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Scaling Out with Celery
=======================

``CeleryExecutor`` is one of the ways you can scale out the number of workers. For this
to work, you need to setup a Celery backend (**RabbitMQ**, **Redis**, ...) and
change your ``airflow.cfg`` to point the executor parameter to
``CeleryExecutor`` and provide the related Celery settings.

For more information about setting up a Celery broker, refer to the
exhaustive `Celery documentation on the topic <http://docs.celeryproject.org/en/latest/getting-started/brokers/index.html>`_.

Here are a few imperative requirements for your workers:

- ``airflow`` needs to be installed, and the CLI needs to be in the path
- Airflow configuration settings should be homogeneous across the cluster
- Operators that are executed on the worker need to have their dependencies
  met in that context. For example, if you use the ``HiveOperator``,
  the hive CLI needs to be installed on that box, or if you use the
  ``MySqlOperator``, the required Python library needs to be available in
  the ``PYTHONPATH`` somehow
- The worker needs to have access to its ``DAGS_FOLDER``, and you need to
  synchronize the filesystems by your own means. A common setup would be to
  store your DAGS_FOLDER in a Git repository and sync it across machines using
  Chef, Puppet, Ansible, or whatever you use to configure machines in your
  environment. If all your boxes have a common mount point, having your
  pipelines files shared there should work as well


To kick off a worker, you need to setup Airflow and kick off the worker
subcommand

.. code-block:: bash

    airflow worker

Your worker should start picking up tasks as soon as they get fired in
its direction.

Note that you can also run "Celery Flower", a web UI built on top of Celery,
to monitor your workers. You can use the shortcut command ``airflow flower``
to start a Flower web server.

Please note that you must have the ``flower`` python library already installed on your system. The recommend way is to install the airflow celery bundle.

.. code-block:: bash

    pip install 'apache-airflow[celery]'


Some caveats:

- Make sure to use a database backed result backend
- Make sure to set a visibility timeout in [celery_broker_transport_options] that exceeds the ETA of your longest running task
- Tasks can consume resources. Make sure your worker has enough resources to run `worker_concurrency` tasks
- Queue names are limited to 256 characters, but each broker backend might have its own restrictions