Maybe Finance on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Maybe (Maybe Finance) is an open-source, self-hosted alternative to Mint/Monarch for personal finance and wealth management — budgeting, net-worth tracking, transaction categorization, and multi-account aggregation, built on Ruby on Rails. This lab takes you through the full operational lifecycle of the Maybe Finance 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 Maybe's 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, including its mandatory PostgreSQL and Redis dependencies.
Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
Understand why the co-located Sidekiq background-job worker needs at least one pod running at all times (min_instance_count = 1).
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, Filestore 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]

Click Deploy in the RAD platform top navigation, open Maybe Finance (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.
The platform builds a thin custom wrapper image FROM ghcr.io/maybe-finance/maybe:stable, deploys the workload into the GKE Autopilot cluster with a cloud-sql-proxy sidecar (enable_cloudsql_volume = true on GKE) listening on 127.0.0.1:5432, provisions a Cloud SQL (PostgreSQL 15) database, mounts the shared Filestore NFS volume at /opt/maybefinance/storage (also the default source of the Redis host), creates the SECRET_KEY_BASE secret in Secret Manager, provisions a storage data bucket, and runs two chained one-shot jobs — db-init (creates the database/user/grants and pre-creates pgcrypto) followed by maybefinance-migrate (rails db:prepare). First deploys take roughly 20–35 minutes (Cloud SQL creation 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 maybefinance | head -1 | cut -d/ -f2)
echo "Cluster: $CLUSTER   Namespace: $NS"
kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]

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"

Confirm the service is healthy. Maybe's Rails app exposes a public, unauthenticated health endpoint that the platform's own startup/liveness probes also target:
```
curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/up"   # expect 200
```
Open http://${EXTERNAL_IP} in a browser. Maybe runs with SELF_HOSTED = "true", so the first visitor to reach the deployment registers the initial administrator account through the web UI — there is no pre-seeded admin credential in Secret Manager. Register the admin account promptly; anyone with the URL who gets there first claims that role.
Confirm the background worker is alive — Sidekiq runs in-process inside the same pod as Rails/Puma, started only if Redis was reachable at boot:
```
kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" \
  --tail=50 | grep -i sidekiq
```

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

Inspect the workload — deployment, pods, and the proxy sidecar:

kubectl get deploy,pods,pvc -n "$NS"
kubectl describe deploy -n "$NS"
kubectl exec -n "$NS" deploy/<service-name> -- ps aux | grep -E 'puma|sidekiq'

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). min_instance_count = 1 is the default on GKE (unlike the Cloud Run variant's min = 0) specifically so the co-located Sidekiq worker always has a pod to run account syncing, import processing, and notifications in — do not scale to zero in production. Session affinity (ClientIP) is set by default to keep authenticated sessions on the same pod.
Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds FROM ghcr.io/maybe-finance/maybe:<tag> (via the app-specific MAYBE_VERSION build ARG) and a rolling update replaces the pods.
Manage secrets, storage, and jobs:
```
kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~maybefinance"
kubectl get jobs -n "$NS"          # db-init and maybefinance-migrate
```
Never rotate the SECRET_KEY_BASE secret after first boot — it invalidates every active session and makes ActiveRecord-encrypted columns permanently unreadable.

Open a database session for inspection or maintenance:

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

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 (the combined Rails + Sidekiq process is memory-hungry under import/sync workloads), 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 Maybe releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets /up with roughly 8 minutes of headroom on first boot (initial_delay_seconds=60, failure_threshold=30 on the startup probe); a stuck cloud-sql-proxy sidecar or unreachable database 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. GKE pods reach it through the Auth Proxy sidecar on 127.0.0.1:5432 (enable_cloudsql_volume = true, required on GKE) with PGSSLMODE=disable on that loopback connection — check the sidecar container's logs alongside the app container's.
Initialisation/migration job failed: inspect the job and its pod logs, checking db-init before maybefinance-migrate (the latter depends on the former):
```
kubectl get jobs -n "$NS"
kubectl logs -n "$NS" job/<db-init-job-name>
kubectl logs -n "$NS" job/<maybefinance-migrate-job-name>
```
Background jobs (account sync, imports, notifications) not firing: usually means Sidekiq never started — check REDIS_URL resolved non-empty:
```
kubectl exec -n "$NS" deploy/<service-name> -- env | grep -E 'REDIS_URL|REDIS_HOST'
```
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 (including the critical rules never to rotate SECRET_KEY_BASE after first boot, and that database_type and enable_redis are enforced by plan-time guards that reject anything but PostgreSQL and a working Redis host).

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 the storage/data GCS buckets. Resources owned by Services_GCP (the VPC, GKE cluster, the shared Filestore NFS/Redis VM, shared Cloud SQL host, Artifact Registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module builds a custom wrapper image, deploys the GKE workload with a Cloud SQL Auth Proxy sidecar, Cloud SQL (PostgreSQL 15), NFS/Redis wiring, secrets, storage buckets, and runs `db-init` + `maybefinance-migrate`
2 — Access & verify	Manual	Connect to the cluster; `/up` health check passes; register the initial admin account in the UI; confirm Sidekiq started
3 — Operate	Manual	Inspect workload, scale (keep `min_instance_count ≥ 1` for Sidekiq), update version, manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and (optional) uptime check
5 — Troubleshoot	Manual	Diagnose pod, database (Auth Proxy sidecar), init/migration-job, background-job, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources; shared NFS/Redis, GKE cluster, and Cloud SQL host are untouched

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​