def setUp(self):
self.dataflow = DataflowTemplatedJobStartOperator(
task_id=TASK_ID,
template=TEMPLATE,
job_name=JOB_NAME,
parameters=PARAMETERS,
dataflow_default_options=DEFAULT_OPTIONS_TEMPLATE,
poll_sleep=POLL_SLEEP)
def setUp(self):
self.dataflow = DataflowTemplatedJobStartOperator(
task_id=TASK_ID,
template=TEMPLATE,
job_name=JOB_NAME,
parameters=PARAMETERS,
options=DEFAULT_OPTIONS_TEMPLATE,
dataflow_default_options={"EXTRA_OPTION": "TEST_A"},
poll_sleep=POLL_SLEEP,
location=TEST_LOCATION,
environment={"maxWorkers": 2},
)
job_id=
"{{task_instance.xcom_pull('start-python-job-async')['job_id']}}",
location='europe-west3',
callback=check_autoscaling_event,
)
# [END howto_sensor_wait_for_job_autoscaling_event]
start_python_job_async >> wait_for_python_job_async_done
start_python_job_async >> wait_for_python_job_async_metric
start_python_job_async >> wait_for_python_job_async_message
start_python_job_async >> wait_for_python_job_async_autoscaling_event
with models.DAG(
"example_gcp_dataflow_template",
default_args=default_args,
start_date=days_ago(1),
schedule_interval=None, # Override to match your needs
tags=['example'],
) as dag_template:
# [START howto_operator_start_template_job]
start_template_job = DataflowTemplatedJobStartOperator(
task_id="start-template-job",
template='gs://dataflow-templates/latest/Word_Count',
parameters={
'inputFile': "gs://dataflow-samples/shakespeare/kinglear.txt",
'output': GCS_OUTPUT
},
location='europe-west3',
)
# [END howto_operator_start_template_job]
with models.DAG(
# The id you will see in the DAG airflow page
"composer_dataflow_dag",
default_args=default_args,
# The interval with which to schedule the DAG
schedule_interval=datetime.timedelta(
days=1), # Override to match your needs
) as dag:
start_template_job = DataflowTemplatedJobStartOperator(
# The task id of your job
task_id="dataflow_operator_transform_csv_to_bq",
# The name of the template that you're using.
# Below is a list of all the templates you can use.
# For versions in non-production environments, use the subfolder 'latest'
# https://cloud.google.com/dataflow/docs/guides/templates/provided-batch#gcstexttobigquery
template="gs://dataflow-templates/latest/GCS_Text_to_BigQuery",
# Use the link above to specify the correct parameters for your template.
parameters={
"javascriptTextTransformFunctionName": "transformCSVtoJSON",
"JSONPath": bucket_path + "/jsonSchema.json",
"javascriptTextTransformGcsPath":
bucket_path + "/transformCSVtoJSON.js",
"inputFilePattern": bucket_path + "/inputFile.txt",
"outputTable": project_id + ":average_weather.average_weather",
"bigQueryLoadingTemporaryDirectory": bucket_path + "/tmp/",
},
)
# [END composer_dataflow_dag]
default_args=default_args,
schedule_interval=None) as dag:
start = dummy.DummyOperator(task_id='start', trigger_rule='all_success')
end = dummy.DummyOperator(task_id='end', trigger_rule='all_success')
# Bigquery Tables automatically created for demo porpuse.
# Consider a dedicated pipeline or tool for a real life scenario.
customers_import = DataflowTemplatedJobStartOperator(
task_id="dataflow_customers_import",
template="gs://dataflow-templates/latest/GCS_Text_to_BigQuery",
project_id=LOD_PRJ,
location=DF_REGION,
parameters={
"javascriptTextTransformFunctionName": "transform",
"JSONPath": ORC_GCS + "/customers_schema.json",
"javascriptTextTransformGcsPath": ORC_GCS + "/customers_udf.js",
"inputFilePattern": DRP_GCS + "/customers.csv",
"outputTable":
DWH_LAND_PRJ + ":" + DWH_LAND_BQ_DATASET + ".customers",
"bigQueryLoadingTemporaryDirectory": LOD_GCS_STAGING + "/tmp/bq/",
},
)
purchases_import = DataflowTemplatedJobStartOperator(
task_id="dataflow_purchases_import",
template="gs://dataflow-templates/latest/GCS_Text_to_BigQuery",
project_id=LOD_PRJ,
location=DF_REGION,
parameters={
"javascriptTextTransformFunctionName": "transform",