 .. 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.

Autoscaling with KEDA
---------------------

*This feature is still experimental.*

KEDA stands for Kubernetes Event Driven Autoscaling.
`KEDA <https://github.com/kedacore/keda>`__ is a custom controller that
allows users to create custom bindings to the Kubernetes `Horizontal Pod
Autoscaler <https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/>`__.
The autoscaler will adjust the number of active celery workers based on the number
of tasks in ``queued`` or ``running`` state.

.. code-block:: bash

   helm repo add kedacore https://kedacore.github.io/charts

   helm repo update

   kubectl create namespace keda

   helm install keda kedacore/keda \
       --namespace keda \
       --version "v2.0.0"

Enable for the airflow instance by setting ``workers.keda.enabled=true`` in your
helm command or in the ``values.yaml``.

.. code-block:: bash

   kubectl create namespace airflow
   helm repo add apache-airflow https://airflow.apache.org
   helm install airflow apache-airflow/airflow \
       --namespace airflow \
       --set executor=CeleryExecutor \
       --set workers.keda.enabled=true \

A ``ScaledObject`` and an ``hpa`` will be created in the airflow namespace.

KEDA will derive the desired number of celery workers by querying
Airflow metadata database:

.. code-block:: none

   SELECT
       ceil(COUNT(*)::decimal / {{ .Values.config.celery.worker_concurrency }})
   FROM task_instance
   WHERE state='running' OR state='queued'

.. note::

   Set celery worker concurrency through the helm value
   ``config.celery.worker_concurrency`` (i.e. instead of airflow.cfg or
   environment variables) so that the KEDA trigger will be consistent with
   the worker concurrency setting.