Source code for airflow.sensors.date_time

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from __future__ import annotations

import datetime
from typing import TYPE_CHECKING, Any, NoReturn, Sequence

from airflow.sensors.base import BaseSensorOperator
from airflow.triggers.base import StartTriggerArgs
from airflow.triggers.temporal import DateTimeTrigger
from airflow.utils import timezone

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class DateTimeSensor(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: .. code-block:: python DateTimeSensor( task_id="wait_for_0100", target_time="{{ next_execution_date.tomorrow().replace(hour=1) }}", ) :param target_time: datetime after which the job succeeds. (templated) """
[docs] template_fields: Sequence[str] = ("target_time",)
def __init__(self, *, target_time: str | datetime.datetime, **kwargs) -> None: super().__init__(**kwargs) # self.target_time can't be a datetime object as it is a template_field if isinstance(target_time, datetime.datetime): self.target_time = target_time.isoformat() elif isinstance(target_time, str): self.target_time = target_time else: raise TypeError( f"Expected str or datetime.datetime type for target_time. Got {type(target_time)}" )
[docs] def poke(self, context: Context) -> bool: self.log.info("Checking if the time (%s) has come", self.target_time) return timezone.utcnow() > timezone.parse(self.target_time)
[docs]class DateTimeSensorAsync(DateTimeSensor): """ Wait until the specified datetime occurs. Deferring itself to avoid taking up a worker slot while it is waiting. It is a drop-in replacement for DateTimeSensor. :param target_time: datetime after which the job succeeds. (templated) :param start_from_trigger: Start the task directly from the triggerer without going into the worker. :param trigger_kwargs: The keyword arguments passed to the trigger when start_from_trigger is set to True during dynamic task mapping. This argument is not used in standard usage. :param end_from_trigger: End the task directly from the triggerer without going into the worker. """
[docs] start_trigger_args = StartTriggerArgs( trigger_cls="airflow.triggers.temporal.DateTimeTrigger", trigger_kwargs={"moment": "", "end_from_trigger": False}, next_method="execute_complete", next_kwargs=None, timeout=None, )
[docs] start_from_trigger = False
def __init__( self, *, start_from_trigger: bool = False, end_from_trigger: bool = False, trigger_kwargs: dict[str, Any] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.end_from_trigger = end_from_trigger self.start_from_trigger = start_from_trigger if self.start_from_trigger: self.start_trigger_args.trigger_kwargs = dict( moment=timezone.parse(self.target_time), end_from_trigger=self.end_from_trigger, )
[docs] def execute(self, context: Context) -> NoReturn: self.defer( method_name="execute_complete", trigger=DateTimeTrigger( moment=timezone.parse(self.target_time), end_from_trigger=self.end_from_trigger, ), )
[docs] def execute_complete(self, context: Context, event: Any = None) -> None: """Handle the event when the trigger fires and return immediately.""" return None

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