`airflow.timetables.interval`¶

Module Contents¶

Classes¶

`CronDataIntervalTimetable`	Timetable that schedules data intervals with a cron expression.
`DeltaDataIntervalTimetable`	Timetable that schedules data intervals with a time delta.

Attributes¶

Delta

airflow.timetables.interval.Delta[source]¶

class airflow.timetables.interval.CronDataIntervalTimetable(cron, timezone)[source]¶

Bases: _DataIntervalTimetable

Timetable that schedules data intervals with a cron expression.

This corresponds to schedule_interval=<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.

classmethod deserialize(cls, data)[source]¶

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.

__eq__(self, other)[source]¶

Both expression and timezone should match.

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

property summary(self)[source]¶

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.

serialize(self)[source]¶

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.

validate(self)[source]¶

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.

infer_manual_data_interval(self, *, run_after)[source]¶

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.

class airflow.timetables.interval.DeltaDataIntervalTimetable(delta)[source]¶