Job launched with flex template fails with "Failed to read the result file"

[justin-aimiable]

Hi, I'm just getting up and running with DataFlow and have been hitting some hiccups along the way. I have a cloud function that is using the `dataflow_v1beta3.FlexTemplateServiceClient` in Python to launch a flex template. I have launched this template using my own credentials from the CLI with a successful run, however when my cloud function successfully launches the template, the job fails with this logged:

Failed to read the result file : gs://dataflow-staging-northamerica-northeast2-31664930760/staging/template_launches/2024-02-13_13_48_32-17616911554794094215/operation_result with error message: (a91b614d0c0827f5): Unable to open template file: gs://dataflow-staging-northamerica-northeast2-31664930760/staging/template_launches/2024-02-13_13_48_32-17616911554794094215/operation_result..

Indeed, there is no file at this path, but I'm not sure what the issue would be.

Some context on how things are set up:
python apache beam pipeline code:

def run():
    class MyPipelineOptions(PipelineOptions):
        @classmethod
        def _add_argparse_args(cls, parser):
            parser.add_argument(
                "--input-file",
                required=True,
                help="Input csv file to process.",
            )
            parser.add_argument(
                "--output-table",
                required=True,
                help="Output table to write results to.",
            )

    options = MyPipelineOptions()

    with beam.Pipeline(options=options) as p:

template manifest.json:

{
  "name": "Airbyte GCS Raw Dump to BigQuery Template",
  "description": "Takes raw data extracted to parquet format from Airbyte, transforms and loads it into BigQuery",
  "parameters": [
    {
      "name": "input-file",
      "label": "Input file",
      "helpText": "The path to the parquet file in GCS",
      "regexes": ["^gs:\\/\\/[^\\n\\r]+$"]
    },
    {
      "name": "output-table",
      "label": "Output table",
      "helpText": "The name of the table to create in BigQuery",
      "regexes": ["^[A-Za-z0-9_:.]+$"]
    }
  ]
}

cloud function call to trigger the job:

    client = dataflow_v1beta3.FlexTemplatesServiceClient()

    parameters = {
        "input-file": f"gs://{bucket_name}/{file_name}",
        "output-table": output_table,
    }

    launch_parameter = dataflow_v1beta3.LaunchFlexTemplateParameter(
        job_name=job_name,
        container_spec_gcs_path=container_spec_gcs_path, # where manifest.json is located in GCS
        parameters=parameters,
        environment={
            "service_account_email": dataflow_service_account, 
            "temp_location": temp_location,
        },
    )

    request = dataflow_v1beta3.LaunchFlexTemplateRequest(
        project_id=project_name,
        location=region,
        launch_parameter=launch_parameter,
    )
    response = client.launch_flex_template(request=request) # succeeds, but then the job fails

[ms4446 · Accepted solution]

This error signifies a problem encountered by Google Cloud Dataflow when attempting to generate or access a crucial result file within Google Cloud Storage (GCS). This file is essential for detailing the execution outcomes of a Dataflow Flex Template job. The error can stem from various issues, including but not limited to:

• Incorrect GCS Permissions: The service account utilized by Dataflow worker nodes may not have the necessary permissions (e.g., "Storage Object Viewer" and "Storage Object Creator") for the GCS bucket in question.
• Template File Issues: It's vital to ensure that the container_spec_gcs_path accurately points to the manifest.json file and that the template is correctly formatted and valid.
• Network Restrictions: Firewalls or network configurations might prevent Dataflow workers from accessing GCS resources.
• Internal Dataflow Error: On rarer occasions, the issue might originate from within the Dataflow service itself.
• Temporary GCS Unavailability: The error could also be due to a temporary issue with GCS, affecting accessibility.
• Worker Startup Issues: Problems during the initialization of Dataflow workers can impact the generation or access of the result file.
• Parameter Matching Issues: Ensuring that the parameters (like input-file and output-table) match the expected data types and constraints defined in your Flex Template is crucial.

Troubleshooting Steps

1. Verify GCS Permissions:

   • Check the IAM settings in the Google Cloud console to identify the service account used by Dataflow workers. Ensure it has the "Storage Object Viewer" and "Storage Object Creator" roles for the necessary GCS bucket.

2. Check File Paths:

   • Confirm the accuracy of the GCS path mentioned in the error message. Implement logging within your cloud function to verify the container_spec_gcs_path at the start, ensuring correctness.

3. Validate Template File:

   • Use a JSON validator to check the format of your manifest.json. Ensure that the parameters and their data types, along with any regex constraints, align with the data you're providing.

4. Enable Robust Diagnostics:

   • Employ a comprehensive logging strategy within your cloud function. Utilize Cloud Logging for centralized error and job run information, ensuring detailed logging of paths, parameters, and errors.

5. Check Job Resources:

   • Review the configuration for machine types and worker counts to ensure the job is provisioned with adequate resources, although this may not directly relate to the specific error.

6. Retry with Monitoring:

   • Implement retries within your cloud function, particularly if temporary GCS unavailability is suspected. Close monitoring during retries can help capture more specific error messages.

7. Contact Google Cloud Support:

   • If the issue persists despite these troubleshooting steps, reaching out to Google Cloud Support with your project ID, job ID, and detailed logs can facilitate further assistance.

[justin-aimiable]

Thank you! I am pretty sure it's a permissions thing... From my cloud function, I was trying to specify the SA for dataflow with this line:

environment={
    "service_account_email": dataflow_service_account, 
}

In this case, the service account is one that I created and gave what I thought were the necessary permissions based on this doc.  I am not sure that this service account is actually being used when the template is launched, though. I don't see any logs around denied permissions, just that the staging/temp console logs were not being written to the temp/staging buckets.

I was able to get the dataflow client in my cloud function to successfully launch the dataflow job after making two changes: 1. remove the "service_account_email" parameter from the request and 2. give my cloud function SA the service account user role for the compute default SA. However, even when I do this, the first step of my pipeline (reading a file on a GCS bucket) hangs indefinitely.

One final thing: I am able to run the script in the cloud function from my own computer using my ADC. It launches the dataflow job, and every step of the dataflow job succeeds.

[ms4446]

Dataflow jobs default to using the Compute Engine default service account unless an alternative is specified via the service_account_email parameter. This default behavior is crucial for setting up the correct permissions.

Removing the service_account_email parameter defaults your Dataflow job to use the Compute Engine default service account. This simplifies execution but necessitates ensuring this account has the appropriate permissions.

The absence of explicit "permission denied" logs requires a thorough examination of both Cloud Function and Dataflow logs. Subtle hints or indirect messages might indicate access issues, necessitating meticulous log analysis.

A Dataflow job hanging during a GCS read operation typically points to insufficient permissions for the Compute Engine default service account. To address this:

• IAM Verification: In the IAM section of the Google Cloud console, locate the Compute Engine default service account ([project-number]-compute@developer.gserviceaccount.com). Ensure it possesses the "Storage Object Viewer" role for the GCS bucket in question.

Discrepancy Between Cloud Function and Local Execution The difference in behavior between local execution using Application Default Credentials (ADC) and Cloud Function execution underscores the importance of consistent permissions. ADCs often have broader access, facilitating local success.

Additional Considerations

• Intra-Project Communication: While default service accounts generally support seamless communication within the same project, ensure explicit permissions are set for required actions, like GCS bucket access.
• IAM Policy Propagation: Be aware that IAM policy updates may not take effect immediately. Allowing time for propagation can sometimes resolve seemingly persistent permission issues.
• Cross-Project Scenarios: If your Cloud Function and Dataflow resources reside in different projects, ensure IAM policies grant cross-project access where needed. In these cases, the "Service Account Token Creator" role can be vital for one service account to impersonate another.

Debugging Suggestions

• Enhanced Cloud Function Logging: Implement detailed logging around file read operations within your Cloud Function, explicitly logging the GCS file path to verify its correctness.
• Cloud Audit Logs: Review Cloud Audit Logs for detailed insights into permission-related events and interactions with Google Cloud services that might not be evident in standard operational logs.

[justin-aimiable]

Thanks again! In terms of the step that was hanging, we examined the arguments passed to it and noticed that the delay happened when we specfied `sdk_container_image` in the arguments. It's possible we are misunderstanding how to use this argument. We were able to make everything work by using default compute engine SA and not specifying that parameter. We are getting the warning that "SDK worker container image pre-building: can be enabled" when running our jobs now, but at least they are succeeding at this point. We're finding it challenging to piece together all the relevant docs around packaging/ running flex template + custom docker image + pre-installing dependencies + python. I've found docs that do some of these things but not all together.

I would definitely prefer to use a specialized service account when running these jobs, since it seems to break the "minimum permissions to do the job" rule when our cloud function SA is able to use the compute default SA and we'll need to ultimately add more roles to the default compute SA as some of our dataflow jobs will require additional permissions, e.g. secret manager.

At any rate, we're unblocked from developing the pipelines for the time being and we can do improved permissions/ pre-build the SDK stuff as a follow-up in the future. Ultimately, we were having a permissions issue with the original question