Kubernetes - Custom Metrics

Introduction

Implementing custom metrics in Kubernetes allows you to monitor specific aspects of your applications and infrastructure that are not covered by built-in metrics. This guide provides an advanced-level overview of how to create and use custom metrics in Kubernetes, including setup, implementation, and best practices.

Setting Up the Custom Metrics Adapter

The Custom Metrics Adapter is used to expose custom metrics to the Kubernetes API. It can be installed using Helm:

# Add the Helm repository for the Custom Metrics Adapter
helm repo add stable https://charts.helm.sh/stable
helm repo update

# Install the Custom Metrics Adapter
helm install custom-metrics-adapter stable/custom-metrics-apiserver
                

Ensure that your adapter is configured correctly to pull metrics from your monitoring system, such as Prometheus.

Exposing Custom Metrics

Using Prometheus

Prometheus is a popular monitoring tool that can be used to expose custom metrics. Here’s how to create and expose a custom metric in Prometheus:

# Define a custom metric in your application code (Python example using Prometheus client)
from prometheus_client import start_http_server, Summary
import random
import time

# Create a metric to track time spent and requests made.
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')

# Decorate function with metric.
@REQUEST_TIME.time()
def process_request(t):
    """A dummy function that takes some time."""
    time.sleep(t)

if __name__ == '__main__':
    # Start up the server to expose the metrics.
    start_http_server(8000)
    # Generate some requests.
    while True:
        process_request(random.random())
                

This example exposes the request_processing_seconds metric at http://localhost:8000/metrics. Ensure Prometheus is configured to scrape this endpoint.

Configuring Prometheus

Configure Prometheus to scrape your custom metrics endpoint:

# Add the following job to your Prometheus configuration file (prometheus.yml)
scrape_configs:
  - job_name: 'custom-metrics'
    static_configs:
      - targets: ['localhost:8000']
                

Accessing Custom Metrics in Kubernetes

Once your custom metrics are exposed and collected by Prometheus, you can access them in Kubernetes using the kubectl command:

# List all custom metrics
kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/"

# Get a specific custom metric for a pod
kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/*/request_processing_seconds"
                

Using Custom Metrics for Autoscaling

Custom metrics can be used with the Horizontal Pod Autoscaler (HPA) to scale your applications based on specific metrics:

# Define an HPA that uses custom metrics (hpa.yaml)
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: custom-metrics-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: your-deployment
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: request_processing_seconds
      target:
        type: AverageValue
        averageValue: 0.5
                

This example scales a deployment based on the request_processing_seconds custom metric.

Best Practices for Implementing Custom Metrics

• Define Clear Metrics: Ensure your custom metrics are well-defined and provide meaningful insights.
• Validate Metrics: Regularly validate the accuracy and reliability of your custom metrics.
• Optimize Performance: Ensure that the collection and exposure of custom metrics do not impact the performance of your applications.
• Secure Metrics Endpoints: Protect your metrics endpoints to prevent unauthorized access and data leakage.
• Integrate with Monitoring Tools: Use monitoring tools to visualize and analyze custom metrics effectively.

Conclusion

Implementing custom metrics in Kubernetes allows for detailed monitoring and fine-grained performance analysis of your applications. By following the steps and best practices outlined in this guide, you can effectively create, expose, and use custom metrics to enhance the observability of your Kubernetes environment.