Kestra on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Kestra is an open-source data orchestration and workflow scheduling platform that uses declarative YAML-based flow definitions and a rich plugin ecosystem for ETL/ELT pipelines, batch jobs, and API automation. This lab takes you through the full operational lifecycle of the Kestra on GKE Autopilot 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 GKE module and the Google Cloud platform, not on Kestra 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.
• Connect to the GKE cluster and access the running workload.
• Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
• Observe the workload 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, GKE Autopilot cluster, Cloud SQL, Artifact Registry, and shared service accounts this module depends on).
• A Google Cloud project with billing enabled.
• gcloud CLI and kubectl installed; gcloud auth login and gcloud auth application-default login completed.
• 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 Kestra (GKE) 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 deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets, a dedicated GCS storage bucket, builds the container image, and runs a one-shot database-initialisation job. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates; Kestra's JVM startup adds additional time on the first boot while database migrations are applied).

3. Connect to the cluster and discover the namespace with name-agnostic filters:

   CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
   gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"
   
   NS=$(kubectl get ns -o name | grep kestra | head -1 | cut -d/ -f2)
   echo "Cluster: $CLUSTER   Namespace: $NS"
   kubectl get all -n "$NS"

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Task 2 — Access & verify [Manual]

1. Confirm the workload is running and find its external address:

   kubectl get pods,svc -n "$NS"
   EXTERNAL_IP=$(kubectl get svc -n "$NS" \
     -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
   echo "External IP: $EXTERNAL_IP"
   curl -s "http://${EXTERNAL_IP}:8080/health"   # expect {"status":"UP"}

   Kestra uses a Java JVM with a slow first boot. If the pod is not yet Running, it may still be completing its database migration. The startup probe allows up to ~14 minutes — wait a few minutes and retry.

2. Retrieve the admin password from Secret Manager and sign in to the Kestra UI at http://${EXTERNAL_IP}:8080:

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

   The admin username is admin. Kestra's own product documentation covers the UI, flow editor, and plugin ecosystem.

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

1. Inspect the workload — deployment, pods, and (if enabled) the horizontal autoscaler and persistent volumes:

   kubectl get deploy,pods,hpa,pvc -n "$NS"
   kubectl describe deploy -n "$NS"

2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the workload spec, so scaling is a configuration change, not a manual kubectl scale (a manual edit would be reverted on the next apply). Note that Kestra's standalone mode requires max_instance_count = 1 to prevent queue-lock conflicts.

3. Update the application version by changing the version input via Update on the deployment details page; a new image builds and a rolling update replaces the pods with schema migrations applied automatically.

4. Manage secrets, storage, and jobs:

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$PROJECT" --filter="name~kestra"
   kubectl get jobs -n "$NS"          # db-init and any scheduled 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 "$INSTANCE" --user=kestra --project="$PROJECT"

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Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from kubectl or the Logs Explorer:

   kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" --tail=50

   Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and HPA status. The module can provision an optional uptime check targeting the /health endpoint; review Monitoring → Uptime checks and Alerting → Policies if enabled.

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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 Kestra releases.

• Pod not Ready / CrashLoopBackOff: inspect events and logs:

  kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
  kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container

• Slow startup / pod not Ready on first boot: Kestra performs Flyway database migrations on startup. The startup probe allows up to ~14 minutes. Check the logs for migration progress before concluding there is a fault.
• Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the db-init job completed.
• Initialisation job failed: inspect the job and its pod logs:

  kubectl get jobs -n "$NS"
  kubectl logs -n "$NS" job/<job-name>

• Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
• Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it.

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 Kubernetes workload and namespace, Cloud SQL database, Secret Manager secrets, GCS buckets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL (PostgreSQL 15), GCS bucket, secrets, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes ({"status":"UP"}); sign in to the Kestra UI
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, JVM startup, database, init-job, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources