Gke App Onboarding

---
name: gke-app-onboarding
description: >-
  Manages GKE application onboarding, covering containerization, deployment
  manifests, and migration. Use when onboarding or deploying an application to
  GKE for the first time, or containerizing an app for GKE. Don't use for
  general GKE cluster administration or upgrades (use gke-basics or
  gke-upgrades instead).
metadata:
  category: Containers
---

# GKE App Onboarding

This reference provides workflows for containerizing and deploying applications
to GKE for the first time.

> **MCP Tools:** `apply_k8s_manifest`, `get_k8s_resource`,
> `get_k8s_rollout_status`, `get_k8s_logs`, `describe_k8s_resource`

## Workflow

### 1. App Assessment

Before containerizing, assess the application:

-   **Language & Framework**: Identify the tech stack
-   **Dependencies**: List required libraries and external services
-   **Configuration**: How is the app configured? (env vars, config files,
    secrets)
-   **Statefulness**: Does it need persistent storage? (databases, file storage)
-   **Networking**: Port mapping and protocol (HTTP, gRPC, TCP)
-   **Health endpoints**: Does the app expose health check endpoints?

### 2. Containerization

Create a container image. A Dockerfile with a multi-stage build is recommended
for most apps — see the Go Dockerfile in
[`references/go-example.md`](./references/go-example.md) for a worked example.

**Best practices:**

-   Use multi-stage builds to keep production images small
-   Use distroless or minimal base images to reduce attack surface
-   Run as non-root user
-   Log to `stdout` and `stderr` for Cloud Logging collection

A complete worked Node.js example is provided in [`assets/`](./assets/):
[`Dockerfile`](./assets/Dockerfile) (non-root `node` user),
[`index.js`](./assets/index.js) (implements distinct `/healthz` and `/readyz`
endpoints), [`package.json`](./assets/package.json), and
[`deployment.yaml`](./assets/deployment.yaml) (hardened Deployment plus
ClusterIP Service, probes wired to `/healthz` and `/readyz`).

For applications where writing a Dockerfile is not preferred, you can use
[**Cloud Native Buildpacks**](https://buildpacks.io/) to automatically detect
the language and build a container image:

```bash
pack build <image> --builder gcr.io/buildpacks/builder:latest
```

### 3. Image Management

Build and store the container image:

```bash
# Configure Docker for Artifact Registry
gcloud auth configure-docker <REGION>-docker.pkg.dev --quiet

# Build and push
docker build -t <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG> .
docker push <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG>
```

**Vulnerability scanning**: Enable automatic scanning in Artifact Registry to
detect issues in base images and dependencies.

```bash
# Check scan results
gcloud artifacts docker images describe \
  <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG> \
  --show-package-vulnerability \
  --quiet
```

### 4. Manifest Generation

Generate Kubernetes manifests for the application. A baseline Deployment +
ClusterIP Service manifest (probes, resource requests/limits, 2 replicas) is in
[`references/go-example.md`](./references/go-example.md).

**Checklist for manifests:**

-   Resource requests and limits set
-   Liveness and readiness probes configured
-   At least 2 replicas for production
-   Service type appropriate (ClusterIP for internal, use Gateway API for
    external)

See [`assets/deployment.yaml`](./assets/deployment.yaml) for a hardened worked
example. A production-hardened pod spec must include ALL of: `runAsNonRoot:
true`, `readOnlyRootFilesystem: true`, `allowPrivilegeEscalation: false`,
`capabilities.drop: ["ALL"]`, `seccompProfile: {type: RuntimeDefault}`,
`automountServiceAccountToken: false` (unless the pod needs the token — then say
why), resource requests, digest-pinned image, and a ClusterIP Service.

That checklist is the baseline for any pod spec produced here. For manifest work
beyond it — Gateway API routes, GCS FUSE and secret volume mounting, `subPath`
overlays, Spot VM targeting, or AI/inference serving specs — see
`gke-manifest-generation`.

### 5. Deploy

```
# MCP (preferred)
apply_k8s_manifest(parent="projects/<PROJECT>/locations/<REGION>/clusters/<CLUSTER>", yamlManifest="<manifest>")

# Verify
get_k8s_rollout_status(parent="...", resourceType="deployment", name="my-app")
get_k8s_resource(parent="...", resourceType="pod", labelSelector="app=my-app")
```

**kubectl fallback:**

```bash
kubectl apply -f manifests/
kubectl rollout status deployment/my-app
kubectl get pods -l app=my-app
```

## Golden Path Onboarding Checklist

For every production application onboarding to GKE:

1.  **Container Security**: Non-root user (`runAsNonRoot: true`), lockfile
    install, minimal/distroless base image.
2.  **Resource Requests**: Explicit CPU and memory requests (mandatory for GKE
    Autopilot).
3.  **Health Probes**: Both liveness (`livenessProbe`) and readiness
    (`readinessProbe`) probes configured.
4.  **Reliability & Availability**: At least 2 replicas and a
    `PodDisruptionBudget` (`minAvailable: 1` or `2`).
5.  **IAM & Workload Identity**: Workload Identity
    (`iam.gke.io/gcp-service-account`) instead of static service account keys.

## Next Steps

Once the application is running on GKE:

-   Configure autoscaling — see the `gke-workload-scaling` skill
-   Set up observability — see the `gke-observability` skill
-   Harden security — see the `gke-workload-security` skill
-   Configure reliability (PDBs, topology spread) — see the `gke-reliability`
    skill