mz-logging-azure-tablestorage 1.0.0

A professional logging module using Azure Table Storage

Masterzdran Azure Table Storage Logging

mz-logging-azure-tablestorage

A professional Python logging module that uses Azure Table Storage for structured logging with support for trace IDs, metadata, and different log levels.

Features

• Asynchronous logging using Azure Table Storage
• Structured logging with comprehensive metadata
• Trace ID support for request tracking
• Automatic caller location tracking
• Configurable log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)
• Dependency injection for flexible storage backends
• Full type hinting support
• Comprehensive test coverage

Installation

Install the package using pip:

pip install mz-logging-azure-tablestorage==1.0.0

For development installation:

pip install -e .[dev]

Quick Start

from mz_logging_azure_tablestorage import AzureLogger
from mz_logging_azure_tablestorage.storage import AzureTableStorage

# Initialize the storage
storage = AzureTableStorage(
    connection_string="your_azure_connection_string",
    table_name="logs"
)

# Create a logger instance
logger = AzureLogger(
    storage=storage,
    logger_name="my_service"
)

# Log messages with different levels
await logger.info("Operation completed", 
    metadata={"user_id": "123", "action": "login"})
await logger.error("Operation failed",
    trace_id="custom-trace",
    metadata={"error_code": "AUTH001"})

Configuration

The logger can be configured with the following parameters:

logger = AzureLogger(
    storage=storage,
    logger_name="my_service",
    default_trace_id="custom-default-trace"  # Optional
)

Log Levels

• DEBUG: Detailed information for debugging
• INFO: General information about program execution
• WARNING: Indicates a potential problem
• ERROR: A more serious problem
• CRITICAL: A critical problem that may prevent program execution

Development

Setup Development Environment

1. Clone the repository:

git clone [e-mail]:masterzdran/mz-logging-azure-tablestorage.git
cd mz-logging-azure-tablestorage

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install development dependencies:

pip install -e .[dev]

Running Tests

Run the test suite:

pytest

Run with coverage:

pytest --cov=src --cov-report=html

Code Quality

Run code quality checks:

black .              # Code formatting
isort .             # Import sorting
pylint src/ tests/  # Linting
mypy src/           # Type checking

Building the Package

1. Install build dependencies:

pip install build wheel

2. Build the package:

python -m build

Publishing

1. Install publishing dependencies:

pip install twine

2. Upload to PyPI:

twine upload dist/*

License

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

Contributing

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