Speedtest Tracker on Cloud Run — Lab Guide

Overview

Estimated time: 45–60 minutes

Speedtest Tracker is an open-source, self-hosted internet speed test monitoring tool that runs automated speed tests on a schedule and charts the results over time. This lab takes you through the full operational lifecycle of the Speedtest Tracker 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 Speedtest Tracker 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 (correctly, given the cron scheduler), 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, including the "looks healthy but the schedule never fires" failure mode.
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 Speedtest Tracker (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 (kept always-on so its cron scheduler fires reliably), a Cloud SQL (MySQL 8.0) database with its Secret Manager secrets (APP_KEY and the database password), and runs a one-shot database-initialisation job. First deploys take roughly 15–25 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="$REGION" \
  --filter="metadata.name~speedtesttracker" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(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 and connected to its database. Speedtest Tracker exposes an unauthenticated health endpoint:
```
curl -s "$SERVICE_URL/api/healthcheck"   # expect a 200 JSON message
```
Open $SERVICE_URL in a browser. On first visit Speedtest Tracker's setup wizard walks you through creating the initial administrator account — no pre-seeded admin credential exists in Secret Manager. After the admin account is created, review the Settings → General page and confirm the speed test schedule (SPEEDTEST_SCHEDULE) matches what you expect; trigger an on-demand test from the dashboard to confirm end-to-end connectivity works before relying on the schedule.

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 carefully — this app is NOT a typical scale-out candidate. Speedtest Tracker's in-process Laravel scheduler has no cross-instance coordination, so max_instance_count must stay at 1 while speedtest_schedule is set (a plan-time validation enforces this). Do not raise max_instance_count unless you disable the schedule and use this deployment purely as a multi-instance dashboard. Also do not set min_instance_count = 0 or cpu_always_allocated = false — either change silently stops the schedule from ever firing while the service still reports healthy.
Update the application version tag by changing the version input in the RAD platform and applying it via Update; a new image is pulled and a new revision rolls out.

Manage secrets and backups:

gcloud secrets list --project="$PROJECT" --filter="name~speedtesttracker"
gcloud run jobs list --project="$PROJECT" --region="$REGION"   # db-init job

Open a database session for inspection or maintenance:

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

Task 4 — Observe: Logging & Monitoring [Manual]

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>".
Monitoring — open the Cloud Run dashboard for the service and review request count, request latency, instance count, and CPU / memory utilisation. Because this service is always-on (min_instance_count = 1), expect a flat 1-instance baseline rather than scale-to-zero behaviour. The module can provision an uptime check (when uptime_check_config.enabled = true — it defaults to false); if enabled, confirm it is green under Monitoring → Uptime checks.
Confirm the schedule is actually firing — check the dashboard's results history for new entries appearing at the expected cadence. A service that is Ready and passes health checks can still have a silently-dead schedule if cpu_always_allocated or min_instance_count were ever changed away from their defaults — the results history is the definitive signal, not the revision status.

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 Speedtest Tracker releases.

Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved.

gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100

"Healthy but no new results ever appear": this is the #1 Speedtest Tracker-specific symptom. Verify cpu_always_allocated = true and min_instance_count >= 1 on the deployed revision — if either was changed, the in-process cron scheduler stops completing its work under Cloud Run's request-based CPU throttling, even though the revision itself reports Ready.
```
gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION" \
  --format='value(spec.template.metadata.annotations)' | grep -i cpu-throttling
```
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the initialisation job completed successfully.

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"

Image pull / exec failures: if the LinuxServer image's s6-overlay init never prints its startup banner in the logs (zero container output before "Application exec likely failed"), this is the documented s6-overlay-under-gVisor incompatibility class — switch container_image to ghcr.io/alexjustesen/speedtest-tracker:<tag> (Alpine-based) as a fallback.
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 critical rule never to rotate APP_KEY after first boot).

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, and Secret Manager secrets. 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 (always-on), Cloud SQL (MySQL 8.0), secrets, and runs DB init
2 — Access & verify	Manual	Health check passes; create the initial admin account in the UI; trigger a test test
3 — Operate	Manual	Inspect revisions, scale carefully (max=1), update version, manage secrets/backups, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics; confirm the schedule is actually producing new results
5 — Troubleshoot	Manual	Diagnose revision, "healthy but no results," database, init-job, and image 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​