scholar-spark-observability 0.5.1

A Python package for monitoring and observability in Apache Spark applications

ScholarSparkObservability

A Python package for monitoring and observability in Apache Spark applications.

Installation

You can install the package using pip:

pip install scholarSparkObservability

Or using Poetry:

poetry add scholarSparkObservability

Usage

Here's a simple example of how to use the package:

from scholarSparkObservability import ScholarSparkObservability

client = ScholarSparkObservability()
result = client.example_method()
print(result)

Features

Features

OpenTelemetry Integration

• Full OpenTelemetry support for distributed tracing and metrics collection
• Configurable exporters for different observability backends (e.g., Jaeger, Zipkin)
• Automatic context propagation across Spark jobs and stages

Comprehensive Monitoring

• Real-time metrics collection for Spark executors and tasks
• Custom span creation for detailed performance tracking
• Exception tracking and error reporting with detailed attributes
• Resource utilization metrics (CPU, memory, I/O)

Flexible Configuration

• Singleton pattern for consistent telemetry setup across your application
• Environment-aware configuration (production, staging, development)
• Customizable export intervals and batch processing
• Debug mode for detailed logging and troubleshooting

Enterprise-Ready

• Low-overhead implementation suitable for production workloads
• Batch span processing for efficient telemetry data export
• Support for multiple exporters and monitoring backends
• Robust error handling and logging capabilities

Easy Integration

• Simple API for creating spans and recording metrics
• Automatic service name and version tracking
• Built-in support for custom attributes and tags
• Seamless integration with existing Spark applications

Development

To contribute to this project:

1. Clone the repository:

git clone https://github.com/pouyaardehkhani/ScholarSparkObservability.git
cd ScholarSparkObservability

2. Install dependencies:

# Using poetry (recommended)
poetry install

# Using pip
pip install -r requirements.txt

3. Run tests:

# Using poetry
poetry run pytest

# Using pytest directly
pytest tests/

4. Set up pre-commit hooks:

pre-commit install

5. Create a new branch for your feature:

git checkout -b feature/your-feature-name

Development Guidelines

• Follow PEP 8 style guidelines
• Write tests for new features
• Update documentation as needed
• Add type hints to all new functions
• Ensure all tests pass before submitting PR

Building Documentation

# Generate documentation
poetry run sphinx-build -b html docs/source docs/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.

Authors

Pouya Ataei- Initial work