Evolution API on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Evolution API is an open-source Node.js WhatsApp Business API gateway (built on the Baileys library) that provisions WhatsApp instances, sends and receives messages, and exposes a REST API plus a manager UI for wiring WhatsApp into other systems. This lab takes you through the full operational lifecycle of the Evolution API 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 Evolution API / WhatsApp 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, access the running workload, and complete first-run WhatsApp setup.
Perform day-2 operations — inspect, update, and manage secrets, cache, 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 (provides the VPC, GKE Autopilot cluster, Cloud SQL, Filestore NFS, Artifact Registry, and shared service accounts this module depends on). You do not need to deploy this yourself first — the platform automatically detects whether it already exists in the target project and provisions it before this module if not (see Task 1).
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.
Bringing your own project? Before the first deploy into it, the deployment confirmation dialog asks you to prove you control it (Get verification code, run the commands it shows as a project Owner, then Verify) and to give the RAD deployment service account the Owner role. A project RAD creates for you needs neither.
Advanced mode for later changes. The create form asks only for the first page of inputs (and, in a project RAD creates for you, little more than the tenant name and region). Every other input in the Configuration Guide — including the scaling and version inputs in the Day-2 tasks — is changed afterwards with Update on the deployment's page after ticking Enable advanced mode, which needs a credit balance that covers the update's estimated build cost (updates never carry a module fee). On a lab environment only an administrator can use Advanced mode.
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]

Open Solutions → Solution Catalog → RAD modules in the RAD platform top navigation, open Evolution API (GKE) from the Platform Modules list to start configuration, choose Configuration Form under How would you like to configure this deployment? (the form opens on the Conversational Assistant if you hold purchased credits or are a partner or administrator), set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Click Deploy Module, review the estimated cost in the Deployment Confirmation dialog when it appears and click Submit (if the dialog then adds a confirmation step, such as verifying a project you bring, complete it and click Confirm), which opens the deployment status page with real-time logs.
The platform deploys the workload into the GKE Autopilot cluster as a single replica (Deployment), provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (the auto-generated AUTHENTICATION_API_KEY admin key and the database password), a Cloud Storage data bucket, a Filestore NFS instance (which also hosts the default Redis endpoint), an external LoadBalancer Service, builds the container image, and runs a one-shot database initialisation job. First deploys take roughly 20–35 minutes (Cloud SQL and NFS creation dominate).

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"

NAMESPACE=$(kubectl get ns -o name | grep evolutionapi | head -1 | cut -d/ -f2)
echo "Cluster: $CLUSTER   Namespace: $NAMESPACE"
kubectl get all -n "$NAMESPACE"

Task 2 — Access & verify [Manual]

Confirm the workload is running and find its external address:

kubectl get pods,svc -n "$NAMESPACE"
EXTERNAL_IP=$(kubectl get svc -n "$NAMESPACE" \
  -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
echo "External IP: $EXTERNAL_IP"

Confirm the service is healthy. Startup and liveness probes target the root / — an unauthenticated status endpoint that responds once the server is up (allow several minutes on first boot while Prisma migrations run):
```
curl -s "http://${EXTERNAL_IP}/"   # expect a JSON status payload, not a connection error
```

Retrieve the auto-generated global admin key from Secret Manager:

API_KEY_SECRET=$(gcloud secrets list --project="$PROJECT" \
  --filter="name~api-key" --format="value(name)" --limit=1)
API_KEY=$(gcloud secrets versions access latest --secret="$API_KEY_SECRET" --project="$PROJECT")
echo "$API_KEY"

Open http://${EXTERNAL_IP}/manager in a browser (or call the REST API directly) using $API_KEY as the apikey header, and create your first WhatsApp instance:

curl -s -X POST "http://${EXTERNAL_IP}/instance/create" \
  -H "apikey: $API_KEY" -H "Content-Type: application/json" \
  -d '{"instanceName":"lab-instance","qrcode":true,"integration":"WHATSAPP-BAILEYS"}'

Fetch the connection QR code and scan it from WhatsApp on your phone (Linked Devices → Link a Device) to connect the number:
```
curl -s "http://${EXTERNAL_IP}/instance/connect/lab-instance" -H "apikey: $API_KEY"
```
Once the external IP is confirmed stable, set SERVER_URL (via environment_variables) to http://${EXTERNAL_IP} and apply via Update, so QR-code and webhook callback URLs use the reachable external address instead of the entrypoint's internal default.

Never rotate AUTHENTICATION_API_KEY after this point — rotating it makes every already-provisioned WhatsApp instance unreachable and returns 401 to every client still holding the old key.

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

Inspect the workload — deployment and pods:

kubectl get deploy,pods -n "$NAMESPACE"
kubectl describe deploy -n "$NAMESPACE"

Do not scale beyond one replica. WhatsApp (Baileys) socket sessions are held in the pod's memory and are not shared across replicas — min_instance_count and max_instance_count are pinned to 1 by design, and ClientIP session affinity keeps each client pinned to that one pod. Raising max_instance_count fragments live connections and duplicates webhook deliveries; leave it alone.
Update the application version by changing the version input (default v2.1.1) in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pod, re-running Prisma migrations on boot.

Manage secrets, cache, and jobs:

kubectl get secrets -n "$NAMESPACE"
gcloud secrets list --project="$PROJECT" --filter="name~evolutionapi"
kubectl get jobs -n "$NAMESPACE"          # DB-init and any scheduled jobs
# Confirm the Redis cache URI is injected into the running pod:
kubectl exec -n "$NAMESPACE" deploy/"$(kubectl get deploy -n "$NAMESPACE" -o jsonpath='{.items[0].metadata.name}')" \
  -- env | grep -i CACHE_REDIS

Open a database session for inspection or maintenance:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
# Role and database are tenant-prefixed (e.g. evolutionapidemo426161cf) — not the bare app name.
DB_USER=$(gcloud sql users list --instance="$INSTANCE" --project="$PROJECT" \
  --format="value(name)" --filter="name~^evolutionapi" --limit=1)
DB_NAME=$(gcloud sql databases list --instance="$INSTANCE" --project="$PROJECT" \
  --format="value(name)" --filter="name~^evolutionapi" --limit=1)
gcloud sql connect "$INSTANCE" --user="$DB_USER" --database="$DB_NAME" --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from kubectl or the Logs Explorer. The entrypoint emits [cloud-entrypoint] markers that confirm the resolved DB/Redis/URL config on boot:
```
kubectl logs -n "$NAMESPACE" deploy/"$(kubectl get deploy -n "$NAMESPACE" -o jsonpath='{.items[0].metadata.name}')" --tail=50
```
Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".
Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. The module can provision an uptime check (when enabled); review Monitoring → Uptime checks and 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 Evolution API releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets /; a connection failure to PostgreSQL will keep the pod from becoming Ready.

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

Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init job completed. On GKE the cloud-sql-proxy sidecar is a TCP loopback (127.0.0.1), so the entrypoint connects with sslmode=disable — this is expected, not a misconfiguration.

Initialisation job failed: inspect the job and its pod logs:

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

Redis cache silently disabled: if enable_redis=true but the cache URI env var is blank, check that either enable_nfs=true (so the NFS server IP is used) or an explicit redis_host is set.
401 on every WhatsApp API call: the apikey header is missing/wrong, or AUTHENTICATION_API_KEY was rotated after instances were already provisioned — the fix is to re-provision the affected WhatsApp instances, not to rotate back.
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 & Sensible Defaults section for setting-specific gotchas (including the critical rule never to rotate AUTHENTICATION_API_KEY or raise max_instance_count 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 (from the same Delete dialog) — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the deployment). 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, NFS, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload (single-replica Deployment), Cloud SQL (PostgreSQL 15), NFS/Redis, secrets, storage bucket, LoadBalancer Service, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; retrieve the admin API key; create and connect a WhatsApp instance via QR code
3 — Operate	Manual	Inspect the workload, update version, manage secrets/cache/jobs, DB access — do not scale beyond one replica
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, Redis, auth-key, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

Need RAD to do something it does not do yet? Request it on the roadmap, or vote on what is already there.

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​