Kubernetes Performance Best Practices

by Bala

Optimize your Kubernetes clusters for maximum performance and efficiency

Kubernetes Performance Best Practices

Optimizing Kubernetes cluster performance is essential for running efficient and cost-effective workloads. This guide covers key performance optimization strategies and best practices.

Prerequisites

  • Basic understanding of Kubernetes
  • Access to a Kubernetes cluster
  • kubectl CLI tool installed
  • Familiarity with monitoring tools

Project Structure

.
├── performance/
│   ├── resource-configs/    # Resource configuration templates
│   ├── hpa/                # Horizontal Pod Autoscaling configs
│   ├── monitoring/         # Monitoring configurations
│   └── benchmarks/         # Performance test configurations
└── optimization/
    ├── node-configs/       # Node optimization settings
    └── network/            # Network optimization configs

Resource Management

1. Resource Requests and Limits

Always specify resource requests and limits:

apiVersion: v1
kind: Pod
metadata:
  name: resource-optimized-pod
spec:
  containers:
  - name: app
    image: nginx
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"
      limits:
        memory: "128Mi"
        cpu: "500m"

2. Horizontal Pod Autoscaling

Implement HPA for automatic scaling:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 80

Node Optimization

1. Node Affinity

Use node affinity for optimal pod placement:

apiVersion: v1
kind: Pod
metadata:
  name: node-affinity-pod
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: kubernetes.io/e2e-az-name
            operator: In
            values:
            - e2e-az1
            - e2e-az2

2. Pod Anti-Affinity

Spread pods across nodes:

apiVersion: v1
kind: Pod
metadata:
  name: anti-affinity-pod
spec:
  affinity:
    podAntiAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
      - labelSelector:
          matchExpressions:
          - key: app
            operator: In
            values:
            - web-server
        topologyKey: "kubernetes.io/hostname"

Storage Optimization

1. Storage Class Configuration

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast
provisioner: kubernetes.io/aws-ebs
parameters:
  type: gp3
  iopsPerGB: "10"
  encrypted: "true"

2. Volume Management

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: optimized-pvc
spec:
  storageClassName: fast
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi

Network Optimization

1. Network Policies

Optimize network traffic:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: optimized-network-policy
spec:
  podSelector:
    matchLabels:
      app: web
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - podSelector:
        matchLabels:
          app: api
    ports:
    - protocol: TCP
      port: 80

2. Service Mesh Configuration

Using Istio for network optimization:

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: optimized-routing
spec:
  hosts:
  - web-service
  http:
  - route:
    - destination:
        host: web-service
        subset: v1
      weight: 90
    - destination:
        host: web-service
        subset: v2
      weight: 10

Monitoring and Metrics

1. Prometheus Configuration

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: app-monitor
spec:
  selector:
    matchLabels:
      app: web
  endpoints:
  - port: web

2. Custom Metrics

apiVersion: custom.metrics.k8s.io/v1beta1
kind: MetricDefinition
metadata:
  name: custom-metric
spec:
  query: rate(http_requests_total[5m])

Performance Checklist

  1. Resource Requests and Limits
  2. Horizontal Pod Autoscaling
  3. Node Affinity Rules
  4. Storage Optimization
  5. Network Policy Configuration
  6. Service Mesh Implementation
  7. Monitoring Setup
  8. Custom Metrics Configuration
  9. Regular Performance Testing
  10. Capacity Planning

Common Performance Pitfalls

  1. Missing resource limits
  2. Improper pod scheduling
  3. Inefficient storage configuration
  4. Network bottlenecks
  5. Inadequate monitoring

Best Practices for Different Workloads

Stateless Applications

  • Use HPA
  • Implement pod disruption budgets
  • Configure readiness probes

Stateful Applications

  • Use local storage when possible
  • Implement proper backup strategies
  • Configure pod anti-affinity

Batch Jobs

  • Set appropriate deadlines
  • Use job parallelism
  • Implement proper retry mechanisms

Conclusion

Following these performance best practices will help ensure your Kubernetes clusters run efficiently and reliably. Regular monitoring and optimization are key to maintaining high performance.

Additional Resources