Hasura on Cloud Run — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Hasura is an open-source engine that gives you an instant, realtime GraphQL and REST API over a PostgreSQL database, with role-based authorization and a built-in admin console. This lab takes you through the full operational lifecycle of the Hasura on Cloud Run module on Google Cloud: deploy it, access and verify it, open the console and run a GraphQL query, 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 Hasura product internals. 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.
• Retrieve the admin secret and open the Hasura console.
• Track a table and run a GraphQL query end to end.
• 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. In the RAD platform, open Hasura (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.

2. The platform provisions the Cloud Run service, a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (HASURA_GRAPHQL_ADMIN_SECRET and the database password), builds the container image (a thin wrapper over hasura/graphql-engine), and runs a one-shot database-initialisation job. 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="$REGION" \
     --filter="metadata.name~hasura" --format="value(metadata.name)" --limit=1)
   SERVICE_URL=$(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. Hasura exposes a public health endpoint that returns 200 only when the engine is up and connected to PostgreSQL:

   curl -s -o /dev/null -w '%{http_code}\n' "$SERVICE_URL/healthz"   # expect 200

2. Retrieve the admin secret from Secret Manager — you need it for the console and for every GraphQL/metadata API call:

   ADMIN_SECRET_NAME=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~admin-secret" --format="value(name)" --limit=1)
   ADMIN=$(gcloud secrets versions access latest --secret="$ADMIN_SECRET_NAME" --project="$PROJECT")
   echo "Admin secret: $ADMIN"

3. Open $SERVICE_URL/console in a browser. Hasura prompts for the admin secret — paste the value from step 2. The console opens on the Data tab, connected to your Cloud SQL database (the default source).

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Task 3 — Worked example: track a table and run a GraphQL query [Manual]

1. Create a table. In the console, go to Data → default → public → Create Table. Name it todos with columns id (Integer, auto-increment, primary key) and title (Text). Click Add Table. (Prefer SQL? Open Data → SQL, run CREATE TABLE todos (id serial primary key, title text);, and tick Track this table.)

2. Track the table. If you created it via SQL, Hasura lists it under Untracked tables — click Track. Tracking is what exposes the table through the GraphQL API; it writes an entry into Hasura's metadata catalog (stored in Postgres, so it survives revisions and restarts).

3. Insert a row via the API (using the admin secret as the x-hasura-admin-secret header):

   curl -s "$SERVICE_URL/v1/graphql" \
     -H "x-hasura-admin-secret: $ADMIN" \
     -H 'Content-Type: application/json' \
     -d '{"query":"mutation { insert_todos_one(object: {title: \"Ship the docs\"}) { id title } }"}'

4. Run a GraphQL query to read it back:

   curl -s "$SERVICE_URL/v1/graphql" \
     -H "x-hasura-admin-secret: $ADMIN" \
     -H 'Content-Type: application/json' \
     -d '{"query":"query { todos { id title } }"}'
   # => {"data":{"todos":[{"id":1,"title":"Ship the docs"}]}}

   You can also run the same query interactively in the console's API (GraphiQL) tab — it sends the admin-secret header for you.

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Task 4 — 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). Hasura scales horizontally safely because all state is in Postgres; set min_instance_count = 1 to remove cold-start latency for a latency-sensitive API.

3. 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. Your tracked-table metadata persists in the database across the upgrade.

4. Manage secrets and backups:

   gcloud secrets list --project="$PROJECT" --filter="name~hasura"
   gcloud run jobs list --project="$PROJECT" --region="$REGION"   # init + 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 "$INSTANCE" --user=hasura --database=hasura --project="$PROJECT"

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Task 5 — 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 /healthz; confirm it is green under Monitoring → Uptime checks, and review Alerting → Policies.

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Task 6 — 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 Hasura releases.

• Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm the DSN was assembled. The startup probe targets /healthz; a database connection failure keeps the revision from becoming Ready.

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

• /console or /v1/graphql returns 401: you are missing or mis-sending the admin secret. Re-fetch it (Task 2 step 2) and send it as x-hasura-admin-secret. Never point health probes at these paths — use /healthz.
• Database connection errors (connection refused, no pg_hba entry): confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the initialisation job completed. On Cloud Run the DSN uses the socket form; a prebuilt image bypasses the entrypoint and has no DSN at all.
• 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.
• 403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including keeping probes on /healthz and never exposing the admin secret).

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Task 7 — 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, 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), secrets, and runs DB init
2 — Access & verify	Manual	Health check passes; retrieve the admin secret; open the console
3 — Worked example	Manual	Track a table and run an insert + GraphQL query end to end
4 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups, DB access
5 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
6 — Troubleshoot	Manual	Diagnose revision, auth (401), database, init-job, build, and IAM issues
7 — Tear down	Automated	Delete (Trash) removes all module resources