EspoCRM on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

EspoCRM is an open-source, GPLv3-licensed Customer Relationship Management platform built on PHP and Apache. This lab takes you through the full operational lifecycle of the EspoCRM 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 EspoCRM product features (contacts, leads, opportunities, workflows). 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, discover the namespace, and access the running workload.
Retrieve the auto-generated admin credential and verify the workload is healthy and connected to its database.
Perform day-2 operations — inspect, scale, update, and manage secrets, NFS storage, and the database.
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, Filestore NFS, 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

Task 1 — Deploy the module [Automated]

Click Deploy in the RAD platform top navigation, open EspoCRM (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.
The platform deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (MySQL 8.0) database with its Secret Manager secrets (ESPOCRM_ADMIN_PASSWORD and the database password), a espocrm-data Cloud Storage bucket, a shared Filestore NFS volume mounted at /var/lib/espocrm for uploads (enable_nfs = true by default), builds the container image, and runs a one-shot database-initialisation job. The pod reaches Cloud SQL through a co-located Auth Proxy sidecar on loopback (enable_cloudsql_volume = true by default). The upstream EspoCRM installer then runs its own install/migrate step automatically on first pod start. First deploys take roughly 20–35 minutes (Cloud SQL and Filestore 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"

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

Task 2 — Access & verify [Manual]

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"

Confirm the workload is serving its login page (EspoCRM's health endpoint is the unauthenticated login screen at /, 200 once the install/migrate step has finished):
```
curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/"   # expect 200
```
On a slow first boot the startup (10s initial delay) and liveness (15s initial delay) probes are noticeably tighter than the Cloud Run variant's — pods can flap briefly while the install/migrate step finishes; give it a few minutes before troubleshooting.
Retrieve the auto-generated administrator password from Secret Manager — EspoCRM's installer creates the admin user with this password on first boot:
```
gcloud secrets versions access latest \
  --secret="secret-<resource_prefix>-espocrm-admin-password" --project="$PROJECT"
```
Open http://${EXTERNAL_IP} in a browser and log in as admin with the retrieved password. Change the password immediately under Administration → Users — the auto-generated value only seeds the first install; losing it later requires a database-level reset.

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

Inspect the workload — deployment, pods, and the horizontal autoscaler:
```
kubectl get deploy,pods,hpa -n "$NS"
kubectl describe deploy -n "$NS"
```
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). max_instance_count defaults to 1; session affinity (ClientIP) is set by default to keep a client's requests on the same pod once you scale beyond one replica. Because the workload is NFS-backed, the foundation uses the Recreate update strategy so two pods never write the same NFS volume during a rollout.
Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and the pods are recreated.

Manage secrets, storage, and jobs:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~espocrm"
gcloud filestore instances list --project="$PROJECT"   # backs /var/lib/espocrm uploads
kubectl get jobs -n "$NS"          # db-init job

Open a database session for inspection or maintenance:

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

Enable Redis (optional) to offload EspoCRM's object cache from MySQL: set enable_redis = true and apply via Update; leave redis_host empty to reuse the NFS server's IP as the Redis endpoint.

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from kubectl or the Logs Explorer. The container prints its resolved ESPOCRM_DATABASE_* and ESPOCRM_SITE_URL values at pod start, a quick way to confirm the DB host and site URL in use:
```
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>".
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 EspoCRM releases.

Pod not Ready / flapping on first boot: the startup probe (10s initial delay, 10s period, 3 failures) and liveness probe (15s initial delay, 30s period, 3 failures) are both HTTP GET /. On a slow first boot (the install/migrate step), pods can flap before EspoCRM finishes initializing; raise the initial delay / failure threshold via startup_probe_config / health_check_config if this persists.
```
kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
```
Database connection errors: confirm the Cloud SQL instance is RUNNABLE and the db-init job completed. The pod reaches MySQL through the Auth Proxy sidecar on 127.0.0.1:3306 (enable_cloudsql_volume = true) — do not override DB_HOST.

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

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

Uploads missing after a restart: confirm enable_nfs = true and the Filestore instance is healthy — without NFS, attachments live on ephemeral pod disk and are lost on restart/reschedule.
Rollout wedged on update: an NFS-backed EspoCRM workload uses Recreate, not RollingUpdate — a surge pod would otherwise deadlock on the shared NFS volume and the database. If you see "Waiting for rollout to finish: old replicas are pending termination", confirm the strategy has not been overridden.
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 (including the immutability of application_database_name/ application_database_user after first deploy and the one-time-only nature of the auto-generated admin password).

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, the Filestore NFS instance, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL (MySQL 8.0), secrets, storage bucket + NFS mount, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; login page returns 200; retrieve the auto-generated admin password and log in
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/NFS, DB access, optional Redis
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, rollout, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

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​