[GitHub] [airflow] turbaszek commented on a change in pull request #10587: @dag decorator

[GitBox <gi...@apache.org>]

turbaszek commented on a change in pull request #10587:
URL: https://github.com/apache/airflow/pull/10587#discussion_r506637517



##########
File path: docs/concepts.rst
##########
@@ -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: 'example@example.com')
+
+    # 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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