Executive Summary

Kubernetes 1.33 represents a significant milestone in container orchestration, introducing powerful enterprise features that address the complex requirements of large-scale production deployments. This comprehensive guide explores the critical features, performance optimizations, and migration strategies that enterprise teams need to successfully deploy and manage Kubernetes 1.33 in production environments.

Key Enterprise Enhancements

In-Place Pod Vertical Scaling: Revolutionary resource management capabilities that allow dynamic CPU and memory adjustments without pod restarts, reducing operational overhead and improving resource utilization efficiency.

Dynamic Resource Allocation (DRA): Advanced resource management framework that provides fine-grained control over specialized hardware resources including GPUs, FPGAs, and custom accelerators.

Enhanced HPA Tolerance Controls: Sophisticated horizontal pod autoscaling with configurable tolerance thresholds that prevent scaling oscillations and improve workload stability.

Production-Grade Security: Comprehensive security enhancements including improved RBAC controls, enhanced pod security standards, and advanced network policy capabilities.

Kubernetes 1.33 Enterprise Architecture Overview

Core Platform Enhancements

Kubernetes 1.33 introduces fundamental improvements to the core platform architecture that directly impact enterprise production deployments:

# Enhanced Cluster Configuration for Kubernetes 1.33
apiVersion: kubeadm.k8s.io/v1beta3
kind: ClusterConfiguration
kubernetesVersion: v1.33.0
clusterName: "enterprise-production-cluster"
controlPlaneEndpoint: "k8s-api.enterprise.com:6443"
networking:
  serviceSubnet: "10.96.0.0/12"
  podSubnet: "10.244.0.0/16"
  dnsDomain: "cluster.local"
apiServer:
  extraArgs:
    # Enhanced security features
    enable-admission-plugins: "NodeRestriction,LimitRanger,ResourceQuota,PodSecurityPolicy,ValidatingAdmissionWebhook,MutatingAdmissionWebhook"
    audit-log-path: "/var/log/audit.log"
    audit-log-maxage: "30"
    audit-log-maxbackup: "10"
    audit-log-maxsize: "100"
    # Performance optimizations
    max-requests-inflight: "400"
    max-mutating-requests-inflight: "200"
    # Enhanced features
    feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true,HPAScaleToZero=true"
  certSANs:
    - "k8s-api.enterprise.com"
    - "kubernetes.default.svc.cluster.local"
controllerManager:
  extraArgs:
    # Enhanced resource management
    feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
    # Improved performance
    concurrent-deployment-syncs: "10"
    concurrent-replicaset-syncs: "10"
    concurrent-service-syncs: "5"
scheduler:
  extraArgs:
    # Advanced scheduling features
    feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
    # Performance tuning
    kube-api-qps: "100"
    kube-api-burst: "200"
etcd:
  local:
    # Performance optimization for enterprise workloads
    extraArgs:
      quota-backend-bytes: "8589934592"  # 8GB
      auto-compaction-retention: "8"
      auto-compaction-mode: "periodic"

In-Place Pod Vertical Scaling Implementation

One of the most significant enterprise features in Kubernetes 1.33 is in-place pod vertical scaling, which allows resource adjustments without pod restarts:

# Example: In-Place Vertical Scaling Configuration
apiVersion: apps/v1
kind: Deployment
metadata:
  name: enterprise-application
  namespace: production
spec:
  replicas: 10
  selector:
    matchLabels:
      app: enterprise-app
  template:
    metadata:
      labels:
        app: enterprise-app
    spec:
      containers:
      - name: main-container
        image: enterprise/app:v2.1.0
        resources:
          requests:
            cpu: "1000m"
            memory: "2Gi"
          limits:
            cpu: "2000m"
            memory: "4Gi"
        # Enable in-place resource updates
        resizePolicy:
        - resourceName: cpu
          restartPolicy: NotRequired
        - resourceName: memory
          restartPolicy: NotRequired
---
# VPA Configuration for In-Place Scaling
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: enterprise-app-vpa
  namespace: production
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: enterprise-application
  updatePolicy:
    updateMode: "Auto"
    # Enable in-place updates
    inPlaceUpdatePolicy:
      enabled: true
      # Configure which resources can be updated in-place
      allowedResources:
      - cpu
      - memory
  resourcePolicy:
    containerPolicies:
    - containerName: main-container
      maxAllowed:
        cpu: 4
        memory: 8Gi
      minAllowed:
        cpu: 100m
        memory: 128Mi
      # Configure scaling behavior
      scalingPolicy:
        scaleUpPolicy:
          stabilizationWindowSeconds: 300
          selectPolicy: Max
          policies:
          - type: Resource
            resource: cpu
            value: 50%
            periodSeconds: 60
        scaleDownPolicy:
          stabilizationWindowSeconds: 600
          selectPolicy: Min
          policies:
          - type: Resource
            resource: memory
            value: 25%
            periodSeconds: 120

Dynamic Resource Allocation Framework

Kubernetes 1.33’s Dynamic Resource Allocation (DRA) provides sophisticated management of specialized hardware resources:

# ResourceClass Definition for GPU Resources
apiVersion: resource.k8s.io/v1alpha3
kind: ResourceClass
metadata:
  name: nvidia-gpu-class
spec:
  driverName: gpu.nvidia.com
  parametersRef:
    apiVersion: gpu.nvidia.com/v1alpha1
    kind: GpuParameters
    name: enterprise-gpu-config
  suitableNodes:
    nodeSelectorTerms:
    - matchExpressions:
      - key: node.kubernetes.io/instance-type
        operator: In
        values: ["g5.xlarge", "g5.2xlarge", "g5.4xlarge"]
---
# GPU Parameters Configuration
apiVersion: gpu.nvidia.com/v1alpha1
kind: GpuParameters
metadata:
  name: enterprise-gpu-config
spec:
  memoryGB: 24
  computeCapability: "8.6"
  allowedUsers:
  - "ml-team"
  - "data-science"
  scheduling:
    strategy: "balanced"
    priority: "high"
---
# ResourceClaim for Application
apiVersion: resource.k8s.io/v1alpha3
kind: ResourceClaim
metadata:
  name: ml-training-gpu-claim
  namespace: ml-workloads
spec:
  resourceClassName: nvidia-gpu-class
  parametersRef:
    apiVersion: gpu.nvidia.com/v1alpha1
    kind: GpuClaimParameters
    name: ml-training-params
---
# Application Using DRA
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-training-workload
  namespace: ml-workloads
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ml-training
  template:
    metadata:
      labels:
        app: ml-training
    spec:
      containers:
      - name: training-container
        image: enterprise/ml-training:v1.5.0
        resources:
          claims:
          - name: gpu-resource
            request: ml-training-gpu-claim
        env:
        - name: CUDA_VISIBLE_DEVICES
          valueFrom:
            resourceFieldRef:
              containerName: training-container
              resource: claims/gpu-resource/gpu.nvidia.com/device-ids

Enhanced HPA with Configurable Tolerance

Kubernetes 1.33 introduces advanced HPA configuration options that provide better control over scaling behavior:

# Advanced HPA Configuration with Tolerance Controls
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: enterprise-app-hpa
  namespace: production
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: enterprise-application
  minReplicas: 5
  maxReplicas: 100
  # Enhanced tolerance configuration
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 180
      selectPolicy: Max
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60
      - type: Pods
        value: 10
        periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300
      selectPolicy: Min
      policies:
      - type: Percent
        value: 10
        periodSeconds: 60
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
    # Configurable tolerance for CPU metrics
    tolerance:
      value: 5
      type: Percent
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
    tolerance:
      value: 10
      type: Percent
  - type: External
    external:
      metric:
        name: queue_depth
        selector:
          matchLabels:
            queue: "enterprise-queue"
      target:
        type: AverageValue
        averageValue: "100"
    tolerance:
      value: 20
      type: Absolute

Production Migration Strategies

Pre-Migration Assessment

Before migrating to Kubernetes 1.33, conduct a comprehensive assessment of your current environment:

#!/bin/bash
# Kubernetes 1.33 Migration Assessment Script

echo "=== Kubernetes 1.33 Migration Assessment ==="

# Check current cluster version
echo "Current Kubernetes Version:"
kubectl version --short

# Check feature gates compatibility
echo -e "\n=== Feature Gates Assessment ==="
kubectl get nodes -o jsonpath='{.items[*].status.nodeInfo.kubeletVersion}' | tr ' ' '\n' | sort -u

# Assess deprecated APIs
echo -e "\n=== Deprecated API Usage ==="
kubectl get apiservices --no-headers | awk '{print $1}' | while read api; do
    if kubectl api-versions | grep -q "$api"; then
        echo "Active API: $api"
    fi
done

# Check for PSP usage (deprecated)
echo -e "\n=== Pod Security Policy Usage ==="
kubectl get psp --no-headers 2>/dev/null | wc -l

# Resource utilization assessment
echo -e "\n=== Resource Utilization Assessment ==="
kubectl top nodes
kubectl top pods --all-namespaces | head -20

# Check for third-party operators
echo -e "\n=== Third-Party Operators ==="
kubectl get crd | grep -v "kubernetes.io\|k8s.io" | head -10

# Network policy assessment
echo -e "\n=== Network Policies ==="
kubectl get networkpolicies --all-namespaces --no-headers | wc -l

# Storage class assessment
echo -e "\n=== Storage Classes ==="
kubectl get storageclass

# Check workload distribution
echo -e "\n=== Workload Distribution ==="
echo "Deployments: $(kubectl get deployments --all-namespaces --no-headers | wc -l)"
echo "StatefulSets: $(kubectl get statefulsets --all-namespaces --no-headers | wc -l)"
echo "DaemonSets: $(kubectl get daemonsets --all-namespaces --no-headers | wc -l)"
echo "Jobs: $(kubectl get jobs --all-namespaces --no-headers | wc -l)"
echo "CronJobs: $(kubectl get cronjobs --all-namespaces --no-headers | wc -l)"

Staging Environment Setup

Create a comprehensive staging environment for testing Kubernetes 1.33 features:

# Staging Cluster Configuration
# File: staging-cluster-config.yaml
apiVersion: kind.x-k8s.io/v1alpha4
kind: Cluster
nodes:
- role: control-plane
  kubeadmConfigPatches:
  - |
    kind: InitConfiguration
    nodeRegistration:
      kubeletExtraArgs:
        feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
  - |
    kind: ClusterConfiguration
    apiServer:
      extraArgs:
        feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
    controllerManager:
      extraArgs:
        feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
    scheduler:
      extraArgs:
        feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
- role: worker
  kubeadmConfigPatches:
  - |
    kind: JoinConfiguration
    nodeRegistration:
      kubeletExtraArgs:
        feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
- role: worker
  kubeadmConfigPatches:
  - |
    kind: JoinConfiguration
    nodeRegistration:
      kubeletExtraArgs:
        feature-gates: "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
containerdConfigPatches:
- |-
  [plugins."io.containerd.grpc.v1.cri".registry]
    [plugins."io.containerd.grpc.v1.cri".registry.mirrors]
      [plugins."io.containerd.grpc.v1.cri".registry.mirrors."localhost:5000"]
        endpoint = ["http://localhost:5000"]

Step-by-Step Migration Process

Execute a controlled migration to Kubernetes 1.33:

#!/bin/bash
# Kubernetes 1.33 Production Migration Script

set -euo pipefail

# Configuration
CLUSTER_NAME="production-cluster"
BACKUP_LOCATION="/backups/k8s-migration-$(date +%Y%m%d)"
KUBECONFIG_BACKUP="${BACKUP_LOCATION}/kubeconfig-backup"

echo "=== Kubernetes 1.33 Production Migration ==="
echo "Cluster: $CLUSTER_NAME"
echo "Backup Location: $BACKUP_LOCATION"

# Pre-migration backup
echo -e "\n=== Creating Pre-Migration Backup ==="
mkdir -p "$BACKUP_LOCATION"

# Backup ETCD
echo "Backing up ETCD..."
kubectl get nodes -o jsonpath='{.items[0].status.addresses[?(@.type=="InternalIP")].address}' > "$BACKUP_LOCATION/control-plane-ip"
CONTROL_PLANE_IP=$(cat "$BACKUP_LOCATION/control-plane-ip")

# Backup critical resources
echo "Backing up critical Kubernetes resources..."
kubectl get all --all-namespaces -o yaml > "$BACKUP_LOCATION/all-resources.yaml"
kubectl get pv -o yaml > "$BACKUP_LOCATION/persistent-volumes.yaml"
kubectl get pvc --all-namespaces -o yaml > "$BACKUP_LOCATION/persistent-volume-claims.yaml"
kubectl get secrets --all-namespaces -o yaml > "$BACKUP_LOCATION/secrets.yaml"
kubectl get configmaps --all-namespaces -o yaml > "$BACKUP_LOCATION/configmaps.yaml"

# Copy current kubeconfig
cp "$KUBECONFIG" "$KUBECONFIG_BACKUP"

# Step 1: Update control plane nodes
echo -e "\n=== Updating Control Plane Nodes ==="
kubectl get nodes -l node-role.kubernetes.io/control-plane --no-headers | while read node _; do
    echo "Updating control plane node: $node"
    
    # Drain node
    kubectl drain "$node" --ignore-daemonsets --delete-emptydir-data --force
    
    # Simulate node update (replace with actual update process)
    echo "Updating node $node to Kubernetes 1.33..."
    # ssh to node and update kubernetes components
    
    # Uncordon node
    kubectl uncordon "$node"
    
    # Wait for node to be ready
    kubectl wait --for=condition=Ready node/"$node" --timeout=300s
    
    echo "Control plane node $node updated successfully"
done

# Step 2: Update worker nodes
echo -e "\n=== Updating Worker Nodes ==="
kubectl get nodes -l '!node-role.kubernetes.io/control-plane' --no-headers | while read node _; do
    echo "Updating worker node: $node"
    
    # Drain node
    kubectl drain "$node" --ignore-daemonsets --delete-emptydir-data --force
    
    # Simulate node update
    echo "Updating node $node to Kubernetes 1.33..."
    # ssh to node and update kubernetes components
    
    # Uncordon node
    kubectl uncordon "$node"
    
    # Wait for node to be ready
    kubectl wait --for=condition=Ready node/"$node" --timeout=300s
    
    echo "Worker node $node updated successfully"
done

# Step 3: Enable new features
echo -e "\n=== Enabling Kubernetes 1.33 Features ==="

# Update API server configuration
echo "Updating API server configuration..."
kubectl patch configmap kube-apiserver-config -n kube-system --type merge -p '{
  "data": {
    "feature-gates": "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true,HPAScaleToZero=true"
  }
}'

# Update controller manager configuration
echo "Updating controller manager configuration..."
kubectl patch configmap kube-controller-manager-config -n kube-system --type merge -p '{
  "data": {
    "feature-gates": "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
  }
}'

# Update scheduler configuration
echo "Updating scheduler configuration..."
kubectl patch configmap kube-scheduler-config -n kube-system --type merge -p '{
  "data": {
    "feature-gates": "InPlacePodVerticalScaling=true,DynamicResourceAllocation=true"
  }
}'

# Step 4: Validation
echo -e "\n=== Post-Migration Validation ==="

# Check cluster version
echo "Verifying cluster version..."
kubectl version --short

# Check node status
echo "Checking node status..."
kubectl get nodes -o wide

# Check system pods
echo "Checking system pods..."
kubectl get pods -n kube-system

# Check workload status
echo "Checking workload status..."
kubectl get pods --all-namespaces | grep -v Running | grep -v Completed || echo "All pods are running"

# Test new features
echo "Testing new features..."

# Test in-place scaling
cat <<EOF | kubectl apply -f -
apiVersion: apps/v1
kind: Deployment
metadata:
  name: test-scaling
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: test-scaling
  template:
    metadata:
      labels:
        app: test-scaling
    spec:
      containers:
      - name: test-container
        image: nginx:1.21
        resources:
          requests:
            cpu: "100m"
            memory: "128Mi"
          limits:
            cpu: "200m"
            memory: "256Mi"
        resizePolicy:
        - resourceName: cpu
          restartPolicy: NotRequired
        - resourceName: memory
          restartPolicy: NotRequired
EOF

echo "Migration to Kubernetes 1.33 completed successfully!"
echo "Backup location: $BACKUP_LOCATION"

Enterprise Security Enhancements

Advanced Pod Security Standards

Kubernetes 1.33 introduces enhanced pod security standards for enterprise environments:

# Enhanced Pod Security Policy
apiVersion: v1
kind: Namespace
metadata:
  name: secure-workloads
  labels:
    pod-security.kubernetes.io/enforce: restricted
    pod-security.kubernetes.io/audit: restricted
    pod-security.kubernetes.io/warn: restricted
    pod-security.kubernetes.io/enforce-version: v1.33
---
# Security Context Constraints
apiVersion: v1
kind: Pod
metadata:
  name: secure-application
  namespace: secure-workloads
spec:
  securityContext:
    runAsNonRoot: true
    runAsUser: 1000
    runAsGroup: 3000
    fsGroup: 2000
    seccompProfile:
      type: RuntimeDefault
    supplementalGroups: [4000]
  containers:
  - name: app-container
    image: enterprise/secure-app:v1.0.0
    securityContext:
      allowPrivilegeEscalation: false
      readOnlyRootFilesystem: true
      runAsNonRoot: true
      runAsUser: 1000
      capabilities:
        drop:
        - ALL
        add:
        - NET_BIND_SERVICE
    resources:
      requests:
        cpu: 100m
        memory: 128Mi
      limits:
        cpu: 500m
        memory: 512Mi
    volumeMounts:
    - name: tmp
      mountPath: /tmp
    - name: var-run
      mountPath: /var/run
  volumes:
  - name: tmp
    emptyDir: {}
  - name: var-run
    emptyDir: {}

Network Security Policies

Enhanced network security with Kubernetes 1.33:

# Advanced Network Policy Configuration
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: enterprise-network-policy
  namespace: production
spec:
  podSelector:
    matchLabels:
      tier: web
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: ingress-nginx
    - podSelector:
        matchLabels:
          tier: load-balancer
    ports:
    - protocol: TCP
      port: 8080
  - from:
    - namespaceSelector:
        matchLabels:
          name: monitoring
    ports:
    - protocol: TCP
      port: 9090
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          name: database
    ports:
    - protocol: TCP
      port: 5432
  - to:
    - namespaceSelector:
        matchLabels:
          name: cache
    ports:
    - protocol: TCP
      port: 6379
  - to: []
    ports:
    - protocol: TCP
      port: 53
    - protocol: UDP
      port: 53
---
# DNS Policy for Secure Communication
apiVersion: networking.k8s.io/v1alpha1
kind: AdminNetworkPolicy
metadata:
  name: enterprise-dns-policy
spec:
  priority: 100
  subject:
    namespaces:
      matchLabels:
        security-tier: "high"
  ingress:
  - name: "allow-dns"
    action: "Allow"
    from:
    - namespaces:
        matchLabels:
          name: "kube-system"
    ports:
    - namedPort: "dns-tcp"
    - namedPort: "dns-udp"
  egress:
  - name: "allow-external-dns"
    action: "Allow"
    to:
    - networks:
      - "8.8.8.8/32"
      - "1.1.1.1/32"
    ports:
    - portNumber:
        protocol: UDP
        port: 53

Performance Optimization Strategies

Resource Management Best Practices

Optimize resource utilization with Kubernetes 1.33 features:

# Resource Quota with Enhanced Controls
apiVersion: v1
kind: ResourceQuota
metadata:
  name: enterprise-resource-quota
  namespace: production
spec:
  hard:
    # Compute resources
    requests.cpu: "100"
    requests.memory: 200Gi
    limits.cpu: "200"
    limits.memory: 400Gi
    # Storage resources
    requests.storage: 1Ti
    persistentvolumeclaims: "50"
    # Object counts
    pods: "100"
    replicationcontrollers: "20"
    resourcequotas: "1"
    secrets: "10"
    configmaps: "10"
    services: "20"
    services.loadbalancers: "5"
    services.nodeports: "10"
    # Enhanced resource controls
    count/deployments.apps: "20"
    count/statefulsets.apps: "10"
    count/jobs.batch: "50"
---
# Limit Range for Resource Constraints
apiVersion: v1
kind: LimitRange
metadata:
  name: enterprise-limit-range
  namespace: production
spec:
  limits:
  - type: Pod
    max:
      cpu: "4"
      memory: 8Gi
    min:
      cpu: 100m
      memory: 128Mi
  - type: Container
    default:
      cpu: 200m
      memory: 256Mi
    defaultRequest:
      cpu: 100m
      memory: 128Mi
    max:
      cpu: "2"
      memory: 4Gi
    min:
      cpu: 50m
      memory: 64Mi
  - type: PersistentVolumeClaim
    max:
      storage: 100Gi
    min:
      storage: 1Gi

Monitoring and Observability

Comprehensive monitoring setup for Kubernetes 1.33:

# Prometheus Configuration for Kubernetes 1.33
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
  namespace: monitoring
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      evaluation_interval: 15s
    
    rule_files:
    - "kubernetes-*.rules"
    
    scrape_configs:
    # Kubernetes API Server
    - job_name: 'kubernetes-apiservers'
      kubernetes_sd_configs:
      - role: endpoints
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
      - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
        action: keep
        regex: default;kubernetes;https
    
    # Kubernetes Nodes
    - job_name: 'kubernetes-nodes'
      kubernetes_sd_configs:
      - role: node
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
      - target_label: __address__
        replacement: kubernetes.default.svc:443
      - source_labels: [__meta_kubernetes_node_name]
        regex: (.+)
        target_label: __metrics_path__
        replacement: /api/v1/nodes/${1}/proxy/metrics
    
    # Kubernetes Pods
    - 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_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        action: replace
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_pod_name]
        action: replace
        target_label: kubernetes_pod_name
    
    # Enhanced metrics for Kubernetes 1.33 features
    - job_name: 'kubernetes-resource-allocation'
      kubernetes_sd_configs:
      - role: pod
        namespaces:
          names: ["kube-system"]
      relabel_configs:
      - source_labels: [__meta_kubernetes_pod_label_component]
        action: keep
        regex: kube-scheduler|kube-controller-manager
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      metric_relabel_configs:
      - source_labels: [__name__]
        regex: 'scheduler_.*|controller_.*'
        action: keep
---
# ServiceMonitor for Enhanced Metrics
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: kubernetes-1-33-metrics
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: kubernetes-metrics
  endpoints:
  - port: metrics
    interval: 30s
    path: /metrics
    honorLabels: true

This comprehensive guide continues with detailed sections on troubleshooting, cost optimization, and enterprise integration patterns. The complete production guide provides enterprise teams with the knowledge and tools needed to successfully deploy and manage Kubernetes 1.33 in large-scale production environments.

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