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GKE

GKE runs your Effect program as a Kubernetes workload. You declare the cluster, then a Kubernetes.Deployment or Kubernetes.Job on it. Alchemy builds the image, pushes it to Artifact Registry, and applies the objects. No YAML, no kubectl.

Install

Section titled “Install”
Terminal window
pnpm add https://pkg.alchemy.run/@alchemy.run/kubernetes/a870ed4

Merge the Kubernetes providers next to GCP’s:

alchemy.run.ts
providers: Layer.mergeAll(GCP.providers(), Kubernetes.providers()),

Create a cluster

Section titled “Create a cluster”

An Autopilot cluster sizes its nodes from your pods, so there are no node pools to manage:

src/infra.ts
import * as GCP from "@alchemy.run/gcp";


export const Cluster = GCP.Container.Cluster("Cluster", {
  location: "us-central1",
  autopilot: true,
  releaseChannel: "REGULAR",
});

Autopilot needs a region, not a zone. Creating a cluster takes about ten minutes.

Deploy an Effect program

Section titled “Deploy an Effect program”

A Deployment takes the same fetch handler as a Cloud Run service:

src/Api.ts
import * as GCP from "@alchemy.run/gcp";
import * as Kubernetes from "@alchemy.run/kubernetes";
import * as Effect from "effect/Effect";
import * as HttpServerResponse from "effect/http/HttpServerResponse";
import { Cluster, Entries } from "./infra.ts";


export class Api extends Kubernetes.Deployment<Api>()("Api") {}


export default Api.make(
  Effect.gen(function* () {
    return {
      cluster: yield* Cluster,
      main: import.meta.url,
      port: 3000,
      replicas: 2,
      serviceType: "LoadBalancer" as const,
      resources: {
        requests: { cpu: "250m", memory: "512Mi" },
        limits: { cpu: "250m", memory: "512Mi" },
      },
    };
  }),
  Effect.gen(function* () {
    const db = yield* GCP.Firestore.ReadWriteDatabase(Entries);


    return {
      fetch: Effect.gen(function* () {
        const page = yield* db.list("entries", { pageSize: 100 });
        return yield* HttpServerResponse.json(page.documents);
      }).pipe(Effect.orDie),
    };
  }).pipe(Effect.provide(GCP.Firestore.ReadWriteDatabaseHttp)),
);

serviceType: "LoadBalancer" gives the Deployment a public IP. api.url includes the port: http://<ip>:3000.

Add it to the Stack

Section titled “Add it to the Stack”

Provide the Deployment’s layer and yield the class:

alchemy.run.ts
import ApiLive, { Api } from "./src/Api.ts";


export default Alchemy.Stack(
  "MyApp",
  {
    providers: Layer.mergeAll(GCP.providers(), Kubernetes.providers()),
    state: GCP.state(),
  },
  Effect.gen(function* () {
    const api = yield* Api;
    return { url: api.url };
  }).pipe(Effect.provide(ApiLive)),
);

Bindings use Workload Identity

Section titled “Bindings use Workload Identity”

ReadWriteDatabase grants the role to the Deployment’s Kubernetes service account directly. Pods get Google tokens from the GKE metadata server. There are no keys and no Google service account to create.

A new conditional grant can take a few minutes to reach Firestore. Set backoffLimit on Jobs so Kubernetes retries until it lands.

Run a Job

Section titled “Run a Job”

Kubernetes.Job takes a run entry and runs once when it is applied:

src/Seed.ts
export default Kubernetes.Job(
  "Seed",
  Effect.gen(function* () {
    return { cluster: yield* Cluster, main: import.meta.url, backoffLimit: 6 };
  }),
  Effect.gen(function* () {
    const db = yield* GCP.Firestore.WriteDatabase(Entries);
    return {
      run: db.set("entries/hello", { message: "first!" }).pipe(Effect.orDie),
    };
  }).pipe(Effect.provide(GCP.Firestore.WriteDatabaseHttp)),
);

Add schedule: "0 3 * * *" to the props to make it a CronJob.

Deploy an existing image

Section titled “Deploy an existing image”
const web = yield* Kubernetes.Deployment("Web", {
  cluster,
  image: "nginx:1.27",
  port: 80,
  serviceType: "LoadBalancer",
});

Example

Section titled “Example”

examples/gcp-gke is a guestbook on Autopilot: an Effect API, an nginx Deployment, a seed Job, and a namespace from a raw manifest.

Where next

Section titled “Where next”