Moodle on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Moodle is an open-source Learning Management System (LMS) used by universities, schools, and online training providers worldwide. This lab takes you through the full operational lifecycle of the Moodle 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 Moodle 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, cron, 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, Cloud SQL, Filestore NFS, Redis, 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 Moodle (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 Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets, a Filestore NFS share for moodledata, optional Redis, builds the container image, runs the db-init and nfs-init one-shot jobs, and provisions a Cloud Scheduler cron job. First deploys take roughly 25–45 minutes (Cloud SQL and Filestore creation dominate).

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 moodle | 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:

   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}/health.php"
   # expect 200

   On first boot, Moodle installs its database schema before the readiness probe passes. If the pod is not yet Ready, monitor startup with kubectl logs -n "$NS" -l app=moodle -f.

2. Retrieve the database password from Secret Manager and note the Moodle cron and SMTP password secrets that were auto-generated:

   gcloud secrets list --project="$PROJECT" --filter="name~moodle"

   The database password secret name is reported in the deployment outputs. To read it:

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

3. Open http://${EXTERNAL_IP} in a browser and sign in to the Moodle admin panel. The initial admin credentials are set during the db-init job (username and email are configurable via environment_variables at deploy time).

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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).

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

4. Manage secrets, Cloud Scheduler cron, storage, and jobs:

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$PROJECT" --filter="name~moodle"
   gcloud scheduler jobs list --project="$PROJECT" --location="$REGION" \
     --filter="name~moodle"
   kubectl get jobs -n "$NS"

   To manually trigger the Moodle cron job:

   CRON_JOB=$(gcloud scheduler jobs list --project="$PROJECT" --location="$REGION" \
     --filter="name~moodle" --format="value(name)" --limit=1)
   gcloud scheduler jobs run "$CRON_JOB" --location="$REGION" --project="$PROJECT"

5. Open a database session for inspection or maintenance:

   INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
   gcloud sql connect "$INSTANCE" --user=moodle --database=moodle --project="$PROJECT"

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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 (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 Moodle releases.

• Pod not Ready / CrashLoopBackOff: the readiness probe targets /health.php; Moodle allows up to 10 minutes for first-boot schema creation. Inspect events and logs:

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

• Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the db-init job completed successfully.
• Initialisation jobs failed (db-init or nfs-init): inspect the jobs and their pod logs:

  kubectl get jobs -n "$NS"
  kubectl logs -n "$NS" job/<job-name>

• Moodle cron not running: confirm the Cloud Scheduler job is enabled and its last run succeeded; check the cron password secret exists in the namespace.

  gcloud scheduler jobs list --project="$PROJECT" --location="$REGION" \
    --filter="name~moodle"

• NFS / moodledata errors: confirm the Filestore instance is READY; the nfs-init job must have completed to set correct www-data ownership. Check the pod's NFS mount with kubectl exec -n "$NS" <pod> -- df -h | grep nfs.
• 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, Cloud SQL database, Filestore NFS share, Cloud Scheduler cron job, Secret Manager secrets, GCS buckets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, 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, Cloud SQL (PostgreSQL 15), Filestore NFS, Redis, Cloud Scheduler cron, secrets, and runs db-init + nfs-init jobs
2 — Access & verify	Manual	Connect to the cluster; health check at /health.php passes; sign in to the Moodle admin panel
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/cron/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, NFS, cron, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources