[GitHub] [airflow] TobKed commented on a change in pull request #13461: Add How To Guide for Dataflow

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TobKed commented on a change in pull request #13461:
URL: https://github.com/apache/airflow/pull/13461#discussion_r554999776



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File path: docs/apache-airflow-providers-google/operators/cloud/dataflow.rst
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@@ -0,0 +1,274 @@
+ .. Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+ ..   http://www.apache.org/licenses/LICENSE-2.0
+
+ .. Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+Google Cloud Dataflow Operators
+===============================
+
+`Dataflow <https://cloud.google.com/dataflow/>`__ is a managed service for
+executing a wide variety of data processing patterns. These pipelines are created
+using the Apache Beam programming model which allows for both batch and streaming.
+
+.. contents::
+  :depth: 1
+  :local:
+
+Prerequisite Tasks
+^^^^^^^^^^^^^^^^^^
+
+.. include::/operators/_partials/prerequisite_tasks.rst
+
+Ways to run a data pipeline
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are multiple options to execute a Dataflow pipeline on Airflow. If looking to execute the pipeline
+code from a source file (Java or Python) it would be best to use the language specific create operators.
+If a process exists to stage the pipeline code in an abstracted manner - a Templated job would be best as
+it allows development of the application without minimal intrusion to the DAG containing operators for it.
+It is also possible to run jobs defined in SQL language.
+
+Starting a new job
+^^^^^^^^^^^^^^^^^^
+
+To create a new pipeline using the source file (JAR in Java or Python file) use
+the create job operators. The source file can be located on GCS or on the local filesystem.
+:class:`~airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator`
+or
+:class:`~airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator`
+
+Please see the notes below on Java and Python specific SDKs as they each have their own set
+of execution options when running pipelines.
+
+Language specific pipelines
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Based on which language (SDK) is used for the Dataflow operators, there are specific options to be wary of.
+
+.. _howto/operator:DataflowCreateJavaJobOperator:
+
+Java SDK pipelines
+""""""""""""""""""
+
+The ``jar`` argument must be specified for
+:class:`~airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator`
+as it contains the pipeline to be executed on Dataflow. The JAR can be available on GCS that Airflow
+has the ability to download or available on the local filesystem (provide the absolute path to it).
+
+Here is an example of creating and running a pipeline in Java with jar stored on GCS:
+
+.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_dataflow.py
+    :language: python
+    :dedent: 4
+    :start-after: [START howto_operator_start_java_job_jar_on_gcs]
+    :end-before: [END howto_operator_start_java_job_jar_on_gcs]
+
+
+Here is an example of creating and running a pipeline in Java with jar stored on GCS:
+
+.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_dataflow.py
+    :language: python
+    :dedent: 4
+    :start-after: [START howto_operator_start_java_job_local_jar]
+    :end-before: [END howto_operator_start_java_job_local_jar]
+
+.. _howto/operator:DataflowCreatePythonJobOperator:
+
+Python SDK pipelines
+""""""""""""""""""""
+
+The ``py_file`` argument must be specified for
+:class:`~airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator`
+as it contains the pipeline to be executed on Dataflow. The Python file can be available on GCS that Airflow
+has the ability to download or available on the local filesystem (provide the absolute path to it).
+
+The ``py_interpreter`` argument specifies the Python version to be used when executing the pipeline, the default
+is ``python3`. If your Airflow instance is running on Python 2 - specify ``python2`` and ensure your ``py_file`` is
+in Python 2. For best results, use Python 3.
+
+If ``py_requirements`` argument is specified a temporary Python virtual environment with specified requirements will be created
+and within it pipeline will run.
+
+The ``py_system_site_packages`` argument specifies whether or not all the Python packages from your Airflow instance,
+will be accessible within virtual environment (if ``py_requirements`` argument is specified),
+recommend avoiding unless the Dataflow job requires it.
+
+.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_dataflow.py
+    :language: python
+    :dedent: 4
+    :start-after: [START howto_operator_start_python_job]
+    :end-before: [END howto_operator_start_python_job]
+
+
+Execution options for pipelines
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Dataflow has multiple options of executing pipelines. It can be done in the following modes:
+batch asynchronously (fire and forget), batch blocking (wait until completion), or streaming (run indefinitely).

Review comment:
       It is based on the Dataflow documentation:
   
   https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#configuring-pipelineoptions-for-execution-on-the-cloud-dataflow-service




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