Change Decorated Flow to TaskFlow API (apache#11895)

* Change Decorated Flow to TaskFlow API In order to better describe what this new data transfer system does, we are changing the name of Decorated Flows to TaskFlow API * Update docs/concepts.rst Co-authored-by: Gerard Casas Saez <[email protected]> * Update docs/concepts.rst Co-authored-by: Kaxil Naik <[email protected]> * Update docs/tutorial_taskflow_api.rst Co-authored-by: Kaxil Naik <[email protected]> Co-authored-by: Gerard Casas Saez <[email protected]> Co-authored-by: Kaxil Naik <[email protected]>
askmeegs · Oct 27, 2020 · 8477efe · 8477efe
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diff --git a/docs/concepts.rst b/docs/concepts.rst
@@ -120,14 +120,14 @@ DAGs can be used as context managers to automatically assign new operators to th
 
     op.dag is dag # True
 
-.. _concepts:decorated_flows:
+.. _concepts:task_flow_api:
 
-Decorated Flows
----------------
+TaskFlow API
+------------
 
 .. versionadded:: 2.0.0
 
-Airflow 2.0 adds a new style of authoring dags called Decorated Flows which removes a lot of the boilerplate
+Airflow 2.0 adds a new style of authoring dags called the TaskFlow API which removes a lot of the boilerplate
 around creating PythonOperators, managing dependencies between task and accessing XCom values. (During
 development this feature was called "Functional DAGs", so if you see or hear any references to that, it's the
 same thing)
@@ -136,7 +136,7 @@ Outputs and inputs are sent between tasks using :ref:`XCom values <concepts:xcom
 functions as tasks using the :ref:`task decorator <concepts:task_decorator>`. Airflow will also automatically
 add dependencies between tasks to ensure that XCom messages are available when operators are executed.
 
-Example DAG with decorated style
+Example DAG built with the TaskFlow API
 
 .. code-block:: python
 

diff --git a/docs/tutorial_decorated_flows.rst → docs/tutorial_taskflow_api.rst b/docs/tutorial_decorated_flows.rst → docs/tutorial_taskflow_api.rst
@@ -18,20 +18,20 @@
 
 
 
-Tutorial on Decorated Flows
-===========================
+Tutorial on the Taskflow API
+============================
 
 This tutorial builds on the regular Airflow Tutorial and focuses specifically
-on writing data pipelines using the Decorated Flow paradigm which is introduced as
+on writing data pipelines using the Taskflow API paradigm which is introduced as
 part of Airflow 2.0 and contrasts this with DAGs written using the traditional paradigm.
 
 The data pipeline chosen here is a simple ETL pattern with
 three separate tasks for Extract, Transform, and Load.
 
-Example "Decorated Flow" ETL Pipeline
--------------------------------------
+Example "Taskflow API" ETL Pipeline
+-----------------------------------
 
-Here is very simple ETL pipeline using the Decorated Flows paradigm. A more detailed
+Here is very simple ETL pipeline using the Taskflow API paradigm. A more detailed
 explanation is given below.
 
 .. exampleinclude:: /../airflow/example_dags/tutorial_decorated_etl_dag.py
@@ -115,7 +115,7 @@ As we see here, the data being processed in the Transform function is passed to
 variables. In turn, the summarized data from the Transform function is also placed
 into another Xcom variable which will then be used by the Load task.
 
-Contrasting that with Decorated Flows in Airflow 2.0 as shown below.
+Contrasting that with Taskflow API in Airflow 2.0 as shown below.
 
 .. exampleinclude:: /../airflow/example_dags/tutorial_decorated_etl_dag.py
     :language: python
@@ -126,7 +126,7 @@ All of the Xcom usage for data passing between these tasks is abstracted away fr
 in Airflow 2.0. However, Xcom variables are used behind the scenes and can be viewed using
 the Airflow UI as necessary for debugging or DAG monitoring.
 
-Similarly, task dependencies are automatically generated within Decorated Flows based on the
+Similarly, task dependencies are automatically generated within TaskFlows based on the
 functional invocation of tasks. In Airflow 1.x, tasks had to be explicitly created and
 dependencies specified as shown below.
 
@@ -135,7 +135,7 @@ dependencies specified as shown below.
     :start-after: [START main_flow]
     :end-before: [END main_flow]
 
-In contrast, with Decorated Flows in Airflow 2.0, the invocation itself automatically generates
+In contrast, with the Taskflow API in Airflow 2.0, the invocation itself automatically generates
 the dependencies as shown below.
 
 .. exampleinclude:: /../airflow/example_dags/tutorial_decorated_etl_dag.py
@@ -147,10 +147,10 @@ the dependencies as shown below.
 What's Next?
 ------------
 
-You have seen how simple it is to write DAGs using the Decorated Flows paradigm within Airflow 2.0. Please do
+You have seen how simple it is to write DAGs using the Taskflow API paradigm within Airflow 2.0. Please do
 read the :ref:`Concepts page<concepts>` for detailed explanation of Airflow concepts such as DAGs, Tasks,
 Operators, etc, and the :ref:`concepts:task_decorator` in particular.
 
-More details about Decorated Flows, can be found as part of the Airflow Improvement Proposal
-`AIP-31: "Decorated Flows" for clearer/simpler DAG definition <https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=148638736>`__
-and specifically within the Concepts guide at :ref:`Concepts - Decorated Flows<concepts:decorated_flows>`.
+More details about the Taskflow API, can be found as part of the Airflow Improvement Proposal
+`AIP-31: "Taskflow API" for clearer/simpler DAG definition <https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=148638736>`__
+and specifically within the Concepts guide at :ref:`Concepts - Taskflow API<concepts:task_flow_api>`.