Use Case: Monitoring Application-Specific Metrics

Imagine you have a microservices-based application with multiple components, and each component produces specific metrics that are crucial for monitoring and optimizing the application's performance. These metrics may include things like response times, error rates, and custom business metrics.

Objective

You want to create a custom Prometheus exporter to collect and expose these application-specific metrics so that you can use Prometheus to monitor and visualize them.

Example - Building a Custom Exporter in Go

Here's a step-by-step example of how to build a custom Prometheus exporter in Go for monitoring a hypothetical microservice:

1. Choose Your Metrics:Identify the metrics you want to collect. In this example, we'll collect the following metrics:http_requests_total: Total number of HTTP requests processed.http_response_time_seconds: Average response time in seconds.custom_business_metric: A custom business metric specific to your application.
2. Write the Go Exporter:

package main

import (
	"fmt"
	"math/rand"
	"net/http"
	"time"

	"github.com/prometheus/client_golang/prometheus"
	"github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
	// Initialize Prometheus metrics
	httpRequestsTotal := prometheus.NewCounterVec(
		prometheus.CounterOpts{
			Name: "http_requests_total",
			Help: "Total number of HTTP requests processed.",
		},
		[]string{"service"},
	)

	httpResponseTime := prometheus.NewGaugeVec(
		prometheus.GaugeOpts{
			Name: "http_response_time_seconds",
			Help: "Average response time in seconds.",
		},
		[]string{"service"},
	)

	customBusinessMetric := prometheus.NewGaugeVec(
		prometheus.GaugeOpts{
			Name: "custom_business_metric",
			Help: "A custom business metric specific to the application.",
		},
		[]string{"service"},
	)

	// Register Prometheus metrics
	prometheus.MustRegister(httpRequestsTotal)
	prometheus.MustRegister(httpResponseTime)
	prometheus.MustRegister(customBusinessMetric)

	// Simulate metric updates
	go func() {
		for {
			serviceName := "my_microservice"
			// Simulate random metric values
			httpRequestsTotal.WithLabelValues(serviceName).Inc()
			httpResponseTime.WithLabelValues(serviceName).Set(rand.Float64() * 0.5) // Random response time between 0 and 0.5 seconds
			customBusinessMetric.WithLabelValues(serviceName).Set(rand.Float64() * 100)

			time.Sleep(time.Second)
		}
	}()

	// Expose Prometheus metrics
	http.Handle("/metrics", promhttp.Handler())
	fmt.Println("Exporter listening on :7777")
	if err := http.ListenAndServe(":7777", nil); err != nil {
		panic(err)
	}
}
        

• In this example, we've created three Prometheus metrics (httpRequestsTotal, httpResponseTime, and customBusinessMetric). The exporter simulates metric updates for a hypothetical microservice, but in a real-world scenario, you would collect these metrics from your application.
• Prometheus Configuration: Use the Prometheus configuration provided earlier to scrape metrics from your custom exporter.

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'custom_exporter'
    static_configs:
      - targets: ['localhost:7777']  # Replace with the address of your exporter if needed
    metrics_path: /metrics
        

• You can save this configuration to a file named prometheus.yml and run Prometheus with the -config.file flag:

prometheus --config.file=prometheus.yml        

Visualization and Alerts

You can now use Prometheus and Grafana to visualize these metrics, set up alerts based on them, and gain insights into the performance of your microservice.

Conclusion

This use case demonstrates how building a custom Prometheus exporter allows you to monitor application-specific metrics that are critical for understanding and optimizing your system's performance.

Further Resources

• Prometheus Documentation
• Instrumenting a GO Application fo Prometheus