SQuiRL

    SQuiRL is a Python library for interacting with any type of data storage structure, including filesystems, databases, caches, and various text file types. It provides a unified API to read, write, query, and manage data across diverse storage systems, abstracting away the complexities of different backends for seamless integration in applications.
    
    ## Features
    
    - **Unified Interface**: Interact with filesystems (local, remote), databases (SQL, NoSQL), caches (in-memory, distributed), and text files (CSV, JSON, XML, etc.) through a consistent Pythonic API.
    - **Extensible Backends**: Support for multiple storage types with pluggable adapters; easily add custom backends.
    - **Data Operations**: Perform CRUD (Create, Read, Update, Delete) operations, queries, and transformations across storage structures.
    - **Configuration-Driven**: Use YAML or Python configs to define storage connections and behaviors.
    - **Cross-Platform Compatibility**: Works on Windows, macOS, and Linux.
    - **Error Handling and Logging**: Built-in mechanisms for robust error management and detailed logging.
    - **Performance Optimizations**: Efficient handling for large datasets, batch operations, and caching layers.
    
    ## Installation
    
    You can install SQuiRL via pip:
    
    ```bash
    pip install squirrl
    ```
    
    Alternatively, clone the repository and install from source:
    
    ```bash
    git clone https://github.com/<USER_OR_ORG>/squirrl.git
    cd squirrl
    pip install -e .
    ```
    
    ### Requirements
    
    - Python 3.<MIN_VERSION> or higher
    - Dependencies: pyyaml, <DB_LIB>, <CACHE_LIB>, <FILE_LIB> (automatically installed via pip where applicable)
    
    ## Quick Start
    
    Import the module, configure a storage backend, and perform operations:
    
    ```python
    import squirrl
    
    # Load configuration for a storage type
    config = squirrl.load_config('path/to/filesystem.yaml')
    
    # Initialize the storage interface
    storage = squirrl.Storage(config)
    
    # Write data
    storage.write('key/path', 'Hello, World!')
    
    # Read data
    data = storage.read('key/path')
    print(data)  # Output: Hello, World!
    ```
    
    ## Usage
    
    ### Loading Configurations
    
    SQuiRL relies on YAML files to define storage backends. A sample YAML for a database might look like:
    
    ```yaml
    storage_type: database
    backend: <DB_LIB>
    connection:
      host: localhost
      port: 5432
      user: <USER>
      password: <PASS>
      db_name: mydb
    operations:
      query: sql_query_method
      insert: insert_method
    ```
    
    Use `squirrl.load_config(yaml_path)` to parse and validate the config.
    
    ### Interacting with Storage
    
    ```python
    # Initialize with config
    config = squirrl.load_config('cache.yaml')
    cache = squirrl.Storage(config)
    
    # Set and get cache values
    cache.set('user_data', {'id': 1, 'name': 'Alice'})
    value = cache.get('user_data')
    
    # Delete
    cache.delete('user_data')
    ```
    
    ### Handling Different Storage Types
    
    - **Filesystems**: Local directories, S3 buckets, etc.
    - **Databases**: SQLite, PostgreSQL, MongoDB, etc.
    - **Caches**: Redis, Memcached, in-memory dicts.
    - **Text Files**: Read/write CSV, JSON, YAML, XML with parsing support.
    
    ```python
    # Example with text file
    config = squirrl.load_config('json_file.yaml')
    file_storage = squirrl.Storage(config)
    data = file_storage.read('data.json')  # Returns parsed dict
    file_storage.write('data.json', {'key': 'value'})
    ```
    
    ## Examples
    
    ### Example 1: Database Querying
    
    ```python
    import squirrl
    
    config = squirrl.load_config('database.yaml')
    db = squirrl.Storage(config)
    db.connect()
    
    # Execute query
    results = db.query('SELECT * FROM users WHERE active = ?', (True,))
    
    # Insert data
    db.insert('INSERT INTO users (name, active) VALUES (?, ?)', ('Bob', True))
    
    db.close()
    ```
    
    ### Example 2: Multi-Storage Workflow
    
    ```python
    import squirrl
    
    configs = ['filesystem.yaml', 'cache.yaml']
    storages = [squirrl.Storage(squirrl.load_config(cfg)) for cfg in configs]
    
    # Read from filesystem, cache it
    data = storages[0].read('file.txt')
    storages[1].set('cached_file', data)
    
    # Retrieve from cache
    cached_data = storages[1].get('cached_file')
    ```
    
    ## Configuration Guide
    
    Each YAML config must include:
    
    - `storage_type`: String identifier (e.g., 'filesystem', 'database', 'cache', 'textfile')
    - `backend`: The underlying library or driver (e.g., '<FS_LIB>', '<DB_LIB>')
    - `connection`: Dictionary of connection parameters
    - `operations`: Mapping of SQuiRL methods to backend-specific calls
    
    For advanced customization, refer to the [docs/config-reference.md](docs/config-reference.md).
    
    ## Contributing
    
    Contributions are welcome! Please follow these steps:
    
    1. Fork the repository.
    2. Create a feature branch (`git checkout -b feature/<FEATURE_NAME>`).
    3. Commit your changes (`git commit -am 'Add some feature'`).
    4. Push to the branch (`git push origin feature/<FEATURE_NAME>`).
    5. Open a Pull Request.
    
    See [CONTRIBUTING.md](CONTRIBUTING.md) for more details.
    
    ## License
    
    This project is licensed under the <LICENSE_TYPE> License - see the [LICENSE](LICENSE) file for details.
    
    ## Acknowledgments
    
    - Built with inspiration from open-source data handling communities.
    - Thanks to contributors of underlying libraries like <DB_LIB>, <CACHE_LIB>, <FILE_LIB>.