chart/docs/quick-start.rst

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Quick start with kind
=====================

This article will show you how to install Airflow using Helm Chart on `Kind <https://kind.sigs.k8s.io/>`__.

Run Airflow on Kind cluster
---------------------------

Install kind and create a cluster
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


.. jinja:: global_ctx

   We recommend testing with Kubernetes {{ min_k8s_version }}+, example:

   .. code-block:: bash

      kind create cluster --image kindest/node:{{ min_k8s_version }}

Confirm it's up:

.. code-block:: bash

   kubectl cluster-info --context kind-kind

Add Airflow Helm Stable Repo
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: bash

   helm repo add apache-airflow https://airflow.apache.org
   helm repo update

Create namespace
^^^^^^^^^^^^^^^^

.. code-block:: bash

   export NAMESPACE=example-namespace
   kubectl create namespace $NAMESPACE

.. note::

   Same exported ``NAMESPACE`` variable will be used in the next instructions.

Install the chart
^^^^^^^^^^^^^^^^^

.. code-block:: bash

   export RELEASE_NAME=example-release
   helm install $RELEASE_NAME apache-airflow/airflow --namespace $NAMESPACE

.. note::

   Same exported ``RELEASE_NAME`` variable will be used in the next instructions.

Use the following code to install the chart with example Dags:

.. code-block:: bash

   helm install $RELEASE_NAME apache-airflow/airflow \
     --namespace $NAMESPACE \
     --set-string "env[0].name=AIRFLOW__CORE__LOAD_EXAMPLES" \
     --set-string "env[0].value=True"

It may take a few minutes. Confirm the pods are up:

.. code-block:: bash

   kubectl get pods --namespace $NAMESPACE
   helm list --namespace $NAMESPACE

Run the following command to port-forward the Airflow UI to http://localhost:8080/ to confirm
Airflow is working.

.. code-block:: bash

   kubectl port-forward svc/$RELEASE_NAME-api-server 8080:8080 --namespace $NAMESPACE

.. _quick-start:extending-airflow-image:

Extending Airflow Image
-----------------------

The Apache Airflow community, releases Docker Images which are ``reference images`` for Apache Airflow.
However, when you try it out you want to add your own Dags, custom dependencies, packages, or even custom providers.

.. note::

   Creating custom images means that you need to maintain also a level of automation as you need to re-create the images
   when either the packages you want to install or Airflow is upgraded. Do not forget about keeping these scripts.
   Also keep in mind, that in cases when you run pure Python tasks, you can use the
   `Python Virtualenv functions <https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html#pythonvirtualenvoperator>`_
   which will dynamically source and install python dependencies during runtime. Virtualenvs can also be cached.

The best way to achieve it, is to build your own, custom image.

Adding Dags to your image
^^^^^^^^^^^^^^^^^^^^^^^^^

1. Create a project:

   .. code-block:: bash

      mkdir my-airflow-project && cd my-airflow-project
      mkdir dags  # put dags here
      cat <<EOM > Dockerfile
      FROM apache/airflow
      COPY . .
      EOM

2. Then build the image:

   .. code-block:: bash

      docker build --pull --tag my-dags:0.0.1 .

3. Load the image into kind:

   .. code-block:: bash

      kind load docker-image my-dags:0.0.1

4. Upgrade Helm deployment:

   .. code-block:: bash

      helm upgrade $RELEASE_NAME apache-airflow/airflow --namespace $NAMESPACE \
        --set images.airflow.repository=my-dags \
        --set images.airflow.tag=0.0.1

Adding ``apt`` packages to your image
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Example below adds ``vim`` apt package.

1. Create a project:

   .. code-block:: bash

      mkdir my-airflow-project && cd my-airflow-project
      cat <<EOM > Dockerfile
      FROM apache/airflow
      USER root
      RUN apt-get update \
        && apt-get install -y --no-install-recommends vim \
        && apt-get autoremove -yqq --purge \
        && apt-get clean \
        && rm -rf /var/lib/apt/lists/*
      USER airflow
      EOM

2. Then build the image:

   .. code-block:: bash

      docker build --pull --tag my-image:0.0.1 .

3. Load the image into kind:

   .. code-block:: bash

      kind load docker-image my-image:0.0.1

4. Upgrade Helm deployment:

   .. code-block:: bash

      helm upgrade $RELEASE_NAME apache-airflow/airflow --namespace $NAMESPACE \
        --set images.airflow.repository=my-image \
        --set images.airflow.tag=0.0.1

Adding ``PyPI`` packages to your image
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Example below adds ``lxml`` PyPI package.

1. Create a project:

   .. code-block:: bash

      mkdir my-airflow-project && cd my-airflow-project
      cat <<EOM > Dockerfile
      FROM apache/airflow
      RUN pip install --no-cache-dir lxml
      EOM

2. Then build the image:

   .. code-block:: bash

      docker build --pull --tag my-image:0.0.1 .

3. Load the image into kind:

   .. code-block:: bash

      kind load docker-image my-image:0.0.1

4. Upgrade Helm deployment:

   .. code-block:: bash

      helm upgrade $RELEASE_NAME apache-airflow/airflow --namespace $NAMESPACE \
        --set images.airflow.repository=my-image \
        --set images.airflow.tag=0.0.1

Further extending and customizing the image
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

See `Building the image <https://airflow.apache.org/docs/docker-stack/build.html>`_ for more
details on how you can extend and customize the Airflow image.