turbaszek commented on a change in pull request #10587:
URL: https://github.com/apache/airflow/pull/10587#discussion_r506637517
##########
File path: docs/concepts.rst
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@@ -162,6 +162,52 @@ Example DAG with decorated style
html_content=email_info['body']
)
+DAG decorator
+-------------
+
+.. versionadded:: 2.0.0
+
+In addition to creating DAGs using context managed, in Airflow 2.0 you can
also create DAGs from a function.
+DAG decorator creates a DAG generator function. This function when called
returns a DAG.
+
+DAG decorator also sets up the parameters you have in the function as DAG
params. This allows you to parametrize
+your DAGs and set the parameters when triggering the DAG manually. See
+:ref:`Passing Parameters when triggering dags <dagrun:parameters>` to learn
how to pass parameters when triggering DAGs.
+
+Example DAG with decorator:
+
+.. code-block:: python
+
+ from airflow.decorators import dag, task
+
+ @dag(default_args=default_args, schedule_interval=None)
+ def send_server_ip(email: '[email protected]')
+
+ # Using default connection as it's set to httpbin.org by default
+ get_ip = SimpleHttpOperator(
+ task_id='get_ip', endpoint='get', method='GET', xcom_push=True
+ )
+
+ @task(multiple_outputs=True)
+ def prepare_email(raw_json: str) -> Dict[str, str]:
+ external_ip = json.loads(raw_json)['origin']
+ return {
+ 'subject':f'Server connected from {external_ip}',
+ 'body': f'Seems like today your server executing Airflow is connected
from the external IP {external_ip}<br>'
+ }
+
+ email_info = prepare_email(get_ip.output)
+
+ send_email = EmailOperator(
+ task_id='send_email',
+ to=email,
+ subject=email_info['subject'],
+ html_content=email_info['body']
+ )
+
+ my_dag = send_server_ip()
Review comment:
I would be in favor of putting this in example dag and using:
```
.. exampleinclude::
/../airflow/providers/google/cloud/example_dags/example_bigquery_operations.py
:language: python
:dedent: 4
:start-after: [START howto_operator_bigquery_create_dataset]
:end-before: [END howto_operator_bigquery_create_dataset]
```
In this way we will have a DAG that can be used for that can be used for
tests
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