Skip to main content

Jellystat on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–75 minutes

Jellystat is an open-source statistics and analytics dashboard for Jellyfin media servers. This lab takes you through the full operational lifecycle of the Jellystat 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 Jellystat 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.
  • 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.
  • (Optional, for the pairing step in Task 2) A running Jellyfin server — deploy one first with the Jellyfin (GKE) or Jellyfin (Cloud Run) module if you don't already have one.

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 Jellystat (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 (the official cyfershepard/jellystat prebuilt image — no build step), provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (JWT_SECRET and the database password), and runs a one-shot database-initialisation job. First deploys take roughly 15–25 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 jellystat | 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. Jellystat exposes a public, unauthenticated endpoint that responds only when the server is up:

    curl -s "http://${EXTERNAL_IP}/auth/isConfigured"   # expect 200 with a JSON body

  3. Open http://${EXTERNAL_IP} in a browser. On first visit Jellystat prompts you to create the initial administrator account — no pre-seeded admin credential exists in Secret Manager.

  4. Pair with a Jellyfin server (manual, cannot be automated). After logging in:

    • In your Jellyfin server's own Dashboard → API Keys, generate a new API key for Jellystat.
    • In Jellystat's settings, enter your Jellyfin server's URL and paste in that API key.
    • Confirm Jellystat begins showing library/user data pulled from Jellyfin. There is no environment variable or Terraform input for this pairing — it is entirely UI-driven by design of the upstream application.

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

  1. Inspect the workload — deployment and pods:

    kubectl get deploy,pods -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 in the RAD platform and applying it via Update; a rolling update replaces the pods with the updated cyfershepard/jellystat:<tag> image.

  4. Manage secrets and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~jellystat"
    kubectl get jobs -n "$NS"          # db-init job

  5. Open a database session for inspection or maintenance:

    INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
    gcloud sql connect "$INSTANCE" --user=jellystat_user --project="$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 and restart counts. The module can provision an uptime check (when enabled); review Monitoring → Uptime checks.

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 Jellystat releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup and liveness probes target /auth/isConfigured; a connection failure to PostgreSQL will keep 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
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init job completed. Confirm the pod actually received POSTGRES_IP/POSTGRES_USER/POSTGRES_DATABASE/POSTGRES_PASSWORD (not just DB_*): 
    kubectl exec -n "$NS" <pod> -- env | grep POSTGRES
  • Initialisation job failed: inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<job-name>
  • Jellystat shows no data even though the pod is healthy: this is almost always the Jellyfin pairing step (Task 2, step 4) not having been completed yet — it is not automated by this module.
  • 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.

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, 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 (PostgreSQL 15), secrets, and runs DB init
2 — Access & verifyManualConnect to the cluster; health check passes; create the admin account; pair with a Jellyfin server
3 — OperateManualInspect workload, scale, update version, manage secrets, 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

Last updated on