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Coder on Cloud Run — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Coder is an open-source, self-hosted platform for provisioning remote development environments (workspaces) defined as code with Terraform. This lab takes you through the full operational lifecycle of the Coder on Cloud Run module on Google Cloud: deploy the control plane, 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 Coder product features such as templates and workspaces. 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 the Coder control plane, create the first admin account, and verify health.
  • 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

Task 1 — Deploy the module [Automated]​

  1. Click Deploy in the RAD platform top navigation, open Coder (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 password secret, a dedicated GCS bucket, mirrors the ghcr.io/coder/coder base image into Artifact Registry, builds the custom entrypoint image with Cloud Build, and runs a one-shot database-initialisation job. Coder applies its own schema migrations on first server boot. 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="$REGION" \
      --filter="metadata.name~coder" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(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]​

  1. Confirm the service is healthy. Coder serves an unauthenticated health endpoint at /healthz (HTTP 200 once the server is up — allow a minute or two on a fresh deploy while first-boot schema migrations run):

    curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/healthz"
    curl -s "$SERVICE_URL/api/v2/buildinfo"     # returns the deployed Coder version

  2. Open $SERVICE_URL in a browser. On first boot Coder presents the setup page — create the initial admin (owner) account with your name, email, and password. Do this promptly: the setup page is publicly reachable until the first account exists. The database password (the only credential stored in Secret Manager) can be retrieved if needed:

    DB_SECRET=$(gcloud secrets list --project="$PROJECT" \
      --filter="name~coder" --format="value(name)" --limit=1)
    gcloud secrets versions access latest --secret="$DB_SECRET" --project="$PROJECT"

  3. Post-deploy hardening and next steps: consider fronting the service with IAP or Cloud Armor for a private team, and note that running actual workspaces requires a day-2 step — create a Coder template (Terraform) pointing at a compute target such as a GKE cluster or GCE VMs, and give the provisioner credentials for it. The control plane alone runs no workspaces.

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). Keep min_instance_count >= 1 and cpu_always_allocated = true: Coder's in-process provisioner daemons poll PostgreSQL for pending workspace builds, and scale-to-zero or CPU throttling silently stalls them.

  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. Coder's tags are semver-prefixed (e.g. v2.24.1); the module maps latest to a pinned tag. Schema migrations run automatically on the new revision's first boot.

  4. Manage secrets, storage, and jobs:

    gcloud secrets list --project="$PROJECT" --filter="name~coder"
    gcloud run jobs list --project="$PROJECT" --region="$REGION"   # db-init + backup jobs
    gcloud storage buckets list --project="$PROJECT" --filter="name~coder"

  5. Open a database session for inspection or maintenance (PostgreSQL):

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

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

  1. Logs — from the CLI or the Logs Explorer. The custom entrypoint logs the resolved PostgreSQL host and access URL at every start:

    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 (should hold steady at the warm minimum), and CPU / memory utilisation. If you enabled uptime_check_config, confirm the check 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 Coder releases.

  • Revision unhealthy / service won't serve: the startup probe targets /healthz with a 60-second initial delay and a generous failure threshold to cover first-boot schema migration. Inspect the latest revision and its logs before concluding the service has failed: 
    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 (PostgreSQL 15) instance is RUNNABLE, the DB password secret exists, and the db-init job completed. The entrypoint log line PG host: <ip> (sslmode=require) shows what it resolved — a URL-parse error in the logs means an explicit CODER_PG_CONNECTION_URL override is malformed (the module's assembled URL percent-encodes the password automatically).
  • 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 build failed: review Cloud Build history for the failed build's log. The container_image_source must be custom — the upstream Coder image lacks the entrypoint that assembles the DB connection URL and access URL. A MANIFEST_UNKNOWN on the base image means a non-existent version tag — Coder tags are semver-prefixed (vX.Y.Z).
  • Workspace builds queued but never start: this is the Coder-specific trap. The provisioner daemons run inside coder server and poll the database — if the service was scaled to zero (min_instance_count = 0) or flipped to request-based billing (cpu_always_allocated = false), they stall between requests. Restore the defaults (min = 1, always-allocated) via the RAD Update flow. Also verify CODER_ACCESS_URL matches the URL developers actually use — a mismatch breaks workspace agent connections.
  • 403 / permission errors: verify the runtime service account's IAM roles.

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, 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.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions Cloud Run, Cloud SQL (PostgreSQL 15), GCS bucket, secrets, builds the image, and runs DB init
2 — Access & verifyManual/healthz returns 200; create the first admin (owner) account
3 — OperateManualInspect revisions, scale (keep min=1/always-on), update version, manage secrets/backups, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose revision, database, init-job, build, stalled-provisioner, and IAM issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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