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Add documentation for Apache Airflow Helm Chart 1.11.0 #867

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.. 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.


Adding Connections, Variables and Environment Variables
=======================================================

You can programmatically add Connections, Variables and arbitrary Environment Variables to your
Airflow deployment using the Helm chart.


Connections and Sensitive Environment Variables
-----------------------------------------------
Under the ``secret`` and ``extraSecret`` sections of the ``values.yaml`` you can pass connection strings and sensitive
environment variables into Airflow using the Helm chart. To illustrate, lets create a yaml file called ``override.yaml``
to override values under these sections of the ``values.yaml`` file.

.. code-block:: yaml

# override.yaml

secret:
- envName: "AIRFLOW_CONN_GCP"
secretName: "my-airflow-connections"
secretKey: "AIRFLOW_CONN_GCP"
- envName: "my-env"
secretName: "my-secret-name"
secretKey: "my-secret-key"

extraSecrets:
my-airflow-connections:
data: |
AIRFLOW_CONN_GCP: 'base64_encoded_gcp_conn_string'
my-secret-name:
stringData: |
my-secret-key: my-secret


Variables
---------
Airflow supports Variables which enable users to craft dynamic DAGs. You can set Variables in Airflow in three ways - UI,
command line, and within your DAG file. See :doc:`apache-airflow:howto/variable` for more.

With the Helm chart, you can also inject environment variables into Airflow. So in the example ``override.yaml`` file,
we can override values of interest in the ``env`` section of the ``values.yaml`` file.

.. code-block:: yaml

env:
- name: "AIRFLOW_VAR_KEY"
value: "value_1"
- name: "AIRFLOW_VAR_ANOTHER_KEY"
value: "value_2"


You can also utilize ``extraEnv`` and ``extraEnvFrom`` if you need the name or value to be templated.

.. code-block:: yaml

extraEnv: |
- name: AIRFLOW_VAR_HELM_RELEASE_NAME
value: '{{ .Release.Name }}'

extraEnvFrom: |
- configMapRef:
name: '{{ .Release.Name }}-airflow-variables'

extraConfigMaps:
'{{ .Release.Name }}-airflow-variables':
data: |
AIRFLOW_VAR_HELLO_MESSAGE: "Hi!"
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.. 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.

Configuring Airflow
-------------------

The chart allows for setting arbitrary Airflow configuration in values under the ``config`` key.
Some of the defaults in the chart differ from those of core Airflow and can be found in
`values.yaml <https://github.com/apache/airflow/blob/main/chart/values.yaml>`__.

As an example of setting arbitrary configuration, the following yaml demonstrates how one would
allow webserver users to view the config from within the UI:

.. code-block:: yaml

config:
webserver:
expose_config: 'True' # by default this is 'False'

Generally speaking, it is useful to familiarize oneself with the Airflow
configuration prior to installing and deploying the service.

.. note::

The recommended way to load example DAGs using the official Docker image and chart is to configure the ``AIRFLOW__CORE__LOAD_EXAMPLES`` environment variable
in ``extraEnv`` (see :doc:`Parameters reference <parameters-ref>`). The official Docker image has ``AIRFLOW__CORE__LOAD_EXAMPLES=False``
set within the image, so you need to override it with an environment variable when deploying the chart in order for the examples to be present.
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.. 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.

Customizing Workers
===================

Both ``CeleryExecutor`` and ``KubernetesExecutor`` workers can be highly customized with the :ref:`workers parameters <parameters:workers>`.
For example, to set resources on workers:

.. code-block:: yaml

workers:
resources:
requests:
cpu: 1
limits:
cpu: 1

See :ref:`workers parameters <parameters:workers>` for a complete list.

One notable exception for ``KubernetesExecutor`` is that the default anti-affinity applied to ``CeleryExecutor`` workers to spread them across nodes
is not applied to ``KubernetesExecutor`` workers, as there is no reason to spread out per-task workers.

Custom ``pod_template_file``
----------------------------

With ``KubernetesExecutor`` or ``CeleryKubernetesExecutor`` you can also provide a complete ``pod_template_file`` to configure Kubernetes workers.
This may be useful if you need different configuration between worker types for ``CeleryKubernetesExecutor``
or if you need to customize something not possible with :ref:`workers parameters <parameters:workers>` alone.

As an example, let's say you want to set ``priorityClassName`` on your workers:

.. note::

The following example is NOT functional, but meant to be illustrative of how you can provide a custom ``pod_template_file``.
You're better off starting with the `default pod_template_file`_ instead.

.. _default pod_template_file: https://github.com/apache/airflow/blob/main/chart/files/pod-template-file.kubernetes-helm-yaml

.. code-block:: yaml

podTemplate: |
apiVersion: v1
kind: Pod
metadata:
name: placeholder-name
labels:
tier: airflow
component: worker
release: {{ .Release.Name }}
spec:
priorityClassName: high-priority
containers:
- name: base
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