How to Monitor Custom Kubernetes Pod Metrics with Prometheus

Monitoring custom Kubernetes pod metrics with Prometheus is a crucial skill for developers and DevOps engineers. This guide will walk you through the process of setting up Prometheus to collect and analyze custom metrics from your Kubernetes pods. You'll learn how to implement, configure, and visualize these metrics for better insights into your application's performance.

Understanding Custom Kubernetes Pod Metrics

Custom pod metrics in Kubernetes are user-defined measurements that provide specific insights into your application's behavior and performance. Unlike core metrics (CPU and memory usage), custom metrics offer tailored information relevant to your application's unique characteristics.

Why are custom metrics important? They:

• Offer deeper insights into application-specific performance
• Enable more precise scaling decisions
• Help identify potential issues before they impact users

Examples of custom metrics include:

• Request latency
• Queue length
• Error rates
• Business-specific metrics (e.g., number of active users)

Introduction to Prometheus for Kubernetes Monitoring

Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It's particularly well-suited for Kubernetes environments due to its:

• Multi-dimensional data model
• Flexible query language (PromQL)
• Pull-based metric collection
• Service discovery capabilities

Prometheus integrates seamlessly with Kubernetes, automatically discovering and scraping metrics from pods and services. This makes it an ideal choice for monitoring custom pod metrics.

Setting Up Prometheus for Kubernetes

To set up Prometheus in your Kubernetes cluster:

1. Create a Kubernetes namespace for Prometheus:

kubectl create namespace monitoring

1. Apply the necessary RBAC rules:

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus
rules:
- apiGroups: [""]
  resources:
  - nodes
  - services
  - endpoints
  - pods
  verbs: ["get", "list", "watch"]
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: prometheus
  namespace: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: prometheus
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
- kind: ServiceAccount
  name: prometheus
  namespace: monitoring

1. Create a ConfigMap for Prometheus configuration:

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
  namespace: monitoring
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
        - role: pod
        relabel_configs:
        - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
          action: keep
          regex: true
        - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
          action: replace
          target_label: __metrics_path__
          regex: (.+)
        - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
          action: replace
          regex: ([^:]+)(?::\\d+)?;(\\d+)
          replacement: $1:$2
          target_label: __address__
        - action: labelmap
          regex: __meta_kubernetes_pod_label_(.+)

1. Deploy Prometheus:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus
  namespace: monitoring
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      serviceAccountName: prometheus
      containers:
      - name: prometheus
        image: prom/prometheus:v2.37.0
        args:
        - "--config.file=/etc/prometheus/prometheus.yml"
        ports:
        - containerPort: 9090
        volumeMounts:
        - name: config
          mountPath: /etc/prometheus
      volumes:
      - name: config
        configMap:
          name: prometheus-config
---
apiVersion: v1
kind: Service
metadata:
  name: prometheus
  namespace: monitoring
spec:
  selector:
    app: prometheus
  ports:
  - port: 9090
    targetPort: 9090

Apply these configurations to your cluster using kubectl apply -f.

Implementing Custom Pod Metrics in Kubernetes

To expose custom metrics from your Kubernetes pods:

1. Instrument your application code using a Prometheus client library for your programming language.
2. Create an HTTP endpoint (usually /metrics) that exposes the metrics.
3. Add Prometheus annotations to your pod specification:

metadata:
  annotations:
    prometheus.io/scrape: "true"
    prometheus.io/port: "8080"
    prometheus.io/path: "/metrics"

Here's an example of a custom metric in Python using the prometheus_client library:

from prometheus_client import start_http_server, Counter

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

def process_request():
    # Your request processing logic here
    REQUEST_COUNT.inc()

if __name__ == '__main__':
    start_http_server(8000)
    # Your main application code here

Querying and Visualizing Custom Pod Metrics

Once Prometheus is collecting your custom metrics, you can query them using PromQL. For example, to get the total number of requests across all pods:

sum(request_count)

To visualize these metrics, integrate Grafana with Prometheus:

1. Deploy Grafana in your Kubernetes cluster.
2. Add Prometheus as a data source in Grafana.
3. Create dashboards using PromQL queries to visualize your custom metrics.

Best Practices for Custom Metric Monitoring

• Follow the USE method: monitor Utilization, Saturation, and Errors for each resource.
• Keep metric names consistent and descriptive.
• Use labels to add dimensions to your metrics for better filtering and aggregation.
• Set up alerting rules for critical custom metrics to proactively identify issues.

Monitoring Custom Kubernetes Pod Metrics with SigNoz

While Prometheus provides a powerful foundation for monitoring custom Kubernetes pod metrics, SigNoz offers an enhanced, user-friendly experience with additional features. SigNoz is an open-source APM tool that combines metrics, traces, and logs, providing a comprehensive monitoring solution.

To set up SigNoz for monitoring your Kubernetes cluster:

SigNoz cloud is the easiest way to run SigNoz. Sign up for a free account and get 30 days of unlimited access to all features.

You can also install and self-host SigNoz yourself since it is open-source. With 19,000+ GitHub stars, open-source SigNoz is loved by developers. Find the instructions to self-host SigNoz.

SigNoz offers several advantages over a standalone Prometheus setup:

• Unified dashboard for metrics, traces, and logs
• Easy-to-use query builder for complex PromQL queries
• Built-in alerting with various notification channels

By using SigNoz, you can streamline your monitoring workflow and gain deeper insights into your Kubernetes applications' performance.

Monitoring custom Kubernetes pod metrics is essential for maintaining healthy, performant applications. By leveraging tools like Prometheus and SigNoz, you can gain valuable insights into your application's behavior and make data-driven decisions to improve its performance and reliability.