Instrumenting a Python App

Introduction

Instrumenting a Python application is essential for achieving observability. This involves adding logging, metrics, and tracing capabilities to your application to monitor its performance and health.

Key Concepts

Setup

Step-by-Step Process

1. Install necessary libraries:

   pip install logging prometheus_client opentracing

2. Configure logging:

   import logging
   
   logging.basicConfig(level=logging.INFO)
   logger = logging.getLogger(__name__)
   
   def main():
       logger.info("Application started")
       
   if __name__ == "__main__":
       main()

3. Add metrics:

   from prometheus_client import start_http_server, Summary
   
   REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
   
   @REQUEST_TIME.time()
   def process_request():
       # Simulate a request processing
       time.sleep(1)
   
   if __name__ == "__main__":
       start_http_server(8000)
       while True:
           process_request()

4. Implement tracing:

   from opentracing import tracer
   
   def traced_function():
       with tracer.start_span('example_span') as span:
           # Perform some work
           logger.info("Inside traced function")
   
   traced_function()

Best Practices

• Use structured logging for better analysis.
• Ensure sensitive data is never logged.
• Regularly review and refine your metrics.
• Monitor the overhead introduced by instrumentation.
• Use a centralized logging system for easier management.

FAQ