How To Trigger A Dataflow With A Cloud Function? (Python SDK)

I have a cloud function that is triggered by cloud Pub/Sub. I want the same function trigger dataflow using Python SDK. Here is my code: import base64 def hello_pubsub(event, cont

Solution 1:

You can use Cloud Dataflow templates to launch your job. You will need to code the following steps:

• Retrieve credentials
• Generate Dataflow service instance
• Get GCP PROJECT_ID
• Generate template body
• Execute template

Here is an example using your base code (feel free to split into multiple methods to reduce code inside hello_pubsub method).

from googleapiclient.discovery import build
import base64
import google.auth
import os

def hello_pubsub(event, context):   
    if 'data' in event:
        message = base64.b64decode(event['data']).decode('utf-8')
    else:
        message = 'hello world!'

    credentials, _ = google.auth.default()
    service = build('dataflow', 'v1b3', credentials=credentials)
    gcp_project = os.environ["GCLOUD_PROJECT"]

    template_path = gs://template_file_path_on_storage/
    template_body = {
        "parameters": {
            "keyA": "valueA",
            "keyB": "valueB",
        },
        "environment": {
            "envVariable": "value"
        }
    }

    request = service.projects().templates().launch(projectId=gcp_project, gcsPath=template_path, body=template_body)
    response = request.execute()

    print(response)

In template_body variable, parameters values are the arguments that will be sent to your pipeline and environment values are used by Dataflow service (serviceAccount, workers and network configuration).

LaunchTemplateParameters documentation

RuntimeEnvironment documentation

Solution 2:

You have to embed your pipeline python code with your function. When your function is called, you simply call the pipeline python main function which executes the pipeline in your file.

If you developed and tried your pipeline in Cloud Shell and you already ran it in Dataflow pipeline, your code should have this structure:

def run(argv=None, save_main_session=True):
  # Parse argument
  # Set options
  # Start Pipeline in p variable
  # Perform your transform in Pipeline
  # Run your Pipeline
  result = p.run()
  # Wait the end of the pipeline
  result.wait_until_finish()

Thus, call this function with the correct argument especially the runner=DataflowRunner to allow the python code to load the pipeline in Dataflow service.

Delete at the end the result.wait_until_finish() because your function won't live all the dataflow process long.

You can also use template if you want.