Getting Started

This guide will help you set up the Qdrant Multi-Node Cluster project on your local environment. Follow these step-by-step instructions to get a fully functional distributed vector database running with monitoring capabilities.

Prerequisites

Before you begin, ensure you have the following installed:

• Docker (version 20.10.0 or higher) and Docker Compose (version 2.0.0 or higher)
• Python (version 3.8 or higher)
• pip (Python package manager)
• git (for cloning the repository)

Installation

1. Clone the Repository

git clone https://github.com/Mohitkr95/qdrant-multi-node-cluster.git
cd qdrant-multi-node-cluster

2. Set Up Python Environment

It’s recommended to use a virtual environment:

# Create a virtual environment
python -m venv env

# Activate the virtual environment
# On Windows:
env\Scripts\activate
# On macOS/Linux:
source env/bin/activate

# Install the package and dependencies
pip install -e .

This will install all required dependencies, including:

• qdrant-client==1.6.1
• numpy==1.24.3
• rich==13.6.0

Deploying the Qdrant Cluster

1. Using Docker Compose

The project includes a pre-configured Docker Compose file for easy deployment:

# Navigate to the Docker deployment directory
cd deployments/docker

# Launch the cluster
docker-compose up -d

This command will start:

• 3 Qdrant nodes in cluster mode
• Prometheus for metrics collection
• Grafana for visualization

2. Verify Deployment

Check if all containers are running:

docker-compose ps

You should see all services in the “Up” state.

Accessing the Services

After successful deployment, you can access:

• Qdrant API at http://localhost:6333
• Prometheus at http://localhost:9090
• Grafana at http://localhost:3000 (default login: admin/admin)

Running the Demo Application

The project includes a demonstration application that shows how to interact with the Qdrant cluster:

# Run the demo with default settings
python src/run_demo.py

# Run with custom settings
python src/run_demo.py --host localhost --port 6333 --points 2000

This will:

1. Create a collection with appropriate sharding configuration
2. Insert vector data with metadata
3. Demonstrate various search operations
4. Showcase scrolling pagination for large result sets
5. Display collection statistics

What’s Next?

Now that you have a running Qdrant cluster, explore the following resources:

• Read the Architecture Overview to understand the system design
• Learn about Configuration Options to customize your deployment
• Study the API Reference for detailed information about available functions
• Check the Performance Tuning guide for optimization tips

Troubleshooting

If you encounter any issues during setup:

1. Ensure all required ports (6333, 9090, 3000) are available
2. Check Docker container logs for errors: docker-compose logs qdrant_node1
3. Verify network connectivity between containers
4. Consult the Troubleshooting Guide for common issues

For additional help, open an issue on the GitHub repository.