user_mapping")
# Use underscore as separator
mapper_underscore = mapper(sep="_", parent_key="")
result_underscore = mapper_underscore.map_dic(input_data, "config.json", "user_mapping")
# Use slash as separator
mapper_bar = mapper(sep="/", parent_key="")
result_bar = mapper_bar.map_dic(input_data, "config.json", "user_mapping")
print("Result with dot:", result_dot)
print("Result with underscore:", result_underscore)
print("Result with slash:", result_bar)
Example 5: Mapping with Parent Key
# Mapper with parent key "api_data"
mapper_with_parent = mapper(sep=".", parent_key="api_data")
result_with_parent = mapper_with_parent.map_dic(input_data, "config.json", "user_mapping")
print("Result with parent key 'api_data':")
print(result_with_parent)
🔧 API Reference
Class mapper
Constructor
mapper(sep: str = "|", parent_key: str = "")
Parameters:
sep: Separator to concatenate nested keys (default: "|")parent_key: Parent key to prefix all keys (default: "")
Methods
map_dic(input_dict: dict, config_path: str, trigger: str = None) -> dict
Main method that combines flattening and field mapping.
Parameters:
input_dict: Input dictionary to be mappedconfig_path: Path to the JSON configuration filetrigger: Specific key in the configuration to be used (optional)
Returns:
- Dictionary with mapped data according to the configuration
Note: This method internally uses the private methods _flatten_dict() and _mapping_fields() to perform processing, but the user only needs to know this public method.
🛠️ Development
Compilation
# Compile C++ extensions
python setup.py build_ext --inplace
# Install in development mode
pip install -e .
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
👥 Contributing
Contributions are welcome! Please feel free to:
- Fork the project
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
📞 Support
For support and questions:
- Author: Gustavo de Oliveira
- Email: devops15@compresuapeca.com.br
- Repository: https://github.com/compre-sua-peca/csp_mapper
🔄 Version History
- v1.0.4 - Current version with cross-platform support
- v1.0.1 - Performance improvements
- v1.0.0 - Initial release
⭐ If this project was useful to you, consider giving it a star in the repository!
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