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Miniflux on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Miniflux is a minimalist, self-hosted RSS/Atom feed reader — a single static Go binary that stores all of its state in PostgreSQL. This lab takes you through the full operational lifecycle of the Miniflux 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 Miniflux 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, find the namespace, and access the running workload.
  • Perform day-2 operations — inspect, scale, update, and manage secrets and jobs.
  • 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 Miniflux (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 as a stateless Deployment (Miniflux keeps all state in PostgreSQL, so no PVC is required), provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (the auto-generated ADMIN_PASSWORD and the database password), builds the container image, and runs a one-shot database-initialisation job that creates the miniflux database/role and installs the hstore extension. First deploys take roughly 20–35 minutes (Cloud SQL creation 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 miniflux | 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. Miniflux's startup and liveness probes default to the root path / (the login page, an unauthenticated 200 OK); a dedicated /healthcheck endpoint is also available unauthenticated:

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

  3. Retrieve the seeded initial owner password from Secret Manager (the account is created on first boot — there is no self-service signup):

    gcloud secrets versions access latest \
      --secret=secret-<resource-prefix>-miniflux-admin-password --project="$PROJECT"

    Substitute <resource-prefix> with the real secret name from gcloud secrets list --project="$PROJECT" --filter="name~miniflux".

  4. Open http://${EXTERNAL_IP} in a browser and log in with username admin (or the ADMIN_USERNAME you configured) and the retrieved password. If you enable a custom domain, set BASE_URL (via environment_variables) to that URL so Miniflux emits correct absolute links.

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

  1. Inspect the workload — deployment, pods, and the horizontal autoscaler:

    kubectl get deploy,pods,hpa -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 min_instance_count = 1 (the default and the GKE minimum) so the in-process feed poller keeps refreshing; extra replicas each poll independently since there is no shared queue to coordinate them. Session affinity (ClientIP) is set by default to keep a client pinned to one pod for a consistent UI session.

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pods. Miniflux applies its own schema migrations on boot, so no separate migrate step is needed — allow extra time on the first boot after an upgrade.

  4. Manage secrets and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~miniflux"
    kubectl get jobs -n "$NS"          # db-init and any scheduled jobs

  5. Open a database session for inspection or maintenance:

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

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

  1. Logs — from kubectl or the Logs Explorer. The entrypoint logs its DATABASE_URL connection mode at start — useful when diagnosing DB connectivity:

    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. 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 Miniflux releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets /; a connection failure to PostgreSQL (via the Cloud SQL Auth Proxy sidecar) 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, enable_cloudsql_volume = true (the proxy sidecar is required on GKE), and the db-init job completed — it creates the miniflux database/role and the hstore extension owned by the app role, then signals the sidecar to exit.
  • Initialisation job failed: inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<job-name>
  • Feeds not refreshing: the feed poller runs in-process on POLLING_FREQUENCY inside every pod. Confirm min_instance_count >= 1 — GKE does not scale to zero, but a workload with zero healthy replicas stops polling entirely.
  • Can't log in / lost the admin password: re-read the ADMIN_PASSWORD secret (see Task 2); CREATE_ADMIN only seeds the account on first boot and is idempotent on later boots.
  • 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 immutability of application_database_name/ application_database_user after first deploy and the binary-unit requirement on quota_memory_requests/quota_memory_limits).

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, the GCS bucket, any Filestore NFS mount, 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 (PostgreSQL 15), secrets, and runs DB init
2 — Access & verifyManualConnect to the cluster; health check passes; retrieve the seeded admin password and log in
3 — OperateManualInspect workload, scale, update version, manage secrets/jobs, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, database, init-job, feed-poller, scheduling, and image-pull issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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