functions

Example Airflow DAG that displays interactions with Google Cloud Functions. It creates a function and then deletes it.

Data Processing


Providers:

Run this DAG

1. Install the Astronomer CLI:Skip if you already have the CLI

2. Initate the project in a local directory:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your

requirements.txt
file:

5. Run the DAG from the local directory where the project was initiated:

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
Example Airflow DAG that displays interactions with Google Cloud Functions.
It creates a function and then deletes it.
This DAG relies on the following OS environment variables
https://airflow.apache.org/concepts.html#variables
* GCP_PROJECT_ID - Google Cloud Project to use for the Cloud Function.
* GCP_LOCATION - Google Cloud Functions region where the function should be
created.
* GCF_ENTRYPOINT - Name of the executable function in the source code.
* and one of the below:
* GCF_SOURCE_ARCHIVE_URL - Path to the zipped source in Google Cloud Storage
* GCF_SOURCE_UPLOAD_URL - Generated upload URL for the zipped source and GCF_ZIP_PATH - Local path to
the zipped source archive
* GCF_SOURCE_REPOSITORY - The URL pointing to the hosted repository where the function
is defined in a supported Cloud Source Repository URL format
https://cloud.google.com/functions/docs/reference/rest/v1/projects.locations.functions#SourceRepository
"""
import os
from airflow import models
from airflow.providers.google.cloud.operators.functions import (
CloudFunctionDeleteFunctionOperator,
CloudFunctionDeployFunctionOperator,
CloudFunctionInvokeFunctionOperator,
)
from airflow.utils import dates
GCP_PROJECT_ID = os.environ.get('GCP_PROJECT_ID', 'example-project')
GCP_LOCATION = os.environ.get('GCP_LOCATION', 'europe-west1')
GCF_SHORT_FUNCTION_NAME = os.environ.get('GCF_SHORT_FUNCTION_NAME', 'hello').replace(
"-", "_"
) # make sure there are no dashes in function name (!)
FUNCTION_NAME = 'projects/{}/locations/{}/functions/{}'.format(
GCP_PROJECT_ID, GCP_LOCATION, GCF_SHORT_FUNCTION_NAME
)
GCF_SOURCE_ARCHIVE_URL = os.environ.get('GCF_SOURCE_ARCHIVE_URL', '')
GCF_SOURCE_UPLOAD_URL = os.environ.get('GCF_SOURCE_UPLOAD_URL', '')
GCF_SOURCE_REPOSITORY = os.environ.get(
'GCF_SOURCE_REPOSITORY',
'https://source.developers.google.com/'
'projects/{}/repos/hello-world/moveable-aliases/master'.format(GCP_PROJECT_ID),
)
GCF_ZIP_PATH = os.environ.get('GCF_ZIP_PATH', '')
GCF_ENTRYPOINT = os.environ.get('GCF_ENTRYPOINT', 'helloWorld')
GCF_RUNTIME = 'nodejs6'
GCP_VALIDATE_BODY = os.environ.get('GCP_VALIDATE_BODY', "True") == "True"
# [START howto_operator_gcf_deploy_body]
body = {"name": FUNCTION_NAME, "entryPoint": GCF_ENTRYPOINT, "runtime": GCF_RUNTIME, "httpsTrigger": {}}
# [END howto_operator_gcf_deploy_body]
# [START howto_operator_gcf_default_args]
default_args = {'owner': 'airflow'}
# [END howto_operator_gcf_default_args]
# [START howto_operator_gcf_deploy_variants]
if GCF_SOURCE_ARCHIVE_URL:
body['sourceArchiveUrl'] = GCF_SOURCE_ARCHIVE_URL
elif GCF_SOURCE_REPOSITORY:
body['sourceRepository'] = {'url': GCF_SOURCE_REPOSITORY}
elif GCF_ZIP_PATH:
body['sourceUploadUrl'] = ''
default_args['zip_path'] = GCF_ZIP_PATH
elif GCF_SOURCE_UPLOAD_URL:
body['sourceUploadUrl'] = GCF_SOURCE_UPLOAD_URL
else:
raise Exception("Please provide one of the source_code parameters")
# [END howto_operator_gcf_deploy_variants]
with models.DAG(
'example_gcp_function',
schedule_interval=None, # Override to match your needs
start_date=dates.days_ago(1),
tags=['example'],
) as dag:
# [START howto_operator_gcf_deploy]
deploy_task = CloudFunctionDeployFunctionOperator(
task_id="gcf_deploy_task",
project_id=GCP_PROJECT_ID,
location=GCP_LOCATION,
body=body,
validate_body=GCP_VALIDATE_BODY,
)
# [END howto_operator_gcf_deploy]
# [START howto_operator_gcf_deploy_no_project_id]
deploy2_task = CloudFunctionDeployFunctionOperator(
task_id="gcf_deploy2_task", location=GCP_LOCATION, body=body, validate_body=GCP_VALIDATE_BODY
)
# [END howto_operator_gcf_deploy_no_project_id]
# [START howto_operator_gcf_invoke_function]
invoke_task = CloudFunctionInvokeFunctionOperator(
task_id="invoke_task",
project_id=GCP_PROJECT_ID,
location=GCP_LOCATION,
input_data={},
function_id=GCF_SHORT_FUNCTION_NAME,
)
# [END howto_operator_gcf_invoke_function]
# [START howto_operator_gcf_delete]
delete_task = CloudFunctionDeleteFunctionOperator(task_id="gcf_delete_task", name=FUNCTION_NAME)
# [END howto_operator_gcf_delete]
deploy_task >> deploy2_task >> invoke_task >> delete_task