68 строки
3.2 KiB
ReStructuredText
68 строки
3.2 KiB
ReStructuredText
.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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Scheduler
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==========
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The Airflow scheduler monitors all tasks and DAGs, then triggers the
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task instances once their dependencies are complete. Behind the scenes,
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the scheduler spins up a subprocess, which monitors and stays in sync with all
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DAGs in the specified DAG directory. Once per minute, by default, the scheduler
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collects DAG parsing results and checks whether any active tasks can be triggered.
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The Airflow scheduler is designed to run as a persistent service in an
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Airflow production environment. To kick it off, all you need to do is
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execute the ``airflow scheduler`` command. It uses the configuration specified in
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``airflow.cfg``.
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The scheduler uses the configured :doc:`Executor </executor/index>` to run tasks that are ready.
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To start a scheduler, simply run the command:
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.. code-block:: bash
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airflow scheduler
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Your DAGs will start executing once the scheduler is running successfully.
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.. note::
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The first DAG Run is created based on the minimum ``start_date`` for the tasks in your DAG.
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Subsequent DAG Runs are created by the scheduler process, based on your DAG’s ``schedule_interval``,
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sequentially.
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The scheduler won't trigger your tasks until the period it covers has ended e.g., A job with ``schedule_interval`` set as ``@daily`` runs after the day
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has ended. This technique makes sure that whatever data is required for that period is fully available before the dag is executed.
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In the UI, it appears as if Airflow is running your tasks a day **late**
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.. note::
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If you run a DAG on a ``schedule_interval`` of one day, the run with ``execution_date`` ``2019-11-21`` triggers soon after ``2019-11-21T23:59``.
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**Let’s Repeat That**, the scheduler runs your job one ``schedule_interval`` AFTER the start date, at the END of the period.
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You should refer to :doc:`dag-run` for details on scheduling a DAG.
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Triggering DAG with Future Date
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-------------------------------
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If you want to use 'external trigger' to run future-dated execution dates, set ``allow_trigger_in_future = True`` in ``scheduler`` section in ``airflow.cfg``.
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This only has effect if your DAG has no ``schedule_interval``.
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If you keep default ``allow_trigger_in_future = False`` and try 'external trigger' to run future-dated execution dates,
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the scheduler won't execute it now but the scheduler will execute it in the future once the current date rolls over to the execution date.
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