prometheus/client_python is the official Python client library for Prometheus, a popular open-source monitoring and alerting toolkit. It provides a way to instrument Python applications with Prometheus metrics, allowing developers to collect and expose application-specific metrics for monitoring and analysis.

Pros

• Easy integration with Python applications
• Supports various metric types (Counter, Gauge, Histogram, Summary)
• Includes built-in exporters for common Python frameworks (e.g., Flask, Django)
• Actively maintained and part of the official Prometheus ecosystem

Cons

• Limited documentation for advanced use cases
• Some users report performance issues with high cardinality metrics
• Learning curve for those new to Prometheus concepts
• Lack of built-in support for some specific Python frameworks

Code Examples

1. Creating and incrementing a Counter:

from prometheus_client import Counter

requests_total = Counter('requests_total', 'Total number of requests')
requests_total.inc()  # Increment the counter

2. Using a Gauge to track a variable value:

from prometheus_client import Gauge

cpu_usage = Gauge('cpu_usage_percent', 'CPU usage percentage')
cpu_usage.set(42.5)  # Set the current CPU usage

3. Creating a Histogram to measure request duration:

from prometheus_client import Histogram

request_duration = Histogram('request_duration_seconds', 'Request duration in seconds')

with request_duration.time():
    # Code to measure goes here
    pass

4. Exposing metrics via HTTP:

from prometheus_client import start_http_server

if __name__ == '__main__':
    start_http_server(8000)  # Start metrics server on port 8000
    # Your application code goes here

Getting Started

To get started with prometheus/client_python:

1. Install the library:

   pip install prometheus_client
   

2. Import and use the desired metric types in your code:

   from prometheus_client import Counter, Gauge, Histogram, start_http_server
   
   # Create and use metrics
   requests_total = Counter('requests_total', 'Total number of requests')
   cpu_usage = Gauge('cpu_usage_percent', 'CPU usage percentage')
   request_duration = Histogram('request_duration_seconds', 'Request duration in seconds')
   
   # Start the metrics server
   start_http_server(8000)
   
   # Your application code goes here
   

3. Run your application and access metrics at http://localhost:8000/metrics