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Kimai on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–60 minutes

Kimai is a free, open-source time-tracking application used by freelancers and agencies for billable-hours tracking, timesheets, and reporting that feeds into invoicing. This lab takes you through the full operational lifecycle of the Kimai 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 Kimai 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, and log in with the bootstrapped administrator account.
  • 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, 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 Kimai (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. If your target project has no reserved static IP or custom domain available, leave enable_custom_domain and reserve_static_ip at their defaults or set them false explicitly — this module's own live-verified deployment did exactly that. 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 (APP_SECRET, ADMINPASS, and the database password), the storage Cloud Storage bucket, builds the custom DATABASE_URL-composing wrapper image, and runs the db-init initialization job (creates the database, user, and grants). First deploys take roughly 15–25 minutes (Cloud SQL creation and the 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 kimai | 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 — Kimai's login page returns HTTP 200:

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

  3. Retrieve the bootstrapped administrator credentials from Secret Manager — the username is always admin (hardcoded by the vendor image), and the password is the auto-generated ADMINPASS secret:

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

  4. Open http://${EXTERNAL_IP} in a browser (or your custom domain, if configured) and log in with admin and the password retrieved above.

  5. Create a test project, activity, and timesheet entry to confirm end-to-end write/read against the real database: Administration → Projects (create one), Administration → Activities (create one), then log a timesheet entry against them. This is the surest sign the pod is actually writing to Cloud SQL through the Auth Proxy sidecar.

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

  1. Inspect the workload — Deployment, pods, and events:

    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). Keep max_instance_count = 1 unless you have verified Kimai's session behaviour under multiple pods.

  3. Update the application version by changing application_version in the RAD platform and applying it via Update; a new image builds FROM kimai/kimai2:<version>-apache and the pod is recreated. kimai:install re-runs safely against the existing schema on the new container's first boot — no manual migration step is needed.

  4. Manage secrets and storage:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~kimai"
    gcloud storage buckets list --project="$PROJECT" --filter="name~kimai"
    kubectl get jobs -n "$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 "$INSTANCE" --user=kimai --project="$PROJECT"

  6. Set up an API token or additional users. With the admin account logged in, go to Profile → API access to generate an API token for time-tracking integrations, or Administration → Users to invite teammates (self-service registration is off by default).

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, plus the Cloud SQL instance dashboard. The module can provision an uptime check; if 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 Kimai releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup probe targets GET /en/login with a generous 20-retry threshold to cover the first-boot kimai:install run — a connection failure to Cloud SQL via the Auth Proxy sidecar (127.0.0.1) is what actually 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 -c <service-name>
    kubectl exec -n "$NS" deploy/<service-name> -c <service-name> -- env | grep -E 'DB_IP|DATABASE_URL'
  • enable_cloudsql_volume was disabled by mistake. This module's wrapper entrypoint relies on the Cloud SQL Auth Proxy sidecar being present (DB_IP resolves to its 127.0.0.1 loopback). If enable_cloudsql_volume is set false on GKE, the pod has no path to Cloud SQL at all. Confirm the sidecar container exists: 
    kubectl get pod -n "$NS" <pod> -o jsonpath='{.spec.containers[*].name}'
  • Wrong port assumption. If you're comparing this deployment against documentation or another Kimai install that assumes port 80, note this module's :apache image variant serves on 8001 — confirmed via local testing and live deployment.
  • Initialisation job failed: inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<db-init-job-name>
  • Rollout stuck on update: check for a stuck DB connection or Auth Proxy sidecar handoff from the old pod if the new pod doesn't reach Ready promptly.
  • 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.
  • Forgot the admin password: it's not lost — ADMINPASS is a persistent Secret Manager secret, re-injected and re-applied to the admin account on every pod boot (idempotent): 
    gcloud secrets versions access latest --secret="$ADMINPASS_SECRET" --project="$PROJECT"

See the Configuration Guide's Configuration Pitfalls & Sensible Defaults 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, 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), secrets, storage bucket, and runs the db-init job
2 — Access & verifyManualConnect to the cluster; health check returns 200 at /en/login; log in as admin with the generated ADMINPASS secret; create a test timesheet entry
3 — OperateManualInspect workload, scale, update version, manage secrets/storage, DB access, API/user setup
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, sidecar, port, and database issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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