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

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

.. py:module:: airflow.timetables.interval


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.timetables.interval.CronDataIntervalTimetable
   airflow.timetables.interval.DeltaDataIntervalTimetable




Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.timetables.interval.Delta


.. py:data:: Delta
   

   

.. py:class:: CronDataIntervalTimetable(cron, timezone)

   Bases: :py:obj:`airflow.timetables._cron.CronMixin`, :py:obj:`_DataIntervalTimetable`

   Timetable that schedules data intervals with a cron expression.

   This corresponds to ``schedule=<cron>``, where ``<cron>`` is either
   a five/six-segment representation, or one of ``cron_presets``.

   The implementation extends on croniter to add timezone awareness. This is
   because croniter works only with naive timestamps, and cannot consider DST
   when determining the next/previous time.

   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:class:: DeltaDataIntervalTimetable(delta)

   Bases: :py:obj:`_DataIntervalTimetable`

   Timetable that schedules data intervals with a time delta.

   This corresponds to ``schedule=<delta>``, where ``<delta>`` is
   either a ``datetime.timedelta`` or ``dateutil.relativedelta.relativedelta``
   instance.

   .. 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:: __eq__(other)

      The offset should match.

      This is only for testing purposes and should not be relied on otherwise.


   .. py:method:: summary()
      :property:

      A short summary for the timetable.

      This is used to display the timetable in the web UI. A cron expression
      timetable, for example, can use this to display the expression. The
      default implementation returns the timetable's type name.


   .. 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:: validate()

      Validate the timetable is correctly specified.

      Override this method to provide run-time validation raised when a DAG
      is put into a dagbag. The default implementation does nothing.

      :raises: AirflowTimetableInvalid on validation failure.


   .. 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``.