.. 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. Scaling Out with Dask ===================== ``DaskExecutor`` allows you to run Airflow tasks in a Dask Distributed cluster. Dask clusters can be run on a single machine or on remote networks. For complete details, consult the `Distributed documentation `_. To create a cluster, first start a Scheduler: .. code-block:: bash # default settings for a local cluster DASK_HOST=127.0.0.1 DASK_PORT=8786 dask-scheduler --host $DASK_HOST --port $DASK_PORT Next start at least one Worker on any machine that can connect to the host: .. code-block:: bash dask-worker $DASK_HOST:$DASK_PORT Edit your ``airflow.cfg`` to set your executor to ``DaskExecutor`` and provide the Dask Scheduler address in the ``[dask]`` section. Please note: - Each Dask worker must be able to import Airflow and any dependencies you require. - Dask does not support queues. If an Airflow task was created with a queue, a warning will be raised but the task will be submitted to the cluster.