ActualBudget on GKE Autopilot — Lab 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 (provides the VPC, GKE Autopilot cluster, Artifact Registry, and shared service accounts this module depends on). You do not need to deploy this yourself first — the platform automatically detects whether it already exists in the target project and provisions it before this module if not (see Task 1).
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.
Bringing your own project? Before the first deploy into it, the deployment confirmation dialog asks you to prove you control it (Get verification code, run the commands it shows as a project Owner, then Verify) and to give the RAD deployment service account the Owner role. A project RAD creates for you needs neither.
Advanced mode for later changes. The create form asks only for the first page of inputs (and, in a project RAD creates for you, little more than the tenant name and region). Every other input in the Configuration Guide — including the scaling and version inputs in the Day-2 tasks — is changed afterwards with Update on the deployment's page after ticking Enable advanced mode, which needs a credit balance that covers the update's estimated build cost (updates never carry a module fee). On a lab environment only an administrator can use Advanced mode.
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]

Open Solutions → Solution Catalog → RAD modules in the RAD platform top navigation, open ActualBudget (GKE) from the Platform Modules list to start configuration, choose Configuration Form under How would you like to configure this deployment? (the form opens on the Conversational Assistant if you hold purchased credits or are a partner or administrator), set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Click Deploy Module, review the estimated cost in the Deployment Confirmation dialog when it appears and click Submit (if the dialog then adds a confirmation step, such as verifying a project you bring, complete it and click Confirm), which opens the deployment status page with real-time logs.
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).

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]

Retrieve the service and verify the liveness probe. service_type defaults to LoadBalancer for this module, so an external IP is provisioned out of the box (it can take a minute or two to be assigned). While the address is still pending — or if you deliberately set service_type = "ClusterIP" — port-forward for a quick check:
```
kubectl get svc -n "$NS"
SVC=$(kubectl get svc -n "$NS" -o jsonpath='{.items[0].metadata.name}')
kubectl port-forward -n "$NS" "svc/$SVC" 5006:5006
```
In a second terminal:
```
curl -s -o /dev/null -w "%{http_code}\n" "http://localhost:5006/"
```
Expect an HTTP 200 response — the same root path the deployment's own startup and liveness probes check. For durable external access, set service_type = "LoadBalancer" via Update on the deployment details page, then retrieve the external IP:
```
ENDPOINT=$(kubectl get svc -n "$NS" "$SVC" -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
curl -s -o /dev/null -w "%{http_code}\n" "http://$ENDPOINT/"
```
Open the ActualBudget UI in your browser (the port-forward URL, or the external IP once service_type = LoadBalancer is set). 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]

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"
```
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).
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.

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]

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>".
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 (from the same Delete dialog) — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the deployment). 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

Task	Type	Outcome
1 — Deploy	Automated	GKE workload and GCS bucket provisioned; image built and deployed
2 — Access & verify	Manual	Liveness endpoint returns 200; budget UI loads in browser
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics
5 — Troubleshoot	Manual	Diagnose pod failures, image pull errors, and permission issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

Need RAD to do something it does not do yet? Request it on the roadmap, or vote on what is already there.

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

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

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

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​