blob: 62c5d94088e138f06d7827ffc8039186e17ecef6 [file] [log] [blame]
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""Example DAG demonstrating the usage of the sensor decorator."""
from __future__ import annotations
# [START tutorial]
# [START import_module]
import pendulum
from airflow.decorators import dag, task
from airflow.sensors.base import PokeReturnValue
# [END import_module]
# [START instantiate_dag]
@dag(
schedule=None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
tags=["example"],
)
def example_sensor_decorator():
# [END instantiate_dag]
# [START wait_function]
# Using a sensor operator to wait for the upstream data to be ready.
@task.sensor(poke_interval=60, timeout=3600, mode="reschedule")
def wait_for_upstream() -> PokeReturnValue:
return PokeReturnValue(is_done=True, xcom_value="xcom_value")
# [END wait_function]
# [START dummy_function]
@task
def dummy_operator() -> None:
pass
# [END dummy_function]
# [START main_flow]
wait_for_upstream() >> dummy_operator()
# [END main_flow]
# [START dag_invocation]
tutorial_etl_dag = example_sensor_decorator()
# [END dag_invocation]
# [END tutorial]