lightbrow 0.1.0

A Fast and S3 or other cloud storage compatible browser application

LightBrow

A Fast and Fully Controlled Data Visualization Library built on top of Plotly, Dash, and Polars.

Features

• Lightweight and fast s3 browser-based or jupyter notebook for s3 or any compatible object storage.
• Built on top of dash and mantine components for a modern UI.
• Supports deep and fast search through large directories.

Installation

pip install lightbrow

Quick Start

#This script demonstrates how to set up and run the Lightbrow application using a MinIO S3-compatible storage service.
import os
from lightbrow.connectors import S3Connector
from lightbrow.browsers import S3Browser

# Example usage
if __name__ == "__main__":
    # IMPORTANT: Configure your MinIO credentials and endpoint here or via environment variables
    # For MinIO, access_key and secret_key are typically required.
    # Ensure AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are set in your environment,
    # OR uncomment and set them in auth_config.
    # If you are using from aws s3 or any other S3-compatible service, you can set the credentials accordingly.
    minio_auth_config = {
        'access_key': os.getenv('MINIO_ACCESS_KEY', 'myaccesskey'),  # Replace or set env var
        'secret_key': os.getenv('MINIO_SECRET_KEY', 'mysecretkey'),  # Replace or set env var
        'endpoint_url': os.getenv('MINIO_ENDPOINT_URL', 'http://127.0.0.1:9000'), # Example: 'http://minio.example.com:9000'
        # 'region': 'us-east-1' # Boto3 requires a region, but MinIO is region-agnostic. 'us-east-1' is a common default.
    }

    # Check if placeholder keys are still present
    if 'YOUR_MINIO_ACCESS_KEY' in minio_auth_config['access_key'] or \
       'YOUR_MINIO_SECRET_KEY' in minio_auth_config['secret_key']:
        print("WARNING: MinIO access_key or secret_key placeholders are being used.")
        print("Please configure your actual MinIO credentials in the script or via environment variables (MINIO_ACCESS_KEY, MINIO_SECRET_KEY, MINIO_ENDPOINT_URL).")
        # You might want to exit or use a default non-functional connector here
        # For demonstration, we'll proceed, but it likely won't connect.
    
    # Create S3 connector instance for MinIO
    minio_s3_connector = S3Connector(auth_config=minio_auth_config, default_prefix="s3a://") # default_prefix is "s3://"
    
    # You can define multiple buckets, they will all use the same connector instance here.
    # If different buckets need different credentials/endpoints, create separate S3Connector instances.
    bucket_connector_list = [
        ('sandbox', minio_s3_connector), # Replace 'sandboxbucket' with your actual MinIO bucket name
        ('experiment', minio_s3_connector), # Add more buckets as needed
        # ('publicbucket', S3Connector()) # Example for a public AWS S3 bucket (no auth_config)
    ]
    
    # Create browser application instance
    # max_depth for background indexing can be adjusted. None means index indefinitely deep.
    s3_browser_app = S3Browser(bucket_connector_pairs=bucket_connector_list, max_depth=None)
    
    # Run the application
    s3_browser_app.run(debug=True)

Visualization Types

Dashboard Layout

• bucket chooser: Allows users to select which bucket to view.
• browser: Displays the contents of the selected bucket.
• address bar: Shows the current path in the bucket.
• copy button: Copies the current path to the clipboard.
• search bar: Enables searching through the contents of the bucket.
• file viewer: Displays the entire information of the selected file - pop up.

Notes

• LightBrow is designed to be a read-only browser for S3-compatible object storage.
• It does not support write operations like uploading, deleting, renaming, or downloading files.
• These operations may be implemented in the future. But currently it still under consideration because of the privacy and security implications.

Write your own connector

You can write your own connector by inheriting from the BaseConnector class and implementing the required methods. This allows you to connect to any object storage service that is compatible with the S3 API.

class BaseConnector(ABC):
    """Base class for storage connectors."""
    def __init__(self, auth_config: Optional[Dict[str, str]] = None, default_prefix: str = "s3://", debug: bool = False):
        self.auth_config = auth_config or {}
        self._index_cache: Dict[str, List[FileItem]] = {} # Cache for list_items results per path
        self._indexing_status: Dict[str, bool] = {}
        self._access_errors: Dict[str, str] = {}  # path -> error message
        self._executor = ThreadPoolExecutor(max_workers=os.cpu_count() or 4) # Use more workers
        
        # New path-based index: bucket -> {item_full_path: FileItem}
        self._path_index: Dict[str, Dict[str, FileItem]] = defaultdict(dict)
        self.default_prefix = default_prefix
        self.debug = debug
    @abstractmethod
    async def list_items(self, path: str = "") -> List[FileItem]:
        """List items in the given path."""
        pass
    
    @abstractmethod
    async def get_item_info(self, path: str) -> FileItem:
        """Get detailed information about a specific item."""
        pass
    
    @abstractmethod
    async def check_access(self, path: str) -> AccessLevel:
        """Check access level for a given path."""
        pass
    @abstractmethod
    def start_background_indexing(self, bucket: str, max_depth: Optional[int] = None):
        """Start background indexing for a bucket."""
        raise NotImplementedError("This method should be implemented in the subclass.")

Check out the base connector for more details on how to implement your own connector.

Development

Setup

1. Clone the repository

git clone https://github.com/FlowFoundation/lightbrow
cd blazingplot

2. Install development dependencies

pip install -e ".[dev]"

Testing

pytest

Building

python -m build

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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