Kubernetes Custom Metrics

What You'll Learn

• Understand what custom metrics are in Kubernetes and why they matter
• Learn how to set up and configure custom metrics
• Explore practical examples and real-world use cases
• Discover Kubernetes best practices for monitoring and logging
• Troubleshoot common issues related to custom metrics

Introduction

Kubernetes, the leading container orchestration platform, offers powerful capabilities for managing workloads. One critical aspect of effective Kubernetes management is monitoring, which includes the use of custom metrics. In this comprehensive guide, you'll learn about Kubernetes custom metrics, why they're important, and how to implement them in your cluster. Whether you're a Kubernetes administrator or developer, understanding custom metrics will enhance your ability to monitor applications and optimize performance in real-time environments.

Understanding Custom Metrics: The Basics

What are Custom Metrics in Kubernetes?

Custom metrics in Kubernetes are user-defined metrics that provide insights into the performance and behavior of applications running in a Kubernetes cluster. Unlike standard metrics, such as CPU and memory usage, custom metrics allow you to track application-specific parameters. For instance, you can monitor request latency, error rates, or custom business metrics like the number of transactions processed.

Think of custom metrics like the dashboard of a car. While a speedometer (standard metric) shows how fast you're going, custom metrics are like gauges for oil temperature or tire pressure—providing deeper insights into the vehicle's health.

Why are Custom Metrics Important?

Custom metrics are vital because they allow you to tailor monitoring to your application's specific needs. By gaining insights into application-specific performance metrics, you can:

• Optimize resource usage and detect anomalies before they impact users.
• Implement auto-scaling based on business-related criteria.
• Improve observability and decision-making through detailed insights.

Key Concepts and Terminology

Metrics Server: A Kubernetes component that aggregates and provides access to resource metrics from nodes and pods.

Prometheus: An open-source monitoring and alerting toolkit widely used for collecting and querying metrics in Kubernetes.

Horizontal Pod Autoscaler (HPA): A Kubernetes resource that automatically scales the number of pod replicas based on observed metrics.

Learning Note: Custom metrics require a metrics backend like Prometheus to collect and expose metrics for use with Kubernetes resources like HPA.

How Custom Metrics Work

Custom metrics in Kubernetes are typically collected and exposed by a metrics backend such as Prometheus. These metrics can then be used by the Kubernetes Horizontal Pod Autoscaler (HPA) to perform scaling operations. Here's a simplified workflow:

1. Collection: A metrics backend like Prometheus scrapes metrics from application endpoints.
2. Exposure: The metrics are exposed via an API that Kubernetes can query.
3. Utilization: Kubernetes resources, such as HPA, use these metrics to make scaling decisions.

Prerequisites

Before working with custom metrics, ensure you have:

• Basic knowledge of Kubernetes deployments and services
• A working Kubernetes cluster with kubectl access
• Prometheus installed and configured in your cluster

Step-by-Step Guide: Getting Started with Custom Metrics

Step 1: Install Prometheus

First, set up Prometheus in your cluster to collect metrics. You can use Helm, a Kubernetes package manager, to simplify this process.

# Add the Prometheus Helm repository
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts

# Update your Helm repository cache
helm repo update

# Install Prometheus using Helm
helm install prometheus prometheus-community/prometheus

Step 2: Expose Application Metrics

Your application must expose metrics in a format that Prometheus can scrape. This is often done by integrating a metrics library (e.g., Prometheus client libraries) into your application code.

// Example: Exposing custom metrics in a Go application
import (
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promhttp"
    "net/http"
)

var customMetric = prometheus.NewCounterVec(
    prometheus.CounterOpts{
        Name: "transactions_processed",
        Help: "Number of transactions processed",
    },
    []string{"status"},
)

func init() {
    prometheus.MustRegister(customMetric)
}

func main() {
    http.Handle("/metrics", promhttp.Handler())
    http.ListenAndServe(":8080", nil)
}

Step 3: Configure Horizontal Pod Autoscaler (HPA)

Create an HPA resource that uses the custom metric to scale your application.

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: custom-metric-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: transactions_processed
      target:
        type: AverageValue
        averageValue: 5

Key Takeaways:

• Custom metrics enable application-specific monitoring and scaling.
• Requires integration with a metrics backend like Prometheus.
• HPA can utilize these metrics for dynamic scaling.

Configuration Examples

Example 1: Basic Configuration

Here's a simple YAML configuration for exposing metrics using Prometheus.

# ServiceMonitor configuration for Prometheus
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: my-app-metrics
spec:
  selector:
    matchLabels:
      app: my-app
  endpoints:
  - port: metrics
    interval: 30s

Key Takeaways:

• ServiceMonitor specifies how Prometheus should scrape metrics.
• Essential for linking application metrics to Prometheus.

Example 2: More Advanced Scenario

This example shows a more advanced Prometheus configuration with multiple endpoints.

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: complex-metrics
spec:
  selector:
    matchLabels:
      app: complex-app
  endpoints:
  - port: metrics
    path: /metrics
    interval: 15s
  - port: admin
    path: /admin/metrics
    interval: 1m

Example 3: Production-Ready Configuration

For production environments, consider using secure endpoints and advanced Prometheus configurations.

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: prod-metrics
spec:
  selector:
    matchLabels:
      app: prod-app
  endpoints:
  - port: metrics
    path: /metrics
    interval: 10s
    tlsConfig:
      insecureSkipVerify: true

Hands-On: Try It Yourself

Let's practice setting up a custom metric and scaling your application.

# Create a namespace for Prometheus
kubectl create namespace monitoring

# Deploy Prometheus and Grafana
kubectl apply -f prometheus-grafana-deployment.yaml

# Expected output: Prometheus and Grafana pods running in the monitoring namespace
kubectl get pods -n monitoring

Check Your Understanding:

• What is a custom metric, and why is it useful?
• How does Prometheus collect and expose metrics?

Real-World Use Cases

Use Case 1: E-commerce Application

Problem: Monitor the number of orders processed per minute.

Solution: Use a custom metric to track orders and auto-scale the backend service when demand increases.

Benefits: Ensures the application can handle spikes in traffic without manual intervention.

Use Case 2: IoT Data Processing

Problem: Track the number of IoT messages processed.

Solution: Implement custom metrics to monitor processing rates and ensure timely data handling.

Benefits: Improved resource utilization and real-time monitoring.

Use Case 3: Advanced Scenario

Problem: Monitor API response time and error rates.

Solution: Use custom metrics to trigger alerts and scale services based on latency and errors.

Benefits: Enhanced reliability and user experience.

Common Patterns and Best Practices

Best Practice 1: Use a Centralized Metrics System

Centralize your metrics collection with a robust system like Prometheus to simplify management and scalability.

Best Practice 2: Implement Metrics Aggregation

Aggregate metrics at the service level to reduce noise and focus on meaningful data.

Best Practice 3: Secure Your Metrics

Ensure that metrics endpoints are secured and encrypted to protect sensitive data.

Pro Tip: Use Grafana for an intuitive visualization of your metrics. It integrates seamlessly with Prometheus.

Troubleshooting Common Issues

Issue 1: Metrics Not Scraped

Symptoms: Custom metrics do not appear in Prometheus.

Cause: Misconfiguration of the ServiceMonitor or incorrect labels.

Solution: Verify ServiceMonitor configuration and labels.

# Check ServiceMonitor logs
kubectl logs -l app=prometheus -n monitoring

Issue 2: HPA Not Scaling

Symptoms: HPA does not scale despite metric changes.

Cause: Incorrect metric configuration or missing metrics API.

Solution: Verify metric names and ensure the metrics API is accessible.

# Check HPA status
kubectl describe hpa custom-metric-hpa

Performance Considerations

• Optimize metric collection intervals to balance performance and data granularity.
• Limit the number of custom metrics to reduce processing overhead.

Security Best Practices

• Use TLS for securing metrics endpoints.
• Restrict access to metrics using Kubernetes RBAC policies.

Advanced Topics

For advanced users, consider exploring custom metric pipelines and integrating with other observability tools like Elastic Stack.

Learning Checklist

Before moving on, make sure you understand:

• What custom metrics are and their importance
• How to set up Prometheus for metrics collection
• How to configure HPA using custom metrics
• Common issues and troubleshooting steps

Related Topics and Further Learning

• Kubernetes Monitoring with Prometheus and Grafana
• Advanced Kubernetes Configuration Techniques
• Official Kubernetes Documentation on Metrics

Conclusion

Understanding and implementing custom metrics in Kubernetes is a powerful way to enhance your monitoring and scaling strategies. By leveraging tools like Prometheus and Grafana, you can gain deeper insights into your applications, optimize resource usage, and ensure high availability. As you continue learning, explore additional Kubernetes tutorials and documentation to further enhance your skills and knowledge.

Quick Reference

• Command for Viewing Metrics: kubectl top pod
• HPA Command Example: kubectl get hpa

By mastering custom metrics, you're well-equipped to tackle complex monitoring challenges in Kubernetes and drive your applications toward greater efficiency and reliability.