Kubernetes 1.32: Real-World Use Cases & Examples

Kubernetes 1.32: Real-World Use Cases & Examples

The Kubernetes 1.32 release, codenamed "Penelope", introduces thoughtful features aimed at making workloads more efficient, observable, and manageable.

In this post, I’ve compiled practical examples for each major feature, making it easier to see how they fit into your everyday Kubernetes workflow.

🎯 1. Dynamic Resource Allocation (DRA) Enhancements

Use Case:

A financial services company needs to train ML models that require GPUs with at least 16GB of memory. Instead of hardcoding node selection, DRA dynamically allocates GPU resources at runtime.

What it does:

• Uses a ResourceClaimTemplate to define GPU access.
• Pods request GPUs without being tied to specific nodes.
• Runs a container that uses an NVIDIA GPU to train a model.

Why it matters:

Template:

apiVersion: resource.k8s.io/v1alpha2
kind: ResourceClaimTemplate
metadata:
  name: gpu-claim-template
spec:
  metadata:
    labels:
      resource: nvidia-gpu
  spec:
    resourceClassName: nvidia.com/gpu

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  resourceClaims:
    - name: gpu
      source:
        resourceClaimTemplateName: gpu-claim-template
  containers:
    - name: ml-trainer
      image: your-ml-image
      command: ["python", "train.py"]
      resources:
        limits:
          nvidia.com/gpu: 1

• ✅ Dynamically provisions GPU at runtime
• ✅ Avoids node pre-binding
• ✅ Ideal for ML training, AI workloads, and GPU-heavy applications

🧹 2. Auto-Removal of PVCs in StatefulSets

Use Case:

Your team deploys short-lived stateful workloads (like test environments). Without cleanup, leftover PVCs accumulate.

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: data-processor
spec:
  serviceName: "data-service"
  replicas: 3
  selector:
    matchLabels:
      app: data-processor
  persistentVolumeClaimRetentionPolicy:
    whenDeleted: Delete
    whenScaled: Delete
  template:
    metadata:
      labels:
        app: data-processor
    spec:
      containers:
        - name: processor
          image: your-data-processor-image
          volumeMounts:
            - name: data-storage
              mountPath: /data
  volumeClaimTemplates:
    - metadata:
        name: data-storage
      spec:
        accessModes: ["ReadWriteOnce"]
        resources:
          requests:
            storage: 10Gi

Why it’s useful:

• ✅ Automatically deletes PVCs when a pod is removed or scaled down
• ✅ Prevents orphaned volumes
• ✅ Great for ephemeral data processing jobs and simulations

🪟 3. Graceful Shutdown for Windows Nodes

Use Case:

You run Windows-based apps in your cluster. During node shutdown, you need those apps to clean up gracefully instead of abruptly terminating.

What’s new:

• Kubernetes 1.32 adds graceful shutdown support for Windows pods.

apiVersion: v1
kind: Pod
metadata:
  name: windows-app
spec:
  nodeSelector:
    kubernetes.io/os: windows
  terminationGracePeriodSeconds: 60
  containers:
    - name: app
      image: your-windows-app-image
      command: ["powershell", "-Command", "Start-Sleep -Seconds 300"]

Why it’s helpful:

• ✅ Preserves app data integrity
• ✅ Simple to test with kubectl delete pod
• ✅ Essential for apps with shutdown routines

💾 4. Change Block Tracking (CBT) – Alpha

Use Case:

You maintain large databases or file systems. Full-volume snapshots are too slow and consume unnecessary storage.

How it works:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: cbt-pvc
  annotations:
    snapshot.storage.kubernetes.io/change-block-tracking: "true"
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 100Gi
  storageClassName: csi-cbt-enabled

• Add a special annotation to your PVC to enable CBT.
• Ensure your CSI driver supports CBT (e.g., csi-cbt-enabled).
• Snapshots capture only changed blocks, not the whole volume.

Why it matters:

• ✅ Faster incremental backups
• ✅ Reduced snapshot size
• ✅ Improves disaster recovery speed

⚙️ 5. Pod-Level Resource Limits

Use Case:

You’re running multiple containers inside a single pod (e.g., app + sidecar in a CI pipeline). Individual container limits are too rigid.

What’s new:

apiVersion: v1
kind: Pod
metadata:
  name: resource-shared-pod
spec:
  containers:
    - name: container-a
      image: your-app-image
    - name: container-b
      image: your-app-image
  resources:
    limits:
      cpu: "2"
      memory: "4Gi"
    requests:
      cpu: "1"
      memory: "2Gi"

• Set resource limits at the pod level, not just per container.
• Containers can share total CPU/memory quotas.

Why it’s great:

• ✅ More efficient resource sharing
• ✅ Great for CI/CD, proxies, and log sidecars
• ✅ Reduces over-provisioning and increases node density

🔍 6. Enhanced Observability with /statusz and /flagz

Use Case:

DevOps and SRE teams can now monitor component health and configuration more efficiently. These endpoints make it easier to audit settings, detect misconfigurations, and ensure runtime consistency during upgrades or debugging.

🔍 /statusz
Reports the health status of the component.
Example output: ok if the component is functioning properly.

⚙️ /flagz
Lists runtime flags and configuration values for the component.
Helps verify the active settings on running nodes or control-plane components.

How it works:

• Enable ComponentStatusz and ComponentFlagz feature gates.
• Access these built-in endpoints:

Final Thoughts

Kubernetes 1.32 isn’t just a list of features—it’s a set of solutions to common challenges faced by teams managing complex workloads.
Whether you’re focused on AI/ML efficiency, storage hygiene, Windows reliability, or control-plane observability, this release has something valuable for you.

👉 I’ve created a GitHub repo with all YAML examples for these use cases:
🔗 https://lnkd.in/emkKCxuY

Let me know which feature you're most excited to try—or if you’re already using it in production!