Grafana on Cloud Run — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Grafana is an open-source observability and analytics platform that provides unified dashboards, alerting, and visualisation for metrics, logs, and traces from a wide range of data sources. This lab takes you through the full operational lifecycle of the Grafana 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 Grafana 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.
• Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
• 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

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Task 1 — Deploy the module [Automated]

1. Click Deploy in the RAD platform top navigation, open Grafana (Cloud Run) 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 provisions the Cloud Run service, a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets, a grafana-data GCS bucket, builds the container image, and starts the service. Grafana auto-migrates its schema on first startup — no separate database-initialisation job is required. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

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

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Task 2 — Access & verify [Manual]

1. Confirm the service is healthy and connected to its database:

   curl -s "$SERVICE_URL/api/health"   # expect {"commit":"...","database":"ok","version":"..."}

2. Grafana does not auto-generate an admin password. If you injected one via secret_environment_variables at deploy time, retrieve it now:

   ADMIN_SECRET=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~grafana-admin-password" --format="value(name)" --limit=1)
   gcloud secrets versions access latest --secret="$ADMIN_SECRET" --project="$PROJECT"

   If no custom password was set, Grafana defaults to admin / admin — change it immediately after first login. Sign in at ${SERVICE_URL}.

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

1. 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"

2. 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).

3. Update the application version by changing the version input via Update on the deployment details page; a new image builds and a new revision rolls out.

4. Manage secrets and storage:

   gcloud secrets list --project="$PROJECT" --filter="name~grafana"
   gcloud storage buckets list --project="$PROJECT" --filter="name~grafana"
   gcloud run jobs list --project="$PROJECT" --region="$REGION"   # scheduled backup 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 "$INSTANCE" --user=grafana --project="$PROJECT"

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Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from the CLI or the Logs Explorer:

   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>".

2. Monitoring — open the Cloud Run dashboard for the service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. The module also provisions an uptime check targeting /api/health; confirm it is green under Monitoring → Uptime checks, and review Alerting → Policies.

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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 Grafana releases.

• Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors. Grafana migrates its schema on first boot — allow up to ~150 seconds before declaring a startup failure.

  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 GF_DATABASE_TYPE=postgres is present in the service environment (it is injected automatically — do not override it).
• Admin password not set: if Grafana was deployed without a GF_SECURITY_ADMIN_PASSWORD secret, it uses the default admin/admin credentials. Create a secret and apply it via Update to inject a strong password.
• 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.

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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, GCS buckets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run, Cloud SQL (PostgreSQL 15), GCS bucket, secrets; Grafana auto-migrates its schema
2 — Access & verify	Manual	Health check passes at /api/health; retrieve admin credential and sign in
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/storage/backups, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, admin-password, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources