Source code for airflow.example_dags.example_datasets

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Example DAG for demonstrating the behavior of the Datasets feature in Airflow, including conditional and
dataset expression-based scheduling.

Notes on usage:

Turn on all the DAGs.

dataset_produces_1 is scheduled to run daily. Once it completes, it triggers several DAGs due to its dataset
being updated. dataset_consumes_1 is triggered immediately, as it depends solely on the dataset produced by
dataset_produces_1. consume_1_or_2_with_dataset_expressions will also be triggered, as its condition of
either dataset_produces_1 or dataset_produces_2 being updated is satisfied with dataset_produces_1.

dataset_consumes_1_and_2 will not be triggered after dataset_produces_1 runs because it requires the dataset
from dataset_produces_2, which has no schedule and must be manually triggered.

After manually triggering dataset_produces_2, several DAGs will be affected. dataset_consumes_1_and_2 should
run because both its dataset dependencies are now met. consume_1_and_2_with_dataset_expressions will be
triggered, as it requires both dataset_produces_1 and dataset_produces_2 datasets to be updated.
consume_1_or_2_with_dataset_expressions will be triggered again, since it's conditionally set to run when
either dataset is updated.

consume_1_or_both_2_and_3_with_dataset_expressions demonstrates complex dataset dependency logic.
This DAG triggers if dataset_produces_1 is updated or if both dataset_produces_2 and dag3_dataset
are updated. This example highlights the capability to combine updates from multiple datasets with logical
expressions for advanced scheduling.

conditional_dataset_and_time_based_timetable illustrates the integration of time-based scheduling with
dataset dependencies. This DAG is configured to execute either when both dataset_produces_1 and
dataset_produces_2 datasets have been updated or according to a specific cron schedule, showcasing
Airflow's versatility in handling mixed triggers for dataset and time-based scheduling.

The DAGs dataset_consumes_1_never_scheduled and dataset_consumes_unknown_never_scheduled will not run
automatically as they depend on datasets that do not get updated or are not produced by any scheduled tasks.

from __future__ import annotations

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [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"})
[docs]dag3_dataset = Dataset("s3://dag3/output_3.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://unrelated/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", ) with DAG( dag_id="consume_1_and_2_with_dataset_expressions", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), schedule=(dag1_dataset & dag2_dataset), ) as dag5: BashOperator( outlets=[Dataset("s3://consuming_2_task/dataset_other_unknown.txt")], task_id="consume_1_and_2_with_dataset_expressions", bash_command="sleep 5", ) with DAG( dag_id="consume_1_or_2_with_dataset_expressions", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), schedule=(dag1_dataset | dag2_dataset), ) as dag6: BashOperator( outlets=[Dataset("s3://consuming_2_task/dataset_other_unknown.txt")], task_id="consume_1_or_2_with_dataset_expressions", bash_command="sleep 5", ) with DAG( dag_id="consume_1_or_both_2_and_3_with_dataset_expressions", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), schedule=(dag1_dataset | (dag2_dataset & dag3_dataset)), ) as dag7: BashOperator( outlets=[Dataset("s3://consuming_2_task/dataset_other_unknown.txt")], task_id="consume_1_or_both_2_and_3_with_dataset_expressions", bash_command="sleep 5", ) with DAG( dag_id="conditional_dataset_and_time_based_timetable", catchup=False, start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), schedule=DatasetOrTimeSchedule( timetable=CronTriggerTimetable("0 1 * * 3", timezone="UTC"), datasets=(dag1_dataset & dag2_dataset) ), tags=["dataset-time-based-timetable"], ) as dag8: BashOperator( outlets=[Dataset("s3://dataset_time_based/dataset_other_unknown.txt")], task_id="conditional_dataset_and_time_based_timetable", bash_command="sleep 5", )

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