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Chatwoot on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Chatwoot is an open-source, multi-channel helpdesk and customer-engagement platform (email, live chat, social, and messaging inboxes, SLA tracking, and reporting) — a GDPR-compliant alternative to Zendesk or Intercom. This lab takes you through the full operational lifecycle of the Chatwoot 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 Chatwoot 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 (and its co-located Sidekiq worker) 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, 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

Task 1 — Deploy the module [Automated]​

  1. Click Deploy in the RAD platform top navigation, open Chatwoot (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 builds a custom Chatwoot container image (chatwoot/chatwoot wrapped with a cloud entrypoint), deploys the workload into the GKE Autopilot cluster behind an external LoadBalancer, provisions a Cloud SQL (PostgreSQL 15, with pgvector) database with its Secret Manager secrets (SECRET_KEY_BASE and the database password), a Cloud Storage bucket, a Filestore NFS mount for attachments, and Redis. It then runs two chained initialization jobs — db-init (creates the database, role, and grants, including cloudsqlsuperuser) followed by chatwoot-prepare (rails db:chatwoot_prepare, using the built app image, to create the schema). First deploys take roughly 20–35 minutes (Cloud SQL creation and the custom image build 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 chatwoot | 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 chained initialization jobs both completed successfully before trusting the schema is ready:

    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<db-init-job-name>
    kubectl logs -n "$NS" job/<chatwoot-prepare-job-name>

  3. Confirm the service is healthy. Chatwoot's login/onboarding page responds with HTTP 200 and needs no authentication:

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

  4. Open http://${EXTERNAL_IP} in a browser. On first visit Chatwoot's onboarding UI prompts you to create the initial administrator account interactively — no pre-seeded admin credential exists in Secret Manager. Fill in your name, email, and a password to finish onboarding. ENABLE_ACCOUNT_SIGNUP defaults to "false", so afterwards only invited users can join; flip it temporarily via environment_variables if you need public self-service signup.

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

  1. Inspect the workload — deployment, pods, and disruption budget:

    kubectl get deploy,pods,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). Chatwoot's Sidekiq worker (background job delivery, notifications, reports) and ActionCable (real-time UI updates) run co-located in the same pod, so keep min_instance_count >= 1 — the workload should not be scaled to zero in production. 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 application_version input (the chatwoot/chatwoot image tag) in the RAD platform and applying it via Update; a new image builds 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~chatwoot"
    kubectl get jobs -n "$NS"          # db-init + chatwoot-prepare
    kubectl get pvc -n "$NS"

  5. Open a database session for inspection or maintenance:

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

  6. Check attachment persistence — uploaded files live on Filestore NFS at /opt/chatwoot/storage, not the auto-provisioned storage-suffixed GCS bucket:

    gcloud storage buckets list --project="$PROJECT" --filter="name~storage"
    gcloud filestore instances list --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]​

  1. Logs — from kubectl or the Logs Explorer. Both the Rails web process and the co-located Sidekiq worker write to the same pod stdout/stderr:

    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 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 Chatwoot releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets GET /; it allows an initial 60-second delay and up to 30 retries at a 15-second period, sized to absorb chatwoot-prepare finishing ahead of the app container. 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • Initialization job failed: chatwoot-prepare depends on db-init completing first; if schema prep fails with must be superuser on CREATE EXTENSION, the cloudsqlsuperuser grant in db-init did not land. Inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<job-name>
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init jobs completed. On GKE, pods reach Postgres via a cloud-sql-proxy sidecar on 127.0.0.1:5432 — a different mechanism from the CloudRun variant's Unix socket.
  • Background jobs/notifications not delivering but the UI loads fine: Sidekiq only runs while the pod is alive. Confirm min_instance_count >= 1 — scaling the workload to zero stops background delivery entirely.
  • Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP (reserve_static_ip = true keeps it stable across redeploys).
  • Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it. A nonexistent application_version tag (e.g. an inherited default from another app) fails the build with MANIFEST_UNKNOWN — confirm the tag exists on Docker Hub for chatwoot/chatwoot.

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

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, Secret Manager secrets, GCS buckets, NFS-hosted attachments, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, NFS/Redis, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule builds the custom Chatwoot image, deploys the GKE workload, Cloud SQL (PostgreSQL 15 + pgvector), secrets, storage, NFS, Redis, and runs the chained db-init → chatwoot-prepare jobs
2 — Access & verifyManualConnect to the cluster; init jobs confirmed successful; health check (GET /) passes; create the initial admin account in the UI
3 — OperateManualInspect workload, scale (keep min >= 1), update version, manage secrets/storage, DB access, verify attachment persistence
4 — ObserveManualQuery Cloud Logging (web + Sidekiq); review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, init-job, database, background-delivery, scheduling, and image-pull issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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