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new fdbb798 Making spelling of "TaskFlow" consistent in docs (#17968)
fdbb798 is described below
commit fdbb798b9d3f58a33200c012cb546d60c06fc84f
Author: Josh Fell <48934154+josh-f...@users.noreply.github.com>
AuthorDate: Wed Sep 1 13:47:49 2021 -0400
Making spelling of "TaskFlow" consistent in docs (#17968)
---
docs/apache-airflow/concepts/taskflow.rst | 2 +-
docs/apache-airflow/tutorial_taskflow_api.rst | 20 ++++++++++----------
docs/spelling_wordlist.txt | 1 +
3 files changed, 12 insertions(+), 11 deletions(-)
diff --git a/docs/apache-airflow/concepts/taskflow.rst
b/docs/apache-airflow/concepts/taskflow.rst
index 9ec8017..4cfc91f 100644
--- a/docs/apache-airflow/concepts/taskflow.rst
+++ b/docs/apache-airflow/concepts/taskflow.rst
@@ -70,4 +70,4 @@ History
The TaskFlow API is new as of Airflow 2.0, and you are likely to encounter
DAGs written for previous versions of Airflow that instead use
``PythonOperator`` to achieve similar goals, albeit with a lot more code.
More context around the addition and design of the TaskFlow API can be found
as part of its Airflow Improvement Proposal
-`AIP-31: "Taskflow API" for clearer/simpler DAG definition
<https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=148638736>`_
+`AIP-31: "TaskFlow API" for clearer/simpler DAG definition
<https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=148638736>`_
diff --git a/docs/apache-airflow/tutorial_taskflow_api.rst
b/docs/apache-airflow/tutorial_taskflow_api.rst
index 03da9e0..b2be763 100644
--- a/docs/apache-airflow/tutorial_taskflow_api.rst
+++ b/docs/apache-airflow/tutorial_taskflow_api.rst
@@ -18,20 +18,20 @@
-Tutorial on the Taskflow API
+Tutorial on the TaskFlow API
============================
This tutorial builds on the regular Airflow Tutorial and focuses specifically
-on writing data pipelines using the Taskflow API 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 "Taskflow API" ETL Pipeline
+Example "TaskFlow API" ETL Pipeline
-----------------------------------
-Here is very simple ETL pipeline using the Taskflow API 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_taskflow_api_etl.py
@@ -129,7 +129,7 @@ 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 Taskflow API in Airflow 2.0 as shown below.
+Contrasting that with TaskFlow API in Airflow 2.0 as shown below.
.. exampleinclude:: /../../airflow/example_dags/tutorial_taskflow_api_etl.py
:language: python
@@ -151,7 +151,7 @@ dependencies specified as shown below.
:start-after: [START main_flow]
:end-before: [END main_flow]
-In contrast, with the Taskflow API 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_taskflow_api_etl.py
@@ -160,12 +160,12 @@ the dependencies as shown below.
:start-after: [START main_flow]
:end-before: [END main_flow]
-Using the Taskflow API with Virtual Environments
+Using the TaskFlow API with Virtual Environments
----------------------------------------------------------
-As of Airflow 2.0.3, you will have the ability to use the Taskflow API with a
+As of Airflow 2.0.3, you will have the ability to use the TaskFlow API with a
virtual environment. This added functionality will allow a much more
-comprehensive range of use-cases for the Taskflow API, as you will not be
limited to the
+comprehensive range of use-cases for the TaskFlow API, as you will not be
limited to the
packages and system libraries of the Airflow worker.
To run your Airflow task in a virtual environment, switch your ``@task``
decorator to a ``@task.virtualenv``
@@ -234,6 +234,6 @@ Finally, a dependency between this Sensor task and the
python-based task is spec
What's Next?
------------
-You have seen how simple it is to write DAGs using the Taskflow API 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 :doc:`Concepts section </concepts/index>` for detailed explanation of
Airflow concepts such as DAGs, Tasks,
Operators, and more. There's also a whole section on the :doc:`TaskFlow API
</concepts/taskflow>` and the ``@task`` decorator.
diff --git a/docs/spelling_wordlist.txt b/docs/spelling_wordlist.txt
index c10c832..67359d9 100644
--- a/docs/spelling_wordlist.txt
+++ b/docs/spelling_wordlist.txt
@@ -342,6 +342,7 @@ TCP
TLS
TTY
TZ
+TaskFlow
TaskGroup
TaskGroups
TaskInstance
