kaxil commented on a change in pull request #6295: [AIRFLOW-XXX] GSoD: Adding Task re-run documentation URL: https://github.com/apache/airflow/pull/6295#discussion_r350230753
########## File path: docs/dag-run.rst ########## @@ -0,0 +1,197 @@ + .. 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. + +DAG Runs +========= +A DAG Run is an object representing an instantiation of the DAG in time. + +Each DAG may or may not have a schedule, which informs how DAG Runs are +created. ``schedule_interval`` is defined as a DAG argument, and receives +preferably a +`cron expression <https://en.wikipedia.org/wiki/Cron#CRON_expression>`_ as +a ``str``, or a ``datetime.timedelta`` object. + +.. tip:: + You can use an online editor for CRON expressions such as `Crontab guru <https://crontab.guru/>`_ + +Alternatively, you can also use one of these cron "presets": + ++--------------+----------------------------------------------------------------+---------------+ +| preset | meaning | cron | ++==============+================================================================+===============+ +| ``None`` | Don't schedule, use for exclusively "externally triggered" | | +| | DAGs | | ++--------------+----------------------------------------------------------------+---------------+ +| ``@once`` | Schedule once and only once | | ++--------------+----------------------------------------------------------------+---------------+ +| ``@hourly`` | Run once an hour at the beginning of the hour | ``0 * * * *`` | ++--------------+----------------------------------------------------------------+---------------+ +| ``@daily`` | Run once a day at midnight | ``0 0 * * *`` | ++--------------+----------------------------------------------------------------+---------------+ +| ``@weekly`` | Run once a week at midnight on Sunday morning | ``0 0 * * 0`` | ++--------------+----------------------------------------------------------------+---------------+ +| ``@monthly`` | Run once a month at midnight of the first day of the month | ``0 0 1 * *`` | ++--------------+----------------------------------------------------------------+---------------+ +| ``@yearly`` | Run once a year at midnight of January 1 | ``0 0 1 1 *`` | ++--------------+----------------------------------------------------------------+---------------+ + +Your DAG will be instantiated for each schedule along with a corresponding +DAG Run entry in backend. + +.. note:: + + If you run a DAG on a schedule_interval of one day, the run stamped 2020-01-01 + will be triggered soon after 2020-01-01T23:59. In other words, the job instance is + started once the period it covers has ended. The ``execution_date`` available in the context + will also be 2020-01-01. + + The first DAG Run is created based on the minimum ``start_date`` for the tasks in your DAG. + Subsequent DAG Runs are created by the scheduler process, based on your DAG’s ``schedule_interval``, + sequentially. If your start_date is 2020-01-01 and schedule_interval is @daily the first run + will be created on 2020-01-02 i.e. after your start date has passed. + +Re-run DAG +'''''''''' +There can be cases where you will want to execute your DAG again. One such case is when the scheduled +DAG run fails. + +.. _dag-catchup: + +Catchup +------- + +An Airflow DAG with a ``start_date``, possibly an ``end_date``, and a ``schedule_interval`` defines a +series of intervals which the scheduler turn into individual DAG Runs and execute. A key capability +of Airflow is that these DAG Runs are atomic and idempotent items. The scheduler, by default, will +kick off a DAG Run for any interval that has not been run since the last execution date (or has been cleared). This concept is called Catchup. + +If your DAG is written to handle its own catchup (i.e. not limited to the interval, but instead to ``Now`` for instance.), +then you will want to turn catchup off. This can be done by setting ``catchup = False`` in DAG or ``catchup_by_default = False`` +in configuration file. When turned off, the scheduler creates a DAG run only for the latest interval. + +.. code:: python + + """ + Code that goes along with the Airflow tutorial located at: + https://github.com/apache/airflow/blob/master/airflow/example_dags/tutorial.py + """ + from airflow import DAG + from airflow.operators.bash_operator import BashOperator + from datetime import datetime, timedelta + + + default_args = { + 'owner': 'Airflow', + 'depends_on_past': False, + 'email': ['[email protected]'], + 'email_on_failure': False, + 'email_on_retry': False, + 'retries': 1, + 'retry_delay': timedelta(minutes=5) + } + + dag = DAG( + 'tutorial', + default_args=default_args, + start_date=datetime(2015, 12, 1), + description='A simple tutorial DAG', + schedule_interval='@daily', + catchup=False) + +In the example above, if the DAG is picked up by the scheduler daemon on 2016-01-02 at 6 AM, +(or from the command line), a single DAG Run will be created, with an `execution_date` of 2016-01-01, +and the next one will be created just after midnight on the morning of 2016-01-03 with an execution date of 2016-01-02. + +If the ``dag.catchup`` value had been True instead, the scheduler would have created a DAG Run Review comment: ```suggestion If the ``dag.catchup`` value had been ``True`` instead, the scheduler would have created a DAG Run ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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