docs-archive/apache-airflow/2.4.0/_sources/_api/airflow/timetables/trigger/index.rst.txt

:py:mod:`airflow.timetables.trigger`
====================================

.. py:module:: airflow.timetables.trigger


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.timetables.trigger.CronTriggerTimetable




.. py:class:: CronTriggerTimetable(cron, *, timezone, interval = datetime.timedelta())

   Bases: :py:obj:`airflow.timetables._cron.CronMixin`, :py:obj:`airflow.timetables.base.Timetable`

   Timetable that triggers DAG runs according to a cron expression.

   This is different from ``CronDataIntervalTimetable``, where the cron
   expression specifies the *data interval* of a DAG run. With this timetable,
   the data intervals are specified independently from the cron expression.
   Also for the same reason, this timetable kicks off a DAG run immediately at
   the start of the period (similar to POSIX cron), instead of needing to wait
   for one data interval to pass.

   Don't pass ``@once`` in here; use ``OnceTimetable`` instead.

   .. py:method:: deserialize(data)
      :classmethod:

      Deserialize a timetable from data.

      This is called when a serialized DAG is deserialized. ``data`` will be
      whatever was returned by ``serialize`` during DAG serialization. The
      default implementation constructs the timetable without any arguments.


   .. py:method:: serialize()

      Serialize the timetable for JSON encoding.

      This is called during DAG serialization to store timetable information
      in the database. This should return a JSON-serializable dict that will
      be fed into ``deserialize`` when the DAG is deserialized. The default
      implementation returns an empty dict.


   .. py:method:: infer_manual_data_interval(*, run_after)

      When a DAG run is manually triggered, infer a data interval for it.

      This is used for e.g. manually-triggered runs, where ``run_after`` would
      be when the user triggers the run. The default implementation raises
      ``NotImplementedError``.


   .. py:method:: next_dagrun_info(*, last_automated_data_interval, restriction)

      Provide information to schedule the next DagRun.

      The default implementation raises ``NotImplementedError``.

      :param last_automated_data_interval: The data interval of the associated
          DAG's last scheduled or backfilled run (manual runs not considered).
      :param restriction: Restriction to apply when scheduling the DAG run.
          See documentation of :class:`TimeRestriction` for details.

      :return: Information on when the next DagRun can be scheduled. None
          means a DagRun will not happen. This does not mean no more runs
          will be scheduled even again for this DAG; the timetable can return
          a DagRunInfo object when asked at another time.