Source code for airflow.example_dags.example_branch_python_dop_operator_3
#
# 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 ``@task.branch`` TaskFlow API decorator with depends_on_past=True,
where tasks may be run or skipped on alternating runs.
"""
from __future__ import annotations
import pendulum
from airflow import DAG
from airflow.decorators import task
from airflow.operators.empty import EmptyOperator
@task.branch()
[docs]def should_run(**kwargs) -> str:
"""
Determine which empty_task should be run based on if the execution date minute is even or odd.
:param dict kwargs: Context
:return: Id of the task to run
"""
print(
f"------------- exec dttm = {kwargs['execution_date']} and minute = {kwargs['execution_date'].minute}"
)
if kwargs["execution_date"].minute % 2 == 0:
return "empty_task_1"
else:
return "empty_task_2"
with DAG(
dag_id="example_branch_dop_operator_v3",
schedule="*/1 * * * *",
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
default_args={"depends_on_past": True},
tags=["example"],
) as dag:
empty_task_1 = EmptyOperator(task_id="empty_task_1")
empty_task_2 = EmptyOperator(task_id="empty_task_2")
cond >> [empty_task_1, empty_task_2]