# mloda Plugins

## Overview
The plugin system is a fundamental part of the mloda framework, providing extensibility and flexibility. Plugins allow for customization of feature definitions, computation technologies, and metadata extraction processes, enabling the framework to adapt to various use cases and environments.

## Plugin Types
mloda supports three main types of plugins, each serving a specific purpose in the data and feature engineering workflow:

### Feature Groups
Feature Groups define feature dependencies and calculations. They specify how features are derived from source data or other features, allowing for complex feature engineering with automatic dependency resolution.

Feature Groups handle tasks such as:
- Defining feature transformations
- Managing feature dependencies
- Specifying data sources and sinks
- Implementing feature-specific logic

### Compute Frameworks
Compute Frameworks define the technology stack used to execute feature transformations and computations. They abstract away the specifics of different computation technologies, allowing feature definitions to remain independent of the underlying implementation.

Compute Frameworks support:
- Different processing engines (e.g., Pandas, PyArrow)
- Various storage formats and systems
- Optimization for different scales of data
- Seamless transitions between development and production environments

### Function Extenders
Function Extenders automate metadata extraction processes, enhancing data governance, compliance, and traceability. They provide additional functionality to the core framework without modifying its fundamental behavior.

Function Extenders enable:
- Automated documentation generation
- Usage tracking and analytics
- Data lineage and provenance tracking
- Custom extensions for specific use cases

## Plugin Development
When developing new plugins for mloda, consider the following steps:

1. **Identify the appropriate plugin type** based on your requirements
2. **Implement the required interfaces** defined in the abstract_plugins module
3. **Test your plugin** thoroughly to ensure compatibility with the framework
4. **Document your plugin** to facilitate adoption by other users

## Best Practices
To create effective and maintainable plugins:

- Follow the single responsibility principle
- Ensure compatibility with different compute frameworks
- Provide comprehensive documentation
- Include tests for various scenarios
- Consider performance implications
- Make plugins configurable for different use cases

## Directory Structure
```
mloda_plugins/
├── compute_framework/   # Compute framework implementations
├── feature_group/       # Feature group implementations
└── function_extender/   # Function extender implementations
