kaxil commented on a change in pull request #8962:
URL: https://github.com/apache/airflow/pull/8962#discussion_r443078056
##########
File path: docs/concepts.rst
##########
@@ -173,6 +213,62 @@ Each task is a node in our DAG, and there is a dependency
from task_1 to task_2:
We can say that task_1 is *upstream* of task_2, and conversely task_2 is
*downstream* of task_1.
When a DAG Run is created, task_1 will start running and task_2 waits for
task_1 to complete successfully before it may start.
+.. _concepts:task_decorator:
+
+Python task decorator
+---------------------
+
+Airflow ``task`` decorator converts any Python decorated function to a Python
Airflow operator.
+The decorated function can be called once to set the arguments and key
arguments for operator execution.
Review comment:
```suggestion
Airflow ``task`` decorator converts any Python function to an Airflow
operator.
The decorated function can be called once to set the arguments and key
arguments for operator execution.
```
##########
File path: docs/concepts.rst
##########
@@ -173,6 +213,62 @@ Each task is a node in our DAG, and there is a dependency
from task_1 to task_2:
We can say that task_1 is *upstream* of task_2, and conversely task_2 is
*downstream* of task_1.
When a DAG Run is created, task_1 will start running and task_2 waits for
task_1 to complete successfully before it may start.
+.. _concepts:task_decorator:
+
+Python task decorator
+---------------------
+
+Airflow ``task`` decorator converts any Python decorated function to a Python
Airflow operator.
+The decorated function can be called once to set the arguments and key
arguments for operator execution.
+
+
+.. code:: python
+
+ with DAG('my_dag', start_date=datetime(2020, 5, 15)) as dag:
+
+ @dag.task
+ def hello_world():
+ print('hello world!')
+
+
+ # Also...
+
+ from airflow.decorators import task
+
+ @task
+ def hello_name(name: str):
+ print(f'hello {name}!')
+
+ hello_name('Airflow users')
+
+Task decorator captures returned values and sends them to the :ref:`XCom
backend <concepts:xcom>`. By default, returned
+value is saved as a single XCom value. You can set ``multiple_outputs`` key
argument to ``True`` to unroll dictionaries,
+lists or tuples into seprate XCom values. This can be used with regular
operators to create
+:ref:`functional DAGs <concepts:functional_dags>`.
+
+Calling a decorated function returns an ``XComArg`` instance. You can use it
to set templated fields on downstream
+operators.
+
+You can call a decorated function more than once in a DAG. The decorated
function will automatically generate unique
+a ``task_id`` for each generated operator.
Review comment:
```suggestion
You can call a decorated function more than once in a DAG. The decorated
function will automatically generate
a unique ``task_id`` for each generated operator.
```
##########
File path: docs/concepts.rst
##########
@@ -173,6 +213,62 @@ Each task is a node in our DAG, and there is a dependency
from task_1 to task_2:
We can say that task_1 is *upstream* of task_2, and conversely task_2 is
*downstream* of task_1.
When a DAG Run is created, task_1 will start running and task_2 waits for
task_1 to complete successfully before it may start.
+.. _concepts:task_decorator:
+
+Python task decorator
+---------------------
+
+Airflow ``task`` decorator converts any Python decorated function to a Python
Airflow operator.
+The decorated function can be called once to set the arguments and key
arguments for operator execution.
+
+
+.. code:: python
+
+ with DAG('my_dag', start_date=datetime(2020, 5, 15)) as dag:
+
+ @dag.task
+ def hello_world():
+ print('hello world!')
+
+
+ # Also...
+
+ from airflow.decorators import task
+
+ @task
+ def hello_name(name: str):
+ print(f'hello {name}!')
+
+ hello_name('Airflow users')
Review comment:
```suggestion
with DAG('my_dag', start_date=datetime(2020, 5, 15)) as dag:
@dag.task
def hello_world():
print('hello world!')
# Also...
from airflow.decorators import task
@task
def hello_name(name: str):
print(f'hello {name}!')
hello_name('Airflow users')
```
##########
File path: docs/concepts.rst
##########
@@ -173,6 +213,62 @@ Each task is a node in our DAG, and there is a dependency
from task_1 to task_2:
We can say that task_1 is *upstream* of task_2, and conversely task_2 is
*downstream* of task_1.
When a DAG Run is created, task_1 will start running and task_2 waits for
task_1 to complete successfully before it may start.
+.. _concepts:task_decorator:
+
+Python task decorator
+---------------------
+
+Airflow ``task`` decorator converts any Python decorated function to a Python
Airflow operator.
+The decorated function can be called once to set the arguments and key
arguments for operator execution.
+
+
+.. code:: python
+
+ with DAG('my_dag', start_date=datetime(2020, 5, 15)) as dag:
+
+ @dag.task
+ def hello_world():
+ print('hello world!')
+
+
+ # Also...
+
+ from airflow.decorators import task
+
+ @task
+ def hello_name(name: str):
+ print(f'hello {name}!')
+
+ hello_name('Airflow users')
+
+Task decorator captures returned values and sends them to the :ref:`XCom
backend <concepts:xcom>`. By default, returned
+value is saved as a single XCom value. You can set ``multiple_outputs`` key
argument to ``True`` to unroll dictionaries,
+lists or tuples into seprate XCom values. This can be used with regular
operators to create
+:ref:`functional DAGs <concepts:functional_dags>`.
+
+Calling a decorated function returns an ``XComArg`` instance. You can use it
to set templated fields on downstream
+operators.
+
+You can call a decorated function more than once in a DAG. The decorated
function will automatically generate unique
+a ``task_id`` for each generated operator.
+
+.. code:: python
+
+ with DAG('my_dag', start_date=datetime(2020, 5, 15)) as dag:
+
+ @dag.task
+ def update_user(user_id: int):
+ ...
+
+ # Avoid generating this list dynamically to keep dag topology stable
between DAG runs
Review comment:
```suggestion
# Avoid generating this list dynamically to keep DAG topology stable
between DAG runs
```
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