Creating a Python Prometheus Exporter with prometheus-client and Docker

Monitoring the performance and health of applications and services is crucial for ensuring reliability and scalability. Prometheus is a popular open-source monitoring and alerting toolkit that collects metrics from configured targets at specified intervals. In this tutorial, we will explore how to create a custom Prometheus exporter in Python using prometheus-client, and integrate it with Prometheus and Grafana using Docker.

Technologies Used

• Prometheus: A monitoring and alerting toolkit.
• Grafana: A platform for analytics and monitoring.
• Docker: Containerization platform for easy deployment.
• prometheus-client: Python library for creating Prometheus exporters.

Architecture

The architecture consists of the following components:

• Python Prometheus Exporter: A custom exporter using prometheus-client to expose metrics.
• Prometheus Server: Collects and stores time series data from exporters.
• Grafana: Visualizes and analyzes metrics collected by Prometheus.

Implementation

Step 1: Setting Up the Python Prometheus Exporter

1.1 Install prometheus-client

First, install the prometheus-client library using pip:

pip install prometheus-client

1.2 Create a Python script to expose metrics

Create a Python script prometheus_exporter.py:

from prometheus_client import start_http_server, Gauge, Summary, Counter
import random
import time

#Create a metric to track time spent and requests made.
#Explain different types of metrics
REQUEST_TIME = Gauge('request_processing_seconds', 'Time spent processing request')
RESPONSE_TIME=Summary('response_processing_seconds', 'Time spent processing response')
REQUEST_COUNTER=Counter('request_counter', 'Number of requests processed')

#Decorate function with metric.
@RESPONSE_TIME.time()
def process_request(t):
    REQUEST_TIME.set(t)
    RESPONSE_TIME.observe(t)
    REQUEST_COUNTER.inc()
    time.sleep(random.random())

#Start up the server to expose the metrics.
start_http_server(8000)

#Generate some requests.
while True:
    process_request(random.random())

This script creates three metrics: request_processing_seconds, response_processing_seconds, and request_counter. The process_request function simulates processing requests and increments the metrics accordingly.

Step 2: Dockerizing the Exporter

2.1 Create a Dockerfile

Create a Dockerfile to build the exporter image:

FROM python:3.9-slim

WORKDIR /app

COPY prometheus_exporter.py .

RUN pip install prometheus-client

CMD ["python", "./prometheus_exporter.py"]

2.2 Build and run the Docker image

Build the Docker image using the following command:

docker build -t prometheus-exporter .

Run the Docker container:

docker run -d -p 8000:8000 prometheus-exporter

The exporter should now be accessible at http://localhost:8000/metrics.

Step 3: Integrating with Prometheus and Grafana

3.1 Docker Compose Configuration

Create a docker-compose.yml file to configure Prometheus and Grafana:

version: '3'

services:

  prometheus-exporter:
    build:
      context: .
      dockerfile: Dockerfile
    ports:
      - 8000:8000
  
  prometheus:
    image: prom/prometheus
    container_name: prometheus
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    ports:
      - 9090:9090

  grafana:
    image: grafana/grafana
    container_name: grafana
    ports:
      - 3000:3000

3.2 Prometheus Configuration

Create a prometheus.yml file to configure Prometheus:

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: prometheus
    static_configs:
      - targets: ['localhost:9090']
  - job_name: python-exporter
    static_configs:
      - targets: ['prometheus-exporter:8000']

3.3 Start the services

Start the services using Docker Compose:

docker-compose up -d

Access Prometheus at http://localhost:9090 and Grafana at http://localhost:3000. Configure Prometheus as a data source in Grafana and create dashboards to visualize metrics.

Conclusion

In this tutorial, we created a custom Prometheus exporter in Python using prometheus-client and integrated it with Prometheus and Grafana using Docker. This setup allows you to monitor and analyze custom metrics from your applications and services, providing valuable insights into system performance and health.