helixml / demo-recipes

Logging and Monitoring

This section outlines the logging and monitoring mechanisms used within the demo-recipes repository.

Logging

The project utilizes the logging module, a standard Python library for generating log messages. This module provides a flexible framework for configuring and routing log messages to various destinations.

  • Configuration: Log configurations are typically handled through a configuration file (e.g., logging.conf) or programmatically within the application’s code.
  • Levels: Log messages are categorized using levels, with the most common ones being DEBUG, INFO, WARNING, ERROR, and CRITICAL. Each level represents a specific severity level for the logged message.
  • Handlers: Log handlers define the destinations for log messages, such as files, consoles, or network sockets.

Example Usage:

import logging
          
          # Configure logging
          logging.basicConfig(filename='app.log', level=logging.DEBUG)
          
          # Log messages at different levels
          logging.debug('This is a debug message.')
          logging.info('Informational message.')
          logging.warning('A warning message.')
          logging.error('An error occurred.')
          logging.critical('Critical error.')

Monitoring

For monitoring, the project leverages Prometheus, an open-source monitoring and alerting system.

  • Metrics: Prometheus collects metrics data from applications and services. These metrics are usually numerical values that describe the state or performance of the monitored system.
  • Exporters: Application code uses exporters to expose metrics to Prometheus. The prometheus_client library is a popular choice for Python applications.
  • Alerting: Prometheus can be configured to trigger alerts when specific metric conditions are met. These alerts can be used to notify teams about potential issues.

Example Usage:

from prometheus_client import Counter, Gauge, start_http_server
          
          # Create a counter metric
          requests_total = Counter('requests_total', 'Total number of requests')
          
          # Create a gauge metric
          active_users = Gauge('active_users', 'Number of active users')
          
          # Increment the counter
          requests_total.inc()
          
          # Set the gauge value
          active_users.set(10)
          
          # Start the Prometheus HTTP server
          start_http_server(8000)

Source: