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LimeSurvey on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

LimeSurvey is a free, open-source online survey and questionnaire platform (PHP/Yii) supporting unlimited surveys, conditional branching, quotas, and multi-language surveys. This lab takes you through the full operational lifecycle of the LimeSurvey 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 LimeSurvey survey-authoring 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, including the first-run super-admin login.
  • 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, Cloud SQL, 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

Task 1 — Deploy the module [Automated]​

  1. Click Deploy in the RAD platform top navigation, open LimeSurvey (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 (MySQL 8.0) database with its Secret Manager secrets (ADMIN_PASSWORD and the database password), a Cloud Filestore (NFS) instance for the upload directory, a limesurvey-uploads Cloud Storage bucket, builds the container image, and runs a one-shot db-init job that creates the empty database and user. LimeSurvey's own console installer then builds the schema on first pod start. First deploys take roughly 20–35 minutes (Cloud SQL and Filestore creation dominate), plus a few extra minutes on the very first boot for the schema install.

  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 limesurvey | head -1 | cut -d/ -f2)
    echo "Cluster: $CLUSTER   Namespace: $NS"
    kubectl get all -n "$NS"

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"

  2. Confirm the service is healthy. LimeSurvey's liveness probe is an unauthenticated GET / on the landing page (300s initial delay, 3 failures) — allow several minutes on first boot for the console installer to finish building the schema:

    curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/"   # expect 200

  3. Retrieve the auto-generated super-admin password from Secret Manager:

    gcloud secrets versions access latest --secret=secret-<resource-prefix>-limesurvey-admin-password \
      --project="$PROJECT"

  4. Set PUBLIC_URL now that the external IP is known — it is not preset on GKE, unlike the Cloud Run variant:

    kubectl patch deploy <service-name> -n "$NS" \
      -p '{"spec":{"template":{"spec":{"containers":[{"name":"limesurvey","env":[
        {"name":"PUBLIC_URL","value":"http://'"${EXTERNAL_IP}"'"}]}]}}}}'

  5. Open http://${EXTERNAL_IP} in a browser and sign in to the admin panel (/admin) as admin / admin@techequity.cloud using the password retrieved above. Create a test survey to confirm the database and NFS upload paths both work end to end.

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

  1. Inspect the workload — deployment, pods, and PVCs:

    kubectl get deploy,pods,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 session-sharing behaviour is verified — LimeSurvey keeps PHP session state, and session_affinity = ClientIP is set by default to keep a client's requests on the same pod.

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pods. latest resolves to the pinned 6-apache tag via the app-specific LIMESURVEY_VERSION build arg — pin it explicitly in production to avoid an unplanned schema upgrade.

  4. Manage secrets, storage, and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~limesurvey"
    kubectl get jobs -n "$NS"          # db-init job
    gcloud filestore instances list --project="$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 "$INSTANCE" --user=limesurvey --project="$PROJECT"

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. Also check the Filestore instance's capacity and throughput metrics, since survey uploads accumulate there. The module can provision an uptime check (when enabled); review Monitoring → Uptime checks and Alerting → Policies.

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 LimeSurvey releases.

  • Pod Ready but every page 500s with "table settings_global not found": the console installer's schema creation silently failed — almost always a storage-engine problem (the image defaults to MyISAM, which Cloud SQL disables; this module forces InnoDB) or the installer could not reach the database via the Auth Proxy sidecar. Check the pod logs from the very first boot. 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • Pod exits immediately on boot: ADMIN_PASSWORD is required — the container exits without it. Confirm the secret materialised into the namespace via the Secret Store CSI driver.
  • db-init job failed: inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<db-init-job-name>
  • Uploaded assets vanish after a pod restart: confirm enable_nfs = true and that the Filestore instance is reachable — without NFS, /var/www/html/upload is ephemeral per-pod.
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE and the cloud-sql-proxy sidecar (loopback 127.0.0.1:3306, requires enable_cloudsql_volume = true) is running alongside the app container.
  • 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 (including the critical rule to never revert the forced InnoDB engine, and to never rename application_database_name/application_database_user after first deploy).

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, Cloud Filestore instance, 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.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule deploys the GKE workload, Cloud SQL (MySQL 8.0), Filestore (NFS), storage bucket, secrets, and runs db-init
2 — Access & verifyManualConnect to the cluster; health check passes; retrieve admin password; set PUBLIC_URL; log in and create a test survey
3 — OperateManualInspect workload, scale, update version, manage secrets/storage, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, database, init-job, scheduling, and image-pull issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

By · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

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