# 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 for demonstrating behavior of Datasets feature.
Notes on usage:
Turn on all the dags.
DAG dataset_produces_1 should run because it's on a schedule.
After dataset_produces_1 runs, dataset_consumes_1 should be triggered immediately
because its only dataset dependency is managed by dataset_produces_1.
No other dags should be triggered. Note that even though dataset_consumes_1_and_2 depends on
the dataset in dataset_produces_1, it will not be triggered until dataset_produces_2 runs
(and dataset_produces_2 is left with no schedule so that we can trigger it manually).
Next, trigger dataset_produces_2. After dataset_produces_2 finishes,
dataset_consumes_1_and_2 should run.
Dags dataset_consumes_1_never_scheduled and dataset_consumes_unknown_never_scheduled should not run because
they depend on datasets that never get updated.
"""
from __future__ import annotations
import pendulum
from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
# [START dataset_def]
[docs]dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
[docs]dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
with DAG(
dag_id="dataset_produces_1",
catchup=False,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule="@daily",
tags=["produces", "dataset-scheduled"],
) as dag1:
# [START task_outlet]
BashOperator(outlets=[dag1_dataset], task_id="producing_task_1", bash_command="sleep 5")
# [END task_outlet]
with DAG(
dag_id="dataset_produces_2",
catchup=False,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule=None,
tags=["produces", "dataset-scheduled"],
) as dag2:
BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")
# [START dag_dep]
with DAG(
dag_id="dataset_consumes_1",
catchup=False,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule=[dag1_dataset],
tags=["consumes", "dataset-scheduled"],
) as dag3:
# [END dag_dep]
BashOperator(
outlets=[Dataset("s3://consuming_1_task/dataset_other.txt")],
task_id="consuming_1",
bash_command="sleep 5",
)
with DAG(
dag_id="dataset_consumes_1_and_2",
catchup=False,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule=[dag1_dataset, dag2_dataset],
tags=["consumes", "dataset-scheduled"],
) as dag4:
BashOperator(
outlets=[Dataset("s3://consuming_2_task/dataset_other_unknown.txt")],
task_id="consuming_2",
bash_command="sleep 5",
)
with DAG(
dag_id="dataset_consumes_1_never_scheduled",
catchup=False,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule=[
dag1_dataset,
Dataset("s3://this-dataset-doesnt-get-triggered"),
],
tags=["consumes", "dataset-scheduled"],
) as dag5:
BashOperator(
outlets=[Dataset("s3://consuming_2_task/dataset_other_unknown.txt")],
task_id="consuming_3",
bash_command="sleep 5",
)
with DAG(
dag_id="dataset_consumes_unknown_never_scheduled",
catchup=False,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule=[
Dataset("s3://unrelated/dataset3.txt"),
Dataset("s3://unrelated/dataset_other_unknown.txt"),
],
tags=["dataset-scheduled"],
) as dag6:
BashOperator(
task_id="unrelated_task",
outlets=[Dataset("s3://unrelated_task/dataset_other_unknown.txt")],
bash_command="sleep 5",
)
