Fider on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Fider is an open-source, self-hosted feedback and feature-voting board — customers post ideas, vote, and comment, and you prioritise your roadmap by demand. This lab takes you through the full operational lifecycle of the Fider 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 Fider 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, access the workload, and complete Fider's first-run site/admin setup.
Perform day-2 operations — inspect, scale, update, and manage secrets and the database.
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, 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 Fider (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 deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (JWT_SECRET and the database password), a Cloud Storage data bucket, a Cloud Filestore NFS mount for attachments (enabled by default), builds the container image, and runs a one-shot database-initialisation job that creates the fider role and database. 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 fider | 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. Fider exposes an unauthenticated /_health endpoint that returns 200 once the server has booted and run its schema migrations. The container listens on port 3000 — on GKE the PORT env is not auto-injected, so container_port and the probe port must both be 3000 or the pod never becomes Ready even though the app is healthy:
```
curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/_health"   # expect 200
```
Open http://${EXTERNAL_IP} in a browser. There are no default credentials — the first visit walks you through creating the site and its admin owner account. Complete this immediately after deploy.
Email is disabled for the demo (EMAIL_NOEMAIL = true), so sign-up and invite links are printed to the pod log rather than sent. Check the logs if you invite additional users before wiring real SMTP:
```
kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" \
  --tail=100 | grep -i "sign-in\|invite"
```

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

Inspect the workload — deployment, pods, and the horizontal autoscaler:
```
kubectl get deploy,pods,hpa -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). GKE does not support scale-to-zero, so min_instance_count stays at least 1.
Update the application version by changing the application_version input in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pods. Because Fider is NFS-backed, App_GKE deploys it with the Recreate update strategy rather than RollingUpdate — two pods sharing the same NFS volume and database would deadlock, so expect a brief moment of downtime during an update, not a zero-downtime rollout. Note getfider/fider has no :latest tag — the module pins latest to stable; pin an explicit SHA tag for reproducible upgrades.
Manage secrets and jobs:
```
kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~fider"
kubectl get jobs -n "$NS"          # db-init job
```
JWT_SECRET signs all authentication and session tokens (including emailed magic sign-in links) — never rotate it after first boot; doing so invalidates every active session and pending sign-in link.

Open a database session for inspection or maintenance:

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

Check the NFS mount:

gcloud filestore instances list --project="$PROJECT"
kubectl get pvc,pv -n "$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>". When email is disabled, sign-up / invite links appear here — this is expected, not an error.
Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, 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 Fider releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets /_health on port 3000; a mismatched container_port (GKE does not auto-inject PORT) or a slow database connection 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 db-init job completed (it idempotently creates the fider role/database and is safe to re-run).
```
kubectl get jobs -n "$NS"
kubectl logs -n "$NS" job/<db-init-job-name>
```
Rollout appears stuck after an update: this is expected for one moment — NFS-backed apps use the Recreate strategy, so the old pod is fully terminated before the new one starts (unlike a RollingUpdate surge). Confirm progress with:
```
kubectl rollout status deploy/<deployment-name> -n "$NS"
```
Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
```
kubectl get svc -n "$NS" -o wide
```
NFS-related mount failures: confirm the shared Filestore NFS VM (managed by Services_GCP) is RUNNING before this app was deployed; a stopped/absent NFS server at deploy time is a common cause of storage mount errors.
```
gcloud filestore instances list --project="$PROJECT"
```
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 & Sensible Defaults section for setting-specific gotchas (including the critical rule never to rotate JWT_SECRET after first boot, why application_database_name/ application_database_user are immutable after first deploy, and why quota_memory_requests/quota_memory_limits require binary unit suffixes).

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, Filestore NFS, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL (PostgreSQL 15), secrets, storage bucket, NFS mount, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; create the site and admin owner on first visit
3 — Operate	Manual	Inspect workload, scale, update version (Recreate strategy), manage secrets, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, rollout, scheduling, NFS, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

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​