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ActualBudget on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 20–40 minutes

A personal budgeting application using envelope-based budgeting to track income and expenses. This lab takes you through the full operational lifecycle of the ActualBudget 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 ActualBudget 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 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, 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 ActualBudget (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 GCS data bucket, and builds the container image. No database or initialisation job is required. First deploys take roughly 10–20 minutes (image build 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 actualbudget | head -1 | cut -d/ -f2)
    echo "Cluster: $CLUSTER   Namespace: $NS"
    kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]​

  1. Retrieve the service endpoint and verify the liveness probe:

    kubectl get svc -n "$NS"
    # For an external IP or ingress hostname:
    ENDPOINT=$(kubectl get svc -n "$NS" -o jsonpath='{.items[0].status.loadBalancer.ingress[0].ip}')
    curl -s "http://$ENDPOINT/health/live"

    Expect an HTTP 200 response. If the service uses a Gateway or Ingress, retrieve the hostname from kubectl get gateway,ingress -n "$NS" instead.

  2. Open the ActualBudget UI in your browser. ActualBudget does not require initial credentials — you will be prompted to create or import a budget file on first access. No password retrieval is needed before you can begin.

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

  1. Inspect the workload — deployment or StatefulSet, pods, and (if enabled) the horizontal autoscaler and persistent volumes:

    kubectl get deploy,statefulset,pods,hpa,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).

  3. Update the application version by changing the version input via Update on the deployment details page; 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~actualbudget"
    gsutil ls -p "$PROJECT" | grep actualbudget
    kubectl get jobs,cronjobs -n "$NS"

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. 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 ActualBudget releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • Pending pod / resource constraints: check kubectl describe pod events for Autopilot resource or quota issues.
  • 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, Secret Manager secrets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedGKE workload and GCS bucket provisioned; image built and deployed
2 — Access & verifyManualLiveness endpoint returns 200; budget UI loads in browser
3 — OperateManualInspect workload, scale, update version, manage secrets/storage
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics
5 — TroubleshootManualDiagnose pod failures, image pull errors, and permission issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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