Running dataflow templete from Cloud Schedule

[jhonjamoca1989]

Hi, Could you help me please, I am getting problem running Dataflow Template from Cloud Schedule

Workflow failed. Causes: There was a problem refreshing your credentials. Please check: 1. Dataflow API is enabled for your project. 2. Make sure both the Dataflow service account and the controller service account have sufficient permissions. If you are not specifying a controller service account, ensure the default Compute Engine service account [PROJECT_NUMBER]-compute@developer.gserviceaccount.com exists and has sufficient permissions. If you have deleted the default Compute Engine service account, you must specify a controller service account. For more information, see: https://cloud.google.com/dataflow/docs/concepts/security-and-permissions#security_and_permissions_fo.... 3. Make sure the controller service account you use is enabled. For more information on how to enable a service account, see: https://cloud.google.com/iam/docs/creating-managing-service-accounts#enabling. , Please make sure the service account exists and is enabled.

This is my request param:

{
"jobName": "test-cloud-scheduler",
"parameters": {
"project":"project",
"region":"region",
"serviceAccount":"sa",
"subnetwork":"subnetwork",
"projectId":"transfers"
}
}

[ms4446]

The core message, "There was a problem refreshing your credentials," suggests issues related to API access or service account permissions. Here's how you can address these:

1. API Access

• Ensure that the Dataflow API is enabled in your Google Cloud Project.
• Go to the APIs & Services dashboard in your Cloud Console.
• Search for "Dataflow API" and make sure it's enabled.

2. Service Account Permissions

• Dataflow Service Account (sa):
• Usually specified as sa in your request.
• Needs these roles:
• roles/dataflow.worker
• roles/dataflow.admin (if the template performs administrative tasks)
• Controller Service Account:
• This is often the default Compute Engine service account.
• Requires these roles:
• roles/dataflow.worker
• roles/compute.instanceAdmin (or roles/compute.admin if more access is needed)
• roles/iam.serviceAccountUser

3. Service Account Existence and Enablement

• Verify the designated service accounts exist and are enabled.
• Check in the IAM & Admin section of the Cloud Console.

Troubleshooting Steps

1. Verify API Activation:
• Follow the instructions above to check the Dataflow API status.
2. Check Dataflow Service Account (sa) Permissions:
• Find the service account used as sa and confirm it has the correct roles.
3. Check Controller Service Account Permissions:
• Find the controller service account and make sure it has the required roles.
4. Confirm Service Account Existence and Status:
• Ensure the service account exists and is enabled.

Additional Tips:

• Logging: Enable Cloud Logging for Dataflow to get more detailed error messages.
• Cloud Scheduler Role: Check the Cloud Scheduler service account has the roles/dataflow.admin role.
• Network Access: If your Dataflow job uses resources in a VPC, verify service accounts have network access.

Example Request Modification (If not using default controller):

JSON

{

  "jobName": "test-cloud-scheduler",

  "parameters": {

    "project": "your-project-id",

    "region": "your-region",

    "serviceAccount": "your-service-account@email.com",

    "controllerServiceAccount": "[PROJECT_NUMBER]-compute@developer.gserviceaccount.com",

    "subnetwork": "projects/your-project/regions/your-region/subnetworks/your-subnetwork",

    "projectId": "your-project-id"

  }

}

[jhonjamoca1989]

Thank so much, I will apply some for your tips, however we are running the project from Cloud Shell and everything work out perfectly wit the service account that we have been using to run the dataflow project from Cloud Shell. But when it comes to run the template from Cloud Schedule we get that error mentioned in the primary post.

[ms4446]

It's interesting that your project runs smoothly from Cloud Shell but encounters issues when initiated from Cloud Scheduler. This discrepancy often points to differences in the environment configuration or permissions between the two execution methods. Here are a few specific areas to investigate and steps to take:

1. Service Account Used by Cloud Scheduler

The Cloud Scheduler itself uses a service account to trigger jobs, which might be different from the one you use in Cloud Shell. Here's how to check and ensure it has the necessary permissions:

• Identify the Cloud Scheduler Service Account: In the Google Cloud Console, go to the Cloud Scheduler page and identify the service account it uses. This is often the App Engine default service account, but it could be a custom one if you configured it so.

• Assign Necessary Roles: Make sure this service account has the roles/dataflow.admin and roles/iam.serviceAccountUser roles. The latter is crucial because it allows the scheduler's service account to act on behalf of other service accounts (like your Dataflow service account).

2. Explicit Service Account Specification

Since your job runs correctly from Cloud Shell using a specific service account, ensure that this same service account is explicitly specified in the Dataflow template parameters when triggered by Cloud Scheduler. Sometimes, specifying the service account explicitly in the job configuration helps resolve permission issues.

• Modify Scheduler Job Configuration: Adjust your Cloud Scheduler job configuration to explicitly include the service account you use in Cloud Shell as the controllerServiceAccount or serviceAccount in the Dataflow parameters, depending on which one is appropriate.

3. Permissions Check

Ensure that all involved service accounts (the one used in Cloud Shell and any specified in your Dataflow job) have sufficient permissions not just for Dataflow, but also for any other resources your job accesses (e.g., GCS buckets, Pub/Sub topics).

• Cross-Verify Permissions: Compare the roles and permissions of your service account in the IAM section when logged in via Cloud Shell and when the job is triggered via Cloud Scheduler.

4. Test with Minimal Configuration

Sometimes, simplifying the configuration can help isolate the issue. Try creating a simple Dataflow job with minimal dependencies and see if it can be triggered via Cloud Scheduler.

• Simplify and Test: This can help determine if the problem is with specific resources or broader permission issues.

5. Logging and Monitoring

Utilize Google Cloud's logging and monitoring tools to get more detailed insights into what might be going wrong when the job is triggered by Cloud Scheduler.

• Enable Detailed Logs: Make sure Cloud Logging is enabled for both Dataflow and Cloud Scheduler to track down exactly where the failure occurs.

If after these steps the issue persists, it may be useful to consult Google Cloud Support.