Custom Integrations | Integrations

Introduction

Custom integrations allow you to extend the functionality of Prometheus by connecting it to other systems and services. This tutorial will guide you through the process of creating your own custom integrations, focusing on how to gather metrics from an application and expose them in a format that Prometheus can scrape.

Understanding Metrics

Before diving into custom integrations, it's essential to understand the types of metrics that Prometheus can collect. Common types include:

Counter: A cumulative metric that increases over time, often used to count requests or events.
Gauge: A metric that represents a single numerical value that can arbitrarily go up and down, such as memory usage.
Histogram: A metric that samples observations and counts them in configurable buckets, useful for measuring latencies.
Summary: Similar to a histogram, but provides a total count and sum of observed values.

Setting Up Your Application

To create a custom integration, you will need an application that generates metrics. For this example, we'll create a simple web server in Python that exposes metrics to Prometheus.

Example: Simple Web Server

Below is a sample Python code using the Flask framework and the prometheus_client library:

from flask import Flask
from prometheus_client import start_http_server, Counter

app = Flask(__name__)
REQUEST_COUNT = Counter('request_count', 'Total number of requests')

@app.route('/')
def index():
    REQUEST_COUNT.inc()
    return "Hello, World!"

if __name__ == '__main__':
    start_http_server(8000)
    app.run(host='0.0.0.0', port=5000)

This code creates a simple web server that counts the number of requests received.

Exposing Metrics

To expose metrics in a format that Prometheus can scrape, you need to set up an endpoint. The prometheus_client library automatically creates a /metrics endpoint when you start the HTTP server.

Accessing Metrics

After running the server, you can access the metrics by navigating to http://localhost:8000/metrics in your web browser. You should see output similar to the following:

# HELP request_count Total number of requests
# TYPE request_count counter
request_count 0.0

Configuring Prometheus

Now that your application is exposing metrics, you need to configure Prometheus to scrape them. Edit your prometheus.yml configuration file to add your application as a scrape target:

Example: prometheus.yml

scrape_configs:
  - job_name: 'my_application'
    static_configs:
      - targets: ['localhost:8000']

After updating the configuration, restart Prometheus to apply the changes.

Testing Your Integration

To verify that the integration is working, access the Prometheus web interface at http://localhost:9090 and navigate to the "Targets" page. You should see your application listed as an active target. You can also run queries to visualize the metrics.

Conclusion

In this tutorial, you learned how to create a custom integration with Prometheus by setting up a web server that exposes metrics. This is just the beginning; you can extend this integration by adding more metrics, using different metric types, or integrating with various services to gather valuable performance data.

Custom Integrations with Prometheus