airflow.sensors.date_time

Module Contents

class airflow.sensors.date_time.DateTimeSensor(*, target_time: Union[str, datetime.datetime], **kwargs)[source]

Bases: airflow.sensors.base.BaseSensorOperator

Waits until the specified datetime.

A major advantage of this sensor is idempotence for the target_time. It handles some cases for which TimeSensor and TimeDeltaSensor are not suited.

Example 1 :

If a task needs to wait for 11am on each execution_date. Using TimeSensor or TimeDeltaSensor, all backfill tasks started at 1am have to wait for 10 hours. This is unnecessary, e.g. a backfill task with {{ ds }} = '1970-01-01' does not need to wait because 1970-01-01T11:00:00 has already passed.

Example 2 :

If a DAG is scheduled to run at 23:00 daily, but one of the tasks is required to run at 01:00 next day, using TimeSensor will return True immediately because 23:00 > 01:00. Instead, we can do this:

DateTimeSensor(
    task_id="wait_for_0100",
    target_time="{{ next_execution_date.tomorrow().replace(hour=1) }}",
)
Parameters

target_time (str or datetime.datetime) -- datetime after which the job succeeds. (templated)

template_fields = ['target_time'][source]
poke(self, context: Dict)[source]
class airflow.sensors.date_time.DateTimeSensorAsync[source]

Bases: airflow.sensors.date_time.DateTimeSensor

Waits until the specified datetime, deferring itself to avoid taking up a worker slot while it is waiting.

It is a drop-in replacement for DateTimeSensor.

Parameters

target_time (str or datetime.datetime) -- datetime after which the job succeeds. (templated)

execute(self, context)[source]
execute_complete(self, context, event=None)[source]

Callback for when the trigger fires - returns immediately.

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