Miniflux on Cloud Run — Lab 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 Cloud Run 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 Cloud Run 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.
Access and verify the running service, and log in with the seeded admin account.
Perform day-2 operations — inspect revisions, scale, update the version, and manage secrets and the feed poller.
Observe the service 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, Cloud SQL, Artifact Registry, and shared service accounts this module depends on).
A Google Cloud project with billing enabled.
gcloud CLI authenticated: gcloud auth login and gcloud auth application-default login.
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]

In the RAD platform, open Miniflux (Cloud Run), 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 provisions the Cloud Run service (2 vCPU / 4 GiB, always-CPU-allocated with min = 1 so the in-process feed poller keeps running), a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (the auto-generated ADMIN_PASSWORD and the database password), a default (unused) data Cloud Storage bucket, 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).

When it completes, discover the resources with name-agnostic filters (so the commands keep working regardless of the deployment suffix):

SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
  --filter="metadata.name~miniflux" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(gcloud run services describe "$SERVICE" \
  --project="$PROJECT" --region="$REGION" --format="value(status.url)")
echo "Service: $SERVICE"
echo "URL:     $SERVICE_URL"

Task 2 — Access & verify [Manual]

Confirm the service is healthy. Miniflux serves an unauthenticated 200 OK at the root path, which is also the probe target:
```
curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/"   # expect 200
```
Retrieve the seeded initial owner password from Secret Manager (the account is created on first boot — there is no self-service signup):
```
gcloud secrets versions access latest \
  --secret=secret-<resource-prefix>-miniflux-admin-password --project="$PROJECT"
```
Substitute <resource-prefix> with the real secret name from gcloud secrets list --project="$PROJECT" --filter="name~miniflux".
Open $SERVICE_URL in a browser and log in with username admin (or the ADMIN_USERNAME you configured) and the retrieved password. If you front the service with a custom domain or load balancer, set BASE_URL (via environment_variables) to that URL so Miniflux emits correct absolute links and feed-proxy image URLs.

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

Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (a manual edit would be reverted on the next apply). Keep min_instance_count = 1 (the default) so the in-process feed poller keeps refreshing between requests; dropping to 0 (scale-to-zero) stops background polling unless you externalize it with a Cloud Scheduler hit to /v1/feeds/refresh.
Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a new revision rolls out. 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.

Manage secrets and jobs:

gcloud secrets list --project="$PROJECT" --filter="name~miniflux"
gcloud run jobs list --project="$PROJECT" --region="$REGION"        # db-init + any scheduled jobs
gcloud run jobs executions list --job="${SERVICE}-db-init" \
  --project="$PROJECT" --region="$REGION"

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]

Logs — from the CLI or the Logs Explorer. The entrypoint logs its DATABASE_URL connection mode (socket / loopback / private-IP TCP) at start — useful when diagnosing DB connectivity:
```
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50
```
Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".
Monitoring — open the Cloud Run dashboard for the service and review request count, request latency (P50/P95/P99), instance count, and CPU / memory utilisation. Because cpu_always_allocated = true and min = 1 are the defaults, expect one instance to remain warm continuously (this is the trade-off for the in-process feed poller). The module can also provision an uptime check; if enabled, confirm it is green under Monitoring → Uptime checks, and review 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.

Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved. The startup probe targets / (root, unauthenticated) with a generous first-boot window.
```
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
```
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the db-init job completed successfully (it creates the miniflux database/role and the hstore extension owned by the app role).

Initialisation job failed: list executions and read the failed one's logs:

gcloud run jobs executions list --job="${SERVICE}-db-init" \
  --project="$PROJECT" --region="$REGION"

Feeds not refreshing: the feed poller runs in-process on POLLING_FREQUENCY. If the service is scaled to zero (cpu_always_allocated = false / min_instance_count = 0), polling stops while idle unless you drive /v1/feeds/refresh externally via Cloud Scheduler.
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.
Image build failed: review Cloud Build history for the failed build's log.
403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the immutability of db_name/db_user after first deploy and the consequences of disabling cpu_always_allocated/min_instance_count).

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 Cloud Run service, Cloud SQL database, Secret Manager secrets, the GCS bucket, and any Filestore NFS mount and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run, Cloud SQL (PostgreSQL 15), secrets, a default storage bucket, and runs DB init
2 — Access & verify	Manual	Health check passes; retrieve the seeded admin password and log in
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/jobs, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, init-job, feed-poller, and build/IAM 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​