evolvishub-minio-adapter 0.1.1

A professional MinIO adapter library with configuration management

Evolvishub MinIO Adapter

A professional Python library for interacting with MinIO object storage, featuring configuration management, health checks, and comprehensive logging.

Developed by Evolvis.ai - Advancing AI-driven solutions for modern enterprises.

Features

• 🚀 Dual Client Support: Both synchronous and asynchronous implementations
• ⚙️ Flexible Configuration: Support for YAML and INI configuration files
• 🔐 Security: Environment variable support for sensitive data
• 🏥 Health Checks: Built-in health monitoring
• 📝 Logging: Comprehensive logging configuration
• 🧪 Testing: Complete test suite
• 🐳 Docker Support: Ready-to-use Docker configuration
• ☸️ Kubernetes: Deployment configurations included
• 📦 Type Hints: Full type annotation support

Installation

# Basic installation
pip install evolvishub-minio-adapter

# Development installation with all tools
pip install evolvishub-minio-adapter[dev]

Quick Start

Basic Usage

from minio_adapter import MinioClient, MinioConfig

# Load configuration from YAML
config = MinioConfig.from_yaml("config/minio_config.yaml")

# Initialize client
client = MinioClient(config)

# Upload a file
client.upload_file(
    file_path="path/to/file.txt",
    object_name="file.txt"
)

# Download a file
client.download_file(
    object_name="file.txt",
    file_path="downloaded_file.txt"
)

Async Usage

import asyncio
from minio_adapter import AsyncMinioClient, MinioConfig

async def main():
    config = MinioConfig.from_yaml("config/minio_config.yaml")
    client = AsyncMinioClient(config)
    
    # Upload file asynchronously
    await client.upload_file(
        file_path="path/to/file.txt",
        object_name="file.txt"
    )
    
    # Download file asynchronously
    await client.download_file(
        object_name="file.txt",
        file_path="downloaded_file.txt"
    )

asyncio.run(main())

Configuration

YAML Configuration

Create a YAML configuration file (e.g., config/minio_config.yaml):

# MinIO Configuration
endpoint: "localhost:9000"  # MinIO server endpoint
access_key: "minioadmin"    # Access key
secret_key: "minioadmin"    # Secret key
secure: true               # Use HTTPS
region: "us-east-1"        # Region (optional)
bucket_name: "my-bucket"   # Default bucket name (optional)

INI Configuration

Alternatively, create an INI configuration file (e.g., config/minio_config.ini):

[minio]
endpoint = localhost:9000
access_key = minioadmin
secret_key = minioadmin
secure = true
region = us-east-1
bucket_name = my-bucket

# You can have multiple sections for different environments
[minio_production]
endpoint = minio.example.com:9000
access_key = production_access_key
secret_key = production_secret_key
secure = true
region = us-west-2
bucket_name = production-bucket

Environment Variables

You can override configuration using environment variables:

export MINIO_ENDPOINT="localhost:9000"
export MINIO_ACCESS_KEY="minioadmin"
export MINIO_SECRET_KEY="minioadmin"
export MINIO_SECURE="true"
export MINIO_REGION="us-east-1"
export MINIO_BUCKET_NAME="my-bucket"

Advanced Features

Health Checks

from minio_adapter import MinioClient, MinioConfig
from minio_adapter.health import HealthCheck

config = MinioConfig.from_yaml("config/minio_config.yaml")
client = MinioClient(config)

# Create health check
health_check = HealthCheck(client)

# Check MinIO health
health_status = health_check.check_health()
print(f"MinIO Health: {health_status}")

# Get detailed health information
health_info = health_check.get_health_info()
print(f"Health Info: {health_info}")

Logging Configuration

from minio_adapter import MinioClient, MinioConfig
from minio_adapter.logging_config import setup_logging

# Setup logging
setup_logging(
    log_level="INFO",
    log_file="minio.log",
    log_format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)

# Initialize client with logging
client = MinioClient(config)

Bucket Operations

# Create bucket
client.ensure_bucket_exists("my-bucket")

# List objects
objects = client.list_objects(prefix="", recursive=True)
for obj in objects:
    print(f"Object: {obj['object_name']}, Size: {obj['size']}")

# Get presigned URL
url = client.get_presigned_url(
    object_name="file.txt",
    expires=3600  # URL expires in 1 hour
)

Bucket Policies

# Get bucket policy
policy = client.get_bucket_policy()

# Set bucket policy
new_policy = {
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {"AWS": "*"},
            "Action": ["s3:GetObject"],
            "Resource": ["arn:aws:s3:::my-bucket/*"]
        }
    ]
}
client.set_bucket_policy(new_policy)

Docker Support

The package includes Docker support. Build and run using:

# Build the image
docker build -t evolvishub-minio-adapter .

# Run the container
docker run -p 9000:9000 evolvishub-minio-adapter

Kubernetes Deployment

Kubernetes deployment configurations are included in the k8s/ directory:

# Deploy to Kubernetes
kubectl apply -f k8s/namespace.yaml
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/secret.yaml
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

Development

Running Tests

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Run tests with coverage
pytest tests/ --cov=minio_adapter

Code Style

The project uses:

• Black for code formatting
• isort for import sorting
• mypy for type checking
• flake8 for linting

# Format code
black .

# Sort imports
isort .

# Type checking
mypy .

# Linting
flake8

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.

Author

• Alban Maxhuni, PhD
• Email: [e-mail]

🏢 About Evolvis.ai

Evolvis.ai is at the forefront of AI innovation, developing cutting-edge solutions that empower enterprises to harness the full potential of artificial intelligence. Our mission is to create robust, scalable, and production-ready AI tools that solve real-world business challenges.

Our Focus Areas:

• 🤖 AI-Powered Enterprise Solutions - Custom AI systems for business automation
• ☁️ Cloud-Native AI Infrastructure - Scalable AI deployment platforms
• 📊 Data Engineering & MLOps - End-to-end ML pipeline solutions
• 🔧 Open Source Tools - Contributing to the AI/ML community

Why Choose Evolvis.ai Solutions:

• ✅ Production-Ready - Battle-tested in enterprise environments
• ✅ Scalable Architecture - Designed for cloud-native deployment
• ✅ Expert Support - Backed by PhD-level AI expertise
• ✅ Open Source Commitment - Contributing to the community

🙏 Acknowledgments

• MinIO for the excellent object storage server
• Python MinIO SDK for the underlying client library
• The open-source community for inspiration and best practices
• The AI/ML community for driving innovation in data infrastructure

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Made with ❤️ by Evolvis.ai
Advancing AI-driven solutions for modern enterprises