connector-compute

Create, start, stop VM using Compute Connector

In this sample, you will see how to use Workflows Connectors.

More specifically, you will use Compute Engine Connector's insert to create and start a VM and stop to stop the running VM.

Create a VM - Compute Engine API

Before trying Workflows, let's take a look at how to create a VM with Compute Engine insert API directly for project workflows-atamel in zone europe-west1-b:

POST https://compute.googleapis.com/compute/v1/projects/workflows-atamel/zones/europe-west1-b/instances?key=[YOUR_API_KEY] HTTP/1.1

Authorization: Bearer [YOUR_ACCESS_TOKEN]
Accept: application/json
Content-Type: application/json

{
  "name": "my-vm1",
  "machineType": "projects/workflows-atamel/zones/europe-west1-b/machineTypes/e2-small",
  "disks": [
    {
      "initializeParams": {
        "sourceImage": "projects/debian-cloud/global/images/debian-10-buster-v20210316"
      },
      "boot": true,
      "autoDelete": true
    }
  ],
  "networkInterfaces": [
    {
      "network": "global/networks/default"
    }
  ]
}

Create a VM - Workflows without connector

First, let's try creating the same VM with Workflows but without the compute connector.

You can see the create-vm.yaml for the workflow definition that makes the http.post call with OAuth2 authentication. You will also realize that there's an assert_running step that needs to check whether the VM has started and if not, the workflow sleeps and checks again in 3 seconds.

Deploy workflow:

gcloud workflows deploy create-vm --source=create-vm.yaml

Execute workflow:

gcloud workflows execute create-vm --data='{"instanceName":"my-vm"}'

Stop a VM - Workflows without connector

Next, stop the VM with Workflows but without the compute connector.

You can see the stop-vm.yaml for the workflow definition that makes the http.post call with OAuth2 authentication.

The workflow uses the default retry policy for HTTP calls: retry requests on status codes 429 Too Many Requests, 502 Bad Gateway, and 503 Service Unavailable.

The instances.stop API is a long running operation. This workflow polls the operation status periodically until either the operation is successfully completed or an error occurs.

Deploy workflow:

gcloud workflows deploy stop-vm --source=stop-vm.yaml

Execute workflow:

gcloud workflows execute stop-vm --data='{"instanceName":"my-vm"}'

Create a VM - Workflows with compute connector

Next, let's try creating the same VM with Workflows and the compute connector.

You can see the create-vm-connector.yaml for the workflow definition. You'll realize that assert_running step does not need to sleep anymore because the connector does the job of waiting for the VM to be created.

Deploy workflow:

gcloud workflows deploy create-vm-connector --source=create-vm-connector.yaml

Execute workflow:

gcloud workflows execute create-vm-connector --data='{"instanceName":"my-vm"}'

Create and stop a VM - Workflows with compute connector

Finally, let's try to stop the created & running VM with the connector.

You can see the create-stop-vm-connector.yaml for the workflow definition.

Deploy workflow:

gcloud workflows deploy create-stop-vm-connector --source=create-stop-vm-connector.yaml

Execute workflow:

gcloud workflows execute create-stop-vm-connector --data='{"instanceName":"my-vm"}'