FalkorDB Graph Exporter

A command-line tool to export FalkorDB graph data to CSV files. This tool connects to a FalkorDB instance and exports both nodes and edges to separate CSV files for analysis, backup, or data migration purposes.

Features

• Export graph nodes to CSV with properties and labels
• Export graph edges/relationships to CSV with properties and types
• Connect to local or remote FalkorDB instances
• Simple command-line interface
• Handles graph properties and metadata

Installation

Prerequisites

• Python 3.8+
• pipenv (recommended) or pip

Method 1: Using pipenv (Recommended)

If you don't have pipenv installed:

pip install pipenv

Install and run:

# Clone the repository
git clone https://github.com/FalkorDB/graph-exporter.git
cd graph-exporter

# Install dependencies using pipenv
pipenv install

# Activate the virtual environment
pipenv shell

Usage

Basic Usage

Export a graph from a local FalkorDB instance:

python main.py <graph_name>

Advanced Usage

Export from a remote FalkorDB instance:

python main.py <graph_name> --host <hostname> --port <port>

Command Line Options

usage: main.py [-h] [--host HOST] [--port PORT] graph_name

Export FalkorDB graph nodes and edges to CSV.

positional arguments:
  graph_name   Name of the graph to export

options:
  -h, --help   show this help message and exit
  --host HOST  FalkorDB host (default: localhost)
  --port PORT  FalkorDB port (default: 6379)

Examples

Export a graph named "social" from local FalkorDB:

python main.py social

Export from a remote FalkorDB instance:

python main.py social --host redis.example.com --port 6379

Output

The tool generates two CSV files:

nodes.csv

Contains all nodes with the following columns:

• id: Node ID
• label: Node label/type
• Additional columns for each node property

edges.csv

Contains all relationships with the following columns:

• id: Edge ID
• type: Relationship type
• from_id: Source node ID
• to_id: Target node ID
• Additional columns for each edge property

Running FalkorDB

Docker

docker run --rm -p 6379:6379 falkordb/falkordb

FalkorDB Cloud

Use FalkorDB Cloud for a managed FalkorDB instance.

Example Workflow

1. Start FalkorDB:

   docker run --rm -p 6379:6379 falkordb/falkordb

2. Create some graph data using FalkorDB client or FalkorDB Browser

3. Export the graph:

   # Using pipenv
   pipenv shell
   python main.py my_graph

4. Find your exported data in nodes.csv and edges.csv

Development

Using pipenv for Development

# Install development dependencies (if any)
pipenv install --dev

# Add new dependencies
pipenv install <package_name>

Testing

The project includes comprehensive tests that verify the export functionality with real FalkorDB instances.

Running Tests Locally

1. Start a FalkorDB instance:

   docker run --rm -p 6379:6379 falkordb/falkordb:latest

2. Install test dependencies:

   pipenv install --dev

3. Run the tests:

   pytest test_main.py -v

4. Run tests with coverage:

   pytest --cov=main --cov-report=term-missing test_main.py

CI/CD

The project uses GitHub Actions for continuous integration:

• Tests are run against multiple Python versions (3.8-3.12)
• FalkorDB is started as a service container
• Coverage reports are uploaded to Codecov
• All tests must pass before merging PRs

Dependencies

• falkordb: Python client for FalkorDB
• pandas: Data manipulation and CSV export

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Support

• 📖 Documentation
• 💬 Community Forum
• 🐛 Report Issues
• 💬 Discord Community