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risclog.logging

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risclog.logging is a comprehensive structured logging solution for Python applications. It combines Python's built-in logging module with structlog to provide powerful, flexible logging with support for both synchronous and asynchronous code.

Key Features:

  • `getLogger` – Modern factory function for creating logger instances (legacy get_logger deprecated)
  • `log_decorator` – Automatic function logging with execution time, arguments, return values, and exception handling
  • Structured logging – Rich, contextual log entries with automatic JSON serialization
  • Async/Sync support – Unified API for both synchronous and asynchronous code
  • File & Console output – Flexible handler configuration
  • Email notifications – Optional exception alerts via SMTP (with email configuration)
  • Enhanced tracebacks – Beautiful, colored exception display via Rich

Features

  • Structured logging: Combines standard logging with structlog for rich, contextual logs
  • Synchronous and asynchronous logging: Unified API works seamlessly in both environments
  • Automatic function logging: Use @log_decorator to automatically capture function execution details:
    • Function arguments and their types
    • Return values
    • Execution duration (in milliseconds)
    • Exception stack traces and details
  • Email notifications: Optionally send SMTP email alerts when decorated functions raise exceptions
  • Rich tracebacks: Enhanced exception display with colors and source code context
  • Flexible configuration:
    • Set log levels per logger
    • Add file handlers with custom formatters
    • Configure via environment variables (LOG_LEVEL, LOG_EXCLUDED_LOGGERS, etc.)
    • Auto-filters verbose library logs (uvicorn, watchfiles, etc.)
  • Production-ready: Type hints, comprehensive tests, and battle-tested in production applications

Installation

Install via pip:

pip install risclog.logging

Configuration and Usage

Quick Start

from risclog.logging import getLogger

logger = getLogger(__name__)
logger.set_level("DEBUG")

# Log messages
logger.info("Application started")
logger.warning("Something might be wrong", user_id=42)
logger.error("An error occurred", error_code=500)

Creating a Logger

Use the getLogger function to obtain a logger instance:

from risclog.logging import getLogger

logger = getLogger(__name__)

# Set log level (accepts string or logging constant)
logger.set_level("DEBUG")  # or logging.DEBUG

# Add file handler
logger.add_file_handler('app.log', level=logging.DEBUG)

Logging Messages

Synchronous logging:

logger.debug("Debug message")
logger.info("Info message", user_id=42, action="login")
logger.warning("Warning message", retry_count=3)
logger.error("Error message", error_code=500)
logger.critical("Critical failure", severity="high")

Asynchronous logging:

await logger.debug("Async debug message")
await logger.info("Async info message", user_id=42)
# All log methods support both sync and async calls

Automatic Function Logging with Decorators

The @log_decorator automatically logs function execution with comprehensive details:

Synchronous functions:

from risclog.logging import log_decorator

@log_decorator
def calculate_sum(a: int, b: int) -> int:
    """Calculate sum of two numbers."""
    result = a + b
    return result

result = calculate_sum(10, 20)
# Logs: [Decorator start: calculate_sum]
# Logs: [Decorator success: calculate_sum] duration=0.00123sec result=30

Asynchronous functions:

@log_decorator
async def fetch_data(user_id: int) -> dict:
    """Fetch user data from API."""
    await asyncio.sleep(1)  # Simulate API call
    return {"id": user_id, "name": "Alice"}

data = await fetch_data(123)
# Logs: [Decorator start: fetch_data] args=('user_id:int=123',)
# Logs: [Decorator success: fetch_data] duration=1.00234sec result={'id': 123, 'name': 'Alice'}

Error handling with decorator:

@log_decorator
def risky_operation(value: int) -> float:
    """Operation that might fail."""
    return 100 / value  # Will raise ZeroDivisionError if value=0

try:
    risky_operation(0)
except ZeroDivisionError:
    pass
# Logs: [Decorator error in risky_operation] error='division by zero'

Using the Decorator in Classes

Use @log_decorator on class methods:

from risclog.logging import getLogger, log_decorator

class UserService:
    def __init__(self):
        self.logger = getLogger(__name__)

    @log_decorator
    def get_user(self, user_id: int) -> dict:
        """Retrieve user by ID."""
        self.logger.info("Fetching user", user_id=user_id)
        # Simulate database lookup
        return {"id": user_id, "name": "John Doe", "email": "john@example.com"}

    @log_decorator
    async def update_user_async(self, user_id: int, name: str) -> dict:
        """Update user asynchronously."""
        await self.logger.info("Updating user", user_id=user_id, name=name)
        await asyncio.sleep(0.5)  # Simulate API call
        return {"id": user_id, "name": name, "updated": True}

# Usage
service = UserService()
user = service.get_user(42)
# Logs: [Decorator start: get_user] args=('self:UserService=...', 'user_id:int=42')
# Logs: Fetching user (manual log)
# Logs: [Decorator success: get_user] result={'id': 42, 'name': 'John Doe', 'email': 'john@example.com'}

Email Notification on Exceptions

To send email alerts when a decorated function raises an exception, use send_email=True.

Required environment variables:

export logging_email_smtp_user="your-email@gmail.com"
export logging_email_smtp_password="your-app-password"
export logging_email_smtp_server="smtp.gmail.com"
export logging_email_to="admin@example.com"

Usage:

@log_decorator(send_email=True)
def critical_operation():
    """This function will send email on exception."""
    # Your code
    ...

When an exception occurs, the logger will automatically send an email notification with the full error traceback.

Rich Traceback Integration

The package uses Rich for beautiful exception display. Rich tracebacks include syntax highlighting, source code context, and improved readability:

from rich import traceback
traceback.install()  # Install the Rich traceback handler

# Now all exceptions will be displayed with Rich formatting
1 / 0  # Beautiful traceback with colors and context

Full Example

Here's a complete example demonstrating logging with both sync and async functions:

import asyncio
import logging
from risclog.logging import getLogger, log_decorator

# Configure logger
logger = getLogger(__name__)
logger.set_level(logging.DEBUG)
logger.add_file_handler('app.log', level=logging.DEBUG)

# Simple sync function
@log_decorator
def calculate(a: int, b: int) -> int:
    logger.debug("Performing calculation", a=a, b=b)
    result = a + b
    return result

# Simple async function
@log_decorator
async def fetch_user(user_id: int) -> dict:
    await logger.info("Fetching user from API", user_id=user_id)
    await asyncio.sleep(1)  # Simulate API latency
    return {"id": user_id, "name": "John"}

# Class-based logging
class DataProcessor:
    def __init__(self):
        self.logger = getLogger(__name__)

    @log_decorator
    def process(self, data: list) -> int:
        self.logger.info("Processing data", count=len(data))
        total = sum(data)
        self.logger.info("Processing complete", total=total)
        return total

    @log_decorator
    async def process_async(self, data: list) -> int:
        await self.logger.info("Async processing", count=len(data))
        await asyncio.sleep(0.5)
        total = sum(data)
        return total

# Main execution
async def main():
    # Sync calls
    logger.info("Application started")
    result = calculate(5, 10)
    logger.info("Calculation result", result=result)

    # Async calls
    user = await fetch_user(123)
    logger.info("User fetched", user=user)

    # Class-based calls
    processor = DataProcessor()
    total = processor.process([1, 2, 3, 4, 5])
    logger.info("Processing complete", total=total)

    total_async = await processor.process_async([10, 20, 30])
    logger.info("Async processing complete", total=total_async)

if __name__ == "__main__":
    asyncio.run(main())

Example Output

When running the example above, you'll see output similar to this:

2026-01-16 12:16:13 [info] Application started
2026-01-16 12:16:13 [info] [4301639536 Decorator start: calculate] _function=calculate _script=example.py args=('a:int=5', 'b:int=10') kwargs={}
2026-01-16 12:16:13 [debug] Performing calculation a=5 b=10
2026-01-16 12:16:13 [info] [4301639536 Decorator success: calculate] _function=calculate result=15 duration=0.00123sec
2026-01-16 12:16:13 [info] Calculation result result=15
2026-01-16 12:16:13 [info] [4311754480 Decorator start: fetch_user] _function=fetch_user args=('user_id:int=123',)
2026-01-16 12:16:13 [info] Fetching user from API user_id=123
2026-01-16 12:16:14 [info] [4311754480 Decorator success: fetch_user] _function=fetch_user result={'id': 123, 'name': 'John'} duration=1.00234sec
2026-01-16 12:16:14 [info] User fetched user={'id': 123, 'name': 'John'}
2026-01-16 12:16:14 [info] [4312228144 Decorator start: process] _function=process args=('self:DataProcessor=...', 'data:list=[1, 2, 3, 4, 5]')
2026-01-16 12:16:14 [info] Processing data count=5
2026-01-16 12:16:14 [info] Processing complete total=15
2026-01-16 12:16:14 [info] [4312228144 Decorator success: process] _function=process result=15 duration=0.00098sec
2026-01-16 12:16:14 [info] Processing complete total=15

Running Tests

To run the tests for this package, execute:

pytest

Or with verbose output:

pytest -v

Development

To set up a development environment, clone the repository and install in editable mode:

git clone https://github.com/risclog-solution/risclog.logging
cd risclog.logging
pip install -e .[dev]
pytest

Troubleshooting

Logs not showing in console:

If you see logs written to files but not in the console, ensure you've added a console handler:

import logging

# Add console output
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s [%(levelname)s] %(message)s')
console_handler.setFormatter(formatter)
logging.getLogger().addHandler(console_handler)

Email notifications not working:

Verify all required environment variables are set:

echo $logging_email_smtp_user
echo $logging_email_smtp_password
echo $logging_email_smtp_server
echo $logging_email_to

Performance considerations:

  • The @log_decorator adds minimal overhead (typically < 1ms)
  • File I/O is buffered by Python's logging module
  • For high-throughput applications, consider using async logging

Migration from Old API

If you're using the old get_logger function (deprecated), migrate to the new getLogger:

Old code:

from risclog.logging import get_logger  # Deprecated
logger = get_logger(__name__)

New code:

from risclog.logging import getLogger  # Current
logger = getLogger(__name__)

The functionality is the same, but getLogger is the recommended approach.

Credits

This package was created using Cookiecutter and the risclog-solution/risclog-cookiecutter-pypackage project template.

License

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

Contributing

Contributions are welcome! Please see CONTRIBUTING.rst for guidelines.

Support

For issues, questions, or feature requests, please open an issue on GitHub.

Changelog

See CHANGES.rst for version history and updates.

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A logger based on structlog

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