NodeRED on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Node-RED is an open-source flow-based programming tool for wiring together IoT devices, APIs, and online services through a visual browser-based editor. This lab takes you through the full operational lifecycle of the Node-RED on GKE Autopilot module on Google Cloud: deploy it, access and verify it, run it day-to-day, observe it, diagnose common problems, and tear it down.

The lab focuses on operating the GKE module and the Google Cloud platform, not on Node-RED product features. For the complete list of provisioned services and every configuration input (organised by group), see the Configuration Guide — this lab deliberately does not duplicate that detail so it stays accurate over time.

Objectives

By the end of this lab you will be able to:

• Deploy the module from the RAD platform and locate the resources it provisions.
• Connect to the GKE cluster and access the running workload.
• Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
• Observe the workload with Cloud Logging and Cloud Monitoring.
• Diagnose and resolve the most common deployment and runtime issues.
• Tear the deployment down cleanly.

Prerequisites

• Services_GCP deployed in the target project (provides the VPC, GKE Autopilot cluster, Filestore NFS, Artifact Registry, and shared service accounts this module depends on).
• A Google Cloud project with billing enabled.
• gcloud CLI and kubectl installed; gcloud auth login and gcloud auth application-default login completed.
• Project Owner (or equivalent) IAM on the project.
• RAD platform access with permission to deploy modules into the project.

Set these shell variables once; every task below reuses them:

export PROJECT="<your-gcp-project-id>"
export REGION="us-central1"           # the region you deploy into

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Task 1 — Deploy the module [Automated]

1. Click Deploy in the RAD platform top navigation, open NodeRED (GKE) from the Platform Modules list to start configuration, set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Review the estimated cost (if credits are enabled) and click Deploy, which opens the deployment status page with real-time logs.

2. The platform deploys the workload into the GKE Autopilot cluster, provisions a Filestore NFS share mounted at /data for persistent flow storage, a Cloud Storage bucket, a Secret Manager secret for the flow credential encryption key, and mirrors or builds the container image. No database is provisioned. First deploys take roughly 10–20 minutes (Filestore provisioning dominates).

3. Connect to the cluster and discover the namespace with name-agnostic filters:

   CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
   gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"
   
   NS=$(kubectl get ns -o name | grep nodered | head -1 | cut -d/ -f2)
   echo "Cluster: $CLUSTER   Namespace: $NS"
   kubectl get all -n "$NS"

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Task 2 — Access & verify [Manual]

1. Confirm the workload is running and find its external address. Node-RED listens on port 1880; the module exposes it through a LoadBalancer Service:

   kubectl get pods,svc -n "$NS"
   EXTERNAL_IP=$(kubectl get svc -n "$NS" \
     -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
   echo "External IP: $EXTERNAL_IP"
   curl -s -o /dev/null -w "%{http_code}" "http://${EXTERNAL_IP}/"
   # expect: 200

2. Open the Node-RED editor in your browser at http://${EXTERNAL_IP}. No credentials are required by default; for production deployments, IAP is recommended (see the Configuration Guide). The editor exposes full flow editing and credential management — do not leave it publicly accessible in production.

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Task 3 — Operate & keep it running (Day-2) [Manual]

1. Inspect the workload — deployment, pods, and (if enabled) the horizontal autoscaler and persistent volumes:

   kubectl get deploy,pods,hpa,pvc -n "$NS"
   kubectl describe deploy -n "$NS"

2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the workload spec, so scaling is a configuration change, not a manual kubectl scale (a manual edit would be reverted on the next apply). Keep max_instance_count = 1 unless flows are stateless or Redis-backed external context storage is enabled; session affinity (ClientIP) is required for the editor WebSocket connections.

3. Update the application version by changing the version input via Update on the deployment details page; a new image is mirrored or built and a rolling update replaces the pods.

4. Manage secrets, storage, and jobs:

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$PROJECT" --filter="name~nodered"
   kubectl get jobs,cronjobs -n "$NS"          # any custom scheduled jobs

5. Inspect the NFS-backed storage — all flows, credentials, and installed palette nodes are persisted in the Filestore share mounted at /data:

   gcloud filestore instances list --project="$PROJECT"
   kubectl exec -n "$NS" \
     deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" \
     -- ls /data

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Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from kubectl or the Logs Explorer:

   kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" --tail=50

   Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. The module also provisions an uptime check against / (when enabled); review Monitoring → Uptime checks and Alerting → Policies.

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Task 5 — Troubleshoot & debug [Manual]

Durable techniques for the failure modes you are most likely to hit. These are platform-level diagnostics and do not change with Node-RED releases.

• Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup probe targets HTTP GET / with a 30-second initial delay; NFS mount adds to startup time.

  kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
  kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container

• NFS mount failure: confirm the Filestore instance is READY and that the nfsserver network tag (required for NFS firewall rules) is present on the node pool. Verify enable_nfs = true and nfs_mount_path = "/data" are set correctly.
• Flow credentials unreadable after an Update: the NODE_RED_CREDENTIAL_SECRET may have been rotated or changed. Retrieve the current secret value and verify it matches the key used when flows were last deployed.

  CRED_SECRET=$(gcloud secrets list --project="$PROJECT" \
    --filter="name~nodered" --format="value(name)" --limit=1)
  gcloud secrets versions access latest --secret="$CRED_SECRET" --project="$PROJECT"

• Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
• Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

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Task 6 — Tear down [Automated]

On the Deployments page, open the deployment and click the Trash icon (Delete). Delete runs terraform destroy and is irreversible (the deployment record is retained for history). If a deployment is stuck and the RAD platform can no longer manage it (for example after manual changes that conflict with the Terraform state), use Purge instead — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the project). This removes everything the module created — the Kubernetes workload and namespace, Filestore NFS instance, Secret Manager secrets, GCS bucket, static IP, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Filestore NFS, GCS bucket, and credential secret
2 — Access & verify	Manual	Connect to the cluster; health check passes (HTTP 200 from /); editor loads in browser
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage, inspect NFS
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, NFS mount, credential, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources