djlogq 1.0.0

A reusable Django app for asynchronous, thread-safe logging with rich metadata, admin interface, and API support.

Django Async Logger

A reusable Django app that provides asynchronous logging functionality using a separate thread to avoid blocking the main application.

Features

• Asynchronous Logging: All log operations run in a separate thread
• Thread-Safe: Uses a queue system for thread-safe logging
• Rich Metadata: Captures module, function, line number, user ID, request ID, and extra data
• Admin Interface: Beautiful Django admin interface for viewing and managing logs
• API Endpoints: REST API for external logging
• Middleware: Automatic request logging with unique request IDs
• Decorators: Utility decorators for function logging and performance monitoring
• Context Managers: Easy-to-use context managers for operation logging
• Configurable: Customizable queue size, flush intervals, and cleanup policies

Installation

1. Add the app to your Django project:

INSTALLED_APPS = [
    # ...
    'logq',
]

2. Add the middleware to your settings:

MIDDLEWARE = [
    # ...
    'logq.middleware.AsyncLoggingMiddleware',
]

3. Run migrations:

python manage.py makemigrations logq
python manage.py migrate

4. (Optional) Configure logging settings:

ASYNC_LOGGING_CONFIG = {
    'MAX_QUEUE_SIZE': 1000,
    'FLUSH_INTERVAL': 1.0,  # seconds
    'AUTO_CLEANUP_DAYS': 30,
    'ENABLE_REQUEST_LOGGING': True,
    'IGNORE_PATHS': ['/admin/'],  # paths to ignore for request logging
}

Usage

Basic Logging

from logq.async_logger import get_async_logger

logger = get_async_logger()

# Different log levels
logger.debug("Debug message")
logger.info("Info message")
logger.warning("Warning message")
logger.error("Error message")
logger.critical("Critical message")

# With extra data
logger.info("User action", extra_data={'action': 'login', 'ip': '192.168.1.1'})

# Log exceptions
try:
    # some code that might fail
    pass
except Exception as e:
    logger.exception("An error occurred", exc_info=str(e))

Function Decorators

from logq.utils import log_function_call, log_performance

@log_function_call
def my_function():
    return "result"

@log_function_call(level='DEBUG')
def debug_function():
    return "debug result"

@log_performance(threshold_seconds=0.5)
def slow_function():
    time.sleep(1)
    return "slow result"

Context Managers

from logq.utils import LogContext

with LogContext("Processing data", level='INFO'):
    # do some work
    time.sleep(0.1)
    # automatically logs start and completion with timing

API Logging

import requests
import json

# Log via API
data = {
    'level': 'INFO',
    'message': 'External log message',
    'extra_data': {'source': 'external_service'}
}

response = requests.post(
    'http://your-domain/logq/api/log/',
    data=json.dumps(data),
    headers={'Content-Type': 'application/json'}
)

# Retrieve logs via API
response = requests.get('http://your-domain/logq/api/logs/?limit=10')
logs = response.json()['logs']

Admin Interface

Access the admin interface at /admin/ to view and manage logs. Features include:

• Filter by level, module, timestamp, user ID
• Search by message, module, function, request ID
• View extra data in formatted JSON
• Delete old logs
• Export functionality

Management Commands

Clean old logs:

# Delete logs older than 30 days
python manage.py clean_logs

# Delete logs older than 7 days
python manage.py clean_logs --days 7

# Delete only DEBUG and INFO logs older than 30 days
python manage.py clean_logs --level INFO

# Dry run to see what would be deleted
python manage.py clean_logs --dry-run

Configuration Options

Setting	Default	Description
MAX_QUEUE_SIZE	1000	Maximum number of log entries in the queue
FLUSH_INTERVAL	1.0	How often to flush logs to database (seconds)
AUTO_CLEANUP_DAYS	30	Days to keep logs before auto-cleanup
ENABLE_REQUEST_LOGGING	True	Whether to log all HTTP requests

Model Fields

The LogEntry model includes:

• timestamp: When the log was created
• level: Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
• message: The log message
• module: Python module where the log originated
• function: Function name where the log originated
• line_number: Line number where the log originated
• user_id: ID of the user (if authenticated)
• request_id: Unique request identifier
• extra_data: Additional JSON data
• created_at: When the entry was saved to database

Performance Considerations

• The logger runs in a separate thread and won't block your main application
• Log entries are batched and written to the database periodically
• If the queue is full, new entries are dropped (with console fallback)
• Consider setting up database indexes for better query performance
• Use the cleanup command regularly to prevent database bloat

Thread Safety

The logger is completely thread-safe:

• Uses a thread-safe queue for communication
• Database operations are wrapped in transactions
• Multiple threads can safely call the logger simultaneously

Customization

You can extend the logger by:

1. Creating custom log levels
2. Adding new fields to the LogEntry model
3. Customizing the admin interface
4. Adding new API endpoints
5. Creating custom middleware

Troubleshooting

Logs not appearing

• Check that the async logger thread is running
• Verify database migrations are applied
• Check for any database connection issues

Performance issues

• Reduce FLUSH_INTERVAL for more frequent writes
• Increase MAX_QUEUE_SIZE for higher throughput
• Add database indexes for frequently queried fields

Memory usage

• Reduce MAX_QUEUE_SIZE if memory is a concern
• Run cleanup commands more frequently
• Monitor database size and clean old logs

License

This project is open source and available under the MIT License.