Mo AbukarMo Abukar

GKE Upgrade Guide and Rollback Strategy: A Production-Ready Approach

2025-08-15

A Real Talk on GKE Upgrades: What They Don't Tell You in the Docs

Fair warning: This is going to be a long read! But if you're responsible for production GKE clusters, every minute you spend here could save you hours of stress and potential downtime later.

I've been through the trenches of GKE cluster upgrades more times than I care to count — from smooth sailing 15-minute upgrades to absolute nightmare scenarios that had me questioning my career choices at 2 AM. The Google docs will tell you how to click the buttons and run the commands, but they won't prepare you for the reality of what happens when Murphy's Law kicks in during your upgrade window.

This guide isn't just another regurgitation of official documentation. It's battle-tested wisdom from someone who's learned the hard way that "it worked in staging" doesn't guarantee anything, and that the most critical step in any upgrade isn't technical — it's having a solid plan for when everything goes sideways.

Kubernetes Cluster Upgrades: Beyond Basic Operations

Managing Google Kubernetes Engine (GKE) clusters in production isn't just about keeping workloads running — it's about maintaining security, performance, and compatibility while navigating the rapidly evolving Kubernetes ecosystem. GKE automatically upgrades the version of the control plane and nodes to ensure that the cluster receives new features, bug fixes, and security patches, but successful production upgrades require careful planning and execution.

With the frequent updates and patches from upstream Kubernetes, and GKE, we highly recommend testing new releases on testing and/or staging environments before the releases are rolled out into your production environment, especially Kubernetes minor version upgrades. The challenge isn't just technical — it's operational. How do you minimize disruption? What happens when things go wrong? How do you ensure your applications remain available throughout the process?

This comprehensive guide walks through a battle-tested approach to GKE upgrades, from initial planning through rollback procedures, designed for production environments where downtime isn't an option.

Pre-Flight Checks: The Foundation of Safe Upgrades

Before touching any cluster configuration, thorough preparation prevents most upgrade failures. These checks aren't just recommendations — they're insurance against production outages.

API Deprecation Detection

A few deprecated APIs have been kept around in the last couple of K8s versions and finally getting completely removed in Kubernetes 1.16 release and this pattern continues with every release. The most critical step is identifying resources using deprecated APIs that will break in your target version.

Method 1: Using kubent (Recommended)

Kube No Trouble (kubent) is a simple tool to check whether you're using any of these API versions in your cluster and therefore should upgrade your workloads first, before upgrading your Kubernetes cluster.

bash# Install kubent
sh -c "$(curl -sSL https://git.io/install-kubent)"

# Scan your cluster for deprecated APIs
kubent --target-version=1.31.0

# Get JSON output for automation
kubent --target-version=1.31.0 --output=json

Method 2: GCP Log Explorer Query

For clusters with audit logging enabled, use this query to identify deprecated API usage:

sqlresource.type="k8s_cluster"
labels."k8s.io.removed-release"="1.31"
protoPayload.authenticationInfo.principalEmail:("system:serviceaccount" OR "@")
protoPayload.authenticationInfo.principalEmail!~("system:serviceaccount:kube-system")

Method 3: Native kubectl Discovery

bash# Check supported API versions
kubectl api-versions

# Verify specific resources exist in new format
kubectl explain deployment --api-version=apps/v1

Service Mesh Compatibility Verification

If running Istio, Anthos Service Mesh, or other service mesh solutions, verify compatibility with your target GKE version. GKE supports a minor version by providing patch versions of the same minor release, and, on a regular basis, automatically upgrading clusters to those newer patches, but service mesh components may have different support matrices. Check the official compatibility documentation for your service mesh version against the target GKE version before proceeding.

Resource and Workload Assessment

Review your cluster's resources and workloads to ensure they're compatible with the target GKE version. This includes checking for deprecated APIs, unsupported features and any other compatibility issues.

# check cluster resource utils
kubectl top nodes
kubectl top pods --all-namespaces

# check for critical workloads
kubectl get deployments --all-namespaces -o wide
kubectl get statefulsets --all-namespaces -o wide

# review PDBs...
kubectl get pdb --all-namespaces

Strategic Upgrade Planning: Minimize Risk, Maximize Success

Environment-Based Rollout Strategy

As part of your workflow for delivering software updates, we recommend that you use multiple environments. Multiple environments help you minimize risk and unwanted downtime by testing software and infrastructure updates separately from your production environment.

Recommended Progression:

  1. Development/Testing Clusters: Start with non-critical environments
  2. Staging Clusters: Full production-like testing with realistic workloads
  3. Production Clusters: Begin with least critical, progress to most critical

Timing Considerations

Schedule upgrades during maintenance windows when impact on users and engineering teams is minimal. Consider:

  • Peak usage periods for your applications
  • Team availability for monitoring and response
  • Dependency on external services that might be affected

Release Channel Strategy

To keep clusters up-to-date with the latest GKE and Kubernetes updates, here are some recommended environments and the respective release channels the clusters should be enrolled in:

  • Development: Rapid channel for early testing
  • Staging: Regular channel for stability validation
  • Production: Stable or Extended channel for proven reliability

Executing the Upgrade: Step-by-Step Process

Phase 1: Control Plane Upgrade

The control plane upgrade typically completes in 10-15 minutes for zonal clusters, 20-30 minutes for regional clusters.

Via Google Cloud Console:

  1. Navigate to Kubernetes Engine > Clusters
  2. Select target cluster
  3. Click Upgrade Available next to Version
  4. Select desired version (increment by one minor version)
  5. Click Save Changes

Via gcloud CLI:

bash# Check available versions
gcloud container get-server-config --zone=us-central1-a

# Upgrade control plane
gcloud container clusters upgrade CLUSTER_NAME \
    --zone=us-central1-a \
    --master \
    --cluster-version=1.31.0-gke.1234567

Via Terraform:

Update your Terraform configuration and apply:

resource "google_container_cluster" "primary" {
  name               = "my-cluster"
  location           = "us-central1-a"
  min_master_version = "1.31.0-gke.1234567"
  # ... other configuration
}

Phase 2: Node Pool Upgrades

GKE chooses the following strategies for these specific scenarios: In Autopilot clusters, GKE uses surge upgrades. For Standard clusters, configure surge settings for optimal balance between speed and disruption.

Configure Surge Upgrade Settings:

For large clusters where the upgrade process might take longer, you can accelerate the upgrade completion time by concurrently upgrading multiple nodes at a time. Use surge upgrade with maxSurge=20, maxUnavailable=0 to instruct GKE to upgrade 20 nodes at a time, without using any existing capacity.

# Configure surge settings before upgrade
gcloud container node-pools update NODE_POOL_NAME \
    --cluster=CLUSTER_NAME \
    --zone=us-central1-a \
    --max-surge=20 \
    --max-unavailable=0

# Upgrade node pool
gcloud container node-pools upgrade NODE_POOL_NAME \
    --cluster=CLUSTER_NAME \
    --zone=us-central1-a \
    --node-version=1.31.0-gke.1234567

Monitor Upgrade Progress:

bash# Check node status
kubectl get nodes -o wide

# Monitor pod scheduling
kubectl get pods --all-namespaces --field-selector=status.phase=Pending

# Watch for node cordoning/draining events
kubectl get events --sort-by=.metadata.creationTimestamp

Phase 3: Post-Upgrade Validation

System Health Checks:

bash# Verify all pods are running
kubectl get pods --all-namespaces --field-selector=status.phase!=Running

# Check node readiness
kubectl get nodes | grep -v Ready

# Validate cluster components
kubectl get componentstatuses

Application Validation:

  • Test critical application endpoints
  • Verify database connectivity
  • Check monitoring and alerting systems
  • Validate ingress and load balancer functionality

Node Resource Inspection:

# Check for common upgrade issues in node logs
# Look for patterns like "task hung" or blocked processes
kubectl logs --selector=app=node-problem-detector -n kube-system

Monitoring and Observability During Upgrades

Key Metrics to Watch

  • Node resource utilization (CPU, Memory, Disk)
  • Pod scheduling latency
  • Application response times
  • Error rates and HTTP status codes
  • Resource quota usage

Critical Events to Monitor

  • Node cordoning and draining events
  • Pod eviction failures
  • PDB violations
  • Failed pod scheduling
  • Container runtime errors

Logging Strategy

# Implement structured logging to capture upgrade events:
# Monitor upgrade operations

```bash
gcloud container operations list --filter="TYPE:UPGRADE_CLUSTER"

# Get detailed operation status
gcloud container operations describe OPERATION_ID --zone=us-central1-a

Rollback Procedures: When Things Go Wrong

Despite careful planning, upgrades sometimes fail. Having a tested rollback strategy is crucial for production environments.

Immediate Response Protocol

When to Trigger Rollback:

  • Application functionality severely degraded
  • Critical system components failing
  • Node upgrade stuck for >2 hours
  • Widespread pod scheduling failures

Control Plane Rollback

To mitigate an unsuccessful cluster control plane upgrade, you can downgrade your control plane to a previous patch release if the version is an earlier patch release within the same minor version.

# Rollback control plane (within same minor version only)
gcloud container clusters upgrade CLUSTER_NAME \
    --zone=us-central1-a \
    --master \
    --cluster-version=1.30.5-gke.1234567  # Previous patch version

Node Pool Rollback via New Pool Strategy

For comprehensive rollback, create a new node pool with the previous version: Step 1: Create New Node Pool

gcloud container node-pools create rollback-pool \
    --cluster=CLUSTER_NAME \
    --zone=us-central1-a \
    --node-version=1.30.5-gke.1234567 \
    --num-nodes=3 \
    --machine-type=e2-standard-4

Step 2: Scale Up New Pool

bashgcloud container node-pools resize rollback-pool \
    --cluster=CLUSTER_NAME \
    --zone=us-central1-a \
    --num-nodes=10  # Match original capacity

Step 3: Drain and Migrate Workloads

bash# Cordon old nodes
kubectl cordon NODE_NAME

# Force restart all workloads to migrate
for ns in $(kubectl get ns -o name | cut -d'/' -f2); do
    if [[ "$ns" != "kube-system" ]]; then
        echo "Restarting workloads in namespace: $ns"
        kubectl -n $ns rollout restart deployment
        kubectl -n $ns rollout restart statefulset
        kubectl -n $ns rollout restart daemonset
    fi
done

Step 4: Verify and Clean Up

bash# Verify pod distribution
kubectl get pods --all-namespaces -o wide

# Delete old node pool once stable
gcloud container node-pools delete old-pool-name \
    --cluster=CLUSTER_NAME \
    --zone=us-central1-a

Application-Level Recovery

If infrastructure rollback isn't sufficient:

  • Restore application deployments to previous versions
  • Rollback database schema changes if applicable
  • Reset configuration maps and secrets
  • Verify external service integrations

Advanced Upgrade Strategies

Blue-Green Node Pool Upgrades

Blue-green upgrades: Existing nodes are kept available for rolling back while the workloads are validated on the new node configuration. For zero-downtime upgrades of critical workloads:

  • Create new node pool with upgraded version Gradually migrate workloads using node selectors
  • Validate functionality on new nodes
  • Complete migration and remove old pool
  • Keep old pool available for quick rollback if needed

Gradual Workload Migration

Use node taints and tolerations for controlled migration:

# Taint new nodes
kubectl taint nodes NEW_NODE_NAME upgraded=true:NoSchedule

# Update deployments with toleration
spec:
  template:
    spec:
      tolerations:
      - key: "upgraded"
        operator: "Equal"
        value: "true"
        effect: "NoSchedule"

Maintenance Windows and Exclusions

Maintenance Windows and Exclusions
Configure maintenance policies to control when automatic upgrades occur:

```bash
gcloud container clusters update CLUSTER_NAME \
    --maintenance-window-start="2025-01-15T02:00:00Z" \
    --maintenance-window-end="2025-01-15T06:00:00Z" \
    --maintenance-window-recurrence="FREQ=WEEKLY;BYDAY=SU"

Infrastructure as Code Integration

Terraform State Management
After successful upgrades, update Terraform configuration to match actual state:

```hcl
resource "google_container_cluster" "primary" {
  name               = "production-cluster"
  location           = "us-central1-a"
  min_master_version = "1.31.0-gke.1234567"
  
  node_config {
    machine_type = "e2-standard-4"
  }
  
  # Ensure this matches post-upgrade state
  remove_default_node_pool = true
}

Apply changes with plan review:

terraform plan  # Should show no changes
terraform apply

GitOps Integration

Update cluster manifests in your GitOps repositories to reflect new API versions and configurations discovered during the upgrade process.

Post-Upgrade Housekeeping

Documentation Updates
  • Update runbooks with any new procedures discovered
  • Document any application-specific compatibility issues
  • Record upgrade duration and resource consumption
  • Update disaster recovery procedures

Security Review

  • Verify RBAC configurations are intact
  • Check network policies and security contexts
  • Validate service mesh security policies
  • Review admission controller configurations

Performance Baseline

Establish new performance baselines post-upgrade:

  • Application response times
  • Resource utilization patterns
  • Scaling behavior
  • Cost implications

The Reality Check: What Actually Happens

Kubernetes upgrades in production rarely go exactly as planned. Even with perfect preparation, you might encounter:

  • Resource Constraints: Nodes created by surge upgrade are subject to your Google Cloud resource quotas, resource availability, and reservation capacity
  • Application Dependencies: Third-party services may have compatibility issues
  • Timing Conflicts: At X we've been running production workloads on kubernetes in GKE since early 2017. When we were at 20 nodes it might take 90–120 minutes, which is in a tolerable range. Our largest nodepool in production at the time was 55 nodes, which would have taken at least 6 hours to fully upgrade

The key is building processes that expect and handle these realities gracefully.

Best Practices Summary

Before Every Upgrade:

  • Run deprecated API scans with kubent Test in non-production environments first
  • Verify service mesh compatibility
  • Configure appropriate surge settings
  • Plan for rollback scenarios

During Upgrades:

  • Monitor cluster and application health continuously
  • Have rollback procedures ready to execute
  • Communicate status to stakeholders
  • Document any unexpected issues

After Upgrades:

  • Validate all critical functionality
  • Update infrastructure code
  • Review and improve procedures Plan timing for dependent system updates

Successful GKE upgrades aren't just about technical execution — they're about building reliable, repeatable processes that minimize risk while keeping your Kubernetes infrastructure current, secure, and performant. The investment in proper upgrade procedures pays dividends in reduced downtime, improved security posture, and engineering team confidence.

Remember: These actions ensure your cluster remains performant, secure, and up-to-date with the latest features and bug fixes. In the rapidly evolving Kubernetes ecosystem, staying current isn't optional — it's a competitive advantage.

The Unvarnished Truth: Lessons from the Field

After years of managing GKE upgrades across different organizations, environments, and scales, here's what I wish someone had told me when I started:

The docs lie about timing. That "15-30 minute" control plane upgrade? Plan for double that. The "4-5 minutes per node"? Add a buffer. I've seen single node replacements take 20+ minutes because of slow image pulls, stuck pods, or resource constraints nobody anticipated.

Your applications are more fragile than you think. Even when you've tested everything in staging, production has a way of exposing edge cases. That legacy service that "just works"? It might be the one that breaks everything during a rolling restart. Always have a communications plan ready — not just for your team, but for stakeholders who need to know why their dashboard went red.

Rollbacks are harder than upgrades. The docs make rollbacks sound easy, but in reality, rolling back a failed upgrade often involves more complexity than the original upgrade. You're dealing with potentially corrupted state, confused monitoring systems, and applications that might have partially migrated their data models. Practice your rollback procedures as much as your upgrades.

The real cost isn't downtime — it's trust. A botched upgrade doesn't just impact your applications; it impacts your team's confidence in making future changes. Build processes that your team trusts, document everything (especially the failures), and celebrate the upgrades that go smoothly. They're more valuable than you think.

Google's automatic upgrades aren't your enemy. I used to fight the automatic upgrade system, trying to control every aspect. But I've learned that working with GKE's upgrade patterns, rather than against them, leads to more reliable outcomes. Use maintenance windows, embrace the surge upgrade strategies, and let Google handle what they do best.

Your monitoring during upgrades needs to be different. Normal monitoring tells you what's broken; upgrade monitoring needs to tell you what's about to break. Watch leading indicators: pod scheduling latency, resource pressure, API response times. By the time your standard alerts fire, you're already in reactive mode.

The biggest lesson? Upgrades are never just about Kubernetes. They're about organizational change management, risk tolerance, team communication and building systems that can evolve safely. Master those aspects and the technical pieces become much more manageable.

Stay curious, stay prepared and remember — every upgrade is a learning opportunity, even when (especially when) things don't go according to plan. Good luck!!!!