docs-archive/helm-chart/1.2.0/_sources/keda.rst.txt

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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.