Beszel on Cloud Run — Lab Guide

Overview

Estimated time: 45–90 minutes

Beszel is a lightweight, open-source server-monitoring hub — historical resource metrics, Docker container stats, and configurable alerts, built on PocketBase with an embedded SQLite database. This lab takes you through the full operational lifecycle of the Beszel 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 Beszel 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 hub, and complete the first-run admin setup.
Perform day-2 operations — inspect, update, and manage the FUSE-mounted data bucket.
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, 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]

Click Deploy in the RAD platform top navigation, open Beszel (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.
The platform provisions the Cloud Run service (single Go container on port 8090), a dedicated Cloud Storage data bucket FUSE-mounted at /beszel_data (which holds the embedded SQLite database and all monitoring history), and mirrors the Beszel image into Artifact Registry. No Cloud SQL, Redis, or init job is created — Beszel is self-contained, so the deploy is quick (typically 10–20 minutes).

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="$REGION" \
  --filter="metadata.name~beszel" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(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 hub is healthy. Beszel's health path is /api/health, a public, unauthenticated endpoint that returns HTTP 200 once the hub is ready (first boot creates the SQLite schema, so allow up to a minute on a fresh deploy):
```
curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/api/health"
```
Open $SERVICE_URL in a browser. On first boot Beszel presents PocketBase's first-run superuser (admin) setup — enter an admin email and password to complete it. No admin credential is stored in Secret Manager; the account you create here lives in the SQLite database on the data bucket.
After creating the admin, add a system to monitor: the hub shows the agent install command and its public key. Install the Beszel agent on a machine you want to watch and confirm it starts reporting to the hub URL. Note that ingress_settings = "all" (the default) is what lets remote agents reach the hub — keep it that way unless every agent is inside the VPC.

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"

Scaling is deliberately pinned. Beszel runs min_instance_count = 1 and max_instance_count = 1 — one warm instance, one SQLite writer. Do not raise max_instance_count: more than one instance against the shared FUSE-mounted database risks lock contention and corruption. Any change is a configuration change made via Update on the deployment details page, not a manual gcloud edit (a manual edit would be reverted on the next apply).
Update the application version by changing the version input via Update on the deployment details page; the new image tag is mirrored and a new revision rolls out. Beszel migrates its embedded database automatically on upgrade. Pin an explicit tag rather than latest to control when that happens.
Inspect the state that matters — the data bucket. The bucket is the database (SQLite file, config, metric history):
```
gcloud storage buckets list --project="$PROJECT" --filter="name~beszel"
BUCKET=$(gcloud storage buckets list --project="$PROJECT" \
  --filter="name~beszel" --format="value(name)" --limit=1)
gcloud storage ls "gs://$BUCKET/"
```
There is no Cloud SQL session to open in this lab — all persistence is this bucket. Treat it as production data: never delete or clear it while the deployment lives.

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 (P50/P95/P99), instance count (should be flat at 1), and CPU / memory utilisation. The module also provisions an uptime check; confirm it is green under Monitoring → Uptime checks, and review Alerting → Policies. (Meta note: this GCP monitoring observes the hub; Beszel itself monitors the machines its agents run on.)

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 Beszel releases.

Revision unhealthy / service won't serve: the startup probe targets /api/health with a retry window that covers first-boot schema creation. Inspect the latest revision and its logs before concluding the service failed:
```
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
```
State not persisting / history gone after redeploy: verify the execution environment is gen2 (required for the GCS FUSE /beszel_data mount) and that the data bucket exists and is mounted on the running revision (gcloud run services describe "$SERVICE" --format=json and check the volumes).
Agents can't report: confirm ingress_settings = "all" and that IAP is off — IAP blocks all unauthenticated requests, including agent metric posts from machines that cannot present a Google identity.
Database locked / intermittent 500s: check the instance count. If max_instance_count was raised above 1, two writers are fighting over one SQLite file — set it back to 1 immediately.
Image pull failed: the module mirrors the upstream Beszel image into Artifact Registry (enable_image_mirroring = true); review Cloud Build history for the mirror step's log and confirm the tag exists upstream.
403 / permission errors: verify the runtime service account's IAM roles (it needs access to the data bucket for the FUSE mount).

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

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, the Cloud Storage data bucket (which is the SQLite database — all monitoring history and the admin account go with it), and the mirrored Artifact Registry images. Resources owned by Services_GCP (the VPC, shared registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run (port 8090), a GCS FUSE data bucket, and mirrors the image — no DB, no Redis
2 — Access & verify	Manual	`/api/health` returns 200; complete the PocketBase superuser setup and connect an agent
3 — Operate	Manual	Inspect revisions, respect the single-instance pin, update version, inspect the data bucket
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, FUSE persistence, agent-ingress, SQLite-lock, mirror, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources including the data bucket

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​