ephraimbuddy commented on a change in pull request #17927:
URL: https://github.com/apache/airflow/pull/17927#discussion_r699040163
##########
File path: docs/apache-airflow/concepts/tasks.rst
##########
@@ -36,16 +36,30 @@ Relationships
The key part of using Tasks is defining how they relate to each other - their
*dependencies*, or as we say in Airflow, their *upstream* and *downstream*
tasks. You declare your Tasks first, and then you declare their dependencies
second.
-There are two ways of declaring dependencies - using the ``>>`` and ``<<``
(bitshift) operators::
+There are three ways of declaring dependencies:
+
+Option 1: using the ``>>`` and ``<<`` (bitshift) operators::
first_task >> second_task >> [third_task, fourth_task]
-Or the more explicit ``set_upstream`` and ``set_downstream`` methods::
+Option 2: The more explicit ``set_upstream`` and ``set_downstream`` methods::
first_task.set_downstream(second_task)
third_task.set_upstream(second_task)
-
-These both do exactly the same thing, but in general we recommend you use the
bitshift operators, as they are easier to read in most cases.
+
+Option 3: The @task decorator provided by TaskFlow API, see
:ref:`concepts:taskflow`::
+
+ @task
+ def second_task(some_parameter):
+ ...
+ @task
+ def first_task():
+ ...
+
+ second_task(first_task()))
+
+
+The former two do exactly the same thing and work on any type of Operator, but
in general we recommend you use the bitshift operators, as they are easier to
read in most cases. The TaskFlow API likewise connects dependencies between
tasks, but is tailored to python functions written with the @task-decorator,
note that the functions here are not executed inline, rather they are converted
to tasks to be scheduled and sent to Executors, for more information see see
:ref:`concepts:taskflow`.
Review comment:
```suggestion
The former two do exactly the same thing and work on any type of Operator,
but in general we recommend you use the bitshift operators, as they are easier
to read in most cases. The TaskFlow API likewise connects dependencies between
tasks, but is tailored to python functions written with the @task-decorator,
note that the functions here are not executed inline, rather they are converted
to tasks to be scheduled and sent to Executors, for more information see see
:ref:`TaskFlow <concepts:taskflow>`.
```
##########
File path: docs/apache-airflow/concepts/tasks.rst
##########
@@ -36,16 +36,30 @@ Relationships
The key part of using Tasks is defining how they relate to each other - their
*dependencies*, or as we say in Airflow, their *upstream* and *downstream*
tasks. You declare your Tasks first, and then you declare their dependencies
second.
-There are two ways of declaring dependencies - using the ``>>`` and ``<<``
(bitshift) operators::
+There are three ways of declaring dependencies:
+
+Option 1: using the ``>>`` and ``<<`` (bitshift) operators::
first_task >> second_task >> [third_task, fourth_task]
-Or the more explicit ``set_upstream`` and ``set_downstream`` methods::
+Option 2: The more explicit ``set_upstream`` and ``set_downstream`` methods::
first_task.set_downstream(second_task)
third_task.set_upstream(second_task)
-
-These both do exactly the same thing, but in general we recommend you use the
bitshift operators, as they are easier to read in most cases.
+
+Option 3: The @task decorator provided by TaskFlow API, see
:ref:`concepts:taskflow`::
+
+ @task
+ def second_task(some_parameter):
+ ...
+ @task
+ def first_task():
+ ...
+
+ second_task(first_task()))
Review comment:
```suggestion
second_task(first_task())
```
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