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📊 Kubernetes Monitoring and Observability

Master Kubernetes monitoring and observability with this in-depth guide. Learn how to implement Prometheus and Grafana, set up custom metrics, create powerful dashboards, and establish effective alerting strategies. Essential knowledge for maintaining production-grade Kubernetes clusters.

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Kubernetes Monitoring and Observability

Learn how to implement comprehensive monitoring and observability in your Kubernetes clusters to ensure optimal performance and quick troubleshooting.

What We’ll Cover

  1. Setting up Prometheus and Grafana
  2. Custom Metrics and Service Monitors
  3. Alert Management
  4. Log Aggregation
  5. Distributed Tracing

Prerequisites

  • Working Kubernetes cluster
  • Helm installed
  • Basic understanding of monitoring concepts

Installing Prometheus Operator

First, let’s set up Prometheus Operator using Helm:

# Add Prometheus community charts helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update # Install Prometheus Stack helm install monitoring prometheus-community/kube-prometheus-stack \ --namespace monitoring \ --create-namespace

Custom ServiceMonitor Configuration

apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: app-monitor namespace: monitoring spec: selector: matchLabels: app: my-app endpoints: - port: metrics interval: 15s namespaceSelector: matchNames: - default

Creating Custom Metrics

Example of a custom metrics endpoint in Go:

package main import ( "github.com/prometheus/client_golang/prometheus" "github.com/prometheus/client_golang/prometheus/promhttp" ) var ( httpRequestsTotal = prometheus.NewCounterVec( prometheus.CounterOpts{ Name: "http_requests_total", Help: "Total number of HTTP requests", }, []string{"method", "endpoint"}, ) ) func init() { prometheus.MustRegister(httpRequestsTotal) }

Grafana Dashboard Configuration

Example dashboard JSON:

{ "dashboard": { "id": null, "title": "Application Overview", "panels": [ { "title": "Request Rate", "type": "graph", "datasource": "Prometheus", "targets": [ { "expr": "rate(http_requests_total[5m])", "legendFormat": " " } ] } ] } }

Alert Configuration

PrometheusRule example:

apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: application-alerts namespace: monitoring spec: groups: - name: application rules: - alert: HighErrorRate expr: | rate(http_requests_total{status=~"5.*"}[5m]) / rate(http_requests_total[5m]) > 0.1 for: 5m labels: severity: critical annotations: description: Error rate is above 10% for 5 minutes

Log Aggregation with Loki

Installing Loki stack:

helm repo add grafana https://grafana.github.io/helm-charts helm install loki grafana/loki-stack \ --namespace monitoring \ --set grafana.enabled=false

Distributed Tracing with Jaeger

Jaeger deployment:

apiVersion: apps/v1 kind: Deployment metadata: name: jaeger spec: selector: matchLabels: app: jaeger template: metadata: labels: app: jaeger spec: containers: - name: jaeger image: jaegertracing/all-in-one:latest ports: - containerPort: 16686 - containerPort: 14268

Best Practices

  1. Metric Collection:
    • Use meaningful labels
    • Follow naming conventions
    • Keep cardinality under control
  2. Alerting:
    • Define clear severity levels
    • Avoid alert fatigue
    • Include runbooks
  3. Dashboard Design:
    • Start with overview
    • Use consistent layouts
    • Include documentation

Video Resources

Monitoring Fundamentals

Advanced Monitoring

Observability Practices

Additional Resources

Kubernetes, DevOps, Monitoring, Tutorial
This post is licensed under CC BY 4.0 by the author.