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ToolJet on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

ToolJet is an open-source, low-code platform for building and deploying internal tools — dashboards, admin panels, and CRUD apps — with a drag-and-drop builder over your own databases and APIs. This lab takes you through the full operational lifecycle of the ToolJet 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 ToolJet 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.
  • Complete the first-run setup wizard and perform day-2 operations.
  • 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, 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 ToolJet (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) instance with two databases (the metadata DB and the ToolJet Database) and their Secret Manager secrets (SECRET_KEY_BASE, LOCKBOX_MASTER_KEY, PGRST_JWT_SECRET, and the database password), builds the container image, and runs a one-shot database-initialisation job (creating both databases and the CREATEROLE app role). First deploys take roughly 20–35 minutes (Cloud SQL creation 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 tooljet | 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. ToolJet exposes a public health endpoint that returns 200 once the server has finished its on-boot migrations and is listening:

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

  3. Open http://${EXTERNAL_IP} (or the provisioned nip.io HTTPS host) in a browser. On first visit ToolJet presents a setup wizard — because DISABLE_SIGNUPS = "true" ships on, this is the only way to create the first account. Fill in your name, email, and a password to create the initial admin user and workspace; you then land in the ToolJet app builder. There is no pre-seeded admin credential in Secret Manager.

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

  1. Inspect the workload — deployment, pods, and the horizontal autoscaler:

    kubectl get deploy,pods,hpa,pdb -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). Session affinity (ClientIP) is set by default to keep the multiplayer editor's WebSocket connections pinned to one 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. The entrypoint re-runs db:migrate:prod, so schema changes are applied on boot.

  4. Manage secrets, and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~tooljet"
    kubectl get jobs -n "$NS"          # DB-init and any scheduled jobs

  5. Open a database session for inspection or maintenance (note the two databases):

    INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
    gcloud sql connect "$INSTANCE" --user=tooljet --database=tooljet --project="$PROJECT"
    gcloud sql connect "$INSTANCE" --user=tooljet --database=tooljet_db --project="$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>". Look for the [cloud-entrypoint] lines confirming the config and the db:migrate:prod run.

  2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. 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 ToolJet releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets /api/health; a connection failure to PostgreSQL or a failed migration keeps the pod from becoming Ready. 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • permission denied for schema postgrest: the postgrest schema is not app-owned — re-run the db-init job, which drops and recreates it AUTHORIZATION the app user.
  • permission denied to create role when creating a workspace: the app role is missing the CREATEROLE attribute — re-run the db-init job.
  • Pod binds the wrong port / probe never passes: confirm PORT resolves to 80 (the entrypoint defaults it); ToolJet's built-in default is 3000, which the Service/probes do not target.
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init job completed.
  • Initialisation job failed: inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<job-name>
  • Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule never to rotate LOCKBOX_MASTER_KEY after first boot).

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, both Cloud SQL databases, Secret Manager secrets, 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 (two PostgreSQL 15 databases), secrets, and runs DB init
2 — Access & verifyManualConnect to the cluster; health check passes; complete the setup wizard to create the admin + workspace
3 — OperateManualInspect workload, scale, update version, manage secrets, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, PostgREST/role, migration, database, init-job, and scheduling issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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